Elsevier

ISA Transactions

Volume 105, October 2020, Pages 240-255
ISA Transactions

Practice article
An Adaptive–Predictive control scheme with dynamic Hysteresis Modulation applied to a DC–DC buck converter

https://doi.org/10.1016/j.isatra.2020.05.015Get rights and content

Highlights

  • A simple adaptive–predictive control scheme for a DC–DC buck converter is proposed.

  • A hysteresis modulation is introduced to enrich the closed-loop control performance.

  • The control scheme is robust to modeling errors due to an on-line parameter estimation.

  • It is suited to control complex nonlinear electronic devices in renewable energy field.

  • Simulations and experiments in a setup verify the robustness in real cases.

Abstract

This article proposes a recent Adaptive–Predictive (AP) control technique applied to a DC–DC buck converter. This converter topology has a wide range of applications in the current electronic and electrical systems that demand an efficient use of low bus voltage and specific requirements in load current consumption. Nevertheless, this converter, and in general any DC–DC converter topology, presents a control challenge due to its nonlinear nature. Hence, in this article, it is proposed an adaptive–predictive control scheme that has low implementation complexity and improves the buck converter performance since it provides a fast response of the output voltage. Moreover, the output is adequately regulated even when the system is subjected to perturbations in the reference voltage, in the input voltage, in the load or in the converter parameters that may be seen as faults in the system. On the other hand, one of the main contributions of the proposed control technique with respect to other controllers is that the AP control scheme allows to on-line infer the parametric status of the plant thanks to its adaptive stage. In addition, a dynamic Hysteresis Modulator (HM) is properly inserted in the control strategy to improve the dynamic behavior of the Adaptive Mechanism (AM), and in general, of the entire closed-loop control performance. To validate the effectiveness of the control design, a wide range of numerical experiments are carried out by using Matlab/Simulink. Finally, the developed control technique was implemented in a benchmark experimental platform. According to the experimental results, the proposed predictive control is suitable for real scenarios in the power electronics applications.

Introduction

In recent years, the power electronics field has had an enormous growth, mainly in the branch of regulation, conversion and distribution of energy [1], [2]. This, due to the high demand of electrical and electronic applications that require devices that realize these tasks. Among all the variety of power electronics devices, DC–DC converters are one of the most common and most studied by engineers and researchers [1].

Regarding to the control engineering, the DC–DC converters constitute an important challenge due to their switched nonlinear and time-varying characteristics [2], [3], [4], the fast changes in their reference voltage, their high sensitivity to the frequently changing loading conditions [5], their very small sampling period [6] and the changes in the system parameters related to external perturbations. All of these challenging tasks highly depend on the application. For instance, in photovoltaic (PV) systems, the DC–DC converter output voltage may be subjected to external perturbations due to the variations of the solar irradiation on the PV panel. These variations may be rapid, for instance, under fast shading conditions [7], [8]. Hence, the controller of the DC–DC converters applied to a PV system must be robust to changes in the input voltage and external perturbations [9]. In general, the main objective of DC–DC converters is to ensure stability with an adequate dynamic response in order to achieve a desired load output voltage by guaranteeing a good performance while optimizing the utility life of the electronic components [3]. The most common DC–DC converters are the buck converter, which is the one treated in this paper and basically consists in decreasing the output voltage on the load with respect to the input voltage, the boost converter that works to increase the voltage with respect to its input, and finally the buck–boost also known as Cûk converter, which realizes both tasks, decreases and increases the load voltage according to the control switch position [10].

Nowadays, and because of the buck converter advantages, such as its small size, low weight, and high efficiency [2], [11], [12], it is universally used for a great amount of applications that require low bus voltage and low-to-medium load current consumption [7], [13], [14]. This converter has been employed in the realization of battery chargers [15], [16], and battery-operated portable equipment, due to its simple structure and low-cost [17], [18]. In the automotive field, the buck converter is widely used. One example of it is its bidirectional version for applications in dual battery system for hybrid electric vehicles [19], [20]. Furthermore, in the management of energy, the DC–DC converters have been adopted along with an adequate control technique, to improve the optimization of the total cost of fuel cell/battery in hybrid electric vehicles [21], [22]. Last but not least, in the renewable energies area, the buck converter also has won popularity, for instance, to feed power from distributed generators into smart grids [9] and to improve the efficiency of energy provided by photovoltaic panels through maximum power point tracking techniques [23], [24], [25].

