Evolutionary Fuzzy Controller Research Articles

Optimal Adjustment of Evolutionary Algorithm-Based Fuzzy Controller for Driving Electric Motor with Computer Interface

Optimal Adjustment of Evolutionary Algorithm-Based Fuzzy Controller for Driving Electric Motor with Computer Interface

On accommodating the semantic-based interpretability in evolutionary linguistic fuzzy controller

On accommodating the semantic-based interpretability in evolutionary linguistic fuzzy controller

AN EFFICIENT EVOLUTIONARY NEURAL FUZZY CONTROLLER FOR THE INVERTED PENDULUM SYSTEM

In this article we propose an evolutionary neural fuzzy controller for the planetary train–type inverted pendulum system (IPS) and verify its effectiveness. The novel hybrid particle swarm optimization (HPSO) learning algorithm of the proposed controller is based on approaches of the fuzzy entropy clustering (FEC), the modified PSO (MPSO), and recursive singular value decomposition (RSVD). The FEC is applied to generate base particles and the MPSO is proposed to effectively improve the performance of the traditional PSO. There are mainly two different characteristics between the MPSO and its original version; that is, the initial parameters of the MPSO are calculated by an effective local approximation method (ELAM), and the global optimum is chosen by the multi-elites strategy (MES). In addition, we use the RSVD to determine the optimal consequent parameters of fuzzy rules, in order to reduce requirements of the computational time and space. Experimental results show that the proposed approach outperforms the proportional–integral–derivative (PID), PSO, and MPSO in terms of better abilities of tracking and noise rejection for planetary train–type IPS.

Coordinated Control of Flexible AC Transmission System Devices Using an Evolutionary Fuzzy Lead-Lag Controller With Advanced Continuous Ant Colony Optimization

This paper proposes an evolutionary fuzzy lead-lag control approach for coordinated control of flexible AC transmission system (FACTS) devices in a multi-machine power system. The FACTS devices used are a thyristor-controlled series capacitor (TCSC) and a static var compensator (SVC), both of which are equipped with a fuzzy lead-lag controller to improve power system dynamic stability. The fuzzy lead-lag controller uses a fuzzy controller (FC) to adaptively determine the parameters of two lead-lag controllers at each control step according to the deviations of generator rotor speeds. This paper proposes an Advanced Continuous Ant Colony Optimization (ACACO) algorithm to optimize all of the free parameters in the FC, which avoids the time-consuming task of parameter selection by human experts. The effectiveness and efficiency of the proposed evolutionary fuzzy lead-lag controller for oscillation damping control is verified through control of a multi-machine power system and comparisons with other lead-lag controllers and various population-based optimization algorithms.

Evolutionary fuzzy logic control of base-isolated structures in response to earthquake activity

This paper presents a design procedure of an evolutionary fuzzy logic controller (EFLC) for a class of base-isolated structures subjected to seismic activity with the goal to reduce the absolute motion of the base. The structure is controlled through a single force applied from a controllable MR damper placed on the first storey of the structure. The restraining control force is computed in real time, using an evolutionary fuzzy logic controller. The evolutionary fuzzy controller possesses the parameters of membership functions whose optimum values are computed using genetic algorithms (GA). The resultant disturbance mitigation of the proposed controller has been verified through extensive simulation of a six-storey structure, using disparate earthquake ground motions. Three main advantages of the evolutionary fuzzy logic controller can be summarized as follows. First, external disturbances can be considered in the EFLC, second, the proposed EFLC can control a structure with unknown dynamics, and third, the EFLC can easily be applied to semi-active structural control. Copyright © 2005 John Wiley & Sons, Ltd.

Load-frequency control by hybrid evolutionary fuzzy PI controller

Power-system load-frequency control by fuzzy-PI (FPI) controller is proposed. During control, a fuzzy system is used to decide adaptively the proper proportional and integral gains of a PI controller according the area-control error and its change. To ease the design effort and improve the performance of the controller, design of the FPI controller by hybridising a genetic algorithm and particle-swarm optimisation, called FPI–HGAPSO, is proposed. FPI–HGAPSO is based on the hybrid of the genetic algorithm and particle-swarm optimisation. In FPI–HGAPSO, elites in the population of GAs are enhanced by particle-swarm optimisation and these enhanced elites are selected as parents for crossover and mutation operations. Simulations of the proposed evolutionary FPI-control approach on a multiarea interconnected power system with different kinds of perturbations are performed. The performance of the proposed approach is verified from simulations and comparisons.

Temperature control by chip-implemented adaptive recurrent fuzzy controller designed by evolutionary algorithm

Online adaptive temperature control by field-programmable gate array (FPGA) - implemented adaptive recurrent fuzzy controller (ARFC) chip is proposed in this paper. The RFC is realized according to the structure of Takagi-Sugeno-Kang (TSK)-type recurrent fuzzy network. Direct inverse control configuration is used. To design RFC offline, evolutionary fuzzy controller using the hybrid of the Simplex method and particle swarm optimization (SPSO) is proposed. In SPSO, each RFC corresponds to a particle, and all the free parameters in RFC are optimally searched. We use the PSO to find a good solution globally, and the incorporation of the Simplex method helps find a better solution around the local region of the best solution found by PSO so far. Then, online adaptive temperature control with ARFC chip implemented by FPGA is proposed. In the ARFC chip, the consequent parameters of all rules are all tuned online using gradient descent. To verify the performance of the ARFC chip, experiments on a water bath temperature system are performed.

A quantitative analysis of evolvability for an evolutionary fuzzy logic controller

This paper presents a quantitative analysis of evolvability with evolutionary activity statistics in an evolutionary fuzzy system. In general, one can estimate the performance of an evolved fuzzy controller by its fitness. However, it is difficult to

Emergent behaviors of a fuzzy sensory-motor controller evolved by genetic algorithm

Recently, there has been extensive work on the construction of fuzzy controllers for mobile robots by a genetic algorithm (GA); therefore, we can realize evolutionary optimization as a promising method for developing fuzzy controllers. However, much investigation on the evolutionary fuzzy controller remains because most of the previous works have not seriously attempted to analyze the fuzzy controller obtained by evolution. This paper develops a fuzzy logic controller for a mobile robot with a GA in simulation environments and analyzes the behaviors of the controller with a state transition diagram of the internal model. Experimental results show that appropriate control mechanisms of the fuzzy controller are obtained by evolution. The controller has evolved wen enough to smoothly drive the robot in different environments. The robot produces emergent behaviors by the interaction of several fuzzy rules obtained.