Bio-Inspired Jumping Spider Optimization for Controller Tuning/Parameter Estimation of an Uncertain Aerodynamic MIMO System
The practical near impossibility of empirical attempts in estimating optimal controller gains makes the use of metaheuristics strategies inevitable to automatically obtain these gains by an iterative, heuristic simulation procedure. The convergence of the gains values to the local or global solution...
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| Format: | Article |
| Language: | English |
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Akif AKGUL
2024-07-01
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| Series: | Chaos Theory and Applications |
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| Online Access: | https://dergipark.org.tr/en/download/article-file/3563238 |
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| author | Ravi Samikannu Oduetse Matsebe David Ezekiel |
| author_facet | Ravi Samikannu Oduetse Matsebe David Ezekiel |
| author_sort | Ravi Samikannu |
| collection | DOAJ |
| description | The practical near impossibility of empirical attempts in estimating optimal controller gains makes the use of metaheuristics strategies inevitable to automatically obtain these gains by an iterative, heuristic simulation procedure. The convergence of the gains values to the local or global solutions occur with ease. In designing controllers for the Twin-Rotor MIMO System (TRMS), Jumping Spider Optimization Algorithm (JSOA), a novel neoteric population-based bio-inspired metaheuristic approach is used to obtain optimum values for the Proportional, Integral and Derivative (PID) controllers. With the k,p,i controller gains as the decision variables, the JSOA solution to a nonlinear multi-objective optimization problem subject to some intrinsic constraints spawned optimal values for the controllers’ variables. Counter to other algorithms (deterministic and stochastic) that get caught in local minima, JSOA evolved a solution after searchingly rummaging the entire solution search space in a vectorized fashion for an optimal value. Compared with several other versatile controllers (using GA, PSO, Pattern Search and Simulated Annealing), statistical results obtained showed JSOA technique provided a unique solution and found the gains of the PID controllers, marginally in relation to the others like optimization methods. |
| format | Article |
| id | doaj-art-54b1a5868ea34a3c91137e6a8bbac994 |
| institution | Kabale University |
| issn | 2687-4539 |
| language | English |
| publishDate | 2024-07-01 |
| publisher | Akif AKGUL |
| record_format | Article |
| series | Chaos Theory and Applications |
| spelling | doaj-art-54b1a5868ea34a3c91137e6a8bbac9942025-01-23T18:19:33ZengAkif AKGULChaos Theory and Applications2687-45392024-07-016320521710.51537/chaos.13968231971Bio-Inspired Jumping Spider Optimization for Controller Tuning/Parameter Estimation of an Uncertain Aerodynamic MIMO SystemRavi Samikannu0https://orcid.org/0000-0002-6945-6562Oduetse Matsebe1https://orcid.org/0000-0001-6052-7320David Ezekiel2https://orcid.org/0000-0002-1922-0690Botswana International University of Science and TechnologyBotswana International University of Science and TechnologyBotswana International University of Science and TechnologyThe practical near impossibility of empirical attempts in estimating optimal controller gains makes the use of metaheuristics strategies inevitable to automatically obtain these gains by an iterative, heuristic simulation procedure. The convergence of the gains values to the local or global solutions occur with ease. In designing controllers for the Twin-Rotor MIMO System (TRMS), Jumping Spider Optimization Algorithm (JSOA), a novel neoteric population-based bio-inspired metaheuristic approach is used to obtain optimum values for the Proportional, Integral and Derivative (PID) controllers. With the k,p,i controller gains as the decision variables, the JSOA solution to a nonlinear multi-objective optimization problem subject to some intrinsic constraints spawned optimal values for the controllers’ variables. Counter to other algorithms (deterministic and stochastic) that get caught in local minima, JSOA evolved a solution after searchingly rummaging the entire solution search space in a vectorized fashion for an optimal value. Compared with several other versatile controllers (using GA, PSO, Pattern Search and Simulated Annealing), statistical results obtained showed JSOA technique provided a unique solution and found the gains of the PID controllers, marginally in relation to the others like optimization methods.https://dergipark.org.tr/en/download/article-file/3563238jumping spider optimization algorithm (jsoa)meta-heuristicsoptimizationpidintelligent controldynamic systemnonlinear systemlinearizationpitch & yawtwin-rotor mimo system (trms) |
| spellingShingle | Ravi Samikannu Oduetse Matsebe David Ezekiel Bio-Inspired Jumping Spider Optimization for Controller Tuning/Parameter Estimation of an Uncertain Aerodynamic MIMO System Chaos Theory and Applications jumping spider optimization algorithm (jsoa) meta-heuristics optimization pid intelligent control dynamic system nonlinear system linearization pitch & yaw twin-rotor mimo system (trms) |
| title | Bio-Inspired Jumping Spider Optimization for Controller Tuning/Parameter Estimation of an Uncertain Aerodynamic MIMO System |
| title_full | Bio-Inspired Jumping Spider Optimization for Controller Tuning/Parameter Estimation of an Uncertain Aerodynamic MIMO System |
| title_fullStr | Bio-Inspired Jumping Spider Optimization for Controller Tuning/Parameter Estimation of an Uncertain Aerodynamic MIMO System |
| title_full_unstemmed | Bio-Inspired Jumping Spider Optimization for Controller Tuning/Parameter Estimation of an Uncertain Aerodynamic MIMO System |
| title_short | Bio-Inspired Jumping Spider Optimization for Controller Tuning/Parameter Estimation of an Uncertain Aerodynamic MIMO System |
| title_sort | bio inspired jumping spider optimization for controller tuning parameter estimation of an uncertain aerodynamic mimo system |
| topic | jumping spider optimization algorithm (jsoa) meta-heuristics optimization pid intelligent control dynamic system nonlinear system linearization pitch & yaw twin-rotor mimo system (trms) |
| url | https://dergipark.org.tr/en/download/article-file/3563238 |
| work_keys_str_mv | AT ravisamikannu bioinspiredjumpingspideroptimizationforcontrollertuningparameterestimationofanuncertainaerodynamicmimosystem AT oduetsematsebe bioinspiredjumpingspideroptimizationforcontrollertuningparameterestimationofanuncertainaerodynamicmimosystem AT davidezekiel bioinspiredjumpingspideroptimizationforcontrollertuningparameterestimationofanuncertainaerodynamicmimosystem |