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

2024/05/16 by Jianglin Lan, Lan, Jianglin
Engineering · #Advanced Control Systems Optimization #FOS: Computer and information sciences #FOS: Electrical engineering #FOS: Mathematics #Fault Detection and Control Systems #Machine Learning (cs.LG) #Optimization and Control (math.OC) #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2405.10372

openalex publication_date 2024/05/16 · openalex created_date 2024/05/21 · openalex updated_date 2026/07/28

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 an exact solution to the nonlinear MPC, and linear relaxation (LR) methods which generally give suboptimal solutions but are much computationally cheaper. Extensive numerical simulation for an inverted pendulum system modelled by ReLU NNs of various sizes is used to demonstrate and compare performance of the MIP and LR methods.

Related

• Constrained Deep Learning Based Nonlinear Model Predictive Control
• Deep Operator Neural Network Model Predictive Control
• Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systems
• Unmatched uncertainty mitigation through neural network supported model predictive control
• Approximate non-linear model predictive control with safety-augmented neural networks