Instituto Tecnol´ogico y de Estudios Superiores de Monterrey

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Instituto Tecnol´ogico y de Estudios Superiores de Monterrey

Campus Monterrey

School of Engineering and Sciences

Implementation and validation of Tracking Control on a Real Manufacturing System

A thesis presented by

Jos´e Manuel Ch´avez Delgado

Submitted to the

School of Engineering and Sciences

in partial fulfillment of the requirements for the degree of Master of Science

in Engineering

Monterrey, Nuevo Le´on, December, 2020

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Dedication

I would like to dedicate this thesis to my family who are an important pillar for me. Especially to my parents, without their guidance and love, I would not be the person I am. Thanks for all the sacrifice you have made for me, the unconditional confidence, support, patience, and encouragement. You were my main motivation for pushing through this work.

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Acknowledgements

I would like to express my deepest gratitude to all those who have been side by side with me, along the long, but also short hours of work.

To God for giving me all these opportunities in life and making me meet wonderful peo- ple on my way.

To my family who together with me suffered my blocks and frustrations, but also cele- brated my progress and results.

To Ana Beatriz who always supported and helped me. Thanks for your patience, love and affection.

To my thesis advisor Ph.D. Carlos Renato V´azquez who always has the time and patience to clear my doubts, for being an excellent academic guide, and for all their professional invaluable advice.

To my classmates and friends who helped me during the master’s degree making the process more enjoyable and fun.

To all students, laboratory workers, and the research group involved in this project.

Without their help and work the results of this thesis would not be the same.

To Tecnol´ogico de Monterrey for giving me this academic scholarship, high quality education, and allowing us to use infrastructures and equipment.

To CONACyT for the economic support for living during this 2 years and make possible the scientific development in M´exico.

The research leading to these results has received funding from CONACYT Program for Education, project 288470.

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Implementation and validation of Tracking Control on a Real Manufacturing System

by

Jos´e Manuel Ch´avez Delgado Abstract

This master thesis for the degree of Master of Science in Engineering concerns the automation in industrial sectors, where electro-pneumatic components are commonly involved to accomplish required tasks. The more electro-pneumatic components are involved, the more complex becomes the control design. Since most of the processes that use electro-pneumatic components evolve according to the occurrence of events, i.e., they can be seen as discrete event systems, Petri nets arise as a powerful mathematical tool for the analysis and design of the control algorithms required by the automation system. Tracking control is a new control approach under development that is based on interpreted Petri nets to model and control discrete event systems. In the literature, Tracking Control has only been implemented on small cases of study that involve few electro-pneumatic components. In order to validate the required features of the Tracking Control approach under development by the research team, in this thesis we propose to design and build a fully automated manufacturing cell with 43 electro-pneumatic components, interacting in the different workstations. It is expected to implement the Tracking Control methodologies and to validate aspects such as deadlock-freeness, scalability, practical implementability, the possibility to consider industrial networks, and to assure a safe operation of the system on different industrial network architectures.

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Chapter 1 Introduction

1.1 Background

In the industrial sector, automation plays a highly important role for the companies, allowing fast production and improvement in the products’ quality. Nowadays, the complexity of the industrial automation systems is increasing due to new technologies (e.g. additive manufacturing, collaborative robots, automated sorting systems based on AGV’s) and paradigms such as Industry 4.0 (e.g. virtual commissioning, digital twins, production based on product lifecycle management). The incorporation of these new technologies and paradigms allows new possibilities, including customized products, on-line automatic decisions, decentralized decision, collaborative and cooperatives robots and machines, etc [5].

The automation of a process can be decomposed into an operative part and a control part. The operative part is composed of the components that produce the actions in the system (e.g., motors, electro-pneumatic cylinders, valves), including sensors and actuators. The control part involves the algorithms that enforce the sequences needed for the actuation of the operative part [12]. The control algorithms are implemented in a hardware device, which can be a Programmable Logic Controller (PLC) that enforces the operative part to describe the required operation sequences. In automation, it is common to have many operative parts.

However, the more operative parts are involved, the more complex becomes to design the control algorithms for their implementation in the control devices.

Among the different technologies that are used in today’s industrial automation, electro- pneumatic systems have an advantage due to the flexibility and variety of applications in almost all the branches of industrial production processes. The use of electro-pneumatic systems has become important due to the feasibility of having reliable, robust, and low-cost automation. Industries where electro-pneumatic systems are commonly used include the production energy industry, chemical industry, plastic industry, metallurgical industry, non-metal products industry, furniture industry, paper industry, textile industry, fur industry, food industry, beverage industry, transport industry, and construction sector[1].

As the complexity of the industrial automation systems is increasing due to new technologies and paradigms, the design methodologies using formal tools are also evolving to

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CHAPTER 1. INTRODUCTION 2

provide efficient solutions to model, analyze and control large-scale automation systems. In particular, methodologies for the analysis of Discrete Event Systems (DES) based on finite state automata (FSA) and Petri nets (PN) are being rapidly adapted to this new scenario and represent a logical choice to analyze automation systems that evolve according to events oc- currences [6].

In the literature, different control paradigms for DES have been studied. In [10], the framework of Supervisory Control Theory is proposed for FSA. In this approach the specification is given as regular language, and the plant is presented as a language generator. The specification language represents a set of safe trajectories (named words) that the system is allowed to perform. The controller (supervisor) confines the system behavior into the specification by disabling controllable events. The supreme controllable language inside the specification is computed in order to synthesize the controller. In this theory, it is needed to know all the possible event sequences that can occur in the plan. Later, this theory was extended to PN in [2]. Another studied control paradigm is the Generalized Mutal Exclusion (GMEC) posed in [3]. In the GMEC paradigm, the specification is a linear inequality that depends on the system’s marking (state), which implies that the system does not reach unsafe states and deadlocks. The controller consists of places and arcs that are added to the PN. Those added arc and places limit the weighted sum of tokens inside some places, imposing thus the specification. In [11], the regulation control framework is introduced, also called Tracking Control.

