A Neural Controller Using Imc Strategy

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A Neural Controller Using Imc Strategy

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dc.contributor.author Abdul Lateef, K. I. en_US
dc.date.accessioned 2009-11-25T13:06:17Z
dc.date.available 2009-11-25T13:06:17Z
dc.date.issued 2000 en_US
dc.identifier.citation Engineering Journal of Qatar University, 2000, Vol. 13, Pages 245-274. en_US
dc.identifier.uri http://hdl.handle.net/10576/7973
dc.description.abstract In this paper a nonlinear Internal Model Control (IMC) strategy based on a modified NARMA model is proposed. The IMC controller consists of a model inverse controller and a robustness filter with a single tuning parameter. Under such a controller, the control quality and the robustness can be influenced in a direct manner. From the input-output observation data of the plant, a neural network is trained off-line using Back-Propagation algorithm to emulate the feed-forward dynamics of the plant. This neural plant model provides a simple check of the model invertability, which plays a critical role in the IMC strategy. Using the same network used in the feedforward model of the plant, an exact model inverse is obtained directly assuming invertability of the plant model. The exact inverse of the plant model is very important to achieve offset-free performance. Simulation examples demonstrate the simplicity of the design procedure and the good performance characteristics of the proposed nonlinear IMC controller. The examples show that the proposed controller is superior to conventional PID controller and it can also be applied to systems having time delay. en_US
dc.language.iso en en_US
dc.publisher Qatar University en_US
dc.subject Engineering: Electrical Engineering en_US
dc.title A Neural Controller Using Imc Strategy en_US
dc.type Article en_US
dc.identifier.pagination 245-274 en_US
dc.identifier.volume 13 en_US

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