In the last two decades, a considerable amount of control techniques have been applied to these devices, of which mainly focus on the Sliding Mode Control (SMC) technique [26], [27], which is a notable control strategy that has been applied in a wide range of engineering systems [28]. This strategy has received much attention owing to its advantages of simple structure, strong robustness and its immunity towards matched uncertainties [10], [26], [27]. However, its convergence time might be notably long and the infinite switching frequency caused by the sign function in its controller cannot be completely avoided [28]. Some improvements of the conventional SMC also have been presented with the aim of eliminating its main disadvantages, for instance, by proposing a switching sliding surface or a fixed frequency and an adaptive backstepping sliding mode control [29], [30], [31]. On the other hand, the habitual Proportional–Integral–Derivative (PID) controller has been used in DC–DC converters because of its simplicity [4]. Nevertheless, this method does not ensure robustness over a wide range of operating points since it involves linearization around a specific operating point [4], [10]. In spite of it, PID controller has been jointly employed with other techniques to improve its performance taking advantages of the combined strategies, for instance, with a neural network based technique, with current feed-back loop by invoking hysteresis or with a finite-time control strategy [32], [33], [34]. Likewise, the Pulse-Width-Modulation (PWM) control scheme is also popular in DC–DC converters. This technique utilizes a signal modulation by reason of the switching necessity of the actuator to achieve the control objective [9], [35]. Furthermore, optimal controllers seek to solve the control problem statement by minimizing a properly designed cost function. Normally, the cost function may incorporate the uncertainties and constraints of the system which results in a proper control performance. However, the design of an adequate cost function can be a complex mathematical realization [12], [36], [37]. Other control techniques for DC–DC converters are Super-Twisting (STW) algorithms [38], Fuzzy Logic controllers [39], and Neural Networks based techniques [32], [40]. On the other hand, hysteretic controllers are an alternative way to achieve the control objective of DC–DC converters thanks to their simplicity, no need of compensation, instantaneous response and no limitations on the switch conduction time [7]. Still, the necessity of an adequate sensing stage makes that these controllers may be costly in terms of economic saving and computational work [3], [33]. Additionally, the standard Adaptive Control scheme is also an alternative to control the DC–DC converters. By using this technique, the converter not only estimates and adapts the values of the uncertain parameters but also reaches the steady state in limited time. The paper in [5] realizes a very clear summary of the adaptive techniques in the DC–DC converters state-of-art. Finally, in the area of power converters, some Predictive Control techniques have emerged as a good alternative [6], [41]. The Model Predictive Control (MPC) obtains the control action by solving an optimization problem with future prediction over a finite horizon [42]. The main advantage of this technique is that the system constraints and nonlinearities may be considered in the cost function [6], [43]. Nevertheless, the MPC presents disadvantages, such as, the difficulty of the cost function design or the lack of stability guarantees [42], [43]. The Predictive Control technique has been successfully used in photovoltaic applications where a Maximum Power Point Tracking is combined jointly to the DC–DC converter controller to improve the efficiency of the energy provided by the photovoltaic panel [44], [45].

Since there are still many disadvantages to be defeated in the DC–DC converter controllers, this article proposes a recent control scheme for the buck converter based on an Adaptive– Predictive (AP) strategy. The AP control scheme is a well established technique that has been applied in a wide variety of industrial applications [46], [47], [48]. In this article, a remodeled Adaptive–Predictive control is proposed to improve the DC–DC buck converter performance. Generally speaking, the AP control strategy requires the mathematical model of the process to predict its future behavior that is re-planed every sample time. Moreover, the AP scheme utilizes an adaptive system to auto-adjust the changes in the plant due to perturbations or faults. The strategy is established in a linear model even if the process is nonlinear, as it will be seen later in this article. This is one of the advantages of the proposed method since the control scheme is simple to conceive and implement. Besides, unlike the traditional predictive control scheme, the AP control design does not use an optimization problem and the prediction horizon is just realized one period in the future, making it a simple but effective option to reduce computational cost and to obtain a good controller performance. Additionally, an Hysteresis Modulator (HM) is inserted in the control scheme to improve the performance of the controller since it provides persistent excitation to the Adaptive Mechanism to ensure the parameters convergence [48], [49]. This extra stage in the control scheme guarantees to solve problems such as saturation in the control law and parameter drift [47], among other advantages provided by this modulation stage. Finally, it will be exposed how the Adaptive Mechanism provides on-line information about the condition of the process parameters. This allows us to infer the status of the system, which could be useful to post-process the estimated parameters with the objective of detecting faults in the system.