In this tracking control framework the specification and the system (the plant) to be controlled are modelled by interpreted Petri nets (IPN), which is an extension of PN, in which labels are added to places (to represent sensors) and to transitions (to represent actuators). In this framework, the specification produces output signals and the controller enforces the occurrence of controllable events in the plant to make the signals of the plant to be equal to the specification signals.

Few works report practical implementations of DES control techniques; most of the works address small cases of study. However, in [9], a manufacturing system was controlled using Hierarchical and Decentralized Supervisory Control, which is an extension of Supervi- sory Control. The manufacturing system controlled in [9] consists of a scale didactics flexible manufacturing system of Fischertechnik with 107 I/O signals, one stack feeder, sixteen conveyors belts, four rotatory tables, two rail-transport units, two processing stations, two push- ers, and two roll- conveyors. The total model of the system has an estimated 10²⁴states. Just for the conveyor module, there are 63 504 states, while the abstracted conveyor module that results of the hierarchical and decentralized approach results in 458 states. The finite state automata model for the conveyor specification consists of the synchronous composition of 4 automata. The authors developed a C++ library called libFAUDES to support the control synthesis due to implementation of the controller in a PLC results impractical. We can conclude from this work that Supervisory control, even in hierarchical and decentralized architectures, involves a high computational cost and its implementation is complex.

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1.2 Problem statement

As mentioned before, there are control paradigms for DES that can be applied on an automation process. However, the lack of communication between scientists and practitioners results in difficulties for implementing new scientific methods at industries. In practice, the DES control approaches existing in the scientific literature are rarely used, instead, the controller synthesis for automation processes is performed based on heuristic rules. In the case of electro-pneumatic systems, state-phase diagrams and space-time diagrams are used to represent the required behavior of the working elements in the process. However, the design of the controllers based on the diagrams is not straightforward, and their design becomes complex for systems with several components.

As described above, Supervisory Control and GMEC represent formal approaches for the synthesis of controllers in DES, including automation systems. These approaches con- fine the system behaviour into a require language or marking set. Nevertheless, in industrial automation it is frequently required to enforce the system to perform pre-defined tasks consisting of sequences of actuators’ activations. The tracking control addresses this problem.

This control approach is useful in manufacturing, automation and industrial processes, where it is required to perform particular sequences of operations. It involves less computational complexity than supervisory control and more practicality for the engineer, since neither a deep knowledge of the system’s behavior nor on DES theory is required for its application.

The research group has developed methodologies for modeling the system [13], specification definition [7], and synthesis of the controller [4] [5] in different industrial architectures under the paradigm of Tracking Control based on PN.

Nevertheless, Tracking Control has only been applied on small simulations as cases of study. It has never been tested on a real manufacturing cell where many electro-pneumatic components are involved through several workstations in the manufacturing process.

Since the Tracking Control methodologies are new, it is required to test the modelling, specification and controller synthesis methodologies in real systems to evaluate its applicability, detect errors (omissions, ambiguities, and inconsistencies), find limitations, and propose enhancements for the theory to get a better approximation of how this approach could be implemented in the industry.

1.3 Objective

The objective of this thesis is to develop a fully automated manufacturing cell for testing the Tracking Control methodologies that have been reported in the literature [4],[5],[13],[7] for modelling, defining specifications and synthesizing controllers in automated systems, consid- ering centralized and distributed architectures.

The particular objectives are:

• Design, build, assembly and test each workstation of the manufacturing cell for testing

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the Tracking Control methodologies [4],[5],[13],[7] for modelling, defining specifications and synthesizing controllers in the developed manufacturing cell .

• Implement, test and validate the modeling methodology of [13] in each workstation and the complete cell to look for discrepancies between the model and the plant, model omissions, and deadlock states in the plant or in the model.

• Implement, test and validate the specification definition methodology of [7] to look for complexity coherence, controllability, and omissions in the current methodology.

• Implement, test and validate the control synthesis [4] for each workstation to look for deadlock states in the plant after implementing the calculated controller, discrepancies, ambiguities, and omissions in the current control synthesis.

• Implement, test and validate the control synthesis [4] for a distributed architecture without communication [5] to look for deadlock states in the plant after implementing the calculated controller, discrepancies, ambiguities, and omissions in the current control synthesis.

• Implement, test and validate the control synthesis [4] for a coordinate architecture [5]

to know its applicability.

1.4 Thesis Structure

This thesis comprises seven chapters, organized as follows:

In chapter 2, some basic concepts related to Petri nets, Interpreted Petri nets, Regulation Control Theory, and Tracking Control are presented. Moreover, the methodologies used in this thesis are explained.

In chapter 3, the required features in the manufacturing cell and the manufacturing cell design are reported.

In chapter 4 the manufacturing process and the integration of all the components in the cell are reported.

In chapter 5describes the virtual commissioning process developed as an alternative for the implementation of Tracking Control methodologies for plant’s modeling, specification definition, and control synthesis.

In chapter 6 reports the results obtained after the application of the Tracking Control methodologies for plant’s modeling, specification definition, and control synthesis.

Finally, in chapter 7 a summary of results, conclusions and future work are presented.

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Chapter 2 Discrete Event Systems Theory

This chapter presents some basic concepts related to Petri Nets, Interpreted Petri Nets, Regu- lation Control Theory, and Tracking Control. Moreover, the methodologies evaluated in this thesis are explained. All the content in this chapter is retrieved from [5] [6] [7] [11] [13] .

2.1 Petri nets

Definition 2.1.1 ([6]). A Petri net (PN) is bipartite digraph represented by the 4-tuple G = hP, T, I, Oi where:

• P = {p1, p₂, ..., p_n} is a finite set of places.

• T = {t₁, t₂, ..., t_n} is a finite set of transitions.

• I : P × T → Z ≥ 0 is a function representing the weighted arcs connecting places to transitions.

• O : P × T → Z ≥ 0 is a function representing the weighted arcs connecting transitions to places.

Z ≥ 0 represents the non-negative integers.

Pictorially, places are represented by circles, transitions by rectangles, and arcs by ar- rows.

