Affiliations: [a]
Department of Electrical and Electronics Engineering, School of Engineering, Nazarbayev University, Astana, Kazakhstan
| [b]
School of of Mechanical, Materials and Mechatronics Engineering, University of Wollongong, NSW, Australia
Correspondence:
[*]
Corresponding author. Shahid Hussain, School of Mechanical, Materials and Mechatronic Engineering, University of Wollongong, 2522 NSW, Australia. Tel: +61242215058. E-mail: [email protected].
Abstract: Robotic devices can be potentially used to assist physical therapy treatments in order to restore musculoskeletal system malfunctions owing to neurological disorders. Cable actuated parallel robots, despite their obvious benefits such as enhanced workspace, light weight, and flexibility, are not popularly used in ankle rehabilitation treatments, due to their complex mechanism and cable actuation issues. In order to address these issues, it is recommended to carry out robot design optimization. However, design synthesis of the cable actuated parallel ankle robot calls for multi-objective optimization (MOO), since there are multiple and conflicting objectives to achieve. To acquire more choices between actuator forces, overall stiffness of robot (which is crucial for a cable based ankle robot) and other vital design objectives, it is required to explore the extreme ends of the Pareto Front (PF) more carefully. Existing multi-objective evolutionary algorithms (MOEAs) normally focus on the convergence and may not provide solutions at the extremities of PF. Capitalizing on this improvement opportunity, this paper presents a fuzzy based MOEA, namely, biased fuzzy sorting genetic algorithm (BFSGA) which encourages solutions in the extreme zones of the PF. It is shown in this paper that using proposed method, diversity in the populations is supported and in the process wider trade-off choices of objectives can be obtained. During ankle robot design optimization, crisp objectives are defined as fuzzy objectives and competing solutions are provided an overall activation score (OAS). Subsequently OAS is used to assign a fuzzy dominance ranking to the design solutions. It is found that the BFSGA approach performs well in exploring the extreme zones of the Pareto front, which are normally overlooked by other MOEA such as NSGA-II due to their inherent mechanism.
