Evaluación y Preparación para el Censo 2010

At the beginning of the 60s decade, artificial intelligence (AI) has found its way into industrial applications. The area of expert knowledge-based decision making was the most attractive area for the design and monitoring of industrial processes using this technique. The advances in computer technology allow to developed many applications. The invention of Fuzzy chips in the 1980s was the milestone for this technique in industrial application, especially in Japan. Also neural networks and evolutionary computation were also received a high boost in industry and academia. As a result of these events, Soft-Computing was born. Nowadays Soft-Computing continues to play a important role in modeling, system identification, and control systems. The principal techniques of Soft- Computing are Fuzzy logic, neural networks, genetic algorithms, genetic pro- graming, chaos theory and probabilistic reasoning. One of the principal compo- nents is Fuzzy logic. From a control theoretical point of view, Fuzzy logic has been used for all the important aspects of systems theory, that is modeling, iden- tification, analysis, stability, synthesis, filtering, and estimation. Fuzzy control provides a formal methodology for representing, manipulating, and implement- ing a human’s heuristic knowledge about how to control a system.

14 2.4. Fuzzy Control

for challenging real-world applications. As opposed to classical control ap- proaches, like proportional-integral-derivative (PID), lead-lag, and state feed- back control, where the focus is on modeling and the use of this model to con- struct a controller that is described by differential equations. in Fuzzy control we focus on gaining an intuitive understanding of how to best control the process, then we load this information directly into the Fuzzy controller. Basically, the motivation of use Fuzzy control is to avoid the difficult task of modeling and simulating complex real-world systems for control system developments.

There are a huge amount of uses of Fuzzy control in different research fields. In the early 1970s during the world’s first big energy crisis the concept of the building energy management (BEMS) was introduced. The Fuzzy control was used to manage the “smart” buildings by simultaneous sensing, control, and monitoring of the internal environment responding to the external climate fac- tors (Talebu-Daryani, 1999). In (Talebu-Daryani, 1995) the system structure were established. A control application for manage the air conditional system is presented in (Lygouras et al., 2008), (Eftekhari et al., 2003). An extended review of Fuzzy control application for BEMS is presented in (Talebu-Daryani and Plass, 1998), (Talebu-Daryani and Luther, 1998).

Several applications of FCSs with Mamdani FCs are reported in manufac- turing. They include control of industrial weigh belt feeders (Zhao and Collins, 2003), the realization of specific controllers (Dvorak et al., 2003), (Galichet and Foulloy, 2003), control of machining processes (Haber et al., 2003), (Nandi and Davim, 2009), (Haber et al., 2009), (E.Haber et al., 2010), laser tracking systems (Bai et al., 2005), plastic injection molding (Chen et al., 2008) and vibration sup- pression (Marinaki et al., 2010). The manufacturing area is related to robotics. Mamdani FCs concern control of both manipulators.

The automotive industry is one special successful area of Mamdani FCs. Problems and practical issues related to suspension control are discussed in (Caponetto et al., 2003). The control of hybrid electric vehicles is treated in (Schouten et al., 2002) and the complexity of all related control strategies is emphasized in (Salmasi, 2007). The control of anti-lock braking systems is an- alyzed in (Mirzaei et al., 2006), (Zhao et al., 2006). Process industries include Mamdani fuzzy control. The applications reported in this context tackle the con- trol of furnaces (Moon and Lee, 2003), filtration processes (Onat and Dogruel, 2004), heat exchangers (Maidi et al., 2008) or forging machines (Lee and Kopp, 2001). The control of the temperature inside a liquid helium cryostat is presented in (Santos and Dexter, 2001), (Santos and Dexter, 2002). Fuzzy control has re- cently been applied to a variety of servo systems and actuators in mechatronics (Ahmed et al., 2001), (Cho and Lee, 2002), (Precup et al., 2003), (Kalyoncu and Haydim, 2009).

