Stable and adaptive PID control based on neural network

Classic PID control method which is based on precise mathematical model has poor adaptivity and is not adaptive to nonlinear and time-variant plants. Conventional neural network is always complicated and its stability often suffers from the effect of initial weight value selecting. A simple stable direct adaptive PID control algorithm is proposed, which is based on RBF neural network. To guarantee the system stability and improve the system precision, initial weight value selecting problem for the neural network is discussed and corresponding iterative algorithm is provided. Simulation results indicate that the system robustness and tracking performance are superior to those of classic PID method.