Abstract
A model based predictive control (MPC) strategy for nonlinear process systems is presented. The sensitivity between the controlled system input and output is identified in the implementation of this strategy. The comparisons of MPC, gain scheduling control and conventional PID control highlights their consistency as well as differences, and the advantages of the adaptive controller. A decomposed neural network (DNN) model is applied to the MPC scheme. Stability analysis of the MPC system is presented based on Lyapunov theory. From the stability investigation, the stability condition for the DNN-based control system is obtained. Benchmark example results show that the proposed MPC method can effectively control unknown nonlinear systems
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