Dynamic State-Feedback Control for LPV Systems: Ensuring Stability and LQR Performance

TL;DR

This paper introduces a dynamic state-feedback controller for LPV systems that guarantees stability and LQR performance through a gradient flow method, verified by convex optimization, with proven convergence and stability properties.

Contribution

It presents a novel gradient flow-based dynamic controller for LPV systems that converges to the LQR gain and ensures stability under fast parameter variations.

Findings

Controller maintains stability under rapid parameter changes

Convergence to optimal LQR gain is proven

Simulation shows improved transient performance

Abstract

In this paper, we propose a novel dynamic state-feedback controller for polytopic linear parameter-varying (LPV) systems with constant input matrix. The controller employs a projected gradient flow method to continuously improve its control law and, under established conditions, converges to the optimal feedback gain of the corresponding linear quadratic regulator (LQR) problem associated with constant parameter trajectories. We derive conditions for quadratic stability, which can be verified via convex optimization, to ensure exponential stability of the LPV system even under arbitrarily fast parameter variations. Additionally, we provide sufficient conditions to guarantee the boundedness of the trajectories of the dynamic controller for any parameter trajectory and the convergence of its feedback gains to the optimal LQR gains for constant parameter trajectories. Furthermore, we show that the closed-loop system is asymptotically stable for constant parameter trajectories under these conditions. Simulation results demonstrate that the controller maintains stability and improves transient performance.