Automatic creation of both the topology and parameters for a robust controller by means of genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8120

@InProceedings{koza:1999:ISIC,

• author = "John R. Koza and Martin A. Keane and Forrest H {Bennett III} and Jessen Yu and William Mydlowec and Oscar Stiffelman",
• title = "Automatic creation of both the topology and parameters for a robust controller by means of genetic programming",
• booktitle = "Proceedings of the 1999 IEEE International Symposium on Intelligent Control, Intelligent Systems, and Semiotics",
• year = "1999",
• pages = "344--352",
• publisher_address = "Piscataway, NJ, USA",
• organisation = "IEEE",
• keywords = "genetic algorithms, genetic programming",
• URL = "http://www.genetic-programming.com/jkpdf/ieeeisic1999.pdf",
• abstract = "The paper describes a general automated method for synthesizing the design of both the topology and parameter values for controllers. The automated method automatically makes decisions concerning the total number of processing blocks to be employed in the controller, the type of each block, the topological interconnections between the blocks, the values of all parameters for the blocks, and the existence, if any, of internal feedback between the blocks of the overall controller. Incorporation of time-domain, frequency-domain, and other constraints on the control or state variables (often analytically intractable using conventional methods) can be readily accommodated. The automatic method described in the paper (genetic programming) is applied to a problem of synthesizing the design of a robust controller for a plant with a second-order lag. A textbook PID compensator preceded by a lowpass pre-filter delivers credible performance on this problem. However, the automatically created controller employs a second derivative processing block (in addition to proportional, integrative, and derivative blocks and a pre-filter). It is better than twice as effective as the textbook controller as measured by the integral of the time-weighted absolute error, has only two-thirds of the rise time in response to the reference (command) input, and is 10 times better in terms of suppressing the effects of disturbance at the plant input.",
• notes = "IEEE ISIC-99",

}

Genetic Programming entries for John Koza Martin A Keane Forrest Bennett Jessen Yu William J Mydlowec Oscar Stiffelman

Citations