A novel adaptive neuro linear quadratic regulator (ANLQR) controller design on DC‐DC buck converter

Roosta, Vahid; Ghamari, Seyyed Morteza; Mollaee, Hasan; Zarif, Mohammad Hadad

doi:10.1049/rpg2.12679

Cited by 11 publications

(6 citation statements)

References 47 publications

(56 reference statements)

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“…Moreover, by replacing the control law in the system equations, the error dynamics equations can be formed as (24).…”

Section: State Augmented Adaptive Backstepping Methodsmentioning

confidence: 99%

“…In addition, adaptive-based approaches are proposed with higher efficiency in challenging cases considering their flexibility and better outcomes. Some of the most recent adaptive controllers used for power converters are listed here: neural network-based adaptive [22][23][24][25], adaptive predictive [26][27][28][29], optimized adaptive sliding mode [30], and Lyaponuv-based adaptive [31,32] strategies. The main benefits presented by these approaches are effectiveness in ill-defined models, higher robustness in uncertainties, faster dynamical operation, and better external disturbance rejection.…”

Section: Introductionmentioning

confidence: 99%

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Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

Azarinfar,

Khosravi,

Soroush

et al. 2024

IET Power Electronics

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A type‐2 fuzzy logic‐based adaptive backstepping control (T2FABSC) approach is designed for an H‐bridge inverter. This inverter has an LC filter to decrease the level of total harmonic distortion (THD) that can affect the efficiency of the system. Reduction of THD and stability insurance of the filter are challenging trade‐offs, tackled here by a controller design. The performance, however, is not suitable for actual applications under a wider range of disturbances, and the parameters need to be adjusted once more for more dependable operations. Backstepping control is given a Lyapunov definition‐based adaptive mechanism that can improve this scheme's stability and robustness in the face of numerous disturbances. Additionally, the system is viewed as a “Black box” without the need for a precise mathematical model, which might lead to a lighter computing burden and simpler implementation. Moreover, a fuzzy type‐2 structure is adopted that can optimize the gains of the adaptive Backstepping controller (ABSC) in challenging conditions. The antlion optimization algorithm is employed to optimize the parameters of the membership functions in the fuzzy system, hence improving the performance of the controller. In addition, compared with different optimization algorithms, it can more quickly and accurately locate the best solutions. Finally, the findings of both the simulations and the experimental outputs are examined, proving the T2FABSC's significant robustness and faster dynamics in a variety of challenging circumstances.

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“…Moreover, by replacing the control law in the system equations, the error dynamics equations can be formed as (24).…”

Section: State Augmented Adaptive Backstepping Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

Azarinfar,

Khosravi,

Soroush

et al. 2024

IET Power Electronics

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“…[7,8] That leads to extensive research on the analysis and control of BDCs. For the application of a BDC, adjusting the output voltage and ensuring robust dynamic performance [9,10] are among the most important features that require the improvement of the control performance of the BDC system.…”

Section: Motivationmentioning

confidence: 99%

Model‐free adaptive control for ultracapacitor based three‐phase interleaved bidirectional DC–DC converter

Wang,

Peng

et al. 2023

IET Power Electronics

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The lack of an accurate mathematical model of non‐linear systems, such as bidirectional DC–DC converters, brings certain limits to design a formidable controller for such systems. This article proposes a model‐free adaptive control (MFAC) approach for an ultracapacitor based three‐phase interleaved bidirectional DC–DC converter (BDC) to ameliorate the controller design limitations due to the demand for system model information. This article designs a pseudo gradient (PG) estimation algorithm based on the input and output (I/O) data of a three‐phase interleaved BDC and designs a control input estimation algorithm through the PG estimation value, then, the design of a model‐free adaptive controller is completed. Moreover, the Black‐box technique is applied to the system, which does not require a precise mathematical model resulting in a lower computational burden, faster dynamics, and easier implementation. Furthermore, the feasibility and effectiveness of the proposed MFAC scheme are experimentally tested and verified on an energy storage system microgrid platform.

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“…In this context, it is well-known that non-minimum phase (NMP) dynamics are characterized by unsuitable behavior. Considering the non-minimum phase nature of dynamics causes physical limitations to the open-loop bandwidth, thus restricting the benefits of the feedback control system [6]. This can be seen in the tracking control problem, where the feedback controller can track the reference signal perfectly.…”

Section: Introductionmentioning

confidence: 99%

Design of a novel robust adaptive cascade controller for DC‐DC buck‐boost converter optimized with neural network and fractional‐order PID strategies

Ghamari

Jouybari

Mollaee

et al. 2023

The Journal of Engineering

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A cascade technique with two control loops is designed for a DC\DC Buck-Boost converter that is a right half-plane zero (RHPZ) structure called a non-minimum phase system. This concept presents several challenging constraints for designing well-behaved control techniques. Cascade controllers can provide various benefits compared with single loop controllers such as higher safety, higher robustness, and higher stability. This strategy assumes the system as a black-box structure without the need for a mathematical model of the system. This benefit can decrease the computational burden and provides faster dynamics along with ease of implementation. This technique consisted of an outer Fractional-order PID voltage controller tuned with the Antlion Optimizer (ALO) algorithm, which provides a reference current for the inner control loop of the Neural Network-based LQR (NN-LQR) controller. The basic principle in cascade controllers is a more rapid performance of the inner loop that has been satisfied with the NN-LQR strategy, which optimizes and tunes the gains of the LQR controller and shows faster dynamics and higher robustness. It should be mentioned that the number of neurons is limited to 2 and 4 in each layer to decrease the computational burden with lower complexity. Also, the ALO algorithm is a modern nature-inspired algorithm used to tune the PID gains with better results under-constrained problems with diverse search spaces. Considering the negative impacts of various disturbances on a power converter, a Fractional-order-based PID (FO-PID) control technique is a proper alternative since it shows higher robustness in load uncertainties along with better dynamical responses based on its extra degree of freedom. Moreover, to evaluate the superiority of this controller, two other controllers are designed using the PSO algorithm for PID and FO-PID controllers. Finally, the presented cascade controller has been tested in various working conditions through simulation and experiment results.

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A novel adaptive neuro linear quadratic regulator (ANLQR) controller design on DC‐DC buck converter

Cited by 11 publications

References 47 publications

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

Robust adaptive backstepping control of H‐bridge inverter based on type‐2 fuzzy optimization of parameters

Model‐free adaptive control for ultracapacitor based three‐phase interleaved bidirectional DC–DC converter

Design of a novel robust adaptive cascade controller for DC‐DC buck‐boost converter optimized with neural network and fractional‐order PID strategies

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