A large overview of different control applications using Soft-Computing techniques is presented in (Zilouchian and Jamshidi, 2000) (Malhotra et al., 2011a), (Precup and Hellendoorn, 2011a), (Lee, 1990b), (Lee, 1990a).

Chapter 2. State of Art 15

proaches for mobile robots or unmanned ground vehicles are mention next. Navigation tasks are implemented with z fuzzy controller in (P.Moustris and G.Tzafestas, 2011); and (Pradhan et al., 2009) in which a big number of mobile robots are controlled. A hybrid controller with Fuzzy and Petri-potential is used for the same purpose in (Parhi and Mohanta, 2011). Onieva et. altres presents in (E.Onieva et al., 2011) an lateral control for unmanned vehicles using a Fuzzy controller optimized with genetic algorithms. The diagonal parking with a car- like autonomous vehicle is presented in (Baturone et al., 2004). The control of the steering wheel of a nonholonomic mobile robot in (Yang et al., 2004). A fuzzy-path planning for a tracked mobile robot (an excavator) is presented in (Saeedi et al., 2005). A motion control of multiple vehicle for passenger comfort in (Raimondi and Melluso, 2008). A system on chip of a fuzzy control for path planning with a Pionner mobile robot is presented in (Tzafestas et al., 2010).

Because of the the complexity of the dynamics of the aerial robots, the con- trol of this type of robots is one of the most fruitful field of the Fuzzy control. The way that the Fuzzy logic manage the uncertainty caused by noisy informa- tion of sensors allow to work with different type of sensors like radar, laser and GPS. The first work with Fuzzy control an unmanned aerial vehicles is presented by Sugeno in (Sugeno et al., 1995), after that a lot of research were done in this field. The automated altitude hold of a UAV was developed in a NASA project and presented in (Dufrene, 2004). In (Kadmiry and Driankov, 2004) is presented the simulation results of the altitude, pitch, roll and yaw control of an unmanned helicopter. (Phillips et al., 1996) presents a fuzzy control of a UH-1 helicopter of the US-Army. The stabilization of an unmanned helicopter is also presented in (Antequera et al., 2006), and the hovering using visual information is shown in (Shin and Oh, 1993). The control of an unmanned Kiteplane is presented in (Kumon et al., 2006), the control of a fixed wind is presented in (Gomez and Jamshidi, 2010), and a quadrotor control is presented in (Nicol et al., 2011), (Santos et al., 2010). A close formation flight control of multi-UAVs (Smith, 2007). A fire detection using an unmanned helicopter is presented in (Nikolos et al., 2004). An aerial river searching and tracking is presented in (Rathinam et al., 2007). Simulated obstacles avoidance and 3D path planning for UAVs are presented in (Hrabar, 2008). A control, navigation and collision avoidance without vision is presented in (Chee and Zhong, 2013).

Chapter

3

Fuzzy Logic Control for Ground

Vehicles

In this chapter is presented the works using Fuzzy control with a commercial car “Citro¨en C3 Pluriel”. These works were part of an industrial project with Siemens Spain. The aim of this project was transforming a commercial car into a driverless car using just vision. Based on the requirements of the project we have to create a system that can follows a line painted on the road under inner city conditions of speed and low radius curves. The car must go inside a circuit and its position must be know. But in the case that the system loss any loca- tion information, the car must continue working. For this reason two system approaches were done. The first one does not use any information about the circuit. No curve and straight characteristic are known. The only information used is the line to follow. The second one uses the line information and a visual mark detection. Using these visual marks the system is able to know its exactly location inside the circuit and the curves and straight characteristics. For both approaches a Fuzzy controller was developed to control the steering wheel of the

publications related to this chapter:

-“A Visual AGV−urban Car using Fuzzy Control, IEEE−ICARA’11

-“Autonomous Guided Car using a Fuzzy Controller”, Recent Advances in Robotics and Au- tomation. Springer Studies in Computational Intelligence, 2013

-“Inner-City Driverless Vehicle: A Low Cost Approach with Vision for Public Transport inside Cities”, Robotics and Automation Magazine (under review)