Radial basis function neural network–based adaptive sliding mode suspension control for maglev yaw system of wind turbines

Cui, Guodong; Cai, Bin; Su, Baili; Chu, Xiaoguang

doi:10.1177/09596518211022068

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…This is the sliding variable's maximum deviation from its instant sampled value. The maximum value of the band can then be determined for the case s t i ð Þ = 0 and is given in equation (28). Thus, the above statements are satisfied.…”

Section: Event-triggering-based Sliding Mode Control Designmentioning

confidence: 94%

“…In an radial basis function (RBF)-based neural network, there are three layers: one input layer, one hidden layer, and one output layer, whereas, in other multilayer neural networks, there is more than one hidden layer (Figure 3). 28 The hidden layer calculates the norm of input from a neuron that passes through the activation function. Thus, it can be said that the hidden units provide a set of functions that comprise a random basis for the input patterns.…”

Section: Event-triggering-based Adaptive Gain Scheduling Control Designmentioning

confidence: 99%

Section: Robust Control Design For Ambmentioning

confidence: 99%

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Adaptive gain scheduling based event-triggering control design for robust performance of active magnetic bearing

Saini,

Pratap,

Kumar

2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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This article proposes an efficient adaptive robust control for the eight-pole active magnetic bearing based on a heteropolar structure. Due to the uncertain behavior of active magnetic bearing, the mathematical model of active magnetic bearing is considered to be highly nonlinear and uncertain. As the rotor displacement and velocity are the measurable states, sliding mode control is designed to estimate state variables. Also, a matched disturbance term is used to deal with undesirable disturbances in the active magnetic bearing system. The controller is developed for an active magnetic bearing using the event-triggering-based sliding mode control technique. However, the stability of the proposed scheme has been achieved with the help of Lyapunov theory. Furthermore, the adaptive gain scheduling approach based on a neural network has been augmented to adjust the gain of the proposed controller for active magnetic bearing adaptively. The simulation studies have been performed in detail to demonstrate the use of proposed scheme for the robust control of active magnetic bearings. Finally, a comparative analysis of the proposed control design scheme with a conventional controller has been performed to achieve improved performance satisfying the plant constraints.

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Section: Event-triggering-based Sliding Mode Control Designmentioning

confidence: 94%

Section: Event-triggering-based Adaptive Gain Scheduling Control Designmentioning

confidence: 99%

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Adaptive gain scheduling based event-triggering control design for robust performance of active magnetic bearing

Saini,

Pratap,

Kumar

2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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Research on the Innovation of Marketing Management Mode Under the Digital Transformation of Textile and Garment Enterprises

Liu,

2024

J. Info. Know. Mgmt.

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With the rapid development of China’s economy, the proportion of people’s disposable income is increasing. The consumption of daily necessities is increasing day by day. The traditional marketing channels of the existing clothing industry have been difficult to meet the development needs of clothing enterprises and consumer consumption needs. In response to this problem, this research introduces in-depth learning into the digital transformation of garment enterprises, focusing on how to combine in-depth learning with the innovation of marketing management mode in the digital transformation. This paper proposes an omni channel marketing management mode based on the improved PGBN user profile construction model and the improved RBF sales volume prediction model. Simulation experiments and comparative experiments are designed to test the effect of the sales volume prediction model and the new marketing management model. The test results show that the performance of the sales volume prediction model is improved by 15.97-19.87%. The new marketing management model improves the sales volume by more than 30%. The improvement effect is significant, which can provide an effective reference for the digital transformation of garment enterprises.

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Neural adaptive dynamic surface control for position tracking of electrohydraulic servo systems based on extended state observer

Liu,

Shen,

Wang

et al. 2024

Proceedings of the Institution of Mechanical Engineers, Part I:

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High-performance motion tracking of electro-hydraulic servo systems plays an important role in developing industrial technology. However, the system itself has parameter uncertainty, uncertain nonlinearity, and measurement noise, significantly decreasing the performance of conventional controllers designed based on nominal models. To improve the tracking performance of electro-hydraulic servo systems, an adaptive dynamic surface control method combining extended state observer and radial basis function neural network is proposed. Firstly, an extended state observer combining neural network output and parameter adaptation technology is designed without the need for speed and pressure feedback to observe the system’s unknown state variables and nonlinear matched disturbance. The design of neural networks is used to approximate mismatched disturbance with significant nonlinearity online. Then, the proposed extended state observer is introduced into the neural adaptive backstepping design, effectively compensating for simultaneous matched and mismatched disturbances, thereby suppressing most of the uncertain nonlinearity of the system. Meanwhile, online update of uncertain parameters further improves the tracking performance of the controller. In addition, the introduction of dynamic surface overcomes the “explosion of complexity” issue in backstepping design. The rigorous stability of the closed-loop system is proved by the Lyapunov stability theory. Finally, the effectiveness of the proposed controller is validated through comparative experiments on valve-controlled hydraulic cylinder.

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Radial basis function neural network–based adaptive sliding mode suspension control for maglev yaw system of wind turbines

Cited by 4 publications

References 31 publications

Adaptive gain scheduling based event-triggering control design for robust performance of active magnetic bearing

Adaptive gain scheduling based event-triggering control design for robust performance of active magnetic bearing

Research on the Innovation of Marketing Management Mode Under the Digital Transformation of Textile and Garment Enterprises

Neural adaptive dynamic surface control for position tracking of electrohydraulic servo systems based on extended state observer

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