A Novel Decentralized Fixed-Time Tracking Control for Modular Robot Manipulators: Theoretical and Experimental Verification

This paper presents a novel decentralized fixed-time tracking control approach, which realizes the advantages of modular robot manipulators (MRMs) with fixed-time convergence, strong robustness, and high tracking performance. First, to estimate the total uncertainty of MRMs, the fixed-time observer based on the extended state is developed. Then, combined with the disturbance observer, a novel decentralized control method based on a fixed-time control strategy was devised to accomplish global fixed-time convergence of MRMs. And, stability analysis based on Lyapunov is utilized to obtain the fixed-time stability as well as convergence time of MRMs. Finally, numerical analysis and experiment respectively verify the excellent tracking ability of the presented decentralized fixed-time tracking control.

Event-triggered-based Cooperative Game Optimal Tracking Control for Modular Robot Manipulator with Constrained Input

In this paper, a cooperative game optimal tracking control method based on event-triggered mechanism for constrained input modular robot manipulators (MRMs) system is introduced. According to the joint torque feedback (JTF) technique, the dynamics model of the constrained input subsystem is established and the global state space equation is derived. The control inputs of $n$ joints in the MRM system with constrained input are taken as $n$ participants of cooperative game, the tracking control problem of the manipulator system is transformed into the optimal control problem based on the cooperative game. Next, a fusion function containing position and velocity errors is defined to construct the performance index function. In order to improve the control performance and robustness of the manipulator system, part of the known model information is used to devise controller, the model uncertainty is dealt by the neural network (NN) observer, and the optimal compensation control strategy is used to deal with internal disturbance such as sensor measurement error and transmission ripple due to power fluctuations, electromagnetic effects, noise and vibration. Based on the adaptive dynamic programming (ADP) algorithm and event-triggered mechanism, the optimal tracking control strategy is obtained by approximately solving the event-triggered Hamilton-Jacobi-Bellman (HJB) equation with the critic NN. The Lyapunov theory proves that trajectory tracking error of MRM system with constrained input is uniformly ultimately bounded (UUB). Finally, the experimental results demonstrate the effectiveness of the proposed control method.

Inter-Module Physical Interactions: A Force-Transmissive Modular Structure for Whole-Body Robot Motion

Robots are required to be significantly compliant and versatile to work in unstructured environments. In a number of studies, robots have positively exploited the environments during interactions and completed tasks from a morphological viewpoint. Modular robots can help realize real-world adaptive robots. Researchers have been investigating the actuation, coupling, and communication mechanisms among these robots to realize versatility. However, the diverse force transmission among modules needs to be further studied to achieve the adaptive whole-body dynamics of a robot. In this study, we fabricated a modular robot and proposed the realization of force transmission on this robot, by constructing fluid transferable network systems on the actuation modules. By exploiting the physical property variations of the modular robot, our experimental results prove that the robot’s motion can be changed by switching the connection pattern of the system.