Self-tuning PID controller based on quantum evolution algorithm and neural network

At present, neural network and quantum evolution algorithm have been used in controller tuning problems, separately. In the past, quantum evolution algorithm, the result of fusing of quantum computing and evolution computing, is attractive as one way to give us suitable answers for optimization problems. For a long time, PID control schemes are widely used in most of control system. PID optimal parameters have a great influence on the stability and the performance of the control system, so, how to determine or tune them, is still a very important problem. In this paper, we propose to combine quantum evolution algorithm and neural network joint use. Simulation results show that this is a feasible method, and the controller has enhanced response speed and robustness, and can be used for different kinds of objects and processes.