Functions I and O are characterized by | P | × | T | matrices Pre and Post, respectively, in such a way that Pre[i, j] = I(p_i, t_j) and Post[i, j] = O(p_i, t_j).

The incidence matrix of a PN is a | P | × | T | matrix C defined such that C[i, j] = Post[i, j] − Pre[i, j]

.

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CHAPTER 2. DISCRETE EVENT SYSTEMS THEORY 6

The symbols • tj (resp. • pi) denotes the set of all places pi (resp. transitions tj) such that I(pi, t_j) 6= 0 (resp. O(p_i, t_j) 6= 0 ). Similarly t_j• (resp. p_i•) denotes the set of all places p_i(resp. transitions t_j) such that O(p_i, t_j) 6= 0 (resp. I(p_i, t_j) 6= 0).

A PN is said to be a state machine if | •tj |=| tj• |= 1, ∀tj ∈ T .

Definition 2.1.2 ([6]). Given a PN structure, the marking distribution is defined as a function M : P → Z ≥ 0, where M (p_i) the number of tokens residing inside the place represents pi (tokens are depicted as dots). The marking distribution is expressed as a column vector M of legth | P |, where M[i] = M (p_i), ∀p_i ∈ P . A PN system is the duple hG, M₀i, where G is a PN structure and M₀ the initial marking distribution. The marking distribution evolves according to the firing of transitions. A transition tj is enabled at marking Mk iff ∀pi ∈ •tj, M_k(p_i) ≥ I(p_i, t_j), this is denoted as M_k[t_ji. A transition t_j can fire if it is enabled. The firing of an enabled transition t_j leads to a new marking M_k+1that can be computed by

Mk+1= Mk+ Cej

where e_j[i] = 0 for i 6= j and e_j[j] = 1. This is denoted as M_k[t_ji M_k+1.

Given a PN system hG, M₀i, a sequence of transitions σ = t₁, t₂, ..., t_kis said to be a firing sequenceor fireable from M0 if there exist markings M1, M2, ..., Mk+1such that M0 [t1i M1,

M₁ [t₂i M₂, ..., M_k−1[t_ki.

The marking M’ reached after the firing of a fireable σ from marking M can be computed by the PN fundamental equation

M’ = M + C~σ

where ~σ is a vector, named Parikh vector, defined as a column vector of size | T | such that

~

σ[j] = k if t_j is fired k times in the sequence σ.

This is denoted as M[σiM’. Moreover, M’ is said to be reachable from M. A PN system is said to be safe if, for every reachable marking, there is at most one token in each place. The

reachability setof a PN is the set of all reachable markings from M0, and it is denoted as R(G, M₀). A PN is said to be deadlock-free if ∀M ∈ R(G, M₀), ∃t ∈ T such that M[ti.

2.2 Interpreted Petri nets

Definition 2.2.1 ([6]). An Interpreted Petri net (IPN) system is a 8-tuple Q = hG, M₀, Σ_I, Σ_idΣ_O, λ_I, λ_id, ϕi where:

• hG, M_oi is a PN system,

• ΣI is the input alphabet, where each element of the ΣI is an input symbol,

• Σ_idis the identity alphabet, where each element of the set Σ_idis an identity symbol,

• P

Ois the output alphabet, where each element of theP

Ois an output symbol,

• λ_I : T → 2^P^I is the input-labeling function of transitions (a single transition can be associated with more than one symbol from the input alphabetP

I),

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• λid : T → 2^P^idS{} is the identity-labeling function of transitions, where is associated to transitions having no identity. Transitions have at most one identity symbol, which cannot be shared with other transitions i.e., ∀t_i, t_j ∈ T either λ_id(t_i) = λ_id(t_j) =

or λid(ti) 6= λid(tj),

• ϕ : P → 2^P^O is the labeling function of places (a single place can be associated with more than one output symbol).

If λI(t_j) 6= ∅, the transition t_j is said to be controllable, otherwise it is uncontrollable.

A place p ∈ P is said to be measurable if ϕ(p) 6= ∅, otherwise, it is non-measurable.

The evolution of an IPN is similar to that of the PN system with the addition of the following rules:

1. A transition t_j is said to be marking-enabled at M if ∀p_i ∈ •t_j, M (p) ≥ I(p_i, t_j) 2. A symbol a ∈ P

I is said to be indicated if either it is activated by an external device (for instance a controller or a user) or ∃p ∈ P such that a ∈ ϕ(p) and M (p) > 0 (notice thatP

I may include symbols ofP

O);

3. If λ(tj) = a is indicated and t_j is marking-enabled then tj must fire;

4. If λ(t_j) = a and t_j is marking-enabled, but the symbol λ(t_j) is not indicated, then t_j cannot fire;

5. If λ(t_j) = ∅ and t_j is marking-enabled, then t_j can fire at any moment;

6. At any reachable marking M_k, an external observer simultaneously reads the symbols ϕ(p_i) if M (p_i) > 0.

The reachability set of an IPN Q will be denoted as R(Q, M₀), in order to make ex- plicit that in these models the reachability depends not only on the PN structure and the initial marking, but also on the input and output labelling functions.

Definition 2.2.2 ([13]). (Synchronous product) Let Q¹ = hG¹, M¹₀, Σ¹_I, Σ¹_O, λ¹, ϕ¹i and Q² = hG², M²₀, Σ²_I, Σ²_O, λ², ϕ²i be two IPN models, where G¹ = hP¹, T¹, I¹, O¹i and G² = hP², T², I², O²i. The synchronous product results in an IPN Q³ = hG³, M³₀, Σ³_I, Σ³_O, λ³, ϕ³i, where G³ = hP³, T³, I³, O³i, denoted as Q³ = Q¹ || Q². The IPN Q³is composed as follows [13]:

• The net structure is computed as follows: P³ = P¹S P² and T³ = T¹S T², enumerating first the nodes in G¹. Next I³ = diag(I¹, I²) and O³ = diag(O¹, O²), where diag(•, •) is a matrix built with the argument matrices as diagonal blocks, and other entries are null.

