An intelligent adaptive control of DC–DC buck converters

Nizami, Tousif Khan; Mahanta, Chitralekha

doi:10.1016/j.jfranklin.2016.04.008

Cited by 69 publications

(41 citation statements)

References 30 publications

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“…In order to achieve a satisfactory control effect, a controller is required to have stronger capacity of resisting disturbance, smaller steady-state error, faster dynamical response, and so on. To enhance output-voltage performance and estimate the uncertain parameters, many control approaches have been developed with better robustness and adaptability, such as adaptive backstepping technique [20], [21], sliding mode control method [22], [23], adaptive Chebyshev neural network technique [24], output feedback control with reducedorder observer [25], [26], and robust control [27]. Remark that these advanced control methods are only considered for point regulating of continuous average models.…”

Section: Introductionmentioning

confidence: 99%

“…Compared with the literature [20], [21], an equivalent continuous adaptive backstepping controler is designed such that all signals of the closed-loop error continuous system are asymptotically stable. For the switched Buck converter, PWM-based adaptive tracking with the equivalent control input is proposed such that the closed-loop error digital system is practically asymptotically stable, and the tracking error tends to an arbitrarily small neighborhood of the origin by tuning design parameters in contrast with [24], [26], [27].…”

Section: Introductionmentioning

confidence: 99%

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PWM-Based Adaptive Trajectory Tracking of Switched Buck Power Converters

Zhang

2020

IEEE Access

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Based on pulse width modulation (PWM) technique, adaptive trajectory tracking of switched Buck power converters with parameter uncertainty is investigated in this paper. By the aid of the backstepping technique, an equivalent continuous adaptive tracking controller is designed to guarantee the asymptotic stability of the closed-loop error continuous system. For the switched Buck converter with unknown parameters, PWM-based adaptive tracking with the equivalent adaptive continuous control input is proposed such that the closed-loop error digital system is practically asymptotically stable, and the tracking error converges to an arbitrarily small neighborhood of the origin. Simulation results are shown to illustrate the effectiveness of the proposed new control schemes.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

PWM-Based Adaptive Trajectory Tracking of Switched Buck Power Converters

Zhang

2020

IEEE Access

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“…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, fractional‐order PID controller, and adaptive controller . 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 .…”

Section: Introductionmentioning

confidence: 99%

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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“…Moreover, in practice, a large rule base puts excessive computational burden on the system. Similarly, the neural controllers require large sets of training data to deliver a robust control effort . The robust control effort derived from the sliding model controllers comes at the expense of large control energy exertion and injection of chattering in the response .…”

Section: Introductionmentioning

confidence: 99%

Self‐adaptive fractional‐order LQ‐PID voltage controller for robust disturbance compensation in DC‐DC buck converters

Saleem

Awan

Mahmood-ul-Hasan

et al. 2019

Int J Numerical Modelling

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This paper presents a state‐dependent self‐tuning fractional control strategy for a DC‐DC buck converter in order to enhance its output voltage regulation and disturbance attenuation capability. The proposed control scheme primarily employs a ubiquitous proportional‐integral‐derivative (PID) controller, where gains are optimally selected using a linear‐quadratic state‐space tuning approach. The optimal PID controller is then augmented with fractional‐order integral and derivative operators in order to improve the controller's degrees‐of‐freedom as well as the system's overall time‐domain performance. The fractional controller's robustness against bounded exogenous disturbances, contributed by the input fluctuations and load‐step transients, is further enhanced by adaptively modulating the fractional‐orders of the integro‐differential operators as a smooth nonlinear function of controlled‐variable's error‐dynamics. An online dynamic adjustment law, comprising of a zero‐mean Gaussian function of error and its derivative, is used to individually update the two fractional orders after every sampling interval. The error derivative is evaluated by measuring the output capacitor's current in order to compensate the noise injected by parasitic impedance. The other controller parameters are tuned via particle‐swarm‐optimization algorithm. The proposed self‐adaptive control strategy renders rapid transits, minimum transient recovery time, and minimal fluctuations around steady state in the response. Its efficacy is validated through hardware in‐the‐loop experiments conducted on a buck converter prototype.

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An intelligent adaptive control of DC–DC buck converters

Cited by 69 publications

References 30 publications

PWM-Based Adaptive Trajectory Tracking of Switched Buck Power Converters

PWM-Based Adaptive Trajectory Tracking of Switched Buck Power Converters

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

Self‐adaptive fractional‐order LQ‐PID voltage controller for robust disturbance compensation in DC‐DC buck converters

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