Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator
This manuscript introduces a new adaptive inverse neural (AIN) control method applied to precisely track the piezoelectric (PZT) actuator displacement. First, a 3‐layer neural network optimized by the enhanced differential evolution technique which modifies a mutation scheme and provides suggestions...
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| Veröffentlicht in: | International journal of intelligent systems 2021-10, Vol.36 (10), p.5472-5492 |
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| container_title | International journal of intelligent systems |
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| creator | Son, Nguyen N. Van Kien, Cao Anh, Ho P. H. |
| description | This manuscript introduces a new adaptive inverse neural (AIN) control method applied to precisely track the piezoelectric (PZT) actuator displacement. First, a 3‐layer neural network optimized by the enhanced differential evolution technique which modifies a mutation scheme and provides suggestions for selecting mutant coefficient F, crossover coefficient CR, and population size NP, is used to identify the inverse nonlinearity hysteresis structure of the PZT actuator. Second, a feed‐forward control based on the identified model is proposed to compensate for the PZT hysteresis effect. Third, the Lyapunov stability principle is used to design and implement an adaptive law‐based neural sliding mode model plus the feed‐forward compensator to ensure that the whole PZT plant is operated in asymptotical stability. The experiment results demonstrate the proposed AIN controller proves superiority in comparison with other advanced control methods. |
| doi_str_mv | 10.1002/int.22519 |
| format | Article |
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H.</creatorcontrib><title>Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator</title><title>International journal of intelligent systems</title><description>This manuscript introduces a new adaptive inverse neural (AIN) control method applied to precisely track the piezoelectric (PZT) actuator displacement. First, a 3‐layer neural network optimized by the enhanced differential evolution technique which modifies a mutation scheme and provides suggestions for selecting mutant coefficient F, crossover coefficient CR, and population size NP, is used to identify the inverse nonlinearity hysteresis structure of the PZT actuator. Second, a feed‐forward control based on the identified model is proposed to compensate for the PZT hysteresis effect. Third, the Lyapunov stability principle is used to design and implement an adaptive law‐based neural sliding mode model plus the feed‐forward compensator to ensure that the whole PZT plant is operated in asymptotical stability. The experiment results demonstrate the proposed AIN controller proves superiority in comparison with other advanced control methods.</description><subject>Adaptive control</subject><subject>adaptive inverse neural controller</subject><subject>back‐propagation</subject><subject>Compensators</subject><subject>Control methods</subject><subject>Control stability</subject><subject>enhanced differential evolution</subject><subject>Evolutionary computation</subject><subject>hybrid adaptive inverse neural control</subject><subject>Hysteresis</subject><subject>Intelligent systems</subject><subject>Lyapunov stability concept</subject><subject>Mutation</subject><subject>Neural networks</subject><subject>Piezoelectric actuators</subject><subject>Sliding mode control</subject><issn>0884-8173</issn><issn>1098-111X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp1kLFOwzAQhi0EEqUw8AaWmBjS2kncOCOqgFaqYOnAZjn2WXJJ42A7rcrT4xJWbvmH--5O9yF0T8mMEpLPbRdnec5ofYEmlNQ8o5R-XKIJ4bzMOK2Ka3QTwo4QSquSTdBudQoRPAQbsHL7Hrogo3Udlp3GUss-2gOkThe9a3EjA2gMB9cOZ0j6E-5g8LJNEY_OfwZsnMe9hW8HLajorcJSxUFG52_RlZFtgLu_nKLty_N2uco276_r5dMmU0WR1xmHsqyBskYrXXBVcJZXBnRTMq0YGGZoaSShUiuzqHnFCPDG1FrmmhakosUUPYxre---BghR7Nzgu3RR5GxRploQnqjHkVLeheDBiN7bfXpIUCLOJkUyKX5NJnY-skfbwul_UKzftuPED2K_eMw</recordid><startdate>202110</startdate><enddate>202110</enddate><creator>Son, Nguyen N.</creator><creator>Van Kien, Cao</creator><creator>Anh, Ho P. H.</creator><general>Hindawi Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0001-7353-8205</orcidid><orcidid>https://orcid.org/0000-0002-9796-9357</orcidid><orcidid>https://orcid.org/0000-0003-3380-1317</orcidid></search><sort><creationdate>202110</creationdate><title>Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator</title><author>Son, Nguyen N. ; Van Kien, Cao ; Anh, Ho P. 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| language | eng |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Adaptive control adaptive inverse neural controller back‐propagation Compensators Control methods Control stability enhanced differential evolution Evolutionary computation hybrid adaptive inverse neural control Hysteresis Intelligent systems Lyapunov stability concept Mutation Neural networks Piezoelectric actuators Sliding mode control |
| title | Hysteresis compensation and adaptive control based evolutionary neural networks for piezoelectric actuator |
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