LQR controller design for affine LPV systems using reinforcement learning

Hosein Ranjbarpur, Hajar Atrianfar and Mohammad Bagher Menhaj

International Journal of Systems Science, 2024, vol. 55, issue 9, 1807-1819

Abstract: In this paper, a data-driven sub-optimal state feedback is designed for a continuous time linear parameter varying (LPV) system using reinforcement learning. Time-varying parameters lie in a poly top and the system matrix has an affine representation for the parameters. Two novels, on-policy and off-policy algorithms, are proposed using available data from vertex systems of polytop to minimise a performance index and admit a common Lyapunov function (CLF). A convex optimisation problem is derived for each iteration based on Lyapunov inequality. Algorithms yield stabilising feedback gain in each iteration and convergence to a common lyapunov function. We demonstrate the efficacy of the proposed method by simulation of two case studies.

Date: 2024
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DOI: 10.1080/00207721.2024.2321370

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