Abstract
An adaptive intelligent control strategy based on a brain emotional learning model is investigated in the application of the rate regulation of suborbital reentry payloads. Because of nonlinear time-varying dynamics of these payloads, choosing an appropriate control mechanism and stability strategy can be an engineering challenge. Thus in a new approach, a moving mass control system in conjunction with brain emotional learning-based intelligent control is used to fulfill payload de-tumbling. The contribution of brain emotional learning-based intelligent control in handling the nonlinear time-varying dynamics is shown by comparison with results obtained from a linear proportional–integral controller. The results demonstrate excellent performance of brain emotional learning-based intelligent control in learning dynamic couplings and improvement of behavior without any considerable control system complexity.
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Schedule a callMore From: Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering
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