Multiple models and neural networks based adaptive PID decoupling control of mine main fan switchover system

Wang, Qianjin; Dai, Wei; Ma, Xiaoping; Yang, Chunyu

doi:10.1049/iet-cta.2017.0701

Cited by 17 publications

(7 citation statements)

References 31 publications

(27 reference statements)

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“…Two techniques have generally been used to achieve this goal: unfalsified adaptive control [3] [4] [5] and multiple model adaptive control [6] [7] [8]. In both cases, the switching process is supervised by a unit that provides the best controller k for the feedback loop from the controller set K based on the plant Intelligent Control and Automation input/output data and performance criterion ( ) t µ .…”

Section: Introductionmentioning

confidence: 99%

Improving Local Priority Hysteresis Switching Logic Convergence

Alhajri¹,

Alsubaie²,

Salamah³

2020

ICA

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Local priority hysteresis switching logic is associated with adaptive control convergence when using an infinite set of candidate parameters via constraints added to the switching scheme. In this paper, we reevaluate these constraints on the basis of the persistent excitation assumption. This makes room for the adaptive control to converge to its optimum, resulting in improved performance. Unconstrained local priority hysteresis switching logic is investigated, and global convergence conditions are proposed. This paper expands on the preliminary version of a conference paper [1] by adding numerical simulation examples to validate both the application and the advantage of the theory.

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Section: Introductionmentioning

confidence: 99%

Improving Local Priority Hysteresis Switching Logic Convergence

Alhajri¹,

Alsubaie²,

Salamah³

2020

ICA

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“…In recent years, intelligent and feedback linearisation decoupling control methods have attracted more and more attention. Intelligent decoupling methods [8][9][10] are insensitive to the accuracy of system model, with strong robustness. However, these methods have some disadvantages such as large computation, complex programming and bad real time, which restrict their applications in the AMB-HFRS.…”

Section: Introductionmentioning

confidence: 99%

Radial position control for magnetically suspended high‐speed flywheel energy storage system with inverse system method and extended 2‐DOF PID controller

Chen

Zhu

Zhang

et al. 2019

IET Electric Power Applications

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To achieve high-precision position control for the active magnetic bearing high-speed flywheel rotor system (AMB-HFRS), a novel control strategy based on inverse system method and extended two-degree-of-freedom (2-DOF) proportionalintegral-derivative (PID) controller is proposed in this study. First, the inverse system method is employed to decouple the AMB flywheel rotor system with strong non-linear and coupling, into four independent subsystems. Subsequently, extended 2-DOF PID controllers are used to regulate the decoupled subsystems, to obtain good performances of disturbance rejection and tracking simultaneously. In the extended 2-DOF PID controller, a differential signal is produced by a velocity observer to improve the ability of against noise. Finally, the characteristics of stability, tracking, disturbance rejection and robustness of the control strategy proposed are analysed in theory, and its ability and effectiveness to control the radial position of the AMB-HFRS are further studied by simulations and experiments. It is shown that the control strategy proposed can keep the AMB-HFRS suspending stably from static state to the speed of 24,000 rpm, has the advantages of good tracking, high disturbance rejection, strong robustness and good ability of against noise.

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“…However, these methods require a large amount of computation. Over time, neural networks have been developed to approximate any nonlinear function (Wang et al, 2018). Recently, a combination of a neural network and predictive control became an effective control method for nonlinear systems because a neural network can approximate any nonlinear function (Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear system predictive control using a multi-dimensional Taylor network

Zhang

2019

Transactions of the Institute of Measurement and Control

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A predictive control scheme on the basis of multi-dimensional Taylor network (MTN), named as MTN predictive compensation control, is proposed for single-input single-output nonlinear systems in this paper. We consider the MTN model as a one-step-ahead predictive model and train it by back-propagation (BP) algorithm with a momentum term, and then control the system by the predictive control law. Furthermore, to improve the anti-disturbance performance of the system, another MTN model is considered as the compensator and trained by a recursive least-squares algorithm to counteract the disturbance. An example is used to verify the effectiveness of the proposed scheme, in which the noise disturbance is considered. For comparison, the scheme that combines a radial basis function (RBF) neural network with a proportional-integral-derivative (PID) controller (RBF-PID), the predictive control scheme that combines a predictive neural network with a control neural network based on a BP neural network, and MTN predictive control are introduced. In addition, the difference in computational complexity between the MTN predictive compensation control scheme and the RBF-PID scheme is also considered. The experimental results show that the proposed scheme is effective, has good anti-disturbance performance in predictive control for the nonlinear system and is superior to the other comparison schemes.

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Multiple models and neural networks based adaptive PID decoupling control of mine main fan switchover system

Cited by 17 publications

References 31 publications

Improving Local Priority Hysteresis Switching Logic Convergence

Improving Local Priority Hysteresis Switching Logic Convergence

Radial position control for magnetically suspended high‐speed flywheel energy storage system with inverse system method and extended 2‐DOF PID controller

Nonlinear system predictive control using a multi-dimensional Taylor network

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