This paper presents a redundant 4-revolute (4R) spherical wrist mechanism for a shoulder exoskeleton, which overcomes several major issues with the 3R mechanisms used in the past. An analysis of the 3R mechanism is done to highlight the limitations in its range of motion and problems caused by operating near singular configurations. To ensure that the redundancy in the 4R mechanism is efficiently utilized, genetic algorithm is used to optimize the mechanism design and identify the optimal operating configurations of the mechanism. The capability to reach the entire shoulder workspace is guaranteed and the joint velocities are minimized by considering the joint displacements required to move the end-effector throughout the workspace and the condition number of joint configurations for reaching 89 positions in the workspace. Analysis of the 4R mechanism obtained from the optimization process indicates that it can move throughout the entire shoulder workspace with feasibly low joint velocities.
Abstract-In rehabilitation, passive control mode is common used at early stages of the post-stroke therapy, when the impaired limb is usually unresponsive. The simplest is the use of a proportional-integral-derivative (PID) feedback control which usually regulates the position or the interaction force along a known reference. Nonetheless PID method cannot achieve an ideal tracking performance due to dynamical uncertainties and unknown time-varying periodic disturbances from the environment. In order to minimize steady-state error with respect to uncertainties in exoskeleton passive control, Iterative Learning Control(ILC) and Neural PID control are proposed to improve the control effective of conventional linear PID. In this paper, two different control algorithms are introduced. Moreover, an experimental study on a 5-DOF upper limb exoskeleton with them is addressed for comparison.
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