Prescribed Performance Control of Unknown Euler-Lagrange Systems Under Input Constraints

TL;DR

This paper introduces a control framework for Euler-Lagrange systems that ensures trajectory tracking within predefined error bounds while respecting input constraints, using novel approximation-free strategies demonstrated through simulations and experiments.

Contribution

It proposes a new prescribed performance control method with feasibility conditions and two approximation-free strategies for systems with unknown dynamics and input limits.

Findings

Ensures tracking errors stay within predefined funnels.

Guarantees bounded control inputs under input constraints.

Validated through simulations and hardware experiments.

Abstract

In this paper, we present a prescribed performance control framework for trajectory tracking in Euler-Lagrange systems with unknown dynamics and prescribed input constraints. The proposed approach enforces hard funnel constraints, meaning that the prescribed performance bounds must not be violated during operation. We derive feasibility conditions that guarantee the tracking error evolves within these predefined funnels while ensuring bounded control inputs. To handle situations where the feasibility conditions are not satisfied, we introduce two approximation-free control strategies: one that actively drives the error back toward the funnel and another that prioritizes safety by preventing further deviation. The effectiveness and robustness of the proposed method are demonstrated through simulation studies and hardware experiments, highlighting its suitability for real-world robotic systems operating under strict input limits.