Neural network based control of unknown nonlinear systems via contraction analysis

TL;DR

This paper presents a neural network control framework for unknown nonlinear systems using contraction analysis, ensuring convergence to equilibrium neighborhoods with convex conditions for efficiency.

Contribution

It introduces a contraction-based neural ODE approach with convex reformulation of conditions, enabling effective control of unknown nonlinear systems.

Findings

Contractive neural ODE systems guarantee convergence to equilibrium neighborhoods.

Convex LMI conditions improve computational efficiency.

Controller design ensures trajectories converge to the neighborhood of the unknown equilibrium.

Abstract

This paper studies the design of neural network (NN)-based controllers for unknown nonlinear systems, using contraction analysis. A Neural Ordinary Differential Equation (NODE) system is constructed by approximating the unknown draft dynamics with a feedforward NN. Incremental sector bounds and contraction theory are applied to the activation functions and the weights of the NN, respectively. It is demonstrated that if the incremental sector bounds and the weights satisfy some non-convex conditions, the NODE system is contractive. To improve computational efficiency, these non-convex conditions are reformulated as convex LMI conditions. Additionally, it is proven that when the NODE system is contractive, the trajectories of the original autonomous system converge to a neighborhood of the unknown equilibrium, with the size of this neighborhood determined by the approximation error. For a single-layer NN, the NODE system is simplified to a continuous-time Hopfield NN. If the NODE system does not satisfy the contraction conditions, an NN-based controller is designed to enforce contractivity. This controller integrates a linear component, which ensures contraction through suitable control gains, and an NN component, which compensates for the NODE system's nonlinearities. This integrated controller guarantees that the trajectories of the original affine system converge to a neighborhood of the unknown equilibrium. The effectiveness of the proposed approach is demonstrated through two illustrative examples.