A neural network–based sliding mode controller of folding-boom aerial work platform

Aerial work platform is a special vehicle for carrying personnel to the appointed site in the air for operations. Therefore, the work platform requires high stability. This article proposes a sliding mode controller based on neural network for tracking control of folding-boom aerial work platform. Since the chattering caused by sliding mode controller with high-speed switching control may lead to system performance degradation, continuous control obtained from neural network system replaces discontinuous switching control to eliminate chattering. Furthermore, the whole system is proved to be stable by Lyapunov stability theorem. Finally, numerical results show that the designed controller can eliminate the chattering resulting from switching control in sliding mode controller and inhibit the vibration of work platform when there exists system uncertainty. Moreover, the controller is effective for the reduction of tracking error.

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