Title

Three-stage design analysis and multicriteria optimization of a parallel ankle rehabilitation robot using genetic algorithm

RIS ID

106046

Publication Details

Jamwal, P. K., Hussain, S. & Xie, S. Q. (2015). Three-stage design analysis and multicriteria optimization of a parallel ankle rehabilitation robot using genetic algorithm. IEEE Transactions on Automation Science and Engineering, 12 (4), 1433-1446.

Abstract

This paper describes the design analysis and optimization of a novel 3-degrees of freedom (DOF) wearable parallel robot developed for ankle rehabilitation treatments. To address the challenges arising from the use of a parallel mechanism, flexible actuators, and the constraints imposed by the ankle rehabilitation treatment, a complete robot design analysis is performed. Three design stages of the robot, namely, kinematic design, actuation design, and structural design are identified and investigated, and, in the process, six important performance objectives are identified which are vital to achieve design goals. Initially, the optimization is performed by considering only a single objective. Further analysis revealed that some of these objectives are conflicting, and hence these are required to be simultaneously optimized. To investigate a further improvement in the optimal values of design objectives, a preference-based approach and evolutionary-algorithm-based nondominated sorting algorithm (NSGA II) are adapted to the present design optimization problem. Results from NSGA II are compared with the results obtained from the single objective optimization and preference-based optimization approaches. It is found that NSGA II is able to provide better design solutions and is adequate to optimize all of the objective functions concurrently. Finally, a fuzzy-based ranking method has been devised and implemented in order to select the final design solution from the set of nondominated solutions obtained through NSGA II. The proposed design analysis of parallel robots together with the multiobjective optimization and subsequent fuzzy-based ranking can be generalized with modest efforts for the development of all of the classes of parallel robots.

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Link to publisher version (DOI)

http://dx.doi.org/10.1109/TASE.2014.2331241