Abstract
This paper proposes an intelligent tuning methods of linear and nonlinear parameters for composite nonlinear feedback (CNF) control using multi objective particle swarm optimization (MOPSO) and multi objective genetic algorithm (MOGA). The main advantage of the methods lies in its efficient fitness/objective evaluation approach of the algorithms such that it can be computed rapidly to obtain an optimal CNF with good system response. In order to yield an efficient technique for fitness evaluation, it is achieved by utilizing a multi objective approach, thus avoiding the use of single objective approach to evaluate the fitness. MATLAB simulations are used to test the effectiveness of the proposed techniques. Nonlinear vehicle model is constructed to validate the controller performance. The model is also simplified to a linear model for designing the CNF. The superiority of the proposed methods over the manual tuning method are improved with 98 percent reduction in error.
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© 2016 Springer Science+Business Media Singapore
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Ramli, L., Sam, Y.M., Mohamed, Z. (2016). A Comparison of Particle Swarm Optimization and Genetic Algorithm Based on Multi-objective Approach for Optimal Composite Nonlinear Feedback Control of Vehicle Stability System. In: Zhang, L., Song, X., Wu, Y. (eds) Theory, Methodology, Tools and Applications for Modeling and Simulation of Complex Systems. AsiaSim SCS AutumnSim 2016 2016. Communications in Computer and Information Science, vol 643. Springer, Singapore. https://doi.org/10.1007/978-981-10-2663-8_67
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DOI: https://doi.org/10.1007/978-981-10-2663-8_67
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