The Voltage Regulation of a Buck Converter Using a Neural Network Predictive Controller

Saadatmand, Sepehr; Shamsi, Pourya; Ferdowsi, Mehdi

doi:10.1109/tpec48276.2020.9042588

Cited by 35 publications

(17 citation statements)

References 21 publications

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“…Furthermore, the Lyapunov function introduced for the subsystem of Equation ( 14) is shown in Equation (15).…”

Section: State Augmented Adaptive Backstepping Methodsmentioning

confidence: 99%

“…In refs. [15][16][17][18][19][20][21][22], various adaptive-based controllers are designed as follows: adaptive neural network-based controllers, adaptive sliding mode controllers, adaptive Lyapunov-based control, and adaptive predictive controllers. The main benefits presented by these methods are effective to control in ill-defined systems, robustness toward load uncertainties, faster dynamics performance, and better external disturbance compensation.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive backstepping controller design on Buck converter with a novel improved identification method

Saadat

Ghamari

Mollaee

2022

IET Control Theory & Appl

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An adaptive backstepping method is presented by this paper for a DC-DC Buck converter utilising a strategy for system identification with pulse width modulation in the presence of parametric uncertainties, load variations, and high variance noises. In this control structure, the system is assumed as a black-box block that can decrease the computational burden providing faster dynamics. An adaptive mechanism is adopted for the BSM using the Lyapunov definition, providing robust dynamics for the controller against various disturbances. In addition, a novel improved exponential recursive least-squares identification algorithm is proposed, which shows higher robustness in parametric estimations and can decrease the negative impact of disrupting factors on the estimator. Moreover, a particle swarm optimisation algorithm-based PID controller is designed to be compared with the proposed controller. Finally, the merits of the presented controller are validated for various working conditions through simulations and experiments. It can be seen that the adaptive backstepping method with the improved identification technique provides much better results with faster dynamics.

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“…Furthermore, the Lyapunov function introduced for the subsystem of Equation ( 14) is shown in Equation (15).…”

Section: State Augmented Adaptive Backstepping Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive backstepping controller design on Buck converter with a novel improved identification method

Saadat

Ghamari

Mollaee

2022

IET Control Theory & Appl

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“…An artificial neural network (ANN) controller for the regulation of voltage in distribution systems is presented in [13]. In [14], an ANN-based predictive controller to regulate the voltage of a buck converter is introduced. However, proper data training is crucial to acquire good performance of ANN-based controllers.…”

Section: A Literature Overviewmentioning

confidence: 99%

Resilient Design of Robust Multi-Objectives PID Controllers for Automatic Voltage Regulators: D-Decomposition Approach

et al. 2021

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Resilient design of robust multi-objectives PID controllers via the D-decomposition method is presented in this paper for automatic voltage regulators (AVRs). The stabilizing interval of derivative gain (k d ) is analytically calculated by the Routh-Hurwitz criterion. The k p − k i domain, for a fixed value of k d , is decomposed in root invariant regions by mapping the stability boundary from the complex plane. Two regions, described by fixed damping isoclines, are assigned for pole-clustering in the open-left half plane (LHP). Other than regional pole clustering, gain and phase margins, as frequency domain specifications, are considered. Both robust stability and robust performance are considered by stabilizing a set of principle segment plants simultaneously. Optimal pole-placer PID controllers are computed analytically. If a robust control basin does exist for a specific compromise of control objective, the criterion of the maximum inscribed circle is considered to compute the maximum radius of controller resiliency. The merit of the proposed design is the simultaneous consideration of three control concerns, namely performance optimality, stability robustness and controller resiliency. Computation, validation, and simulation results are presented to show the simplicity and efficacy of the suggested method in tracing control basins (CBs) of all admissible PID controllers.

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“…In [26], ANN is dedicated to voltage control in a distributed power system. A voltage regulation scheme is introduced for a buck converter utilizing an ANN combined with a predictive controller in [27]. The main issue against the implementation of the ANNs is demonstrated in the availability of the proper training data to prove a good performance.…”

Section: Introductionmentioning

confidence: 99%

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

et al. 2021

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This paper introduces a robust model predictive controller (MPC) to operate an automatic voltage regulator (AVR). The design strategy tends to handle the uncertainty issue of the AVR parameters. Frequency domain conditions are derived from the Hermite–Biehler theorem to maintain the stability of the perturbed system. The tuning of the MPC parameters is performed based on a new evolutionary algorithm named arithmetic optimization algorithm (AOA), while the expert designers use trial and error methods to achieve this target. The stability constraints are handled during the tuning process. An effective time-domain objective is formulated to guarantee good performance for the AVR by minimizing the voltage maximum overshoot and the response settling time simultaneously. The results of the suggested AOA-based robust MPC are compared with various techniques in the literature. The system response demonstrates the effectiveness and robustness of the proposed strategy with low control effort against the voltage variations and the parameters’ uncertainty compared with other techniques.

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The Voltage Regulation of a Buck Converter Using a Neural Network Predictive Controller

Cited by 35 publications

References 21 publications

Adaptive backstepping controller design on Buck converter with a novel improved identification method

Adaptive backstepping controller design on Buck converter with a novel improved identification method

Resilient Design of Robust Multi-Objectives PID Controllers for Automatic Voltage Regulators: D-Decomposition Approach

Robust Model Predictive Control Paradigm for Automatic Voltage Regulators against Uncertainty Based on Optimization Algorithms

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