Legendre Neural Network based Intelligent Control of DC-DC Step Down Converter-PMDC Motor Combination

Nizami, Tousif Khan; Gangula, Sasank Das; Reddy, Ramanjaneya; Dhiman, Harsh S.

doi:10.1016/j.ifacol.2022.04.027

Cited by 11 publications

(10 citation statements)

References 13 publications

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“…Control of DC motors is a classic topic with recent novel methods such as deterministic artificial intelligence (DAI) presented here, neural networks [7][8][9][10][11][12], and reinforcement learning [13] and other instantiations of neural network-based, stochastic artificial intelligence [14][15][16].…”

Section: (A) (B)mentioning

confidence: 99%

“…Nizami et, al. [14] proposed a single functional layer Legendre neural network integrated adaptive backstepping control technique and favorably compared the performance to the response obtained from proportional-integral-derivative controller. Lei, et. al, [15] proposed self-tuning and approximation via RBF neural networks, validating control system accuracy owing to its robustness and adaptability.…”

Section: (A) (B)mentioning

confidence: 99%

“…The vector of knowns corresponds to the desired trajectory, while the vector of unknowns represents the learned parameters obtained from proportional plus derivative feedback used to generate the process input. Therefore, the regression form of the process input u(t) is expressed as 𝑢𝑢 * (𝑡𝑡) in Equations ( 13) and (14).…”

Section: Deterministic Artificial Intelligencementioning

confidence: 99%

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Autonomous Drone Electronics Amplified With Pontryagin–Based Optimization

Sands¹

2023

Preprint

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In the era of electrification and artificial intelligence, direct current motors are widely utilized with numerous innovative adaptive and learning methods. Traditional methods utilize model-based algebraic techniques with system identification, such as recursive least squares, extended least squares, and autoregressive moving averages. The new method known as deterministic artificial intelligence asserts physical-based process dynamics to achieve target trajectory tracking. There are two common autonomous trajectory generation algorithms: sinusoidal function and Pontryagin's based generation algorithm. This thesis aims to simulate model-following and deterministic artificial intelligence methods using sinusoidal and Pontryagin's methods and compare their performance difference when following challenging step function slew maneuver.

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Section: (A) (B)mentioning

confidence: 99%

Section: (A) (B)mentioning

confidence: 99%

Section: Deterministic Artificial Intelligencementioning

confidence: 99%

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Autonomous Drone Electronics Amplified With Pontryagin–Based Optimization

Sands¹

2023

Preprint

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“…Additionally, Srinivasan et al [28,29] introduced a sensitivity analysis applied to the DC/DC Buck converter-DC motor system by exploiting the exact tracking error dynamics passive output feedback (ETEDPOF) methodology. Other recent control techniques investigated in the literature include neuroadaptive backstepping based control, intelligent nonlinear adaptive control, and neural network based intelligent control by Nizami et al [30][31][32], piecewise affine PI-based control by Hanif et al [33], and adaptive neurofuzzy H ∞ -based control by Rigatos et al [34], while Kazemi and Montazeri in [35] elaborated a fault detection control algorithm by combining a switching observer with the bond graph method.…”

Section: Unidirectional "Dc/dc Buck Converter-dc Motor" Systemmentioning

confidence: 99%

Robust Flatness-Based Tracking Control for a “Full-Bridge Buck Inverter–DC Motor” System

et al. 2022

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By developing a robust control strategy based on the differential flatness concept, this paper presents a solution for the bidirectional trajectory tracking task in the “full-bridge Buck inverter–DC motor” system. The robustness of the proposed control is achieved by taking advantage of the differential flatness property related to the mathematical model of the system. The performance of the control, designed via the flatness concept, is verified in two ways. The first is by implementing experimentally the flatness control and proposing different shapes for the desired angular velocity profiles. For this aim, a built prototype of the “full-bridge Buck inverter–DC motor” system, along with Matlab–Simulink and a DS1104 board from dSPACE are used. The second is via simulation results, i.e., by programming the system in closed-loop with the proposed control algorithm through Matlab–Simulink. The experimental and the simulation results are similar, thus demonstrating the effectiveness of the designed robust control even when abrupt electrical variations are considered in the system.

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“…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 [18,19], adaptive predictive [20], optimized adaptive sliding mode [21,22], and Lyapunovbased adaptive [23] strategies. The main benefits presented by these approaches are effectiveness in ill-defined models, higher robustness in uncertainties, and faster dynamical operation.…”

Section: Introductionmentioning

confidence: 99%

Design of a novel robust type‐2 fuzzy‐based adaptive backstepping controller optimized with antlion algorithm for buck converter

Khavari

Ghamari

Abdollahzadeh

et al. 2023

IET Control Theory & Appl

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A type‐2 fuzzy logic‐based adaptive backstepping control (T2F‐ABSC) approach is presented for a DC/DC Buck converter. Lyapunov‐based backstepping control (BSC), which can guarantee convergence along with asymptotic stability of the system. Also, the black‐box technique is applied for the proposed system, which does not require an accurate mathematical model resulting in a lower computational burden, easy implementation, and lower dependency on the states of the model. On the other hand, for wider ranges of disturbances, including parametric variations, load uncertainty, supply voltage variation, and noise, this approach shows an unsuitable practical application based on its fixed gain values; therefore, the control parameters need to be optimized again to provide ideal operations. Type‐2 fuzzy structure is adopted here to optimize the gains of the ABSM in challenging conditions. Type‐2 fuzzy (T2F) has higher efficiency and faster dynamics with more adaptability to the system. To enhance the performance of the T2F, antlion optimization (ALO) has been used in this structure. ALO is a modern nature‐inspired algorithm, and it has some advantages over other optimizing algorithms. Furthermore, it has ability to find optimal answers in a shorter time, more accurately in contrast to the other optimization algorithms and is used to tune the membership function parameters of the (T2F) under the diverse search spaces. To depict the strength of the designed work, fuzzy‐PID and backstepping schemes proposed to carry out an analysis with this work.

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Legendre Neural Network based Intelligent Control of DC-DC Step Down Converter-PMDC Motor Combination

Cited by 11 publications

References 13 publications

Autonomous Drone Electronics Amplified With Pontryagin–Based Optimization

Autonomous Drone Electronics Amplified With Pontryagin–Based Optimization

Robust Flatness-Based Tracking Control for a “Full-Bridge Buck Inverter–DC Motor” System

Design of a novel robust type‐2 fuzzy‐based adaptive backstepping controller optimized with antlion algorithm for buck converter

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