Task-space tracking for networked heterogeneous robotic systems via adaptive neural fixed-time control

The task-space distributed adaptive neural network (NN) fixed-time tracking problem is studied for networked heterogeneous robotic systems (NHRSs). In order to address this complex problem, we propose a NN-based fixed-time hierarchical control approach that transforms the problem into two sub-problems: a distributed fixed-time estimation problem and a local fixed-time tracking problem, respectively. Specifically, distributed estimators are constructed so that each follower can acquire the dynamic leader's state in a fixed time. Then, the neural networks (NNs) are employed to approximate the compounded uncertainty consisting of the unknown dynamics of robotic systems and the boundary of the compounded disturbance. More importantly, to guarantee that the tracking errors can converge into a small neighborhood of equilibrium in a fixed time independent of the initial state, the adaptive neural fixed-time local tracking controller is proposed. Another merit of the proposed controller is that the approximation errors are addressed in a novel way, eliminating the need for prior precise knowledge of uncertainties and improving the robustness and convergence speed of unknown robotic systems. Finally, the experimental results demonstrate the effectiveness and advantages of the proposed control method.