• The initial marking is M³₀ = [(M¹₀)^T, (M²₀)^T]^T.

• The input and output alphabets are Σ³_I = Σ¹_IS Σ²_I and Σ³_O= Σ¹_OS Σ²_O, respectively.

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• The input function is defined as: ∀t ∈ T³set λ³(t) = λⁱ(t), where t is a node of Tⁱ with i ∈ {1, 2}. Similarly, the output function is defined as: ∀p ∈ P³ set ϕ³(p) = ϕⁱ(p), where p is a node of Pⁱwith i ∈ {1, 2}.

Example 1Figure 2.1 shows an IPN Q where P = {p1, p₂, p₃, p₄}, T = {t₁, t₂, t₃, t₄}, Σ_I = {a, b}, Σ_O= {A, B, C, b}. The incidence matrix of Q is given by

C =







−1 0 1

1 0 −1

0 −1 1

0 1 −1







Figure 2.1: IPN system described in Example 1

The initial marking is M₀ = [1, 0, 1, 0]^T, describing that only p₁ and p₃ have a token.

The initial marking of the IPN indicates the output signals {A, B, C}. The transitions t1and t2

are enabled by the marking, but none of them can be fired since both have input symbols that must be indicated to be fired. These input symbols indicate that the transitions are controllable.

The transition t1 requires symbols a and b to be simultaneously indicated for its firing. If an external agent indicates the symbol a, t₂ will fire. In such case, a token is transfered from p₃ to p₄.

M₁ = M₀+ Ce₂ =

₁

0 01

where e2 = [0, 1, 0]^T (i.e., the elementary vector associated to t2). Thus p1and p4are the marked places. At the new marking, the symbols A, B, b are indicated. As mentioned before, t₁ can be fired until {a, b} are indicated, since b is indicated by the current marking, t₁can be fired until an external agent indicates the a symbol again. The firing of t1 transfers a token from p₁ to p₂. The new marking enables t₃and since it is not labeled (uncontrollable), it can be fired at any time, leading the system to its initial marking. Then, the firing sequence from the initial marking is σ = t1t₂t₃. As an example of a non-fireable sequence, σ⁰ = t₁t₂t₁is not fireable from the initial marking, since t₁cannot fire twice without firing t₃.

The function ϕ can be represented by a | ΣO| × | P | matrix ϕ. Enumerating the output symbols as Σ_O = {a₁, a₂, ..., a|P

O|}, the output matrix ϕ is defined such that ϕ[i, j] = 1 if

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a ∈ ϕ(pj) and ϕ[i, j] = 0 if a 6∈ ϕ(pj). In this way, each row of ϕ represents a symbol from P

O. The output vector of the IPN at the marking M is defined as y = ϕM

where y[i] > 0 iff a_i is indicated by M.

Example 2Consider again the IPN depicted in Figure 2.1, if we order the output symbols as Σ_O = {A, B, C, b}, then the output matrix is defined as

ϕ =







1 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1







Then, the first row of ϕ means that the symbol A is indicated by the marking of p₁, the second row means that B is also indicated by the marking of p1, the third and fourth rows mean that C is indicated by the marking of p3and symbol b is indicated by the marking of p4, respectively. If the sequence σ = t₂t₁t₃is fired from the initial marking, the sequence of output symbols is {A, B, C}, {A, B, b}, b, {A, B, C} or [1, 1, 1, 0]^T, [1, 1, 0, 1]^T, [0, 0, 0, 1]^T, [1, 1, 1, 0]^T as output vectors.

2.3 Regulation Control Framework

The regulation control framework has been introduced and studied for discrete event systems (DES) in [11] to address the enforcement of a DES to perform predefined tasks, described as sequences of actuators’ (or machines) activations. In this framework, the desired behavior is named specification, the system to be controlled is called the Plant, and both are IPN, in which some transitions are enabled or disable by the application of certain input symbols, while some places have symbols that are observable by external agents. The objective is to design a controller that, for every firing in the specification, executes a sequence of transitions in the Plant so the output symbols of the specification and the Plant become equal. In other words, the Regulation problem consists of finding out a controller to track the output trajectories of the specification model [13].

Definition 2.3.1 ([5]). The Plant is a safe event-detectable IPN Q = hG, M₀, Σ_I, Σ_O, λ_I, ϕi that models the system to be controlled, where G = hP, T, I, Oi. It is assumed that two plant places cannot generate the same output symbol (i.e. a sensor cannot be turned on by two different variables), moreover, the input and output plant alphabets are disjoint (e.i., ΣIT Σ_O= ∅).

Definition 2.3.2 ([5]). Given a plant as defined in Definition 2.3.1, a Specification is a deadlock- free state-machine IPN Q^s = hG^s, M^s₀, Σ^s_I, Σ^s_O, λ^s, ϕ^si. The output alphabet fulfills Σ_IT Σ^s_O =

∅. The specification input alphabet contains symbols from the plant’s alphabets and other external symbols, whose set is denoted as Σext= Σ^s_I\{Σ_IS ΣO}.

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The regulation problem can be formulated as the synthesis of a controller function H that provides the input symbols that have to be indicated to the plant in order to produce an output equal to that of the specification.

Definition 2.3.3 ([11]). Let Q = hG, M₀, ΣI, Σ_O, λ_I, ϕi and Q^s = hG^s, M^s₀, Σ^s_I, Σ^s_O, λ^s, ϕ^si be the IPN modeling the plant and specification, respectively. The regulation problem consists in the computation of a controller function

H : R(Q, M₀) × R(Q^s, M^s₀) × T^s → Σ_I

such that ∀Mi ∈ R(Q,M₀), ∀ M^s_i ∈ R(Q^s,M^s₀), the indication of the input symbols H(M_i,M^s_j, t^s_k), where t^s_j is the specification transition previously fired, will eventually lead the plant to a marking M_j such that ϕ(M_j) = ϕ^s(M_j^s).

2.3.1 Modeling methodology for electro-pneumatic systems

The construction of a plant IPN model for an automation system can be built by using the synchronous products of small IPN models defined for each of the components.

