Search Results (6)

While networked multi-agent systems have been widely explored, the challenges introduced by underactuation still impede safety-critical cooperative control of multiple underactuated manipulators. This paper introduces a distributed framework for end-effector formation control and obstacle avoidance in planar n-link manipulators with a passive first joint and active remaining joints—termed PAⁿ⁻¹ manipulators. By exploiting the integrability of each PAⁿ⁻¹ manipulator’s second-order nonholonomic constraint, we reformulate the dynamics into a cascaded structure and derive a reduced-order model driven solely by active joint velocities. Building on this reduced-order model, we design safety-critical distributed formation control laws for the reduced-order dynamics, which serve as the manipulators’ desired active joint velocities. Then, we employ the backstepping method to obtain control inputs for the full-order dynamics. To guarantee safety, we treat backstepping tracking errors as matched disturbances and address them within a robust control barrier function framework. Numerical simulations and comparative studies confirm the effectiveness of the proposed approach. Full article

Underactuated robotic systems are appealing for industrial use due to their reduced actuator number, which lowers energy consumption and system complexity. Underactuated systems are, however, often affected by residual vibrations. This paper addresses the challenge of generating energy-optimal trajectories while imposing theoretical null residual (and yet practical low) vibration in underactuated systems. The trajectory planning problem is cast as a constrained optimal control problem (OCP) for a two-degree-of-freedom revolute–revolute planar manipulator. The proposed method produces energy-efficient motion while limiting residual vibrations under motor torque limitations. Experiments compare the proposed trajectories to input shaping techniques (ZV, ZVD, NZV, NZVD). Results show energy savings that range from 12% to 69% with comparable and negligible residual oscillations. Full article

Planar underactuated mechanical systems have been a popular research issue in the area of mechanical systems and nonlinear control. This paper reviews the current research status of control methods for a class of planar underactuated manipulator (PUM) systems containing a single passive joint. Firstly, the general dynamics model and kinematics model of the PUM are given, and its control characteristics are introduced; secondly, according to the distribution position characteristics of the passive joints, the PUM is classified into the passive first joint system, the passive last joint system, and the passive intermediate joint system, and the analysis and discussion are carried out in respect to the existing intelligent control methods. Finally, in response to the above discussion, we provide a brief theoretical analysis and summarize the challenges faced by PUM, i.e., uncertainty and robustness of the system, unified control methods and research on underactuated systems with uncontrollable multi-passive joints; at the same time, the practical applications have certain limitations that need to be implemented subsequently, i.e., anti-jamming, multi-planar underactuated robotic arm co-control and spatial underactuated robotic arm system development. Aiming at the above challenges and problems in the control of PUM systems, we elaborate on them in points, and put forward the research directions and related ideas for future work, taking into account the contributions of the current work. Full article

This article proposes a comprehensive stable control strategy for the planar multi-link underactuated manipulator (PMLUM), considering several uncertainties. According to the nilpotent approximation property, the control procedure is split into two stages. In the first stage of control, we postulate the idea of model degradation, reducing the PMLUM to a planar virtual Pendubot (PVP). This occurs by controlling the active link (AL) to a specific desired position and the passive link (PL) moves along with it. When the AL moves to the desired position, the second phase of control is entered. Meanwhile, all ALs are regarded as a whole, so the PMLUM can be regarded as a mechanical arm with 2-DOF. In the second stage of control, due to the nilpotent approximation feature of the PVP, the PVP is guided to the desired angle using the iterative steering technique. Simulation experiments are carried out on active–active–passive (AAP) and active–active–active–passive (AAAP) systems under major uncertainties, which contain initial velocity and torque disturbances. The final results validate the effectiveness of the method proposed. Full article

In this paper, we propose a general control method via the intelligent algorithm for a planar R-type underactuated robot. This control method solves the unified control problem of R-type underactuated manipulator. Meanwhile, the proposed method is also applicable to cases of nonzero initial velocity and interference rejection. Our total control program includes two stages. In the first stage, we design the trajectory based on the states of the actuated link, and then the controller is designed to track the planned trajectory to realize the objective of the actuated link. In the second stage, the trajectory with adjustable parameters is planned for the actuated link. Then, the adjustable parameters are calculated by the intelligent algorithm based on the underactuated constraints. Subsequently, the controller is designed to track the second trajectory to realize the objective of the actuated manipulator and the underactuated manipulator. Finally, the performance of the proposed method is verified through simulations. Full article

For the position control of a planar 3-linkage underactuated manipulator (PTUM) with a passive first linkage, a continuous control strategy is developed in this paper. In particular, a broad neural network (BNN)-based model is first established to accurately describe the motion coupling relationship between the passive linkage and the second linkage. Based on this model, by using the particle swarm optimization algorithm, the target angles of all linkages are calculated combining the start states of all linkages and the target position of the PTUM. Then, the target angles of the active linkages are directly achieved by their respective actuators, and that of the passive linkage is also achieved by the rotation of the second linkage. By carrying out several experiments, the effectiveness of the above strategy is verified. Full article