Data-Driven LPV Tracking Control Design and Application to Partially Unknown Nonlinear Systems

This paper addresses the problem of data-driven control design for systems with partially known dynamics. We first present a generalized data-driven LPV algorithm capable of tracking time-varying reference trajectories with practical stability. This formulation extends existing data-driven approaches, which typically focus on regulation or constant reference tracking, by explicitly handling bounded, time-varying signals. Building on this result, we illustrate how the proposed method can be applied to the stabilization of nonlinear systems in strict-feedback form, where part of the dynamics is known and part is unknown, without involving any model reconstruction. As a motivating case study, we consider a marine vessel with partially known dynamics. Simulation results confirm the effectiveness of the proposed approach, demonstrating both accurate time-varying reference tracking and successful stabilization of the nonlinear vessel model.