Acuerdo que Debe Regir las Actividades de los Estados en la Luna y otros Cuerpos

The pro le-controller algorithm can now be summarised into the

following steps:

ial desired closed-loop system poles and zeros polynomials T and

ns _{p i d}

ured pole-zero placement controller.

ontroller Algorithm Summary: SISO Case

posed intelligent multip

K and

Step 1: Select the init

H~ respectively.

Step 2: The system current controller is initially set to work with the Pole-Zero Placement controller (i.e. _{Cη =[1,1]}) in order to avoid high control action, at the start of

the control process, and consequently prevent output signal overshooting [100].

Step 4: Read the current values of y(t) and w(t).

Step 3: Select F and the initial value for the gain v for the desired PID control structure.

Step 5: Compute the control input u(t) using (4.34) when the current controller is pole- zero placement, or using equation (4.29) when the controller is the conventional PID controller.

Step 6: Estimate the process linear parameters Aˆ and Bˆ using the least squares algorithm of the linear sub-model in the GLM.

Step 7: Compute f₀(.,.)= y(t)−yˆ(t)_{, where yˆ(t) is the output of the linear sub-model.}

user requirements.

Step 10: The behaviour recogniser will report the system performance to the switching and tuning subsystems as concluded in (t)

Step 8: Apply the RBF based nonlinear sub-model of the GLM to obtain (.,.)f₀ by using equations (4.16)-(4.21).

Step 9: The behaviour recogniser will assess the current performance of the control system using the system output y(t), the set-point w(t), the control input u(t) and the

Ξ .

Step 11: The fuzzy logic switching subsystem will employ Ξ_s(t) to make the switching decision for the next controller to be activated, that is achieved by setting _Cη to [1,1] for

a Pole-Zero Placement controller or to [0,0] for a conventional PID controller.

Step 12: The fuzzy logic tuning subsystem will decide the tuning values for the current controller parameters’ based on the input Ξ_t(t).

4.6 Summary

A central theme in the study of intelligent control is the modelling and control of complex systems. Every control system, from the simplest (e.g. the thermostat or a simple positioning servo) to the most complex currently in use (e.g. control of unmanned air vehicle) utilize feedback in one form or another. The essence of the concept involves the triad: measurement, comparison, and correction [98]. That is, measurement of relevant variables, comparison with desired values, and using the errors to correct behaviour. The complexity of the control systems used nowadays emphasise

In this chapter a new intelligent nonlinear multiple-controller framework incorporating a fuzzy logic based switching and tuning supervisor is developed to control complex SISO systems. The framework integrates the simple fuzzy rule based supervisor with

nonlinear-controllers along with a GLM framework. In the GLM, the unknown complex process to be controlled is represented by an equivalent stochastic model consisting of a linear time-varying sub-model plus a computationally-efficient RBF neural-network the involvement of more sophisticated and intelligent techniques, that is to cope with the measurements, comparisons, and corrections required for the control decision making process. By considering the design of a multi-controller, the automation capabilities provided by the field of Artificial Intelligence can be integrated with the concepts and techniques from this field to the multiple controller approach of designing control systems may be advantageous, from a practical perspective, to solve such complex control problems.

based learning sub-model. The proposed methodology provides the designer the choice between the conventional PID adaptive controller, or the PID structure based

with online tuning of the controller parameters, is made using a fuzzy logic based supervisor operating at the highest level of the system. The proposed intelligent multiple-controller works to adaptively tracking a desired reference signal, achieving the desired output signal performance and penalising excessive control actions, in

It is often the case that higher-level knowledge about how to control a process is

[156

(simultaneous) pole and zero placement controller. Both controllers (multiple controller modes 1 and 2) benefit from the simplicity of having a PID structure, operate using the same adaptive procedure and can be selected on the basis of the required performance measure.

The switching decision between the two nonlinear fixed structure controllers, along

response to the current performance of the control systems as assessed by the behaviour recogniser. The stability analysis of the proposed intelligent multiple-controller framework for SISO complex systems will be considered as a subset of the general multivariable case in the next chapter.

available along with the lower-level data on which simple control systems operate ]. In the proposed intelligent framework, the tasks of fuzzy logic based coordination between multiple-controllers and tuning of the controller parameters are

o ,

ut the system to be controlled was essential to design the fuzzy logic high- based on information about the application operating points, including system transfer function poles and zeros, and the controller PID gains. Acc rdingly information acquired abo

level supervisor. Taking into account the real-time implementation

minimizing the amount of memory used and the time that it takes to compute the fuzzy constraints, such as

outputs using the given inputs [4], the fuzzy logic based switching and tuning

supervisor is d signe fu y rules with minimum input and

output parameters.

e d with a minimum number of zz

The next chapter presents the intelligent multivariable multiple-controller framework for the general case of complex MIMO systems.