Admittance-Based Adaptive Cooperative Control for Multiple Manipulators With Output Constraints

Abstract

This paper proposes a novel adaptive control methodology based on admittance model for multiple manipulators transporting a rigid object cooperatively along a predefined desired trajectory. Firstly, an admittance model is creatively applied to generate reference trajectory online for each manipulator according to the desired path of the rigid object, which is the reference input of the controller. Then, an innovative integral Barrier Lyapunov function (iBLF) is utilized to tackle the constraints due to the physical and environmental limits. Adaptive neural networks (NN) are also employed to approximate the uncertainties of the manipulator dynamics. Different from the conventional NN approximation method, which is usually semiglobally uniformly ultimately bounded (SGUUB), a switching function is presented to guarantee the global stability of the closed-loop. Finally, the simulation studies are conducted on planar 2-link robot manipulators to validate the efficacy of the proposed approach.

Adaptive Reinforcement Learning-Enhanced Motion/Force Control Strategy for Multirobot Systems

“…It is worth emphasizing that, in the general case of timevarying system (14), the optimal control strategy needs to be considered as a time-varying function u * (Z, t). Hence, the time-varying Bellman function with respect to arbitrary time V * (Z(t), t) can be given as…”

“…It is worth emphasizing that, unlike nonlinear multiagent systems [8,9] studying the relation between agents, CMMs consider the interaction with the rigid object. In the literature of CMMs' control objectives, they can be classified into two categories, i.e., multiple mobile robot manipulators in cooperation carrying a common object with unknown parameters and disturbances [7,[10][11][12][13][14][15][16][17][18]; one of them tightly holds the object by the end effector, and the remaining mobile manipulators' end effector follows a trajectory on the surface of the object [19,20]. Most of the existing control literature of networked robotics mainly focuses on the implementation of nonlinear controllers.…”

Output Feedback Control via Linear Extended State Observer for an Uncertain Manipulator with Output Constraints and Input Dead-Zone

“…Transgression of the constraints may produce not only decay of the system performance [27], but also unsafe operation for both the robot and the human. By designing the advanced controllers with a BLF whose output is infinite at corresponding limits, these approaches guarantee that the barriers will not be broken [28,29]. Consequently, the constraints are ensured to be valid all the time.…”

Adaptive Neural Network Stabilization Control of Underactuated Unmanned Surface Vessels With State Constraints

“…Besides realistic mathematic model and environment, researchers have payed considerable critical attention to constraints and output constraints to improve steady-state and transient performance. Research works focus on barrier Lyapunov function (BLF) [23]- [28] were recently proposed to guarantee system signals with prescribed performance. Considering fully actuated system, an adaptive controller based on time-varying barrier Lyapunov function was proposed in [23], which can ensure output constraint satisfaction and achieve asymptotically tracking.…”

“…However, a USV cannot be transformed into a driftless chained system. Some of the above results such as for robots [24], [28], nonuniform gantry cranes [25], robotic manipulators [26], multiple manipulator [27], and fully actuated system [23] cannot be applied to the control of USVs. Due to the nonholonomic constraint, regulation/stabilization is much more difficult than trajectory-tracking, path-tracking and path-following for underactuated vessels [16], [31].…”