Data-Driven Tracking Control Based on LM and PID Neural Network With Relay Feedback for Discrete Nonlinear Systems

Hao, Jun; Zhang, Guoshan; Liu, Wanquan; Zheng, Yuqing; Ren, Ling

doi:10.1109/tie.2020.3032872

Cited by 21 publications

(9 citation statements)

References 30 publications

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“…The value of the partial derivative; ∂ y(k) ∂u(k) has no major effect on the learning algorithm in Eqs. (20)(21)(22)(23)(24)(25) because it can be absorbed by the learning rate; h(k) [23]. Therefore, it is considered a constant value in this work, while ∂u (k) ∂w(k) can be calculated as the following remark:…”

Section: Update Rule Based On the Lyapunov Stability Criterionmentioning

confidence: 99%

“…Remark: For deriving the formulas of ∂u(k) ∂w(k) in Eqs. (20)(21)(22)(23)(24)(25), since the two hidden neurons used in this structure generate the same output value from Eq. ( 5), and then, let, F j (k) F, accordingly Eqs.…”

Section: Update Rule Based On the Lyapunov Stability Criterionmentioning

confidence: 99%

“…(26-28) can be updated directly by inserting the updated weights Eqs. (20)(21)(22)(23)(24)(25) into Eqs. (26)(27)(28).…”

Section: Update Rule Based On the Lyapunov Stability Criterionmentioning

confidence: 99%

“…In 2020, a PID controller based on an RBF neural network (PID-RBFNN) was introduced for a speed profile control of a subway train using (3-5-3) neural network structure [20]. In 2020, a neural network-based PID controller using Levenberg-Marquardt identifier (NNPID-LM) was introduced in [21]. The NNPID-LM controller, which was optimized by using an LM learning algorithm and an adaptive learning rate, used a neural network structure (2-5-1) and a 'log-sigmoid' activation function and it was used to control nonlinear dynamical systems.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Polynomial Recurrent Neural Network-Based Adaptive PID Controller With Stable Learning Algorithm

Hanna

Khater

El-Nagar

et al. 2022

Neural Process Lett

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This paper introduces a novel structure of a polynomial weighted output recurrent neural network (PWORNN) for designing an adaptive proportional—integral—derivative (PID) controller. The proposed adaptive PID controller structure based on a polynomial weighted output recurrent neural network (APID-PWORNN) is introduced. In this structure, the number of tunable parameters for the PWORNN only depends on the number of hidden neurons and it is independent of the number of external inputs. The proposed structure of the PWORNN aims to reduce the number of tunable parameters, which reflects on the reduction of the computation time of the proposed algorithm. To guarantee the stability, the optimization, and speed up the convergence of the tunable parameters, i.e., output weights, the proposed network is trained using Lyapunov stability criterion based on an adaptive learning rate. Moreover, by applying the proposed scheme to a nonlinear mathematical system and the heat exchanger system, the robustness of the proposed APID-PWORNN controller has been investigated in this paper and proven its superiority to deal with the nonlinear dynamical systems considering the system parameters uncertainties, disturbances, set-point change, and sensor measurement uncertainty.

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Section: Update Rule Based On the Lyapunov Stability Criterionmentioning

confidence: 99%

Section: Update Rule Based On the Lyapunov Stability Criterionmentioning

confidence: 99%

“…(26-28) can be updated directly by inserting the updated weights Eqs. (20)(21)(22)(23)(24)(25) into Eqs. (26)(27)(28).…”

Section: Update Rule Based On the Lyapunov Stability Criterionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Polynomial Recurrent Neural Network-Based Adaptive PID Controller With Stable Learning Algorithm

Hanna

Khater

El-Nagar

et al. 2022

Neural Process Lett

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“…Traditional decoupling controllers are designed for linear systems [5] or for weak coupling systems [6].…”

Section: Introductionmentioning

confidence: 99%

Fractional-order Learning Algorithm for PID Neural Network Decoupling Control Based on Sparrow Search Algorithm

Ding

Yuan

et al. 2021

Preprint

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Research on control of multi-variable system with strong coupling has been a significant issue in industry. To accurately eliminate the coupling between system variables and improve the control effect, decoupling control techniques are investigated. In this paper, a decoupling control scheme based on fractional-order proportion integration differentiation neural network and sparrow search algorithm (SSA-FPIDNN) is proposed, where sparrow search algorithm is employed to derive the optimal initial weights, preventing the weights from falling into the local optimum, while the fractional-order algorithm is used to correct its connection weights to improve control accuracy. Compared with traditional PIDNN, the proposed SSA-FPIDNN has better decoupling control performance, and the tracking time can be reduced significantly. Numerical simulation and engineering examples verified its effectiveness.

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Parallel Hybrid Control Model of CNC Machine Tool Based on Neural Network and PID Algorithm

Luo,

Ji,

Zhang

2024

Lecture Notes in Electrical Engineering

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Data-Driven Tracking Control Based on LM and PID Neural Network With Relay Feedback for Discrete Nonlinear Systems

Cited by 21 publications

References 30 publications

Polynomial Recurrent Neural Network-Based Adaptive PID Controller With Stable Learning Algorithm

Polynomial Recurrent Neural Network-Based Adaptive PID Controller With Stable Learning Algorithm

Fractional-order Learning Algorithm for PID Neural Network Decoupling Control Based on Sparrow Search Algorithm

Parallel Hybrid Control Model of CNC Machine Tool Based on Neural Network and PID Algorithm

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