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Design of a minimum variance multiple input-multiple output neuro self-tuning proportional-integral-derivative controller for non-linear dynamic systems

Guo, L.Z., Zhu, Q.M. and Warwick, K. (2007) Design of a minimum variance multiple input-multiple output neuro self-tuning proportional-integral-derivative controller for non-linear dynamic systems. Proceedings of the Institution of Mechanical Engineers Part I-Journal of Systems and Control Engineering, 221 (1). pp. 75-88. ISSN 0959-6518

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To link to this article DOI: 10.1243/095965118i12503

Abstract/Summary

In this study a minimum variance neuro self-tuning proportional-integral-derivative (PID) controller is designed for complex multiple input-multiple output (MIMO) dynamic systems. An approximation model is constructed, which consists of two functional blocks. The first block uses a linear submodel to approximate dominant system dynamics around a selected number of operating points. The second block is used as an error agent, implemented by a neural network, to accommodate the inaccuracy possibly introduced by the linear submodel approximation, various complexities/uncertainties, and complicated coupling effects frequently exhibited in non-linear MIMO dynamic systems. With the proposed model structure, controller design of an MIMO plant with n inputs and n outputs could be, for example, decomposed into n independent single input-single output (SISO) subsystem designs. The effectiveness of the controller design procedure is initially verified through simulations of industrial examples.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Systems Engineering
ID Code:15240
Uncontrolled Keywords:MIMO dynamic systems, neuro PID controller, self-tuning control, DISCRETE-TIME-SYSTEMS, ADAPTIVE-CONTROL, NETWORKS

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