This work develops a fixed-time control scheme to address the tracking control problem for a wheeled mobile robot (WMR) in lumped disturbance conditions involving slipping, skidding, and external disturbances. A novel fixed-time velocity control law is first developed for the error kinematics of the WMR system based on an intermediate-term design. To ensure high-performance tracking for the generated velocity in lumped disturbance cases, the impacts of slipping and skidding are incorporated into the dynamics of the WMR system. A fixed-time trajectory tracking control law that integrates a fast fixed-time disturbance observer is also proposed. Specifically, the well-designed observer is developed to estimate the lumped disturbances. The proposed control scheme avoids the commonly existing singular problem and ensures that the error states of WMR can converge into a neighborhood of the origin within a fixed time during slipping and skidding. Based on comparison experiments, the proposed control scheme exhibits superior performance and effectiveness.

This paper proposes an asymptotically stable learning-based formation control scheme built upon the adaptive dynamic programming (ADP) technique, addressing the coupling uncertainties and obstacle avoidance issues in multi-quadrotor unmanned aerial vehicles. The scheme formulates the control strategy and coupling uncertainties as opposing objectives within a zero-sum game framework, solved using a critic-only ADP technique. For obstacle avoidance, an improved repulsive force is developed based on the repulsive potential field with continuous differentiable repulsive force adjustment mechanisms to guide quadrotors around obstacles while eliminating the effects of obstacles outside the detection range. To satisfy attitude tracking requirements for the formation, the critic-only structure is also employed to develop the attitude tracking control strategy. Furthermore, asymptotic convergence critic neural network weight updating laws are proposed to learn the control strategies. The proposed scheme guarantees the asymptotic stability of the multi-quadrotor system, effectively addresses worst-case coupling uncertainties, and reduces unnecessary chattering during obstacle avoidance. Comparative simulation results demonstrate the superior effectiveness of the proposed scheme.

Thiswork addresses the trajectory-tracking-control problem for a quadrotor unmanned aerial vehicle with external disturbances and parameter uncertainties. A novel adaptive-dynamic-programming-based robust control method is proposed to eliminate the effects of lumped uncertainties (including external disturbances and parameter uncertainties) and to ensure the approximate optimal control performance. Its novelty lies in that two radial basis function neural network observers with fixed-time convergence properties were first established to reconstruct the lumped uncertainties. Notably, they tune only the scalar parameters online and have low computational complexities. Subsequently, two actor–critic neural networks were designed to approximate the optimal cost functions and control policies for the nominal system. In this design, two new actor–critic neural network weight update laws are proposed to eliminate the persistent excitation condition. Then, two adaptive-dynamic-programming-based robust control laws were obtained by integrating the observer reconstruction information and the nominal control policies. The uniformly ultimately bounded stability of the closed-loop tracking control systems was ensured using the Lyapunov methodology. Finally, numerical results are shown to verify the effectiveness and superiority of the proposed control scheme.

This paper investigates a fixed-time reinforcement learning framework for containment coordination of networked aerial vehicles in dynamic inspection tasks. The proposed framework integrates sliding mode concepts to enhance the robustness of the system. To address the challenge of limited leader information sharing among followers, a distributed estimation mechanism is designed to reconstruct leader states in a fixed time, thereby eliminating the need for fully informed followers. Based on this, a critic-only adaptive dynamic programming (ADP) framework is employed to learn optimal coordination strategies for both containment behavior and attitude tracking under time-varying environments. Within this ADP framework, the critic neural network (NN) weights are adjusted according to a fixed-time convergent update rule constructed from auxiliary errors associated with the Bellman residual. The framework ensures that the networked aerial vehicle system converges within a fixed time, and the corresponding convergence properties are rigorously verified through comprehensive mathematical analysis. Simulation results demonstrate that the proposed method achieves rapid convergence and improved containment performance in inspection tasks.