Since electro-pneumatic system (ENS) are common components in industrial automation, in [13] a library of IPN models was proposed for the most frequently used components in electro-pneumatic systems, including switches, selectors, proximity sensors, electro- pneumatic valves and actuators. Since in the models proposed in [13] the labels of the transitions are module disjoint, then the plant is a disjoint collection of component submodels. In other words, the plant model is a collection of disjoint ENS IPN modules [13]. Figure 2.2 - 2.5 shows the IPN models proposed in [13].

Figure 2.2 shows IPN models for different kind of switches. In these, output symbols are defined in the places that represent a state in which the switch conducts current providing a signal to the Programmable Logic Controller (PLC) device.

Figure 2.2: IPN models for different kinds of switches [13]

Figure 2.3 shows the electro-pneumatic symbols for different kind of proximity sensors (capacitive sensor, inductive sensor, magnetic sensor, optical sensor) and its IPN model. All of them are proximity sensors commonly used in ENS to detect a limit position of an actuator, the presence of a part or a machine condition. These sensors frequently provide two output

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signals one NO and one NC to detect the presence and absence of parts, respectively. For that reason, the IPN model includes two output symbols, B for NC and A for NO.

Figure 2.3: IPN models for different proximity sensors [13]

Figure 2.4 shows a vacuum actuator assembly, consisting of a 3-ways 2-positions valve, a Venturi nozzle to produce vacuum, a suction cup and a vacuum sensor, which provides an activation signal when vacuum is detected (when a part is grasped).

Figure 2.4: IPN model for vacuum assembly [13]

Figure 2.5 represents the most typical valve-actuator assemblies: double acting actuator controlled by a 5-ways 2-positions valve (a), spring return actuator controlled by a 3-ways 2-positions spring return valve (b), and a rotary actuator controlled by a 5-ways 2-positions valve (c). The same IPN model is valid for all the assemblies.

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Figure 2.5: IPN models for electro-pneumatic assemblies [13]

2.3.2 Specification Definition

According to the Definition 2.3.2 a specification is represented as a safe state-machine IPN that describes the required plant output sequences.

The complexity of the specification design will vary depending on the system. The construction of the specification relay on the designer skills and knowledge of the TC framework. Therefore, in [7] a methodology is proposed to design a specification in such way that it can be automatically computed from minimum information provided by a practitioner. The methodology can be summarized in four main steps:

1. The sets of tasks, and the devices that will perform each set of task, are defined.

2. The required plant conditions to execute each task are declared.

3. Some tables with the necessary information required for the specification construction are filled.

4. The information provided in the tables is used to generate an IPN specification.

Definition 2.3.4 ( [7]). (Subtask) A subtask subtask_a = (s⁰â1, ..., s⁰ân) is an ordered set of operations s⁰â1, ..., s⁰ân ∈ 2^ΣÔ, each one defined as a subset of actuators’ output signals. The set of all the subtasks of the plant is denoted as τ⁰ = {subtask₁, ..., subtask_m}, and the alphabet of τ⁰ is denoted as Σ_sub = {sub₁, ..., sub_m}, where each sub_i ∈ Σ_subis defined as a new symbol associated with a subtask subtaski ∈ τ⁰.

Definition 2.3.5 ([7]). (Task) A task taska = (s⁰â1, ..., s⁰ân) is an ordered set of operations s⁰â1, ..., s⁰ân ∈ 2^ΣÔ^{S Σ}^sub, each one defined as a subset of actuators’ output signals or subtasks symbols. The set τ = {task₁, ..., task_m} is the set of all the tasks of the plant. A task represents a required sequence of actuators signals that the controlled plant must accomplish.

Definition 2.3.6 ([7]). (Workstation) Consider the plant as a collection of IPN models of the devices set D = {d1, ..., d_n}, where Σ^dk_I and Σ^dk_O are the input and output alphabet of the k-th device, respectively. In practice, it holds that Σ^dk_O T Σ^dl_O = ∅, Σ^dk_I T Σ^dl_I = ∅, for all k 6= 1.

Given a set of tasks τ_i ⊆ τ , the workstation of τ_i is defined as the smallest subset of devices w_i ⊆ D such that Σ^τⁱ ⊆ 2^Σ^o^wⁱ where Σ^τⁱ = {s | s ∈ task, task ∈ τ_i} denotes the set of operations involved in τ_i and Σ^w_Oⁱ denotes the set of output symbols of all the devices in w_i.

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It is assumed that the plant is split in a set of workstations W = {w1, ..., wu} covering all the tasks. Moreover, it is assumed that ∀taska, task_b ∈ τ_i, s^a¹ = s^b¹, i.e., all the tasks of a workstation have the same initial states.

It is also possible to indicate when does a task or subtask of a work station finish its sequence by using an auxiliary signal. The auxiliary signals that tell if a task or subtask is completed are defined as flag signals.

Definition 2.3.7 ([7]). A flag signal in a task_ais a duple (sâl, f lag_oâ), where sâ₁ is an operation of taskaand f lag_oâ ∈ Σf lag is a set of symbols of virtual sensors fulfilling Σf lag ⊆ Σ^S_O\ΣO. Flag signals can also be defined in subtasks in a similar way.

The activation of certain sensors signals and flag signals can be considered as guards.

Those guards can be used as conditions for the tasks or subtasks to perform their sequences.

These conditions are named event conditions and are formally defined as follows.

Definition 2.3.8 ([7]). An event condition of a task_a is a triplet (sâ₁, sâ_(l+1), condâ_f), where sâ₁ and sâ_(l+1) are operations of the task_a, and condâ_f is a logical expression composed by logical variables in Σf lagS ΣO and logical operators ∧ and ∨. Here, condâ_f represents the necessary conditions of sensors in the plant and virtual sensors for the task_ato advance from the operation sâ_l to the operation sâ_(l+1). Event conditions can also be defined in subtasks in a similar way.

