Bilateral teleoperation system has raised expansive concern as it's excellent behaviors in executing the tasks in the remote, unstructured and dangerous areas via the cooperative operation systems. In this paper, an RBF-neural-network-based adaptive robust control design is proposed for nonlinear bilateral teleoperation manipulators to cope with the main issues including the communication time delay, various nonlinearities and uncertainties. Specifically, the slave environmental dynamics is modeled by a general RBF neural network, and its parameters are estimated and then transmitted for the environmental torque reconstruction in the master side. Since the parameters of neural network (which are non-power signals) are transmitted instead of the traditional environmental torque in the communication channel, the previous existing passivity problem under time delay is avoided. In both of master and slave sides, the trajectory creators are applied to generate the desired trajectories, and the RBF-neural-network-based adaptive robust controllers are designed subsequently to handle the nonlinearities and uncertainties. Theoretically, the proposed control algorithm can guarantee the global stability of bilateral teleoperation manipulators under time delay, and the good transparency performance with both position tracking and force feedback is also achieved simultaneously. The real platform comparative experiments are carried out, and the results show the good position tracking to execute precise operation and the good force feedback to detect the sudden disturbance in the environment dynamics.

Multilateral tele-robotic system have potential applications in the industry environments with the advantages of cooperative manipulation for the remote and hazardous tasks, and its control design is quite challenging due to several coupling issues such as stability, position tracking, force feedback and cooperative manipulation under time delays, various uncertainties and external disturbance. In this paper, a novel RBFNN ( $Radial\ Basis\ Function\ Neural\ Network$ )-based adaptive sliding mode control design is proposed for nonlinear multilateral tele-robotic system with $n$ -master- $n$ -slave manipulators. The environment force is modeled with a general form via the RBFNN-based environment parameters estimation in the slave side. The estimated environment parameters (non-power signals) are transmitted to rebuild the environment dynamics in the master side and provide the good force feedback for the human operators. The RBFNN-based adaptive sliding mode controllers are designed separately for master and slave manipulators to achieve good position tracking under parameter variations and external disturbance. The coordinated force distribution algorithm is designed to achieve cooperative manipulation with the balance of force acting on the target object. The theoretical analysis is given and the comparative experiment for a nonlinear multilateral tele-robotic system with 2-master-2-slave manipulators is implemented. The results show the good performance of our design.

To realize high-performance contouring tracking of dual-linear-motor-driven (DLMD) gantry, both motion coordination of multi-axes and synchronization of redundant actuators are indispensable factors. However, earlier contouring methods of multi-axes systems only focus on motion coordination, leading to certain inherent limitations and uncertainties when applied to DLMD gantry. Especially, the usually ignored synchronization issue may directly affect the normal operation of system. To this end, this paper proposes a novel contouring control method by integrating both motion coordination between axes and synchronization of redundant actuators. With the knowledge of the complete system dynamics, corresponding modification and extension have been done within the framework of global task coordinate frame (GTCF). Subsequently, both associated issues are effectively dealt with in the task space to simultaneously obtain efficient motion coordination ability and high-level synchronization, along with more accurate contouring error calculation. The proposed method is compared to the earlier GTCF based contouring control method without consideration of synchronization issue. The experimental results fully indicate the effectiveness and superiority of the proposed method to indeed guarantee and further improve the contouring performance.

Dual-linear-motor-driven gantry systems have been widely used in high-speed or heavy payload precision motion systems. Due to the unique physical properties such as mechanical coupling, the precise synchronization control of such kinds of systems is crucial to achieve good tracking and smooth operation performance. However, the existing synchronization schemes usually focus on the pure motion synchronization only and, thus, have certain inherent performance limitations. In this paper, an accurate multi-input multi-output mathematical model of a dual-driven gantry is presented first, where the complete planar motions of the crossbeam include the traditional linear motion along the guide rails and the previously ignored rotational motion around the mass center. With the better understanding of the mechanical coupling and the internal forces caused by the rotational dynamics, an advanced synchronization control scheme by directly taking into account the additive rotational dynamics is presented, which not only synchronizes the motions of two parallel motors but also regulates the internal forces. In the proposed scheme, the adaptive robust control algorithm is also applied to obtain a guaranteed robust performance in the presence of both parametric uncertainties and uncertain nonlinearities. Comparative experiments with previous control schemes are carried out to show the better synchronization performance and verify the effectiveness of the proposed method.