A new controller for DC-DC converters based on particle swarm optimization

Fermeiro, João; Pombo, J.A.N.; Calado, M.R.A.; Mariano, S.J.P.S.

doi:10.1016/j.asoc.2016.10.025

Cited by 28 publications

(12 citation statements)

References 40 publications

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“…30 The PI gains, the fractional order of integral operator, and the center locations of input/output MF of the SIFLC are metaheuristically selected using the PSO algorithm, owing to its recent popularity in optimizing the model-free feedback controllers for switching power electronic systems. 35 The organization of the remaining paper is as follows. The theoretical background and mathematical model of the buck converter are presented in Section 2.…”

Section: Proposed Approachmentioning

confidence: 99%

“…The PSO algorithm is a population-based stochastic parameter optimization technique. 35 In this research, the PSO algorithm serves to offline optimize the PI gains (k P , k I ), the fractional order (δ) of FPI controller, and the centers of input and output fuzzy MFs (c in,1 , c in,2 , c o,1 , c o,2 , c o,3 ). The algorithm initiates with a randomly selected search space consisting of possible candidate solutions, denoted as the "particles."…”

Section: Parameter Optimizationmentioning

confidence: 99%

“…The error derivative term in SIFLC is computed by measuring the instantaneous variations in the output‐capacitor current, in real time . The PI gains, the fractional order of integral operator, and the center locations of input/output MF of the SIFLC are metaheuristically selected using the PSO algorithm, owing to its recent popularity in optimizing the model‐free feedback controllers for switching power electronic systems …”

Section: Introductionmentioning

confidence: 99%

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Time‐optimal control of DC‐DC buck converter using single‐input fuzzy augmented fractional‐order PI controller

Saleem

Shami

Mahmood-ul-Hasan

2019

Int Trans Electr Energ Syst

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Summary This paper presents a robust and optimal output‐voltage tracking controller for a DC‐DC buck converter. A fractional‐order proportional‐integral (FPI) controller is primarily used to eliminate the steady‐state error and damp the oscillations. In order to improve the error convergence rate of the response, the FPI controller is augmented with a single‐input fuzzy‐logic based precompensator stage. The fuzzy precompensator aggregates the information regarding the error and error derivative using the signed‐distance method. The derived aggregated signal is used to infer logical control decisions based on a predefined one‐dimensional rule base. The simplification yields shorter computation time and a piecewise linear control surface that improves the system's immunity against exogenous disturbances and unprecedented nonlinearities. The error derivative is indirectly computed by measuring the capacitor current in real time. This modification further enhances the computational efficiency, offers minimum‐time transient recovery, and compensates the hysteresis effect rendered by parasitic impedances. The controller gains are optimally selected using the particle swarm optimization algorithm. Credible hardware‐in‐the‐loop experiments are conducted to validate the time optimality and robustness of the proposed controller. The experimental results clearly demonstrate the significant improvement rendered by the proposed controller in the system's reference tracking performance, disturbance‐rejection capability, and transient recovery time.

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Section: Proposed Approachmentioning

confidence: 99%

Section: Parameter Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Time‐optimal control of DC‐DC buck converter using single‐input fuzzy augmented fractional‐order PI controller

Saleem

Shami

Mahmood-ul-Hasan

2019

Int Trans Electr Energ Syst

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“…The steady-state value of capacitors voltage (V C1 and V C2 ) and inductors current (I L1 and I L2 ) can be calculated by solving (24) to (27).…”

Section: Small-signal Analysis Of Hgbbcmentioning

confidence: 99%

“…To study the closed-loop converter's dynamic performances, a generalized optimized Type III controller is implemented on the basis of conventional "K-factor" method [20][21][22] and PSO technique. 23,24 Similar specifications are considered in controller design procedure for three converters (ie, HGBBC, TLBBC, and SSQBBC) to achieve the comparable performances. The complete design procedure of the proposed Type III controller is described below.…”

Section: Optimized Type III Controller Designmentioning

confidence: 99%

Closed‐loop control and performance analysis of a high‐gain buck‐boost converter with optimized Type III controller

