Discrete-Time Noise-Resilient Neural Dynamics for Model Predictive Motion-Force Control of Redundant Manipulators

Motion-force control is one of the critical technologies for a manipulator to accomplish some tasks, such as polishing and burring. Some optimization-based and kinematics-related methods for motion-force control of redundant manipulators have good performance but exist some shortcomings. First, these methods utilize transformation techniques to deal with different levels of joint limits, such as joint angle, velocity, or acceleration limits, which reduces the feasible region of decision variables. Second, these methods require the direction for the end-effector of the manipulator to be perpendicular to the contact surface and thus are not applicable to some scenarios. In response to these shortcomings, this article proposes a noise-resilient neural-dynamics-based planning (NRNDP) scheme, which includes a model predictive motion-force control (MPMFC) strategy and a discrete-time noise-resilient neural dynamics solver. The proposed NRNDP scheme directly handles three levels of joint limits without reducing the feasible region. Meanwhile, it can achieve the desired force with the end-effector of the manipulator being at any suitable angle to the work surface. Moreover, it can reduce the impact of noise and thus improve the control accuracy and operational stability of redundant manipulators. Besides, the MPMFC strategy is improved to achieve motion-force control of pose-varying workpieces. Simulations, comparisons, and experiments demonstrate the effectiveness and superiority of the proposed scheme.