Robust nonlinear control with neural networks

Ramasamy, Selvaraj ; Deshpande, Pradeep B. ; Tambe, Sanjeev S. ; Kulkarni, Bhaskar D. (1995) Robust nonlinear control with neural networks Proceedings of the Royal Society A: Mathematical, Physical & Engineering Sciences, 449 (1937). pp. 655-667. ISSN 1364-5021

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A new method for robust nonlinear control of single-input single-output systems is presented. The control law utilizes the universal approximation characteristic of neural networks augmented with the ability for adaptation. The presence of neural networks obviates the need for a mechanistic model for control law computations and the difficulties associated with model-based approaches become irrelevant. The new control law called N-RNCL incorporates the ability for adaptation through an adjustment of bias neurons and ensures offset-free performance in the presence of load and unmeasured disturbances. The performance of N-RNCL is demonstrated using the examples of a strong-acid strong-base pH control system and a nonlinear heat exchanger system. The state-of-the-art controller shows excellent servo and regulatory performance.

Item Type:Article
Source:Copyright of this article belongs to The Royal Society.
ID Code:85682
Deposited On:05 Mar 2012 13:54
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