Banerjee

Rana

Khuntia

2019

Int Trans Electr Energ Syst

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Summary This paper presents the design and development of an improved high‐gain buck‐boost converter (HGBBC) with optimized Type III controller suitable for power supplies in various electrical/electronics devices. The proposed HGBBC produces higher efficiency, exhibits better performance, is less bulky, and is compact in size. The voltage gain of the proposed HGBBC is the squared times the voltage gain of the conventional buck‐boost converter (BBC). The polarity of source and load (output) voltages are the same (ie, noninverting polarity). This added advantage of the proposed converter allows it to operate the positive load voltage variation in a wider range even with small variation of duty cycle ratio of the power switches. Therefore, its application may be advantageous for electronics/electrical equipment. The proposed converter's validity is determined by its steady‐state analysis, small‐signal analysis, and closed loop control operation. A closed‐loop controlled of HGBBC utilizing optimized Type III controller has been fabricated and tested in the laboratory. The comparative studies in terms of efficiency, dynamic response, and percentage ripple content of the proposed HGBBC and existing converters have been performed. From the results, it is seen that the proposed HGBBC is performed better than other existing quadratic types of BBCs.

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Performance optimization of LQR‐based PID controller for DC‐DC buck converter via iterative‐learning‐tuning of state‐weighting matrix

Saleem

Rizwan

2019

Int J Numerical Modelling

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This paper presents a numerically optimized linear-quadratic-regulator-based tuning mechanism for a ubiquitous proportional-integral-derivative controller to improve the output-voltage regulation capability of a direct-current (DC)-DC buck converter. The linear-quadratic-regulator minimizes the quadratic cost of variations in the control signal and error-dynamics of output-voltage to provide a trivial set of optimized proportional-integral-derivative controller gains in the form of the state-feedback gain vector. In order to further improve the controller's time-domain performance and its disturbance-rejection capability against load-transients and input-fluctuations, an iterative-learningtuning mechanism is adopted to optimize the state-weighting matrix of the linear-quadratic cost function. The proposed optimization mechanism iteratively converges in the direction of the steepest gradient-descent of another performance index that directly captures the transient response characteristics, and thus, optimally selects the weighting matrix to achieve the desired natural frequency and damping ratio of the closed-loop system. Credible hardware-inthe-loop experiments are conducted on a low-power DC-DC buck converter circuit to validate the aforementioned propositions.A lot of research has been done to devise robust and optimal voltage controllers for effectively regulating the v o of the buck converter, despite the exogenous disturbances in load impedance and input voltage. A detailed comparative performance analysis of the conventional and contemporary control techniques has been presented in previous tudies. 4-6 These controllers are divided in two categories, namely, model-free controllers and model-based controllers. The model-free controllers do not depend on the mathematical model of the converter. Hence, they are relatively simpler to construct. 7 Although they can significantly improve the transient and steady-state response of the system, they lack optimality in minimizing the deviations in state-trajectories or energy consumption. The proportional-integralderivative (PID) controllers, and their variants, are the most widely used model-free controllers in the industry. 8,9 However, evaluating a trivial set of controller gains to obtain optimal control effort is a cumbersome process. Several autotuning techniques for PID gains have been proposed in the literature to enhance the robustness of the controller design. [10][11][12][13][14] The fuzzy-logic controllers infer the control decision based on a heuristically synthesized set of logical rules. 15,16 However, these rules are empirically defined and hence cannot completely compensate all the nonlinearities associated with the system's dynamics. Other widely used control schemes include the sliding-mode controller, 17,18 fractional-order PID controller, 19 and adaptive controller. [20][21][22] The model-based controllers depend on the mathematical model of the system and thus use the complete knowledge of system dynamics to provide optimal control effort. 23 Se...

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A new controller for DC-DC converters based on particle swarm optimization

Cited by 28 publications

References 40 publications

Time‐optimal control of DC‐DC buck converter using single‐input fuzzy augmented fractional‐order PI controller

Time‐optimal control of DC‐DC buck converter using single‐input fuzzy augmented fractional‐order PI controller

Closed‐loop control and performance analysis of a high‐gain buck‐boost converter with optimized Type III controller

Performance optimization of LQR‐based PID controller for DC‐DC buck converter via iterative‐learning‐tuning of state‐weighting matrix

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