Online Neural Network Tuned Tube-Based Model Predictive Control for Nonlinear System

This paper proposes a robust control scheme based on the sequential convex programming and learning-based model for nonlinear system subjected to additive uncertainties. For the problem of system nonlinearty and unknown uncertainties, we study the tube-based model predictive control scheme that makes use of feedforward neural network. Based on the characteristics of the bounded limit of the average cost function while time approaching infinity, a min-max optimization problem (referred to as min-max OP) is formulated to design the controller. The feasibility of this optimization problem and the practical stability of the controlled system are ensured. To demonstrate the efficacy of the proposed approach, a numerical simulation on a double-tank system is conducted. The results of the simulation serve as verification of the effectualness of the proposed scheme.