Timeliness and accuracy are critical performance metrics for evaluating the effectiveness of control strategies, particularly in high-maneuverability systems such as quadrotor unmanned aerial vehicle (UAV) swarms that demand precise and coordinated control. However, most existing control methods for quadrotor UAV swarms primarily focus on ensuring system stability, often overlooking the stringent requirements on transient response and steady-state accuracy. To this end, this paper proposes a novel prescribed performance formation tracking control scheme for quadrotor UAVs, enhanced by a human-in-the-loop control mechanism. The proposed framework integrates a distributed prescribed-time observer and a prescribed performance controller, enabling the estimation of the human operator's dynamic influence even when the control input is inaccessible to the UAVs. This facilitates accurate formation tracking for the networked UAV system. The framework guarantees that both the estimation error of the human-in-the-loop system and the formation tracking error converge within predefined performance envelopes, with convergence achieved strictly within preset time bounds. Theoretical analysis based on Lyapunov stability theory is provided to ensure rigorous performance guarantees, and simulation results further validate the effectiveness of the proposed scheme, demonstrating its superior capability in achieving rapid convergence and maintaining high-precision tracking under performance constraints.

Actuator faults and external disturbances, which are inevitable due to material fatigue, operational wear and tear, and unforeseen environmental impacts, cause significant threats to the control reliability and performance of networked systems. Therefore, this paper primarily focuses on the distributed adaptive bipartite consensus tracking control problem of networked Euler–Lagrange systems (ELSs) subject to actuator faults and external disturbances. A robust distributed control scheme is developed by combining the adaptive distributed observer and neural-network-based tracking controller. On the one hand, a new positive definite diagonal matrix associated with an asymmetric Laplacian matrix is constructed in the distributed observer, which can be used to estimate the leader’s information. On the other hand, neural networks are adopted to approximate the lumped uncertainties composed of unknown matrices and external disturbances in the follower model. The adaptive update laws are designed for the unknown parameters in neural networks and the actuator fault factors to ensure the boundedness of estimation errors. Finally, the proposed control scheme’s effectiveness is validated through numerical simulations using two types of typical ELS models: two-link robot manipulators and quadrotor drones. The simulation results demonstrate the robustness and reliability of the proposed control approach in the presence of actuator faults and external disturbances.

Control timeliness, safety, and robustness are critical performance metrics in modern industrial and cooperative robotics, significantly impacting the effectiveness of cooperative tasks. However, external disturbances, actuator faults, and unknown reference input signals can significantly degrade the above critical performance metrics. To address these challenges, this article investigates the human-in-the-loop finite-time practical consensus tracking problem for networked robot manipulators in the presence of external disturbances and actuator faults. In the considered framework, the reference trajectory is generated by a human-in-the-loop system, where the human operator actively regulates the signal via an unknown control input that is inaccessible to all manipulators. To address this challenge, an extended state observer (ESO) is designed to simultaneously estimate the system states and the unknown human control input. Leveraging the observer estimates, an adaptive fault-tolerant control scheme is subsequently developed to guarantee that all manipulators achieve practical consensus tracking, with the tracking errors converging to prescribed residual sets. The proposed approach ensures that both the estimation and tracking errors converge within the finite time. Rigorous Lyapunov-based analysis is conducted to establish the finite-time stability of the closed-loop system. Simulation results are presented to validate the effectiveness and robustness of the proposed control strategy.

In the era of Industry 5.0, achieving rapid and robust coordination in networked agent systems (NASs) is essential for enabling intelligent collaborative autonomy. This paper investigates the distributed fixed-time containment tracking problem for NASs subject to input saturation and lumped uncertainties. A novel fixed-time extended state observer (FxTESO) is first proposed to estimate uncertain leader dynamics without requiring prior knowledge of system bounds. Then, a neural network-based adaptive estimator is developed to compensate for unknown model uncertainties and external disturbances affecting follower agents. To guarantee control feasibility under actuator limitations, an auxiliary dynamic system (ADS) is introduced to suppress control inputs while preserving fixed-time convergence. Rigorous Lyapunov-based analysis establishes the closed-loop stability and provides an explicit, initial-condition-independent bound on the settling time. Simulation results validate the effectiveness of the proposed control scheme. Comparative studies further demonstrate that, in contrast to existing fixed-time controllers, the proposed approach improves tracking accuracy and ensures input constraint compliance, thereby enhancing robustness and practical implementability. To sum up, this distributed control scheme addresses the fixed-time coordination problem of NASs, enhancing cooperative control robustness in networked multiple quadrotors or large-scale robot groups in engineering applications for Industry 5.0.