Bisheban, M., Mahmoodabadi, M. (2015). Pareto Optimal Design Of Decoupled Sliding Mode Control Based On A New Multi-Objective Particle Swarm Optimization Algorithm. AUT Journal of Modeling and Simulation, 45(2), 31-40. doi: 10.22060/miscj.2015.527

M. Bisheban; M.J. Mahmoodabadi. "Pareto Optimal Design Of Decoupled Sliding Mode Control Based On A New Multi-Objective Particle Swarm Optimization Algorithm". AUT Journal of Modeling and Simulation, 45, 2, 2015, 31-40. doi: 10.22060/miscj.2015.527

Bisheban, M., Mahmoodabadi, M. (2015). 'Pareto Optimal Design Of Decoupled Sliding Mode Control Based On A New Multi-Objective Particle Swarm Optimization Algorithm', AUT Journal of Modeling and Simulation, 45(2), pp. 31-40. doi: 10.22060/miscj.2015.527

Bisheban, M., Mahmoodabadi, M. Pareto Optimal Design Of Decoupled Sliding Mode Control Based On A New Multi-Objective Particle Swarm Optimization Algorithm. AUT Journal of Modeling and Simulation, 2015; 45(2): 31-40. doi: 10.22060/miscj.2015.527

Pareto Optimal Design Of Decoupled Sliding Mode Control Based On A New Multi-Objective Particle Swarm Optimization Algorithm

^{1}School of Mechanical and Aerospace Engineering, the George Washington University, Washington DC, USA.

^{2}Department of Mechanical Engineering, Sirjan University of Technology, Sirjan, Iran.

Abstract

One of the most important applications of multi-objective optimization is adjusting parameters ofpractical engineering problems in order to produce a more desirable outcome. In this paper, the decoupled sliding mode control technique (DSMC) is employed to stabilize an inverted pendulum which is a classic example of inherently unstable systems. Furthermore, a new Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is implemented for optimizing the DSMC parameters in order to decrease the normalized angle error of the pole and normalized distance error of the cart, simultaneously. The results of simulation are presented which consist of results with and without disturbances. The proposed Pareto front for the DSMC problem demonstrates that the Ingenious-MOPSO operates much better than other multi-objective evolutionary algorithms.

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