Composite recurrent Laguerre orthogonal polynomials neural network dynamic control for continuously variable transmission system using altered particle swarm optimization
The composite recurrent Laguerre orthogonal polynomials neural network (NN) control system using altered particle swarm optimization (PSO) is developed for controlling the V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor to obtain better control per...
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| Veröffentlicht in: | Nonlinear dynamics 2015-08, Vol.81 (3), p.1219-1245 |
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| container_title | Nonlinear dynamics |
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| creator | Lin, Chih-Hong |
| description | The composite recurrent Laguerre orthogonal polynomials neural network (NN) control system using altered particle swarm optimization (PSO) is developed for controlling the V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor to obtain better control performance. The simplified dynamic and kinematic models of a V-belt CVT system are derived by law of conservation. The control system consists of an inspector control, a recurrent Laguerre orthogonal polynomials NN control with adaptation law, and a recouped control with estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomials NN is originated from Lyapunov stability theorem. Additionally, two optimal learning rates of the parameters by means of altered PSO are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated to demonstrate the control performance of the proposed control scheme. |
| doi_str_mv | 10.1007/s11071-015-2064-7 |
| format | Article |
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The simplified dynamic and kinematic models of a V-belt CVT system are derived by law of conservation. The control system consists of an inspector control, a recurrent Laguerre orthogonal polynomials NN control with adaptation law, and a recouped control with estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomials NN is originated from Lyapunov stability theorem. Additionally, two optimal learning rates of the parameters by means of altered PSO are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated to demonstrate the control performance of the proposed control scheme.</description><identifier>ISSN: 0924-090X</identifier><identifier>EISSN: 1573-269X</identifier><identifier>DOI: 10.1007/s11071-015-2064-7</identifier><language>eng</language><publisher>Dordrecht: Springer Netherlands</publisher><subject>Adaptation ; Automotive Engineering ; Classical Mechanics ; Comparative studies ; Continuously variable ; Control ; Control systems ; Dynamic control ; Dynamical Systems ; Engineering ; Mechanical Engineering ; Neural networks ; Original Paper ; Parameters ; Particle swarm optimization ; Permanent magnets ; Polynomials ; Synchronous motors ; Transmissions (automotive) ; Vibration</subject><ispartof>Nonlinear dynamics, 2015-08, Vol.81 (3), p.1219-1245</ispartof><rights>Springer Science+Business Media Dordrecht 2015</rights><rights>Nonlinear Dynamics is a copyright of Springer, (2015). 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The simplified dynamic and kinematic models of a V-belt CVT system are derived by law of conservation. The control system consists of an inspector control, a recurrent Laguerre orthogonal polynomials NN control with adaptation law, and a recouped control with estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomials NN is originated from Lyapunov stability theorem. Additionally, two optimal learning rates of the parameters by means of altered PSO are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated to demonstrate the control performance of the proposed control scheme.</description><subject>Adaptation</subject><subject>Automotive Engineering</subject><subject>Classical Mechanics</subject><subject>Comparative studies</subject><subject>Continuously variable</subject><subject>Control</subject><subject>Control systems</subject><subject>Dynamic control</subject><subject>Dynamical Systems</subject><subject>Engineering</subject><subject>Mechanical Engineering</subject><subject>Neural networks</subject><subject>Original Paper</subject><subject>Parameters</subject><subject>Particle swarm optimization</subject><subject>Permanent magnets</subject><subject>Polynomials</subject><subject>Synchronous motors</subject><subject>Transmissions 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studies</topic><topic>Continuously variable</topic><topic>Control</topic><topic>Control systems</topic><topic>Dynamic control</topic><topic>Dynamical Systems</topic><topic>Engineering</topic><topic>Mechanical Engineering</topic><topic>Neural networks</topic><topic>Original Paper</topic><topic>Parameters</topic><topic>Particle swarm optimization</topic><topic>Permanent magnets</topic><topic>Polynomials</topic><topic>Synchronous motors</topic><topic>Transmissions (automotive)</topic><topic>Vibration</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Chih-Hong</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One 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Dyn</stitle><date>2015-08-01</date><risdate>2015</risdate><volume>81</volume><issue>3</issue><spage>1219</spage><epage>1245</epage><pages>1219-1245</pages><issn>0924-090X</issn><eissn>1573-269X</eissn><abstract>The composite recurrent Laguerre orthogonal polynomials neural network (NN) control system using altered particle swarm optimization (PSO) is developed for controlling the V-belt continuously variable transmission (CVT) system driven by permanent magnet synchronous motor to obtain better control performance. The simplified dynamic and kinematic models of a V-belt CVT system are derived by law of conservation. The control system consists of an inspector control, a recurrent Laguerre orthogonal polynomials NN control with adaptation law, and a recouped control with estimation law. Moreover, the adaptation law of online parameters in the recurrent Laguerre orthogonal polynomials NN is originated from Lyapunov stability theorem. Additionally, two optimal learning rates of the parameters by means of altered PSO are posed in order to achieve better convergence. At last, comparative studies shown by experimental results are illustrated to demonstrate the control performance of the proposed control scheme.</abstract><cop>Dordrecht</cop><pub>Springer Netherlands</pub><doi>10.1007/s11071-015-2064-7</doi><tpages>27</tpages></addata></record> |
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| language | eng |
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| source | SpringerLink Journals - AutoHoldings |
| subjects | Adaptation Automotive Engineering Classical Mechanics Comparative studies Continuously variable Control Control systems Dynamic control Dynamical Systems Engineering Mechanical Engineering Neural networks Original Paper Parameters Particle swarm optimization Permanent magnets Polynomials Synchronous motors Transmissions (automotive) Vibration |
| title | Composite recurrent Laguerre orthogonal polynomials neural network dynamic control for continuously variable transmission system using altered particle swarm optimization |
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