Adaptive Tracking Control of Euler-Lagrange Systems with Time-Varying State and Input Constraints

TL;DR

This paper introduces an adaptive control method for Euler-Lagrange systems that ensures time-varying state and input constraints are satisfied despite uncertainties, validated through experiments on a helicopter model.

Contribution

It develops a novel control framework combining TVBLF and saturated control laws with an offline feasibility check for constraints.

Findings

Guarantees constraint satisfaction without real-time optimization

Proves boundedness of all closed-loop signals

Validates approach through helicopter experiments

Abstract

This paper presents an adaptive control framework for Euler-Lagrange (E-L) systems that enforces user-defined time-varying state and input constraints in the presence of parametric uncertainties and bounded disturbances. The proposed design integrates a time-varying barrier Lyapunov Function (TVBLF) with a saturated control law to guarantee constraint satisfaction without resorting to real-time optimization. A key contribution is the development of an offline, verifiable feasibility condition that certifies the existence of a feasible control policy for any prescribed pair of time-varying state and input envelopes. Additionally, we prove boundedness of all closed-loop signals. Real-time experiments conducted on a 2-DoF helicopter model validate the efficacy and practical viability of the proposed method.