The self-tuning PID decoupling control based on the diagonal recurrent neural network

The diagonal recurrent neural networks (DRNN) is a powerful computational tools that have been used extensively in the areas of pattern recognition, systems modeling and identification. This paper proposes a self-tuning PID decoupling control based on DRNN neural networks for solving the time-varying coupling nonlinear control problems. The approach can on-line identify the controlled plant using the DRNN identifier and tune the parameters of the PID controller automatically. The simulation results show that the proposed control algorithm is an efficient method to solve nonlinear coupling problems. From the simulation results we see that the system output can tract and decouple the reference input satisfactorily, and the performance of the proposed controller is better than that of the conventional PID decoupling controller in the time-varying coupling nonlinear system, such as good adaptability, strong robustness and fast response speed.