Trajectory-Attracted Adaptive Tracking Control for Robotic Systems Based on a Hybrid Guiding Vector Field in Flexible Environments

This paper proposes a trajectory-attracted adaptive tracking control (TAATC) scheme based on a hybrid guiding vector field (HGVF) for robotic systems, addressing the high operational difficulty and safety concerns inherent in flexible environments. The HGVF is constructed using the characteristics of different task spaces in flexible environments to enable smooth transitions between free space and contact space with adjustable operating velocity, thereby enhancing robustness against environmental interactions and uncertainties. The HGVF can attract the state trajectories of the robotic systems by path convergence of an auxiliary dynamic system, simplifying the trajectory planning process with a time-independent representation of the desired path. In addition, a neural network is employed to compensate for uncertainties in the robotic systems, while a nonlinear duffing function represents the dynamic contact force model of flexible environments. In this way, the TAATC scheme is constructed by using the HGVF and the adaptive neural network, which unifies trajectory planning and tracking control. By using the proposed TAATC scheme, the robotic systems can achieve smooth interaction with flexible environments and obtain the desired tracking force without complex trajectory planning. The stability of the HGVF and the whole control scheme are analyzed by using the Lyapunov theorem. Finally, the effectiveness of the proposed method is validated through both simulation and experimental tests.