确保机器人在未知动力学下严格遵守输入限制，实时跟踪误差不越界。
Prescribed Performance Control of Unknown Euler-Lagrange Systems Under Input Constraints
- 设计硬性动态边界约束，强制跟踪误差始终在预设范围内。
- 推导可行性条件，保证控制输入有界且误差不超出性能界限。
- 无需模型近似，支持安全优先或主动纠错两种控制策略。
本文针对具有未知动力学和预设输入约束的Euler-Lagrange系统，提出一种规定性能控制框架，用于轨迹跟踪。所提方法施加硬性漏斗约束，即运行过程中性能边界不可被突破。我们推导了保障跟踪误差在预设漏斗内演化且控制输入有界的可行性条件。当可行性条件不满足时，引入两种无近似控制策略：一种主动将误差拉回漏斗内，另一种优先保证安全以防止进一步偏离。通过仿真与硬件实验验证了该方法的有效性与鲁棒性，表明其适用于受严格输入限制的真实机器人系统。
原文摘要 · Abstract (English)
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.
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