A quick control strategy based on hybrid intelligent optimization algorithm for planar n-link underactuated manipulators

• Published in: Information sciences 2017-12, Vol.420, p.148-158
• Main Authors: Wang, Yawu, Lai, Xuzhi, Chen, Luefeng, Ding, Huafeng, Wu, Min
• Format: Article
• Language: English
• Subjects: Angle constraint; Genetic algorithm; Particle swarm optimization algorithm; Planar underactuated manipulator
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2017.08.052

This paper presents a quick two-stage position control strategy based on a hybrid intelligent optimization algorithm for a planar n-link underactuated manipulator with a passive first joint. In stage 1, the system is directly reduced to a planar virtual Acrobot by controlling n-2 active links to their target angles. A hybrid intelligent optimization algorithm, which includes genetic algorithm (GA) and particle swarm optimization algorithm (PSO), is used to solve all link target angles according to the target position of the system. By coordinating GA and PSO, the hybrid intelligent optimization algorithm ensures that all link target angles, the angle of the passive link at the end of stage 1, and the initial angle of the active link of the planar virtual Acrobot meet the angle constraint of the planar virtual Acrobot. So, the position control objective of the planar n-link underactuated manipulator is realized by controlling the active link of the planar virtual Acrobot to its target angle in stage 2.