Task-Oriented Adaptive Position/Force Control for Robotic Systems Under Hybrid Constraints

By mapping the performances of the task requirement and the inherent physical characteristics of the robotic systems to the hybrid constraints, this article proposes a task-oriented adaptive position/force control (TOAPFC) scheme for the robotic systems to ensure the execution of the predefined tasks and the safety of robotic manipulators and humans in the task workspace. In the proposed scheme, a reference trajectory generation strategy and admittance model are regarded as the outer loop of TOAPFC to obtain and shape the robotic system's task trajectory that guarantees the safety of the interaction system. An admittance-based adaptive position/force control scheme unifying the position and force into a control law is used as the inner loop of TOAPFC to track the shaped task trajectory, where a barrier Lyapunov function is utilized to constrain the tracking errors within permitted ranges. Moreover, the system uncertainties and lumped disturbances are compensated by the radial basis function neural network and robust compensator, respectively. Meanwhile, the stability of the proposed admittance-based adaptive position/force control scheme is analyzed by using the Lyapunov stability theory. Finally, the effectiveness of the proposed scheme is verified by experiments on a real robotic system.