Table 2.1 shows an example of a Specification table to be filled in order to summarize the information of each workstation. This table requires on its first column the task’s workstation, in its second column the name of the task is given, on its third column the operation sequences are described, in its the fourth column the event conditions are declared, and on its fifth column, the flag signals are described if it is needed.

If there are subtasks a table for them must be filled in the same way as the Table 2.1.

Table 2.1: Specification definition table Specification Definition Table Workstation Task

Name Event Conditions Flag Signals

w₁ task₁ (s¹_l, s¹_m, cond¹_l), · · ·, (s¹_p, s¹_q, cond¹_p) (s¹_j, f lag_l¹), · · ·, (s¹_k, f lag_o¹)

... ... ... ...

w_u task_n (sⁿ_l, sⁿ_m, condⁿ_l), · · ·, (sⁿ_p, sⁿ_q, condⁿ_p) (sⁿ_j, f lag_lⁿ), · · ·, (sⁿ_k, f lag_oⁿ)

Once the tables are filled their information is translated into an IPN model. The Algo- rithm 4.1 in [7] presents the computation for the IPN model. This algorithm generates a state machine IPN, where the transitions are labelled based on the event conditions and the places are labelled based on the operations alphabet and flag signals.

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Figure 2.6: IPN model of a plant composed of 4 pneumatic actuators, 1 proximity sensor, 1 selector, and 1 emergency stop.

Example 3: Consider tha plant of Figure 2.6. This plant consists of 4 pneumatic actuators, 1 proximity sensor, 1 selector, and 1 emergency stop. In this system, it is required to perform two different sequences. At the beginning all the actuators are in their retracted position (A1, A2, A3, A4).

1. The first sequence is performed when the proximity sensor detects a piece (ON ), the selector indicates the first sequence is allowed (Sec1), and the emergency stop is not pressed (Run). The sequence performed is (B1, B2, B3, B4).

2. The second sequence is performed when the proximity sensor detects a piece (ON ), the selector indicates the second sequence is allowed (Sec2), and the emergency stop is not pressed (Run). The sequence performed is (B1, B4, A1, B3). When the sequence is performed a flag signal F inishSeq2 will be activated .

In this case, the set of task performed by the system is defined as τ_R= {Sequence1, Sequence2}.

The task are formally defined as

Sequence1 = ({A1, A2, A3, A4}, B1, B2, B3, B4) Sequence2 = ({A1, A2, A3, A4}, B1, B4, A1, B3).

The event conditions are defined for each task as follows,

Sequence1 = ({A1, A2, A3, A4}, B1, (ON, Sec1, Run)) Sequence2 = ({A1, A2, A3, A4}, B1, (ON, Sec2, Run)).

The flag signal (B3, F inishSeq2) means the flag symbol F nishSeq2 is activated when the Actuator 3 is extended after performing the sequence.

Table 2.2 shows the filling of the specification table for both Sequence1 and Sequence2.

Notices that for this example a subtask table is not required.

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Table 2.2: Specification definition table for Example 3

Specification Definition Table for Example 3 Workstation Task

Name Operations Sequence Event Conditions Flag Signals

Machine1 Sequence1 ({A1, A2, A3, A4}, B1, B2, B3, B4) ({A1, A2, A3, A4}, B1, (ONV Sec1 V Run)) - Machine1 Sequence2 ({A1, A2, A3, A4}, B1, B4, A1, B3) ({A1, A2, A3, A4}, B1, (ONV Sec2 V Run)) (B3, F inishSeq2)

Once the table is filled its information is translated into an IPN model. The computation for the IPN model of the specification is made with the algorithm presented in [7]. Figure 2.7 shows the IPN model of the specification of the Example 3 generated by applying the algorithm presented in [7].

Figure 2.7: IPN model of the specification of the Example 3.

2.3.3 Control synthesis

In [4], a method for the synthesis of a regulation controller was presented. The plant’s sets, functions and markings will be denoted with a superscript^P to distinguish them from those of the specification, denoted with a superscript^S.

The controller synthesis consists in three main steps described bellow:

• each specification reachable marking M_i^S ∈ R(Q^S, M₀^S) is associated to a plant reachable marking M_i^P ∈ R(Q^P, M₀^P) such that ϕ^SM^S_i = ϕ^PM^P_i , this association can be described by a linear function Π : R(Q^S, M₀^S) → R(Q^P, M₀^P), defined such that ΠM^S_i = M^P_i . The computation of the function can be performed by an integer program problem;

• each specification transition t^S_i ∈ T^Sis associated to a controllable plant firing sequence σ^P_i such that M^P_i [σ^P_i iM^P_j, where M^S_i[t^S_iiM^S_j, Π M^S_i = M^P_i and ΠM^S_j = M^P_j . The sequences can be computed by exploring the plant reachability graph, for instance by using the A∗ algorithm;

• based on the knowledge of the mapping Π and the controllable sequences σ_i^P, a controller can be synthesized as an agent that indicates suitable plant input symbols so the

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sequences σ_i^P are enforced when required by the specification. This controller can be realized as a function on the plant and the specification markings or as an IPN

The Algorithm 4.1 of [13] presents the computation of an IPN that represents the closed- loop system of the Plant under a proper tracking controller. Here, it is assumed that the Spec- ification is a state machine IPN.

Example 4:Consider the system of Example 3 (Figure 2.6) and the specification defined in Example 3 (Figure 2.7). By applying the Algorithm 4.1 of [13], the closed-loop system shown in Figure 2.8 is computed. This algorithm removes the original output labels from the specification places just by adding the character ∗ in the closed-loop.

Figure 2.8: IPN model of the closed-loop system of the system presented in Figure 2.6 under the specification of Figure 2.7.

The behavior of the closed-loop is described as follows. From the initial marking, if the conditions for the first task hold (ON,Sec1,Run), the tokens at the initial marking in the controller are moved from the initially marked places to the places labelled with b1 and ∗B1.

