Discrete-time Contraction-based Control of Nonlinear Systems with Parametric Uncertainties using Neural Networks

by   Lai Wei, et al.

Flexible manufacturing in the process industry requires control systems to achieve time-varying setpoints (e.g., product specifications) based on market demand. Contraction theory provides a useful framework for reference-independent system analysis and tracking control for nonlinear systems. However, determination of the control contraction metrics and control laws can be very difficult for general nonlinear systems. This work develops an approach to discrete-time contraction analysis and control using neural networks. The methodology involves training a neural network to learn a contraction metric and feedback gain. The resulting contraction-based controller embeds the trained neural network and is capable of achieving efficient tracking of time-varying references, with a full range of model uncertainty, without the need for controller structure redesign. This is a robust approach that can deal with bounded parametric uncertainties in the process model, which are commonly encountered in industrial (chemical) processes. Simulation examples are provided to illustrate the above approach.



There are no comments yet.


page 1


Learning-based Adaptive Control via Contraction Theory

We present a new deep learning-based adaptive control framework for nonl...

A Theoretical Overview of Neural Contraction Metrics for Learning-based Control with Guaranteed Stability

This paper presents a theoretical overview of a Neural Contraction Metri...

Robust Control Synthesis and Verification for Wire-Borne Underactuated Brachiating Robots Using Sum-of-Squares Optimization

Control of wire-borne underactuated brachiating robots requires a robust...

Robust Controller Design for Stochastic Nonlinear Systems via Convex Optimization

This paper presents ConVex optimization-based Stochastic steady-state Tr...

Universal Adaptive Control for Uncertain Nonlinear Systems

Precise motion planning and control require accurate models which are of...

Learning Stabilizable Dynamical Systems via Control Contraction Metrics

We propose a novel framework for learning stabilizable nonlinear dynamic...

Neural Contraction Metrics for Robust Estimation and Control: A Convex Optimization Approach

This paper presents a new deep learning-based framework for robust nonli...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.