Cable-driven parallel robots (CDPRs) can provide a high speed and a heavy payload in a large workspace. The main challenge of CDPRs stems from the dynamic control, in which all cables must coordinate with each other to remain in tension, and the cable-driven form may lead to model uncertainties. To solve it, the second-order sliding-mode (SOSM) is combined with the multi-cable synchronization idea to propose a new SOSM-based synchronization control (SOSM-SC) strategy for CDPRs. The goal of the cross-coupled control (CCC) approach is to improve the synchronization motion relationship between all cables and guarantee better trajectory tracking control, while the SOSM can restrain the model uncertainties and external disturbances. The Lyapunov theory is utilized to prove the asymptotic stability of the closed-loop control system of CDPRs. Trajectory tracking experiments indicate that compared with the existing synchronization control (SC) strategy and the augmented proportional-derivative (APD) strategy, the proposed SOSM-SC strategy significantly reduces the cable tracking error and the cable synchronization error and ultimately improves the control accuracy for the mobile platform. Tracking experiments of the anti-disturbance indicate that the SOSM-SC strategy can retain good tracking accuracy under external disturbances.

Cable-driven parallel robots (CDPRs) have low moving inertia and provide high-speed motion over large workspace. The main challenges of CDPRs stem from the fact that cables should be in tension during motion control. Cable tension is closely related to the synchronization motion relation between cables which is often omitted in the existing controllers for CDPRs. To solve it, this paper proposes two synchronization controllers in the cable space (CS) to realize the synchronization motion between cables, and finally increase the tracking accuracy of the moving platform. The two synchronization controllers are proven to guarantee asymptotic convergence to zero of both tracking error and synchronization error. The trajectory tracking experiments are implemented on a self-built 3-DOF CDPR, and the experiments are compared with the traditional augmented PD (APD) controllers neglecting synchronization. The experimental results indicate that, the tracking error and the synchronization error of the moving platform decrease greatly by using the synchronization controllers.

To achieve high-accuracy grasping of unknown objects, we present novel multilevel convolutional neural networks (CNNs) for robotic grasping with a parallel gripper or multifingered dexterous hand. The multilevel CNNs include four levels with different structures and functions. The first level is constructed to get the approximate position of the grasped object. The second level aims to obtain the preselected grasping rectangles. The third level is constructed to re-evaluate the preselected grasping rectangles and obtain substantially detailed features with quite a large network, so as to assess each preselected grasping rectangle exactly. By using a selection algorithm, the optimal grasping rectangle can be determined and unknown object grasping can be achieved with a parallel gripper. The purpose of the fourth level is to obtain the finger position distribution to complete the accurate grasping of unknown objects with a multifingered dexterous hand. The test results indicate that, compared to state-of-the-art methods, the proposed multilevel CNNs can greatly increase the precision of the grasping rectangle. Grasping experiments were implemented on a Youbot arm with five degrees of freedom and a Shadow four-fingered dexterous hand. The results show that the multilevel CNNs can determine the optimal grasping rectangle and finger position distribution, thereby achieving high-accuracy grasping of various unknown objects, even under several complex environmental conditions.

The condition of a quantum Lyapunov‐based control which can be well used in a closed quantum system is that the method can make the system convergent but not just stable. In the convergence study of the quantum Lyapunov control, two situations are classified: nondegenerate cases and degenerate cases. For these two situations, respectively, in this paper the target state is divided into four categories: the eigenstate, the mixed state which commutes with the internal Hamiltonian, the superposition state, and the mixed state which does not commute with the internal Hamiltonian. For these four categories, the quantum Lyapunov control methods for the closed quantum systems are summarized and analyzed. Particularly, the convergence of the control system to the different target states is reviewed, and how to make the convergence conditions be satisfied is summarized and analyzed.