This paper presents a structured feedback design approach for interconnected first-order systems with symmetric couplings and partially-unknown dynamics. Optimal structured state feedback control laws are commonly designed by solving one or more Lyapunov equations. Reinforcement learning, in conjunction with a preliminary data collecting phase, solves the Lyapunov equations without knowing the state matrix of the interconnected system. To find the optimal structured feedback matrix, a novel algorithm combines the data-driven approach with a gradient-based optimization technique. An application example validates the effectiveness of the proposed design procedure.
Data-Driven Optimal Structured Control for Unknown Symmetric Systems / Massenio, Pr; Rizzello, G; Naso, D; Lewis, Fl; Davoudi, A. - (2020), pp. 179-184. [10.1109/case48305.2020.9216852]
Data-Driven Optimal Structured Control for Unknown Symmetric Systems
Massenio, PR;Rizzello, G;Naso, D;
2020-01-01
Abstract
This paper presents a structured feedback design approach for interconnected first-order systems with symmetric couplings and partially-unknown dynamics. Optimal structured state feedback control laws are commonly designed by solving one or more Lyapunov equations. Reinforcement learning, in conjunction with a preliminary data collecting phase, solves the Lyapunov equations without knowing the state matrix of the interconnected system. To find the optimal structured feedback matrix, a novel algorithm combines the data-driven approach with a gradient-based optimization technique. An application example validates the effectiveness of the proposed design procedure.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
