Adaptive Multi-Objective Optimization of Bionic Shoulder Joint Based on Particle Swarm Optimization

doi:10.1007/s12204-018-1958-1

Abstract

Abstract: To get the movement mode and driving mechanism similar to human shoulder joint, a six degrees of freedom (DOF) serial-parallel bionic shoulder joint mechanism driven by pneumatic muscle actuators (PMAs) was designed. However, the structural parameters of the shoulder joint will affect various performances of the mechanism. To obtain the optimal structure parameters, the particle swarm optimization (PSO) was used. Besides, the mathematical expressions of indexes of rotation ranges, maximum bearing torque, discrete dexterity and muscle shrinkage of the bionic shoulder joint were established respectively to represent its many-sided characteristics. And the multi-objective optimization problem was transformed into a single-objective optimization problem by using the weighted-sum method. The normalization method and adaptive-weight method were used to determine each optimization index’s weight coefficient; then the particle swarm optimization was used to optimize the integrated objective function of the bionic shoulder joint and the optimal solution was obtained. Compared with the average optimization generations and the optimal target values of many experiments, using adaptive-weight method to adjust weights of integrated objective function is better than using normalization method, which validates superiority of the adaptive-weight method.

Key words: multi-objective optimization| particle swarm optimization (PSO)| pneumatic muscle actuator (PMA)| bionic shoulder joint mechanism

摘要： To get the movement mode and driving mechanism similar to human shoulder joint, a six degrees of freedom (DOF) serial-parallel bionic shoulder joint mechanism driven by pneumatic muscle actuators (PMAs) was designed. However, the structural parameters of the shoulder joint will affect various performances of the mechanism. To obtain the optimal structure parameters, the particle swarm optimization (PSO) was used. Besides, the mathematical expressions of indexes of rotation ranges, maximum bearing torque, discrete dexterity and muscle shrinkage of the bionic shoulder joint were established respectively to represent its many-sided characteristics. And the multi-objective optimization problem was transformed into a single-objective optimization problem by using the weighted-sum method. The normalization method and adaptive-weight method were used to determine each optimization index’s weight coefficient; then the particle swarm optimization was used to optimize the integrated objective function of the bionic shoulder joint and the optimal solution was obtained. Compared with the average optimization generations and the optimal target values of many experiments, using adaptive-weight method to adjust weights of integrated objective function is better than using normalization method, which validates superiority of the adaptive-weight method.

关键词: multi-objective optimization| particle swarm optimization (PSO)| pneumatic muscle actuator (PMA)| bionic shoulder joint mechanism

CLC Number:

TP 242
Q 811

LIU Kai (刘凯), WU Yang (吴阳), GE Zhishang (葛志尚), WANG Yangwei (王扬威), XU Jiaqi (许嘉琪), LU Yonghua (陆永华), ZHAO Dongbiao (赵东标). Adaptive Multi-Objective Optimization of Bionic Shoulder Joint Based on Particle Swarm Optimization[J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(4): 550-.

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