Abstract
In this literature survey, the writing reachable for the H∞ adaptive control engineering utilizing neural systems for frameworks whose uncertainty has an ambiguous structure. This engineering merge thoughts from powerful control hypothesis, for example, H∞ control structure, the little addition hypothesis, and L dependability hypothesis with Lyapunov security hypothesis and current hypothetical accomplishments in uncertainty control to build up an adaptive design for frameworks whose vagueness fulfills a neighborhood Lipschitz bound. The strategy enables a control originator to rearrange adaptive tuning method, band limit adaptive control sign, in addition indulgence unequaled vagueness in a solitary plan system. Powerful control configuration limits the impact of uncertainty and nonlinearity to the detriment of diminished execution. The plan outline work is like that utilized in powerful control, yet without giving up execution. The majority of this is adjusted, while giving thoughts of transient execution limits subject to the qualities of two straight frameworks and the adjustment gain. The relevance of neural networks in the control systems, feed forward neural networks direct and indirect adaptive controls, H∞ controller is also reviewed.
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Kashyap, P. (2022). A Literature Review on H∞ Neural Network Adaptive Control. In: Gupta, D., Khanna, A., Kansal, V., Fortino, G., Hassanien, A.E. (eds) Proceedings of Second Doctoral Symposium on Computational Intelligence . Advances in Intelligent Systems and Computing, vol 1374. Springer, Singapore. https://doi.org/10.1007/978-981-16-3346-1_28
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DOI: https://doi.org/10.1007/978-981-16-3346-1_28
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