Anti-lock Braking System (ABS) is a nonlinear and time varying system including uncertainty, so it cannot be controlled by classic methods. Intelligent methods such as fuzzy controller are used in this area extensively; however traditional fuzzy controller using simple More
Anti-lock Braking System (ABS) is a nonlinear and time varying system including uncertainty, so it cannot be controlled by classic methods. Intelligent methods such as fuzzy controller are used in this area extensively; however traditional fuzzy controller using simple type-1 fuzzy sets may not be robust enough to overcome uncertainties. For this reason an interval type-2 fuzzy controller is developed to improve the performance of ABS in presence of uncertainty such as changing road condition. The output membership functions have been optimized by Discrete Action Reinforcement Learning Automata (DARLA) technique. Simulation results show the effectiveness of the proposed controller in comparison to type-1 fuzzy controller.
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Controller design and optimization problems, with more than one objective, are referred as multiple objectives or multiple attributed problems. In this paper, a novel method is proposed for designing optimum PID controller that is called genetic multiple attributed deci More
Controller design and optimization problems, with more than one objective, are referred as multiple objectives or multiple attributed problems. In this paper, a novel method is proposed for designing optimum PID controller that is called genetic multiple attributed decision making method (GMADM). This method is newer than the previous methods and in this paper some options are considered that have not been considered in previous paper for simplicity. The proposed PID controller is applied on the automatic voltage regulator (AVR).An automatic voltage regulator system is the main part of a generator because this system keeps the output voltage in constant level. The simulation results of automatic voltage regulator system are compared with conventional multiobjective algorithm, known as multiobjective genetic algorithm (MOGA).The simulation results of automatic voltage regulator system show that GMADM method is better than MOGA and number of optimum solutions of the proposed method is greater than the other one.
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