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A Comparison among Multi-Agent Stochastic Optimization Algorithms for State Feedback Regulator Design of a Twin Rotor MIMO System

A Comparison among Multi-Agent Stochastic Optimization Algorithms for State Feedback Regulator Design of a Twin Rotor MIMO System

Kaushik Das Sharma
Copyright: © 2016 |Pages: 40
ISBN13: 9781522500582|ISBN10: 1522500588|EISBN13: 9781522500599
DOI: 10.4018/978-1-5225-0058-2.ch018
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MLA

Das Sharma, Kaushik. "A Comparison among Multi-Agent Stochastic Optimization Algorithms for State Feedback Regulator Design of a Twin Rotor MIMO System." Handbook of Research on Natural Computing for Optimization Problems, edited by Jyotsna Kumar Mandal, et al., IGI Global, 2016, pp. 409-448. https://doi.org/10.4018/978-1-5225-0058-2.ch018

APA

Das Sharma, K. (2016). A Comparison among Multi-Agent Stochastic Optimization Algorithms for State Feedback Regulator Design of a Twin Rotor MIMO System. In J. Mandal, S. Mukhopadhyay, & T. Pal (Eds.), Handbook of Research on Natural Computing for Optimization Problems (pp. 409-448). IGI Global. https://doi.org/10.4018/978-1-5225-0058-2.ch018

Chicago

Das Sharma, Kaushik. "A Comparison among Multi-Agent Stochastic Optimization Algorithms for State Feedback Regulator Design of a Twin Rotor MIMO System." In Handbook of Research on Natural Computing for Optimization Problems, edited by Jyotsna Kumar Mandal, Somnath Mukhopadhyay, and Tandra Pal, 409-448. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0058-2.ch018

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Abstract

Multi-agent optimization or population based search techniques are increasingly become popular compared to its single-agent counterpart. The single-agent gradient based search algorithms are very prone to be trapped in local optima and also the computational cost is higher. Multi-Agent Stochastic Optimization (MASO) algorithms are much powerful to overcome most of the drawbacks. This chapter presents a comparison of five MASO algorithms, namely genetic algorithm, particle swarm optimization, differential evolution, harmony search algorithm, and gravitational search algorithm. These MASO algorithms are utilized here to design the state feedback regulator for a Twin Rotor MIMO System (TRMS). TRMS is a multi-modal process and the design of its state feedback regulator is quite difficult using conventional methods available. MASO algorithms are typically suitable for such complex process optimizations. The performances of different MASO algorithms are presented and discussed in light of designing the state regulator for TRMS.

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