In this article a wide range of experiments is presented. Firstly, numerical experiments are realized in Matlab/Simulink. Furthermore, because one of the most typical application of DC–DC converters is in the solar renewable energy field, this article presents an application of the proposed control scheme in a PV system at a numerical experiment level. This is realized by employing a PV panel as the voltage supply to the buck converter. Hence, it is validated that the proposal has a good performance even when the buck converter is subjected to the common irradiation change perturbation presented in the PV panel due to shading conditions. Secondly, the technique exposed here, is also tested in an experimental platform where the results were compared to those obtained with a typical PI controller. Hence, it will be seen that the proposed technique has a good performance since it has a fast convergence response under changes in reference voltage, in input voltage and in the load. Moreover, the controller is robust since it maintains the output voltage well regulated even when there are variations in the inductance which is one of the principal components of the converter.

Hereafter, Section 2 presents the DC–DC buck converter mathematical model. Afterward, Section 3 exposes the proposed Adaptive–Predictive control. Numerical experiment results are depicted in Section 4. On the other hand, the results of the experimental implementation in a real DC–DC buck converter are shown in Section 5; the result discussion is presented in Section 6. Finally, conclusions are drawn in Section 7.

Section snippets

DC–DC buck converter modeling

The conventional electronic circuit of the buck converter is shown in Fig. 1. It is mainly composed of an input voltage source (E), a switch mechanism (S), a diode (D), an inductor (L), a capacitor (C), and finally the load, which usually is a resistor (R). Based on circuit analysis and under ideal assumptions, the buck converter dynamic model may given by [10]: Lż1(t)=z2(t)+u(t)E,Cż2(t)=z1(t)z2(t)R, where, z1=iL is the inductor current, z2=Vo is the output voltage and u is the switching

Adaptive–predictive control design

In this section, the stages of the Adaptive–Predictive control scheme are described. This control methodology is based on an approximated dynamic mathematical model that depicts the process to be controlled. This model is required to obtain the Predictive Model, based on a discrete-time realization, which generates the control law. On the other hand, the process model is also useful to design the Adaptive Mechanism, which serves to auto-adjust the process parameters under any possible change in

Numerical experiment results

In this section, different numerical experiments will be illustrated to evaluate the effectiveness of the proposed predictive control technique. These experiments were realized in Matlab/Simulink 2018b. The realistic buck converter parameters used for these simulations were obtained from a real DC–DC setup and are listed in Table 1. In addition, the parameters of the control scheme are set as follows: αhm=5, ahm=5, bhm=0.1, γ=0.001, λ1=10, λ2=5, α1=α2=100, β1=200. These parameters were tuned

Results of the control implementation in a realistic buck converter setup

In order to experimentally validate the proposed predictive control, a variety of experiments were carried out using a benchmark platform in the Electrical Energy Laboratory (EELAB) in Ghent University. The parameters of each component are listed in Table 1. Furthermore, the setup is the one presented in Fig. 19, which is a three-phase inverter described in [59], [60]. However, it was adjusted such that only one leg is used to get a buck converter configuration where the proposed control

Discussion of results

In this article, the presented results, both numerical and experimental, allow us to draw some conclusions regarding the proposed control technique. Firstly, the results make inference that the Adaptive–Predictive control strategy is an adequate option to control the buck converter device since its main objective is totally accomplished. That is, regulate the output voltage to a desired value. Realistic scenarios have been emulated in numerical simulations and experimental implementation and in

Conclusions

In this paper, a new control scheme for a DC–DC buck converter has been completely developed and tested through numerical and real experiments. The control approach is a simple manner to accomplish the main power control objective of these electronic devices. That is, efficiently regulating the output voltage of a DC–DC converter to a desired value even when the system is subjected to the commonly existing perturbations, such as variation in the input voltage, fast changing in the reference

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgments

This work has been partially funded by the Spanish Ministry of Economy and Competitiveness/Fondos Europeos de Desarrollo Regional (MINECO/FEDER), Spain with grant number DPI2015-64170-R and by the scholarship for doctoral studies abroad provided by the CONACyT, Mexico .

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