This paper presents an efficient and fast method for fine tuning the controller parameters of robot manipulators in constrained motion. The stability of the robotic system is proved using a Lyapunov-based impedance approach whereas the optimal design of the controller parameters are tuned, in offline, by a Particle Swarm Optimization (PSO) algorithm. For designing the PSO method, different index performances are considered in both joint and Cartesian spaces. A 3DOF manipulator constrained to a circular trajectory is finally used to validate the performances of the proposed approach. The simulation results show the stability and the performances of the proposed approach.
Problem statement: The assistance of person with limited ability of arm movement is necessary for rehabilitation reasons. This aid is required not only to cover the human performances of the arm in motion and force but also to have a strictly stable dynamics. In this study, we proposed a cooperative system between a disabled arm and a robotic manipulator to reach such objectives. Desired positions and contact forces were imposed by the disabled human whereas appropriate torques were applied by the manipulator to follow human intension. Approach: Various control strategies were proposed during recent years to solve position/force control problem. The impedance control concept was used in this study. A relationship between the dynamics of the robot and its energy was developed to derive stability conditions of the robotic system at the constrained motion phase using a suitable Lyapunov approach. Results: New sufficient conditions of asymptotic stability were developed. To prove the efficiency of the proposed approach, a prototype of a human arm coupled to cooperative constrained robotic manipulator was used. The simulation results showed the stability and the performances of the proposed approach. Conclusion: Results showed the possibility of their use in a real context of rehabilitation of injured and disabled people
A new design of a robust impedance controller for constrained robotic manipulators is presented. The main objective is to stabilize asymptotically, in the task space, the robotic manipulator's end effectors into a desired position, via a desired contact force under model uncertainties and measurement noise. In this work, the proposed approach is enough straightforward for application without force and position control separation. Robust asymptotic stability in the approach is proved using a HamiltonianLyapunov approach. Besides this, a state/parameter observer and an acceleration estimator are proposed to handle the problems of force estimation, disturbance rejection and acceleration measurement. To ensure high performance, a Particle Swarm Optimization (PSO) algorithm is used finally as an efficient and fast method for the offline fine-tuning of the controller's parameters. In designing the PSO method, the Mean of Root Squared Error (MRSE) is considered as a cost function in the Cartesian space. Finally, the example of the ABB-IRB 140 industrial robot with 6DOFs is used to validate the performances of the proposed approach.
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