Improved Grey Particle Swarm Optimization and New Luus-Jaakola Hybrid Algorithm Optimized IMC-PID Controller for Diverse Wing Vibration Systems
The PID control plays important role in wing vibration control systems. However, how to efficiently optimize the PID parameters for different kinds of wing vibration systems is still an open issue for control designers. The problem of PID control optimization is first converted into internal mode co...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/8283178 |
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| author | Nailu Li Hua Yang Anle Mu |
| author_facet | Nailu Li Hua Yang Anle Mu |
| author_sort | Nailu Li |
| collection | DOAJ |
| description | The PID control plays important role in wing vibration control systems. However, how to efficiently optimize the PID parameters for different kinds of wing vibration systems is still an open issue for control designers. The problem of PID control optimization is first converted into internal mode control based PID (IMC-PID) parameters optimization problem for complex wing vibration systems. To solve this problem, a novel optimization technique, called GNPSO is proposed based on the hybridization of improved grey particle swarm optimization (GPSO) and new Luus-Jaakola algorithm (NLJ). The original GPSO is modified by using small population size/iteration number, employing new grey analysis rule and designing new updating formula of acceleration coefficients. The hybrid GNPSO benefits improved global exploration of GPSO and strong local search of new Luus-Jaakola (NLJ), so as to avoid arbitrary and inefficient search of global optimum and prevent the trap in local optimum. Diverse wing vibration systems, including linear system, nonlinear system and multiple-input-multiple-output system are considered to verify the effectiveness of proposed method. Simulation results show that GNPSO optimized method obtains improved vibration control performance, stronger robustness and wide applicability on all system cases, compared to existing evolutionary algorithm based tuning methods. Enhanced optimization convergence and computation efficiency obtained by GNPSO tuning technique are also verified by statistical analysis. |
| format | Article |
| id | doaj-art-67d3176fb92b482a8c60c311a8b8c419 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-67d3176fb92b482a8c60c311a8b8c4192025-02-03T01:29:16ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/82831788283178Improved Grey Particle Swarm Optimization and New Luus-Jaakola Hybrid Algorithm Optimized IMC-PID Controller for Diverse Wing Vibration SystemsNailu Li0Hua Yang1Anle Mu2College of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, ChinaCollege of Electrical, Energy and Power Engineering, Yangzhou University, Yangzhou 225127, ChinaSchool of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, ChinaThe PID control plays important role in wing vibration control systems. However, how to efficiently optimize the PID parameters for different kinds of wing vibration systems is still an open issue for control designers. The problem of PID control optimization is first converted into internal mode control based PID (IMC-PID) parameters optimization problem for complex wing vibration systems. To solve this problem, a novel optimization technique, called GNPSO is proposed based on the hybridization of improved grey particle swarm optimization (GPSO) and new Luus-Jaakola algorithm (NLJ). The original GPSO is modified by using small population size/iteration number, employing new grey analysis rule and designing new updating formula of acceleration coefficients. The hybrid GNPSO benefits improved global exploration of GPSO and strong local search of new Luus-Jaakola (NLJ), so as to avoid arbitrary and inefficient search of global optimum and prevent the trap in local optimum. Diverse wing vibration systems, including linear system, nonlinear system and multiple-input-multiple-output system are considered to verify the effectiveness of proposed method. Simulation results show that GNPSO optimized method obtains improved vibration control performance, stronger robustness and wide applicability on all system cases, compared to existing evolutionary algorithm based tuning methods. Enhanced optimization convergence and computation efficiency obtained by GNPSO tuning technique are also verified by statistical analysis.http://dx.doi.org/10.1155/2019/8283178 |
| spellingShingle | Nailu Li Hua Yang Anle Mu Improved Grey Particle Swarm Optimization and New Luus-Jaakola Hybrid Algorithm Optimized IMC-PID Controller for Diverse Wing Vibration Systems Complexity |
| title | Improved Grey Particle Swarm Optimization and New Luus-Jaakola Hybrid Algorithm Optimized IMC-PID Controller for Diverse Wing Vibration Systems |
| title_full | Improved Grey Particle Swarm Optimization and New Luus-Jaakola Hybrid Algorithm Optimized IMC-PID Controller for Diverse Wing Vibration Systems |
| title_fullStr | Improved Grey Particle Swarm Optimization and New Luus-Jaakola Hybrid Algorithm Optimized IMC-PID Controller for Diverse Wing Vibration Systems |
| title_full_unstemmed | Improved Grey Particle Swarm Optimization and New Luus-Jaakola Hybrid Algorithm Optimized IMC-PID Controller for Diverse Wing Vibration Systems |
| title_short | Improved Grey Particle Swarm Optimization and New Luus-Jaakola Hybrid Algorithm Optimized IMC-PID Controller for Diverse Wing Vibration Systems |
| title_sort | improved grey particle swarm optimization and new luus jaakola hybrid algorithm optimized imc pid controller for diverse wing vibration systems |
| url | http://dx.doi.org/10.1155/2019/8283178 |
| work_keys_str_mv | AT nailuli improvedgreyparticleswarmoptimizationandnewluusjaakolahybridalgorithmoptimizedimcpidcontrollerfordiversewingvibrationsystems AT huayang improvedgreyparticleswarmoptimizationandnewluusjaakolahybridalgorithmoptimizedimcpidcontrollerfordiversewingvibrationsystems AT anlemu improvedgreyparticleswarmoptimizationandnewluusjaakolahybridalgorithmoptimizedimcpidcontrollerfordiversewingvibrationsystems |