Currently, research on parameter tuning methods is active due to the widespread use of PID controllers in industrial practice. Many studies have been carried out on self-tuning methods to tune the parameters of PID controllers with varying plant parameters, bringing about remarkable successes.
Meanwhile, the Particle Swarm Optimization (PSO) algorithm is a kind of optimization method based on the theory of cluster intelligence, which has been widely applied in the field of function optimization, neural network learning, multiobjective optimization, fuzzy control system, etc. The population optimization algorithm is easily applicable to various types of problems because there are no complex crossover and mutation operations like in genetic algorithms. The greatest advantage of PSO algorithm is its fast convergence speed, simplicity of evolution operation, low computational cost and small number of parameters used in it.
The T-S fuzzy model approximates nonlinear characteristics, and inference using it can be used as a tool to implement controllers or fuzzy models with optimal and adaptive functions because it is simple and convenient for mathematical analysis and it can be easily combined with PID control or adaptive control.
Kim Yong Su, a researcher at the Faculty of Automatics, has proposed a method for designing a PID controller, based on the characteristics of PSO algorithm and T-S fuzzy model.
According to the proposed method, you first use the PSO algorithm to determine the position of a dominant pole which minimizes the judge function consisting of Integral of Absolute Error (IAE) and overshoot value and then express it as a T-S fuzzy model to determine the parameters of a PID controller using fuzzy inference.
The proposed method allows you to get both the optimal dominant pole and the exchanging frequency at the same time, and to get a good control result as the overshoot value is small.
...
