Adaptive neuron PID control of Buck type AC chopper voltage regulator

Jin, Nan; Tang, Houjun; Cui, Guangzhao

doi:10.1109/bicta.2009.5338095

Cited by 9 publications

(4 citation statements)

References 10 publications

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“…To control the converters in use, we adopted fundamental, simple, and widely applied techniques such as PID [15], 2DOFPID [16], and FOPID [17]. Furthermore, more advanced techniques, including fuzzy logic controllers [18], artificial neural networks [19], and genetic algorithms [20], have also been employed.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Güven

2024

IET Control Theory & Appl

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This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system’s adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Güven

2024

IET Control Theory & Appl

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“…In order to improve system performance in relation to the response time without causing overshoots, which could damage the load or circuit elements, and to obtain a steadystate null error, adaptive and nonlinear controllers have being used. The Feedback Linearization, Adaptive Control, Gain Scheduling, Sliding Mode control, and State Dependent Riccati Equation (SDRE), can be highlighted each with its own characteristics regarding their application or parameters such as stability, robustness, and computational cost [13][14][15][16][17][18][19][20][21][22][23][24]. Another possibility of enhancing the system performance is the integration of two or more controllers.…”

Section: Introductionmentioning

confidence: 99%

“…Adaptive control techniques have been highlighted in various applications. Alternative forms of adaptive control techniques applied to power electronic circuits are presented in [18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

A Hyperbolic Tangent Adaptive PID + LQR Control Applied to a Step-Down Converter Using Poles Placement Design Implemented in FPGA

Pedroso

Nascimento

Tusset

et al. 2013

Mathematical Problems in Engineering

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This work presents an adaptive control that integrates two linear control strategies applied to a step-down converter: Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) controls. Considering the converter open loop transfer function and using the poles placement technique, the designs of the two controllers are set so that the operating point of the closed loop system presents the same natural frequency. With poles placement design, the overshoot problems of the LQR controller are avoided. To achieve the best performance of each controller, a hyperbolic tangent weight function is applied. The limits of the hyperbolic tangent function are defined based on the system error range. Simulation results using the Altera DSP Builder software in a MATLAB/SIMULINK environment of the proposed control schemes are presented.

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“…As in the case of the buck mode, to derive the steady state transfer function for the boost mode we take a look at the average inductor voltage over an entire switching period. Using equations (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) and (4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19) this can be seen to be:…”

Section: Transfer Function Derivationsmentioning

confidence: 99%

Modeling and Analysis of a Four-Switch Buck-Boost Dynamic Capacitor

Plasencia¹

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Adaptive neuron PID control of Buck type AC chopper voltage regulator

Cited by 9 publications

References 10 publications

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

A Hyperbolic Tangent Adaptive PID + LQR Control Applied to a Step-Down Converter Using Poles Placement Design Implemented in FPGA

Modeling and Analysis of a Four-Switch Buck-Boost Dynamic Capacitor

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