Efficient model predictive control for nonlinear systems modelled by deep neural networks

arXiv:2405.10372v1 Announce Type: cross
Abstract: This paper presents a model predictive control (MPC) for dynamic systems whose nonlinearity and uncertainty are modelled by deep neural networks (NNs), under input and state constraints. Since the NN output contains a high-order complex nonlinearity of the system state and control input, the MPC problem is nonlinear and challenging to solve for real-time control. This paper proposes two types of methods for solving the MPC problem: the mixed integer programming (MIP) method which produces …

abstract arxiv constraints control cs.lg cs.sy dynamic eess.sy math.oc mpc networks neural networks nns paper predictive state systems type uncertainty