| Abstrakt: |
Several innovative techniques have been developed recently for the intelligent design of the widely used PID controllers for industrial systems where accuracy is of paramount concern. In addition to the classical methods, these include soft computing based methods such as Fuzzy logic, Neural Network, Genetic Algorithm (GA), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO). PSO is a well-established computational technique based on swarm intelligence. This method is motivated by the observation of social interaction and behavior of animals with limited brains in solving complex problems such as fish schooling and bird flocking. In this paper, PID controller parameters have been designed by using PSO for three different types of plants represented by their transfer functions and compared with those obtained by using the standard Ziegler–Nichols (Z–N) method. The proposed methodology was implemented using MATLAB/Simulink software, and the step response characteristics obtained by the application of Z-N method based PID controller and PSO based PID controller have been analyzed. The performance of both strategies is compared in terms of PID controller tuning parameters, peak overshoot (%), and settling time. It has been found that PSO based controller performs better than Z-N based controller for all three plants. |
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