Multi-objective learning control for robotic manipulator

Win, Khin Kyu Kyu; Cheah, Chien Chern

doi:10.1109/icit.2002.1189322

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…These studies are numerical; no experimental testing was performed. Other control tasks that are supported using multi-objective optimization are far from structural engineering: shop floor scheduling (Hong and Prabhu 2004), multi-objective control for a robotic manipulator (Win and Cheah 2004), a power dispatch task (Zhang and Zhen, 2004), portfolio control and optimization (Derigs and Nichel 2004) and ecology (Brouwer and Van Ek 2004).…”

Section: Introductionmentioning

confidence: 99%

Tensegrity Active Control: Multiobjective Approach

Adam

Smith

2007

J. Comput. Civ. Eng.

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A multi-objective search method is adapted for supporting structural control of an active tensegrity structure. Structural control is carried out by modifying the self-stress state of the structure in order to satisfy a serviceability objective and additional robustness objectives. Control commands are defined as sequences of contractions and elongations of active struts to modify the self-stress state of the structure. A two step multi-objective optimization method involving Pareto filtering with hierarchical selection is implemented to determine control commands. Experimental testing on a full-scale active tensegrity structure demonstrates validity of the method. In most cases, control commands are more robust when identified by multi-objective optimization method as compared with a single objective and this robustness leads to better control over successive loading events. Evaluation of multiple objectives provides a more global understanding of tensegrity structure behavior than any single objective. Finally, results reveal opportunities for self-adaptive structures that evolve in unknown environments.

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Section: Introductionmentioning

confidence: 99%