Abstract
The difficulty of addressing the decentralized control problem for a torque sensorless constrained reconfigurable manipulator is associated with decentralized control of the constraint force. This paper studies the force/position decentralized robust control problem for constrained reconfigurable manipulator system with parameter perturbation and unmodeled dynamics. A joint torque estimation scheme based on the motor-side and link-side position measurements along with harmonic drive model is deployed for each joint module. Subsequently, radial basis function (RBF) neural network is applied to compensate the unmodeled dynamics and unknown terms of subsystem, simultaneously. Furthermore, a decentralized force/position robust controller is designed by combining the estimated joint torque with the dynamic output feedback control method. The stability of closed-loop system is proved using the Lyapunov theory and linear matrix inequality (LMI) technique. Finally, simulations are performed to verify the advantage of the proposed method.
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