Data-Driven Control for Nonlinear Euler-Lagrange Systems
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Beschreibung
This dissertation presents data-driven control and fault detection approaches for nonlinear Euler-Lagrange systems with model uncertainties, unmodelled dynamics, external disturbances, input constraints, and underactuation. Based on time-delayed control and time-delayed estimation, several methods are developed, including a universal residual generator for sensor fault detection, a data-driven optimal control approach for fully actuated systems, an adaptive control method for underactuated systems, and a sliding model predictive control framework. The proposed approaches are supported by theoretical stability analysis and validated through simulations and experiments on robotic and aerial systems.
Autor*in
Wenyan Ye
Themen in »Data-Driven Control for Nonlinear Euler-Lagrange Systems«
Data-driven control Euler-Lagrange systems time-delayed control time-delayed estimation adaptive control model predictive control fault detection underactuated systems