Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems

Elkenawy, Ahmed; El-Nagar, Ahmad M.; El-Bardini, Mohammad; El-Rabaie, Nabila M.

doi:10.1177/0142331220921259

Cited by 7 publications

(7 citation statements)

References 33 publications

(51 reference statements)

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“…Inspired by the above research, a linear active disturbance rejection control (LADRC) scheme based on the diagonal recurrent neural network (DRNN) [25][26][27][28] has been designed for adaptive control of the radar position servo system facing dead zone and friction nonlinearities in this paper. For one thing, the LADRC is used for real-time estimates and compensates for disturbance with unknown nondeterminacies.…”

Section: B Contributions Statementmentioning

confidence: 99%

“…The LADRC has self-learning ability and better control performances by introducing the DRNN to tune the parameters of the LSEF in real time, adapting well to the changes of the plant such as parameter and disturbance changes, etc. Moreover, it has higher tracking accuracy for the radar position servo system than the LADRC based on the back propagation neural network (BPNN-LADRC) [30], since DRNN is found to be more suitable to identify dynamic nonlinear systems than those static neural networks such as BPNN, radical basis function neural network (RBFNN) and so on, that contains internal feedback loops which can store the plant information and utilize it at the later stage [25,26].…”

Section: B Contributions Statementmentioning

confidence: 99%

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Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

2022

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This paper proposes a control scheme for the radar position servo system facing dead zone and friction nonlinearities. The controller consists of the linear active disturbance rejection controller (LADRC) and diagonal recurrent neural network (DRNN). The LADRC is designed to estimate in real time and compensate for the disturbance with vast matched and mismatched uncertainties, including the internal dead zone and friction nonlinearities and external noise disturbance. The DRNN is introduced to optimize the parameters in the linear state error feedback (LSEF) of the LADRC in real time and estimate the model information, namely Jacobian information, of the plant on-line. In addition, considering the Cauchy distribution, an adaptive tracking differentiator (ATD) is designed in order to manage the contradiction between filtering performance and tracking speed, which is introduced to the LADRC. Another novel idea is that the back propagation neuron network (BPNN) is also introduced to tune the parameters of the LADRC, just as in the DRNN, and the comparison results show that the DRNN is more suitable for high precision control due to its feedback structure compared with the static BPNN. Moreover, the regular controller performances and robust performance of the proposed control approach are verified based on the radar position servo system by MATLAB simulations.

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Section: B Contributions Statementmentioning

confidence: 99%

Section: B Contributions Statementmentioning

confidence: 99%

Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

2022

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“…Nonlinear system learning in a nonstationary or dynamic setting is a significant and difficult problem. Adaptive control (Elkenawy et al, 2020), pattern classification (Meeker et al, 2017), and self-organizing systems (Mu¨ller et al, 2012) have all been used to investigate the issue of learning in automatic control systems. During operation, these control systems learn unknown knowledge, and the learned knowledge, which is regarded as the controller's experience, is then used to enhance control efficiency once similar control situations arise.…”

Section: Introductionmentioning

confidence: 99%

Feedback error learning neural network for stable control of nonlinear nonaffine systems

Asgari

2022

Transactions of the Institute of Measurement and Control

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This study presents a stable feedback error learning (FEL) scheme for nonlinear nonaffine systems in the presence of uncertainty and disturbances. The distinguishing feature of the FEL method, which can have a significant effect on both transient and steady-state performance, has led us to adopt this approach. The nonlinear system studied here is nonaffine. In other words, the function that describes the dynamic equations of the system is an implicit function of the control input rather than a particular class of systems. We aim to develop a stable FEL control system with three components: a neural network (NN), a linear controller, and a robustifying control term. To this end, all the adaptation laws for the NN weights are derived from a Lyapunov function, ensuring that the closed-loop system is uniformly asymptotically stable (UAS). Thus, an NN learning control approach that effectively improves the transient performance, as well as the steady-state performance, is proposed, and its remarkable effectiveness is illustrated in comparison with the existing methods.

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“…In [29], a flexible manipulator was designed with a DRNN controller to limit backward vibration, which is performed based on a shaking control signal generator and an online identification system. The DRNN was introduced as a controller and an observer for estimating the anonymous dynamics of the nonlinear system [30]. DRNN was developed to determine the optimal parameters of the PID controller for controlling induction motors [31].…”

Section: Introductionmentioning

confidence: 99%

Hybrid deep learning diagonal recurrent neural network controller for nonlinear systems

El-Nagar

Zaki

Soliman

et al. 2022

Neural Comput & Applic

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In the present paper, a hybrid deep learning diagonal recurrent neural network controller (HDL-DRNNC) is proposed for nonlinear systems. The proposed HDL-DRNNC structure consists of a diagonal recurrent neural network (DRNN), whose initial values can be obtained through deep learning (DL). The DL algorithm, which is used in this study, is a hybrid algorithm that is based on a self-organizing map of the Kohonen procedure and restricted Boltzmann machine. The updating weights of the DRNN of the proposed algorithm are developed using the Lyapunov stability criterion. In this concern, simulation tasks such as disturbance signals and parameter variations are performed on mathematical and physical systems to improve the performance and the robustness of the proposed controller. It is clear from the results that the performance of the proposed controller is better than other existent controllers.

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Diagonal recurrent neural network observer-based adaptive control for unknown nonlinear systems

Cited by 7 publications

References 33 publications

Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

Linear Active Disturbance Rejection Control-Based Diagonal Recurrent Neural Network for Radar Position Servo Systems with Dead Zone and Friction

Feedback error learning neural network for stable control of nonlinear nonaffine systems

Hybrid deep learning diagonal recurrent neural network controller for nonlinear systems

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