Nueral network internal model control for MIMO nonlinear processes

An internal model based neural network control is proposed for unknown multi-input multi-output (MIMO) nonlinear processes in non-affine discrete-time state space form under model mismatch and disturbances. Based on the neural state space model built for an unknown nonlinear MIMO state space process, an approximate internal model and approximate decoupling controllers are derived simultaneously. Thus, the learning of the inverse process dynamics is not required. The neural network model based extended Kalman observer is used to estimate the states of a nonlinear process as not all states are accessible. The application to a distributed thermal process shows the effectiveness of the proposed approach on suppressing nonlinear coupling and external disturbance and its feasibility to the control of non-affine nonlinear MIMO processes.