Genetic learning of fuzzy controllers

Dumitrache, I.; Buiu, Cătălin

doi:10.1016/s0378-4754(99)00005-1

Cited by 21 publications

(12 citation statements)

References 2 publications

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“…The first main work related to GA is attributed to Holland [17] as a global optimization method for complex function. GA have been widely and successfully applied to various problems like operation research, image processing, and control problems [18][19][20]. A typical progression of a GA is depicted in Fig.…”

Section: Genetic Algorithmmentioning

confidence: 99%

GA-based nonlinear predictive switching control for a boiler-turbine system

Shen

Krug

et al. 2011

J. Control Theory Appl.

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In this paper, the problem of designing a controller for a highly coupled constrained nonlinear boilerturbine system is addressed with a predictive controller. First, a nonlinear predictive control is implemented by genetic algorithm. Second, to guarantee fast output stabilization, an H-infinity fuzzy state-feedback tracking control is applied with a designed switching principle. The success of such a control structure is based on taking advantage of the optimal input sequence derived from the nonlinear predictive control based on artificial intelligent while ensuring a fast decay of the steady state error. Simulation results of the proposed design are given to illustrate its effectiveness and compared to other control schemes.

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Section: Genetic Algorithmmentioning

confidence: 99%

GA-based nonlinear predictive switching control for a boiler-turbine system

Shen

Krug

et al. 2011

J. Control Theory Appl.

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“…Further to effects laid in releasing the burdens in building the fuzzy system, the use of intelligent [25] and evolutionary computing methods is also a very attractive approach [26]. These attempts include the genetic algorithm [27] which was inspired by the process in which living species evolve according to the principle of the survival of the fittest. Due to its generality, the algorithm is able to tune the fuzzy controller parameters to near-optimal values.…”

Section: Introductionmentioning

confidence: 99%

Design of an Automatically Tuned Fuzzy Controller for a Truck and Multitrailer System

Ren

2013

Advances in Mechanical Engineering

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The use of a truck and multitrailer system is advantageous because of its ability to transport heavy and large parts with a single powered vehicle. On the other hand, when the system is deployed in an autonomous and unmanned scenario, it remains a challenging task to design a drive controller. Since the drive is only applied to the truck and motivated by successful cases of human expert drivers, a fuzzy controller is developed to generate speed and turn rate commands in order to steer the multi-trailer system to reach the target position with minimum position error. Furthermore, in order to make the controller design efficient and effective, the parameters in the fuzzy controller including the membership functions and rules are automatically tuned using the implementation of efficient particle swarm optimization algorithm instead of relying solely on human expert knowledge. Nearoptimal parameters are then derived and adopted in the controller, and drive commands are then generated. The performance of the truck-and-multi-trailer system under fuzzy control is verified through simulation studies, and satisfactory results are obtained.

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“…al, 1998), where up to ten trailers can be controlled representing those as Takagi-Sugeno models and applying linear matrix inequalities method. A simplified version of the control problem has been extensively investigated in the field of fuzzy control (Ramamoorthy & Huang, 1991;Ismail & Abu-Khousa, 1996;Kim, 1998;Dumitrache & Buiu, 1999). Also parking problem has been investigated by many researchers in the field of computational intelligence (for example; chang in (Chang et.…”

Section: Introductionmentioning

confidence: 99%