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Pareto design of state feedback tracking control of a biped robot via multiobjective PSO in comparison with sigma method and genetic algorithms: modified NSGAII and MATLAB's toolbox.

Mahmoodabadi MJ, Taherkhorsandi M, Bagheri A - ScientificWorldJournal (2014)

Bottom Line: An optimal robust state feedback tracking controller is introduced to control a biped robot.Three points are chosen from the nondominated solutions of the obtained Pareto front based on two conflicting objective functions, that is, the normalized summation of angle errors and normalized summation of control effort.Obtained results elucidate the efficiency of the proposed controller in order to control a biped robot.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran.

ABSTRACT
An optimal robust state feedback tracking controller is introduced to control a biped robot. In the literature, the parameters of the controller are usually determined by a tedious trial and error process. To eliminate this process and design the parameters of the proposed controller, the multiobjective evolutionary algorithms, that is, the proposed method, modified NSGAII, Sigma method, and MATLAB's Toolbox MOGA, are employed in this study. Among the used evolutionary optimization algorithms to design the controller for biped robots, the proposed method operates better in the aspect of designing the controller since it provides ample opportunities for designers to choose the most appropriate point based upon the design criteria. Three points are chosen from the nondominated solutions of the obtained Pareto front based on two conflicting objective functions, that is, the normalized summation of angle errors and normalized summation of control effort. Obtained results elucidate the efficiency of the proposed controller in order to control a biped robot.

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The desired trajectory of θ1 and its tracking trajectory for the optimum design points A, B, and C shown in the Pareto front.
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Related In: Results  -  Collection


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fig3: The desired trajectory of θ1 and its tracking trajectory for the optimum design points A, B, and C shown in the Pareto front.

Mentions: By regarding Figure 2, all the optimal points in the Pareto front are nondominated and could be selected to design the controller. However, it is crucial to note that selecting a better amount of any objective function causes a worse amount of another objective. In the Pareto front, there are three crucial points, A, B, and C. By regarding both objective functions of the normalized summation of angles errors and normalized summation of control effort, point B could be the trade-off optimum choice. Moreover, design variables and objective functions corresponding to the optimum design points A, B, and C are illustrated in Table 2. The real tracking trajectories and phase planes of the optimum design points A, B, and C are shown in Figures 3, 4, and 5.


Pareto design of state feedback tracking control of a biped robot via multiobjective PSO in comparison with sigma method and genetic algorithms: modified NSGAII and MATLAB's toolbox.

Mahmoodabadi MJ, Taherkhorsandi M, Bagheri A - ScientificWorldJournal (2014)

The desired trajectory of θ1 and its tracking trajectory for the optimum design points A, B, and C shown in the Pareto front.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3927564&req=5

fig3: The desired trajectory of θ1 and its tracking trajectory for the optimum design points A, B, and C shown in the Pareto front.
Mentions: By regarding Figure 2, all the optimal points in the Pareto front are nondominated and could be selected to design the controller. However, it is crucial to note that selecting a better amount of any objective function causes a worse amount of another objective. In the Pareto front, there are three crucial points, A, B, and C. By regarding both objective functions of the normalized summation of angles errors and normalized summation of control effort, point B could be the trade-off optimum choice. Moreover, design variables and objective functions corresponding to the optimum design points A, B, and C are illustrated in Table 2. The real tracking trajectories and phase planes of the optimum design points A, B, and C are shown in Figures 3, 4, and 5.

Bottom Line: An optimal robust state feedback tracking controller is introduced to control a biped robot.Three points are chosen from the nondominated solutions of the obtained Pareto front based on two conflicting objective functions, that is, the normalized summation of angle errors and normalized summation of control effort.Obtained results elucidate the efficiency of the proposed controller in order to control a biped robot.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran.

ABSTRACT
An optimal robust state feedback tracking controller is introduced to control a biped robot. In the literature, the parameters of the controller are usually determined by a tedious trial and error process. To eliminate this process and design the parameters of the proposed controller, the multiobjective evolutionary algorithms, that is, the proposed method, modified NSGAII, Sigma method, and MATLAB's Toolbox MOGA, are employed in this study. Among the used evolutionary optimization algorithms to design the controller for biped robots, the proposed method operates better in the aspect of designing the controller since it provides ample opportunities for designers to choose the most appropriate point based upon the design criteria. Three points are chosen from the nondominated solutions of the obtained Pareto front based on two conflicting objective functions, that is, the normalized summation of angle errors and normalized summation of control effort. Obtained results elucidate the efficiency of the proposed controller in order to control a biped robot.

Show MeSH