Genetic algorithms in controller design and tuning
A three-phased framework for learning dynamic system control is presented. A genetic algorithm is employed to derive control rules encoded as decision tables. Next, the rules are automatically transformed into comprehensible form by means of inductive machine learning. Finally, a genetic algorithm i...
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| Veröffentlicht in: | IEEE transactions on systems, man, and cybernetics man, and cybernetics, 1993-09, Vol.23 (5), p.1330-1339 |
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| container_end_page | 1339 |
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| container_issue | 5 |
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| container_title | IEEE transactions on systems, man, and cybernetics |
| container_volume | 23 |
| creator | Varsek, A. Urbancic, T. Filipic, B. |
| description | A three-phased framework for learning dynamic system control is presented. A genetic algorithm is employed to derive control rules encoded as decision tables. Next, the rules are automatically transformed into comprehensible form by means of inductive machine learning. Finally, a genetic algorithm is applied again to optimize the numerical parameters of the induced rules. The approach is experimentally verified on a benchmark problem of inverted pendulum control, with special emphasis on robustness and reliability. It is also shown that the proposed framework enables exploiting available domain knowledge. In this case, genetic algorithm makes qualitative control rules operational by providing interpretation of symbols in terms of numerical values.< > |
| doi_str_mv | 10.1109/21.260663 |
| format | Article |
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A genetic algorithm is employed to derive control rules encoded as decision tables. Next, the rules are automatically transformed into comprehensible form by means of inductive machine learning. Finally, a genetic algorithm is applied again to optimize the numerical parameters of the induced rules. The approach is experimentally verified on a benchmark problem of inverted pendulum control, with special emphasis on robustness and reliability. It is also shown that the proposed framework enables exploiting available domain knowledge. 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A genetic algorithm is employed to derive control rules encoded as decision tables. Next, the rules are automatically transformed into comprehensible form by means of inductive machine learning. Finally, a genetic algorithm is applied again to optimize the numerical parameters of the induced rules. The approach is experimentally verified on a benchmark problem of inverted pendulum control, with special emphasis on robustness and reliability. It is also shown that the proposed framework enables exploiting available domain knowledge. 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Systems</subject><subject>Costs</subject><subject>Exact sciences and technology</subject><subject>Genetic algorithms</subject><subject>Machine learning</subject><subject>Mathematical model</subject><subject>Optimization methods</subject><subject>Robust control</subject><issn>0018-9472</issn><issn>2168-2909</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1993</creationdate><recordtype>article</recordtype><recordid>eNo9jzFPwzAQhS0EEqUwsDJlYGFIubNdOx5RRQtSJRaYI2Ofi1HqVHYY-PcEpep0errvvafH2C3CAhHMI8cFV6CUOGMzjqqpuQFzzmYA2NRGan7Jrkr5HqWUZjljfEOJhugq2-36HIevfaliqlyfhtx3HeXKU4m7VNnkq-EnxbS7ZhfBdoVujnfOPtbP76uXevu2eV09bWsnJA61sxY4gRQ6cMkDBo2SI3iPnjhwp42w3qJtUC01GSMEfCovrEDSJjQo5uxhynW5LyVTaA857m3-bRHa_7Etx3YaO7L3E3uwxdkuZJtcLCeDhLFE6RG7m7BIRKfvMeMPliBabg</recordid><startdate>19930901</startdate><enddate>19930901</enddate><creator>Varsek, A.</creator><creator>Urbancic, T.</creator><creator>Filipic, B.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>19930901</creationdate><title>Genetic algorithms in controller design and tuning</title><author>Varsek, A. ; Urbancic, T. ; Filipic, B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c341t-caa02e0437f242f1f714210dd1de202c793ada1a81657e99330b6d3a31e79f813</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1993</creationdate><topic>Algorithm design and analysis</topic><topic>Applied sciences</topic><topic>Automatic control</topic><topic>Computer science</topic><topic>Computer science; control theory; systems</topic><topic>Control system synthesis</topic><topic>Control systems</topic><topic>Control theory. Systems</topic><topic>Costs</topic><topic>Exact sciences and technology</topic><topic>Genetic algorithms</topic><topic>Machine learning</topic><topic>Mathematical model</topic><topic>Optimization methods</topic><topic>Robust control</topic><toplevel>online_resources</toplevel><creatorcontrib>Varsek, A.</creatorcontrib><creatorcontrib>Urbancic, T.</creatorcontrib><creatorcontrib>Filipic, B.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><jtitle>IEEE transactions on systems, man, and cybernetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Varsek, A.</au><au>Urbancic, T.</au><au>Filipic, B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genetic algorithms in controller design and tuning</atitle><jtitle>IEEE transactions on systems, man, and cybernetics</jtitle><stitle>T-SMC</stitle><date>1993-09-01</date><risdate>1993</risdate><volume>23</volume><issue>5</issue><spage>1330</spage><epage>1339</epage><pages>1330-1339</pages><issn>0018-9472</issn><eissn>2168-2909</eissn><coden>ISYMAW</coden><abstract>A three-phased framework for learning dynamic system control is presented. 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| ispartof | IEEE transactions on systems, man, and cybernetics, 1993-09, Vol.23 (5), p.1330-1339 |
| issn | 0018-9472 2168-2909 |
| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Algorithm design and analysis Applied sciences Automatic control Computer science Computer science control theory systems Control system synthesis Control systems Control theory. Systems Costs Exact sciences and technology Genetic algorithms Machine learning Mathematical model Optimization methods Robust control |
| title | Genetic algorithms in controller design and tuning |
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