Fast Genetic Algorithms Used for PID Parameter Optimization

Tzeng

2010 International Conference on Machine Learning and Cybernetics

2010

This paper deals with the optimal design of PID controllers for a proton exchange membrane fuel cell (PEMFC). The dynamic model of the PEMFC with reformer is firstly presented. The inlet hydrogen flow is supplied by reformer and the oxygen flow is supplied by air or pure oxygen according to the hydrogen-oxygen flow ratio. The partial pressures of hydrogen and oxygen are then transformed based on the mole conservation principle. Moreover, we design the PID controllers, which are installed for controlling PEMFC and reformer, using genetic algorithm (GA) to achieve better transient and steady-state performances of PEMFC.

Section: Optimal Tuning Of Pid Controllers Using Genetic Algorithmmentioning

confidence: 99%

Optimization design of PID controllers for PEMFC with reformer using genetic algorithm

Tzeng

2010 International Conference on Machine Learning and Cybernetics

2010

2009 International Conference on Artificial Intelligence and Computational Intelligence

“…There are some problems produced by using binary encoding [7][8][9][10]. For example, the precision of solutions and the size of search space are influenced due to the binary strings.…”

Section: ) Parameters Encodingmentioning

confidence: 99%

Genetic Simulated Annealing Algorithm Used for PID Parameters Optimization

Wang

Jin

Wang

et al. 2009

A type of genetic simulated annealing algorithms (GSAAs) is presented, which is used to optimize the parameters of proportional-integral-derivative (PID) controllers. This approach combines the merits of genetic algorithms (GAs) and simulated annealing algorithms (SAAs). By integrating the global search ability of GA with the local search ability of SAA, the search ability of GSAA is much stronger than GA's and SAA's search ability. So, GSAA could find the global optimal solution of the given problem. Furthermore, the adaptive probability for crossover operator and nonuniform mutation operator is used in the GSAA, which can eliminate the phenomena of premature converge. Computer simulation on the speed control system of a kind of mobile robots is relized by Matlab. The results of computer simulation demonstrate that, comparing with the GA and SAA, the response speed of the PID controller can be improved due to the parameters produced from GSAA.

“…For example, evolutionary algorithms such as Genetic Algorithm (GA) [6] [8][9] and PSO [10][11] have widely been applied to tune PID controllers.…”

Section: Introductionmentioning

confidence: 99%

Evaluation function comparison of particle swarm optimization for buck converter

Altınöz

Erdem

2010

Speedam 2010

PID controller has been frequently applied to various application areas because of mathematical definition and overall performance which is greatly depended on accuracy of the control parameters. Therefore, conventional and evolutionary algorithms have introduced to find these parameters as tuning methods. In present evolutionary algorithms have frequently used in PID tuning applications. However, the tuning performance greatly depends on the evaluation function. This study focuses on design of a particle swarm optimization (PSO) based PID controller by using 8 different fitness (evolution) functions. The performance of the optimized controllers is compared with respect to following criteria: Overshoot, Undershoot, Rise Time, Settling Time, and Steady State Error. To compare the proposed optimized controller, a DC-DC buck converter is selected as a test bed. Firstly, the parameters are optioned in the simulation environment, and then the optimized controllers are compared in the hardware circuit.