Neural network control for multivariable nonlinear systems with a fast learning optimisation algorithm

A radial basis function (RBF) neural network model based predictive control scheme is developed for multivariable nonlinear systems in this paper. A fast convergence algorithm is proposed and employed in multidimensional optimisation in the control scheme to reduce the computing time and save required computer memory. The scheme is applied to a simulated two-input two-output nonlinear process for set-point tracking control. Simulation results demonstrate the effectiveness of the control strategy and the fast learning algorithm for multivariable non-linear processes. Comparison ofthe performance with PID control is included.

کلیدواژه ها:

Fast optimization ، Learning Algorithm ، Nonlinear Systems ، Process control. RBF