Nonlinear Adaptive Model Predictive Control of Constrained Systems with Offset-Free Tracking Behavior

In this paper, a nonlinear model-based predictive control strategy for constrained systems based on an adaptive neural network (NN) predictor is proposed. The proposed controller is robust against the model uncertainties and external bounded disturbances. Moreover, it provides offset-free tracking behavior using the adaptive structure in the model. Based on the uncertainties bounds, the restriction of the system constraints causes robust feasibility and stability of the closed-loop system. It is shown that the output of the NN predictor converges to the system output. Moreover, offset-free behavior of the closed-loop system is investigated using the Lyapunov theorem. Simulation results show the effectiveness of the proposed method as compared to the recently proposed model predictive control methods in the literature.