Research on optimization of fuzzy control rules based on particle swarm optimization algorithm
In order to avoid the complex adjustment of scaling factor and quantization factor in the design of fuzzy controller and the shortcomings that the fuzzy control rules can not be changed once identified, a fuzzy controller with self-optimizing rules is adopted . Particle swarm optimization (PSO) algorithm is used to optimize the parameters of fuzzy controller. A new evaluation function including the system adjusting time, rise time, over-shoot, and steady-state error is defined. A group of parameters of the fuzzy controller that minimize the evaluation function is calculated rapidly by searching in the given controller parameters area. Numerical simulations show that the controller can easily be applied to the first-order and second-order system with time delay, no overshoot, and its performance is also better than the classic PID optimized by PSO.