Learning Controllers From Data via Approximate Nonlinearity Cancellation

2023/01/06 by Claudio De Persis, Monica Rotulo, Pietro Tesi · 8 citations
Engineering · #Control Systems and Identification #Advanced Control Systems Optimization #Fault Detection and Control Systems

paper · pdf · doi:10.1109/tac.2023.3234889

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.

Citations

• Model-free control
• Combining Prior Knowledge and Data for Robust Controller Design

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• Data-Driven Adaptive Second-Order Sliding Mode Control with Noisy Data
• Data-Driven Model Order Reduction for Continuous- and Discrete-Time Nonlinear Systems
• Bipedal Stepping Controller Design Considering Model Uncertainty: A Data-Driven Perspective. [europepmc]

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