Learning Controllers From Data via Approximate Nonlinearity Cancellation

• 成果类型: Article
• 署名作者: De Persis, Claudio; Rotulo, Monica; Tesi, Pietro
• 署名单位: University of Groningen; University of Groningen; University of Florence
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3234889
• 发表日期: 2023
• 页码: 6082-6097
• 关键词: Control design; Data-driven control; learn-ing systems; Linear matrix inequalities; nonlinear control systems; Robust control

摘要

In this article, we introduce a method to deal with the data-driven control design of nonlinear systems. We derive conditions to design controllers via (approximate) nonlinearity cancelation. These conditions take the compact form of data-dependent semidefinite 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.