At this marking, the transition (∗B1•) is not enabled, but the token at the place with label b1 enables the transition b1 in the Plant (Actuator 1). The firing of this transition will eventually drive the actuator to the position B1, i.e., the Plant has reached a marking having the required output. At this new Plant marking, the controller transition with label {B1,A2,A3,A4} becomes enabled, moving the token from the place b1 to the next place. This new place has no label, which means that it is not associated either with the plant or specification. From this

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new marking, the transition ∗B1 becomes enabled and the specification can evolve. In this way, the controller provides symbols to the Plant in order to reach the marking that produces the required output symbols, when this marking is reached, the specification is allowed to evolve, requiring new output symbols.

2.4 Decentralized Control

In industrial applications, several components are integrated, composing large systems. There- fore the implementation of a centralized controller becomes complex and usually and frequently impossible, due to physical hardware limitations. Decentralized architectures allow to regulate a large plant by several controllers, the most frequently used are:

• Distributed control without communication: different controllers (implemented in different devices) control sub-assemblies (sections) of the plant, there are no communication among the controllers;

• Distributed control in an industrial network: different controllers control different sub-assemblies of the plant but they are integrated in an industrial network, allowing to communicate discrete variables;

• Coordinated control: similar to the distributed scheme but with the addition of a coordinator agent (for instance, a SCADA system) in a hierarchically upper level that supervises and provides directions to the local controllers.

Each of the previous decentralized control schemes requires a different control synthesis strategy.

2.4.1 Distributed control without communication

In this scheme, there is a collection of sub-specifications, each one describing sequences (and/or selections of sequences) of desired output signals involving different actuators. Thus, the plant can be split into sub-assemblies according to the actuators involved in the specifications. The interaction of the sub-assemblies in the closed-loop system appears due to the interaction in the plant and sensor/switches whose activation trigger more than one sub-sequence.

Let us provide a formal definition.

Definition 2.4.1 ([5]). A distributed-specification Q^s is the synchronous product of a disjoint collection of r subspecifications, denoted as Q^s1, ..., Q^sr, where each subspecification is defined as in Definition 2.3.2. Moreover, two different subspecifications cannot involve the same actuator, i.e., Σ^si_OT Σ^sj_O T Σ^act_O = ∅ for any Σ^si_O 6= Σ^sj_O, where Σ^act_O denotes the alphabet of actuators’ output signals.

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2.4.2 Distributed control in a network

In this scheme, the controller devices are connected to an industrial network, sending messages between controller agents becomes possible. Thus, in this scheme messages can be communicated at the specification level. Therefore, the interaction of the sub-assemblies in the closed-loop system appears not only because the interaction in the plant but also due to the messages at the specification level.

Definition 2.4.2 ([5]). A distributed-networked-specification Q^s is a safe and live IPN composed of:

1. the synchronous product of a disjoint collection of r subspecifications, denoted as Q^s1, ..., Q^sr, defined as in Definition 2.3.2, such that two different subspecifications do not involve the same actuator (i.e., Σ^sl_OT Σ^sj_O T Σ^act_O = ∅ for anyΣ^si_O 6= Σ^sj_O);

2. additional buffer places (messages) interconnecting subspecifications such that, for any buffer place p, | •p |=| p• |≥ 1, •p belongs to a subspecification Q^siand p• belongs to another Q^sj.

2.4.3 Coordinated control

In a SCADA system, several local controller devices can be implemented in such a way that each device controls a sub-assembly to describe particular sequences and a coordinator controller, implemented in the SCADA system, monitors and gives directions to the local controllers. In this scheme, the communication occurs between the local controllers and the coordinator. Here, the coordinator neither directly control actuators nor observe sensor signals.

Definition 2.4.3 ([5]). A coordinated-specification Q^s is a safe and live IPN composed of:

1. a specification Q^s^C, named coordinator, as defined in Definition 2.3.2 whose output alphabet is given byΣ^C = Σ^CsenS Σ^Ccoor, where Σ^Csen denotes the Plant’s sensor signals and Σ^CcoorT Σ^P = ∅;

2. a collection of r local subspecifications Q^s1, ..., Q^sr, where each subspecification is as defined in Definition 2.3.2 and whose output alphabet is a subset of Σ^PS Σ^Ccor.

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Chapter 3 Manufacturing Cell’s development:

Design

A key aspect of the thesis project was the design and construction of a fully automated manufacturing cell.

As motioned before, the main objective of this automated manufacturing cell is the practical implementation of TC algorithms and the observation of the behavior of the algorithms in real applications. In this way, the algorithms will be submitted to normal conditions that can be found in a factory, with external uncontrollable variables (users interaction, machine interactions, mechanical failures, pneumatic failures, electrical failures, and other variables not considered yet) that could affect the behavior of the TC algorithms. In a previous work [13] a first approach of TC was implemented on simulation achieving good results. However, there is no work in the literature about the physical applications of TC algorithms.

Although in the market exists some Training Platforms that could be acquired for exper- imenting the TC modelling and control methodologies, those Training Platforms are designed to teach practitioners how to perform particular technical tasks, thus these platforms have closed architectures that in some cases cannot show interesting applications for the TC, re- ducing or skewing the scopes of the results.

In the further sections of this chapter, some aspects of the manufacturing cell will be discussed. Firstly, a detailed explanation of the project’s requirement for the manufacturing cell is given. Secondly, the conceptual design of the manufacturing cell is explained. Next, the design of the components of the manufacturing cell is shown and explained. After that, the manufacture processes for the manufacturing cell’s components are described. Then, the development of the electro-pneumatic installation is presented. Last but not least, the manufacturing cell integration and testing are described.

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CHAPTER 3. MANUFACTURING CELL’S DEVELOPMENT: DESIGN 20

3.1 Project’s Requirements

The manufacturing cell to be developed must represent an automated production process that can be commonly found in the industry. The TC algorithms for plant modeling, specification, and control synthesis will be applied to this manufacturing cell. Therefore, the manufacturing cell must fulfill the following requirements to be plenty useful for our purposes:

• Combinatory: This requirement allows that several processes can occur in parallel. In such way, the trajectory that the system could perform is one among many different possible combinations.

• Existence of possible deadlock states: This requirement allows the possibility that the system could reach a deadlock state due to resource availability while performing different tasks.

