In this paper, we consider uncertain high-order nonlinear systems performing dynamic tracking tasks under hard actuator constraints, where only the output error is available for measurement, while the system states and the desired trajectory derivatives are unavailable for feedback. We propose a robust output-feedback controller that guarantees adaptive performance specifications in this framework. The proposed scheme employs a novel Prescribed Performance Observer (PPO) with dynamic gains, which enhances estimation accuracy while avoiding large fixed observer gains. In addition, we introduce an adaptive mechanism that dynamically adjusts the output performance specifications according to the actuator limitations, ensuring bounded closed-loop signals. We establish a separation principle showing that the output-feedback scheme recovers the performance of its state-feedback counterpart. Comparative simulations demonstrate accurate tracking and smoother applied control under actuator limitations, uncertainties, and measurement noise.
This paper investigates the output regulation problem using an observer-based inverse optimal controller within the nonlinear servomechanism framework for asymptotic convergence to desired references and rejection of time-varying disturbances generated by an exosystem. To address the practical constraint of full-state...
This paper proposes a fixed-time trajectory tracking control strategy for robotic manipulators facing dynamic uncertainties and input saturation, combining prescribed performance control with adaptive reinforcement learning. A prescribed performance function is designed to strictly constrain tracking errors within pred...
Yan Li, Xin Liu, Yan Zhang et al.· 2026 IEEE International Conf...· 0 citations
This paper presents an advanced predictive Iterative Learning Control (ILC) methodology tailored for discretetime Linear Parameter-Varying (LPV) systems subject to strict input and state constraints, as well as bounded process and measurement disturbances. The proposed framework operates through a dual-phase architectu...
Marcin Witczak, M. Pazera, B. Sulikowski et al.· International Conference on...· 0 citations
This paper investigates trajectory tracking of a 3-DOF serial manipulator under external disturbances, parameter uncertainties, and nonlinear friction. A model-based adaptive anti-disturbance controller is developed based on nominal dynamics compensation to improve tracking accuracy and robustness. A prescribed perform...
Jun Shao, Runze Shao· 2026 IEEE International Conf...· 0 citations
This paper investigates the predefined-time adaptive neural tracking control problem for a class of nonlinear pure feedback systems with full state constraints, and proves that all closed loop signals are predefined-time-bounded and the tracking error meets the preset performance specifications.
Yang Li, Ya-Qi Yu, Quan-Min Zhu et al.· Mathematics· 0 citations
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