A novel RBF-PID control strategy for turbine governing system based on chaotic PSO

Aiming at the non-linear characteristics such as dead-time and saturation in the turbine governing system, a novel PID control strategy with radial basis function network tuning algorithm was proposed. Thus the governing system can be identified on line, and PID parameters can be adjusted in real time. Additionally, a new chaotic particle swarm algorithm is proposed which combines the particle swarm algorithm and the chaotic optimization in order to avoid the premature convergence of the particle swarm algorithm and the shortcomings of chaotic optimization, such as slow searching speed and low accuracy when used in the multivariable systems or in large search space. The initial values of RBF network parameters are selected by chaotic particle swarm optimization algorithm which can avoid blindness of choosing parameters randomly. By comparing the results of simulation, the method does increase the system´s control precision and enhance the system´s response speed.