Robust Data-Driven Predictive Control of Unknown Nonlinear Systems using Reachability Analysis

TL;DR

This paper introduces a robust data-driven predictive control method for unknown nonlinear systems that uses reachable sets derived from noisy data, eliminating the need for explicit system models and ensuring safety.

Contribution

It presents a novel approach to approximate reachable sets from noisy data and designs a control policy that guarantees safety without requiring noise statistical properties.

Findings

Effective control with bounded noise without system model

Comparable performance to model-based predictive control

Validated through numerical example

Abstract

This work proposes a robust data-driven predictive control approach for unknown nonlinear systems in the presence of bounded process and measurement noise. Data-driven reachable sets are employed for the controller design instead of using an explicit nonlinear system model. Although the process and measurement noise are bounded, the statistical properties of the noise are not required to be known. By using the past noisy input-output data in the learning phase, we propose a novel method to over-approximate reachable sets of an unknown nonlinear system. Then, we propose a data-driven predictive control approach to compute safe and robust control policies from noisy online data. The constraints are guaranteed in the control phase with robust safety margins through the effective use of the predicted output reachable set obtained in the learning phase. Finally, a numerical example validates the efficacy of the proposed approach and demonstrates comparable performance with a model-based predictive control approach.