This article addresses the synchronization control problem for dual-linear-motors-driven systems with model uncertainties and disturbances. An approximation-free robust synchronization control scheme based on cross-coupled control (CCC) frame is proposed to achieve high-precision tracking and synchronization performance, along with excellent uncertainties and disturbances rejection ability. More specifically, the CCC frame is designed to handle the asynchronous motion of two parallel motors. The main advantage is that the proposed method does not require the explicit system model, and any approximations utilized to handle the model uncertainties, such as estimation, identification, and online learning, are not required. Therefore, the computational burden and complexity of the controller are significantly reduced. Considering the importance of the transient and steady-state response, the concept and technology of prescribed performance are adopted to guarantee the control effect and state constraints. In addition, none of the high-order derivatives of desired trajectory, difficult to obtain directly in many applications, are used in the proposed controller. Furthermore, the stability and convergence performance of the closed-loop system are rigorously demonstrated. Finally, comparative experiments show the effectiveness of this study via a dual-driven H-type gantry.

This article proposes a gradient descent (GD) algorithm-based B-spline wavelet neural network (GDBSWNN) learning adaptive controller for linear motor (LM) systems under system uncertainties and actuator saturation constraints. A recursive-least-squares-algorithm-based indirect adaptive strategy is used to effectively estimate model parameters, which can guarantee that they converge to true values. A novel GDBSWNN compensator is proposed to estimate the remaining complex uncertainties, where weights are updated by online GD training. An auxiliary system is integrated into the control scheme to address the saturation problem, which guarantees stability and satisfactory control performance when saturation occurs. In addition, a stability analysis is presented to prove that all the signals of the closed-loop system are bounded using the Lyapunov theory. Experiments have been conducted on an LM-driven motion platform, where different controllers have been tested, demonstrating the effectiveness and advantages of the proposed approach.

Compared with traditional rigid objects’ dynamic throwing and catching by the robot, the in-flight trajectory of nonrigid objects (incredibly variable centroid objects) throwing is more challenging to predict and track. This article proposes a variable centroid trajectory tracking network (VCTTN) with the fusion of vision and force information by introducing force data of throw processing to the vision neural network. The VCTTN-based model-free robot control system is developed to perform highly precise prediction and tracking with a part of the in-flight vision. The flight trajectories dataset of variable centroid objects generated by the robot arm is collected to train VCTTN. The experimental results show that trajectory prediction and tracking with the vision-force VCTTN is superior to the ones with the traditional vision perception and has an excellent tracking performance.

Model predictive current controller is a popular and effective technique to provide fast dynamic response in the field of motor control. However, conventional predictive controllers are susceptible to deteriorating control performance when model mismatch exists, such as changes in motor parameters due to the temperature variations. Therefore, this article proposes a precise model-aid extended state observer (MAESO) compensation-based real-time model predictive current controller with enhanced parameter robustness performance and high bandwidth. The predictive controller is converted into the form of multiparameter quadratic programming for online solution using numerical computational method and the constraints are linearized. In addition, the disturbances estimated by MAESO are fed back to the controller in the form of parameters for cycle-by-cycle compensation without extra controller design. Comparative simulations and experiments under different operating conditions are carried out to verify the effectiveness and superiority of the proposed method.

Thanks to the advancements in micro- and nanomechatronics techniques, the capability to inject foreign materials into target locations inside biosamples has become increasingly crucial. However, technical problems, like orientation and penetration, make manual microinjection challenging, while most available automated platforms are too targeted and often fail to support reconfiguration for different injection tasks. In this article, we first review the state-of-the-art in microinjection techniques for biomedical applications. Then, to solve the aforementioned problems, we report an autonomous omnidirectional microinjection (AOM) system based on effortless updates of manual injection hardware with experiments. Future perspectives on the system are outlined with the possibility of multiplying daily genetic screens and the potential of widespread usage in laboratories and clinics.

This study presents a novel method to realize an augmented reality (AR) and haptic feedback teleoperation ultrasound system, which can reduce the influence of time delay on the operator. The physician uses a haptic device to send commands to a remote site while observing the AR environment. In the virtual environment, the organ model is projected onto the corresponding position according to the human pose, and the robot model can respond to the physician's action in advance. Based on force data, the dynamic environmental model is updated by the recursive least squares method, allowing real-time force feedback. The contact force is computed from the identified parameters together with the position and velocity of the master site. An experimental platform has been built where experiments have been conducted to evaluate the overall architecture, highlighting the role of AR and contact force prediction models, and including clinical validation. Results show that the system can correctly perform remote ultrasound scanning tasks and ensure authenticity and synchronization.