• Open Architecture: This requirement allows flexibility in the final process.

• Modular Structure: This requirement allows to be able to add, remove, or modify some workstation in the manufacturing cell.

• Decentralized Control: This requirement allows to have several control cabinets. Due to the total amount of input/ output signals is not feasible to have all of them connected to one control cabinet. This feature provides the ability to implement different industrial networks and decentralized control architectures on the manufacturing cell.

• Flexibility: This requirement allows to be able to change the production process easily or to modify the product’s specification.

• Integration of Industrial Technology: This requirement allows to integrate components normally used in industry such as electro-pneumatic components, electrical components, conveyors, RFID, computer numerical control machine (CNC), inspection with computer vision, and collaborative robots.

In this way, the manufacturing cell was thought and designed to represent challenging, complex, and interesting cases for the study. Furthermore, all the requirements mentioned above provide features to the manufacturing cell that permit to investigate problems associated with the Industry 4.0 paradigm, such as the implementation of virtual commissioning, implementation of digital twins, and automation based on product lifecycle management (PLM).

3.2 Conceptual Design

The manufacturing cell will assemble, inspect, and pack a rattle toy. This toy consists of two MDF caps of 10 cm with ten holes equally distributed with three different geometric shapes: square, circle, and octagon. The caps are assembled with ten sticks of 10 cm each one with a diameter of 6.35 mm and include three marbles that could have the same color or a combination of three different colors. The operator of the system can be able to select one

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of the three different cap’s geometric shapes and the three marble’s colors to make the toy customizable from the nine possible combinations. Figure 3.1 shows the rattle toy in three possible combinations that the manufacturing cell can produce.

Figure 3.1: Three models of rattle toy’s render.

In general, the process that the manufacturing cell must perform consists in three main stages of the process:

• Process 1: Assembly process. In this process, the raw material is placed on strategic stations. The raw material is modified through several intermediate steps, and later it is used to assemble the rattle.

• Process 2: Detailing and Inspection process. In this process, the rattle previously assembled is submitted to a detailing station and then to an inspection process to verify if the rattle meets the specified order and detect any possible fault.

• Process 3: Packing process. In this process, the verified rattle is packed for storage or delivery.

This thesis focus on Process 1, the assembly process. The distribution of the assembly process can be seen on Figure 3.2.

As mentioned above the raw material is placed manually on strategics workstations. The raw materials are MDF caps, marbles, and wooden posts. In the dispenser workstation, the MDF caps are placed manually on three different dispensers depending on the cap’s shape, here the caps are stored and delivered. The cap is disposed one by one, then each cap goes through a conveyor, when arriving to a physical stop a RFID lector reads if the cap has a RFID tag or not, if not the pneumatic arm 1 takes it to a RFID station where a tag is placed into the cap, then the same pneumatic arm moves the cap to a CNC drill station to make the 10 equally distributed bores. When the CNC drill station finishes its process the pneumatic arm 1 returns the cap to the conveyor. If the cap already has a RFID tag, the pneumatic arm avoids the RFID station and goes directly to the CNC drill station.

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Once the cap has the 10 bores and it is at the end of the conveyor, the pneumatic arm 2 takes the cap from the conveyor and places the cap into a cap mounting platform, where the caps are positioned and wait until a second cap is placed by the pneumatic arm 2. This cap mounting platform has a capacity of two caps. When the two caps are placed in the workstation, another workstation, named wooden post positioner station, places each wooden post and the marbles. Once the wooden posts and marbles are placed the process is completed. The rattle is finally assembled and ready to go to Process 2: the detailing and inspection process.

Figure 3.2: Schematic of the assembly process (Top view).

3.3 Design of the Process 1: Assembly process of the manu- facturing cell

The design of the assembly process is divided into a general mounting structure, a conveyor, and seven workstations that include: cap’s dispensers, 3 degrees of freedom pneumatic arm, CNC driller machine, RFID station, 5 degrees of freedom pneumatic arm, cap mounting platform, and wooden post positioner. The arrangements of all the elements mentioned above can be seen in Figure 3.3.

All the mechanical drawings of the manufacturing cell components can be found in Ap- pendix A.

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Figure 3.3: Manufacturing Cell render.

3.3.1 Mounting structure design

The mounting structure was designed based on commercial Bosch aluminum profiles and its special connectors and accessories.

The mounting structure, shown in Figure 3.4, has a general frame, and two secondary frames based on a ladder design.

Figure 3.4: Manufacturing cell’s structure Render

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The general frame, shown in Figure 3.5 (a), is made with 40 mm Bosch type aluminum profiles and contains four swiveling casters with lock. This general frame supports almost all the loads generated by the components that will be settled into the manufacturing cell. This structure has a general dimension of 1.93 meters length, 1.63 meters width, and 1.13 meters height with two levels, one 0.42 m from the floor to the top of the profile, and the other at 1.13 m from the floor to the top of the profile.

The secondary frame based on a ladder design, shown in Figure 3.5 (b), is replicated two times, one for each level of the general frame. It is made with a 20 mm Bosch type aluminum profile and has five divisions. Over these secondary frames, all the components of the manufacturing cell will be mounted. The general dimensions of this second structure are 1.85 meters in length, 0.84 meters in width, and 0.02 meters in height.

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Figure 3.5: (a) Structure made with 40 mm Bosch type aluminum profile render. (b) Structure made with 20 mm Bosch type aluminum profile render

3.3.2 Cap’s Dispensers design

The cap’s dispensers main objective is to store over 10 caps in a container and to deliver these caps one by one on a conveyor. The caps will be placed manually in the container by an operator.

The design of the cap’s dispensers consists of a container where the caps are stacked, a base where the actuator is hold, and a structure mounted over the stem of the actuator.

The container design is a structure made with 6mm acrylic sheets and 8 aluminum an- gles to join the structure and provide stiffness in the structure. There are two different models for the container with a slight difference between each one depending on the position where

Instituto Tecnol´ogico y de Estudios Superiores de Monterrey