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PID Learning Controller for Multivariable System with Dynamic Friction

Byeong Mook Chung
JKSPE 2007;24(12):57-64.
Published online: December 1, 2007
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There have been many researches for optimal controllers in multivariable systems, and they generally use accurate linear models of the plant dynamics. Real systems, however, contain nonlinearities and high-order dynamics that may be difficult to model using conventional techniques. Therefore, it is necessary a PID gain tuning method without explicit modeling for the multivariable plant dynamics. The PID tuning method utilizes the sign of Jacobian and gradient descent techniques to iteratively reduce the error-related objective function. This paper, especially, focuses on the role of I-controller when there is a steady state error. However, it is not easy to tune I-gain unlike P- and D-gain because I-controller is mainly operated in the steady state. Simulations for an overhead crane system with dynamic friction show that the proposed PID-LC algorithm improves controller performance, even in the steady state error.

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PID Learning Controller for Multivariable System with Dynamic Friction
J. Korean Soc. Precis. Eng.. 2007;24(12):57-64.   Published online December 1, 2007
Download Citation

Download a citation file in RIS format that can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Reference Manager.

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PID Learning Controller for Multivariable System with Dynamic Friction
J. Korean Soc. Precis. Eng.. 2007;24(12):57-64.   Published online December 1, 2007
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