A Vibration Control Method for Hybrid-Structured Flexible Manipulator Based on Sliding Mode Control and Reinforcement Learning

Pedicle drilling force control of a robotic surgical system via spine-soft tissue coupling model and parameters optimization

Bone drilling is a crucial operation in spinal fusion surgery that requires precise control of the applied force to ensure surgical safety. This manuscript aims to enhance the force servo performance of the orthopedic robot during automatic bone drilling operations. Firstly, an analytical model is introduced to describe the spinal mobility of the spine-soft tissue coupling structure. Then, the model is calibrated using force data obtained from stress relaxation tests. Next, optimal force controller parameters are determined through drilling force control simulations based on the identified model. The dynamic performance and robustness of the closed-loop control system are analyzed to ensure safe drilling procedures. Finally, bone drilling experiments are conducted in a force control mode to verify the effectiveness of the proposed method. The step drilling force response's steady-state error is less than 0.15 N, the relative control error is less than 3 %, and there is no noticeable force overshoot. The amplitude of the sinusoidal force response decays to -3 dB when the target force frequency is up to 3.49 rad/s, indicating a wide control bandwidth. These results demonstrate that the proposed method can rapidly and safely provide an adequate force servo to carry out automatic bone drilling.

Robust Adaptive Learning Control of Space Robot for Target Capturing Using Neural Network

This article investigates the robust adaptive learning control for space robots with target capturing. Based on the momentum conservation theory, the impact dynamics is constructed to derive the relationship of generalized velocity in the pre-impact and post-impact phase. Considering the nonlinear dynamics with contact impact, the robust control using nonsingular terminal sliding mode (NTSM) and fast NTSM is designed to achieve the fast realization of the desired states. Furthermore, for the unknown dynamics of the combination system after capturing a target, the adaptive learning control is developed based on neural network and disturbance observer. Through the serial-parallel estimation model, the prediction error is constructed for the update of adaptive law. The system signals involved in the Lyapunov function are proved to be bounded and the sliding mode surface converges in finite time. Simulation studies present the desired tracking and learning performance.

Visual Servoing of Flexible-Link Manipulators by Considering Vibration Suppression Without Deformation Measurements

Visual servoing and vibration suppression of spatial flexible-link manipulators with a fixed camera setup are addressed in this article. The singular perturbation method is adopted to decouple the dynamic equations of the flexible manipulator; hence, two subsystems that represent the rigid robot motion and flexible-link vibration are obtained, respectively. Then, for the slow subsystem related to the rigid motion, an image-based controller is designed to converge the image errors with the consideration of compensating for the errors of approximating the Jacobian matrix. For the fast subsystem corresponding to the elastic vibration, to eliminate the requirements of measuring the vibration states, an observer is designed to estimate the fast states and then a feedback controller of the fast subsystem is presented to suppress the vibration of the flexible manipulator by using the estimation values. The closed-loop stabilities of the slow and fast subsystem are both proved by employing the Lyapunov theory. Numerical simulations demonstrate the effectiveness of the proposed controller, which shows that the image errors approach zero with the vibration of the flexible manipulator damped out simultaneously.