Single and Multiple variables control using Tree Physiology Optimization
This paper presents the tuning of single-input single-output (SISO), and multiple-input multiple-output (MIMO) control system using Tree Physiology Optimization (TPO). TPO is a metaheuristic optimization algorithm that has a clustered diversification search strategy inspired from plant shoots growth...
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| Main Authors: | , |
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| Format: | Article |
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EDP Sciences
2017
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85033214550&doi=10.1051%2fmatecconf%2f201713103017&partnerID=40&md5=5f1a43e92dcda29c2838af787c562066 http://eprints.utp.edu.my/19960/ |
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| Summary: | This paper presents the tuning of single-input single-output (SISO), and multiple-input multiple-output (MIMO) control system using Tree Physiology Optimization (TPO). TPO is a metaheuristic optimization algorithm that has a clustered diversification search strategy inspired from plant shoots growth. The clustered diversification is referred as tree branch and leaves. The exploration is amplified from roots growth counterparts. In the proposed method, each shoot from each branch search for possible solution in parallel and the fitness is evaluated based on all best values found by branch search. The proposed algorithm is also compared with deterministic gradient-free algorithm: Nelder-Mead simplex (NMS) and another metaheuristic algorithm: Particle Swarm Optimization (PSO). Results shown that TPO is able to find good PID parameters with lesser settling time for SISO and MIMO process. NMS is also able to find good PID parameters for SISO with lesser performance index, however not able to find better solution in MIMO control. PSO converged prematurely in SISO control and has high overshoot for MIMO control optimization. © The authors, published by EDP Sciences, 2017. |
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