Learning Controllers from Data via Approximate Nonlinearity Cancellation

2022/01/25 by Claudio De Persis, Monica Rotulo, De Persis, Claudio +3 · 7 citations
Engineering · #Advanced Control Systems Optimization #Control Systems and Identification #FOS: Electrical engineering #Fault Detection and Control Systems #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2201.10232

openalex publication_date 2022/01/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We introduce a method to deal with the data-driven control design of nonlinear systems. We derive conditions to design controllers via (approximate) nonlinearity cancellation. These conditions take the compact form of data-dependent semi-definite programs. The method returns controllers that can be certified to stabilize the system even when data are perturbed and disturbances affect the dynamics of the system during the execution of the control task, in which case an estimate of the robustly positively invariant set is provided.

Cited by

• Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
• Event-triggered control of nonlinear systems from data
• Data-driven stabilization of nonlinear systems via descriptor embedding
• Stabilization of Nonlinear Systems with State-Dependent Representation: From Model-Based to Direct Data-Driven Control
• Data driven feedback linearization of nonlinear control systems via Lie derivatives and stacked regression approach
• Data-Driven Adaptive Second-Order Sliding Mode Control with Noisy Data
• Data-Driven Model Order Reduction for Continuous- and Discrete-Time Nonlinear Systems

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