This study develops an adaptive recurrent neural network (NN) intelligent sliding-mode controller (ARNISMC) for magnetic levitation system (MLS). First, a non-linear dynamic model of the MLS is derived. Thereafter, a SM controller (SMC) method is presented to compensate for the uncertainties in the MLS. In addition, to enhance the control effort of a conventional SMC and further increase the tracking performance of the MLS, the uncertainty terms of the system dynamics can be estimated online by using an AR radial basis function NN estimator. Accordingly, the proposed controller not only offers the accurate positioning tracking control, minimises steady-state error, and improves conventional controller performance but also provides global positioning tracking control, which has not been previously reported in the literatures. It can effectively solve problems associated with typical MLS controller design. Moreover, the satisfactory tracking performance can be observed from the experimental results by adopting the proposed ARNISMC scheme.

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This article presents a double-integral sliding-mode controller with an adaptive proportional-integral-derivative observer for brushless direct current motor speed controller. First, an integral sliding-mode control is designed based on the motor dynamic model and system uncertainties. Accordingly, a novel double-integral sliding-mode controller is proposed to enhance the steady-state performance by employing the double-integral sliding surface with its inherent integral control feature. In addition, the control gains of the double-integral sliding-mode controller can be online adjusted using an adaptive proportional-integral-derivative observer. Thus, the proposed double-integral sliding-mode controller possesses the merits of integral sliding-mode control, proportional-integral-derivative, and adaptive law. An experimental setup including a digital signal processor is applied to verify the brushless direct current motor control system using different control scheme. The measured results show that satisfactory acceleration/deceleration speed tracking and load disturbance speed regulation characteristics are obtained via conventional proportional-integral-derivative controller and the developed integral sliding-mode control scheme under the various testing cases. Moreover, the maximum tracking error can be further reduced more than 50% by adopting the proposed double-integral sliding-mode controller with adaptive proportional-integral-derivative observer.

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