Composite control of RBF neural network and PD for nonlinear dynamic plants using U-model

In this paper, based on a pole placement PID controller designed, a composite control of RBF and PD is proposed for the tracking control of nonlinear dynamic systems. The proposed scheme combines the stability of PD and the ability of the RBF to approximate any function with any precision, with the control-oriented nature of the U-model to achieve exact tracking of nonlinear plants. The proposed structure has a more general appeal than many other models, such as polynomial NARMAX (Nonlinear Autoregressive Moving Average with Exogenous inputs) model and the Hammerstein model, etc. In addition, the control law is shown to be more simplistic in nature. The effectiveness of the proposed scheme is demonstrated with the help of simulations for the pole placement PID controller and the controller without the Newton-Raphson algorithm.