Research Article  Open Access
Nixuan Liu, Siqi Cao, Juntao Fei, "FractionalOrder PID Controller for Active Power Filter Using Active Disturbance Rejection Control", Mathematical Problems in Engineering, vol. 2019, Article ID 6907570, 10 pages, 2019. https://doi.org/10.1155/2019/6907570
FractionalOrder PID Controller for Active Power Filter Using Active Disturbance Rejection Control
Abstract
This paper proposed a fractionalorder PID controller and active disturbance rejection control (ADRC) method for the current compensation of active power filter (APF). The control method consists of two closed loops. One is a reference current tracking loop based on the ADRC controller, which can treat the internal and external uncertainties of the system as a whole. The other is the voltage control loop with the fractionalorder PID controller for more flexibility. Simulation results demonstrate that the proposed control method has a stronger robustness and higher compensating precision comparing with the doubleloop PID control method.
1. Introduction
It is generally acknowledged that using APF is an effective way to deal with the grid harmonic and reactive power in order to improve the grid quality. However, the research for APF has not been mature, yet there are still lots of problems to be studied and solved. To improve the capacity of APF, recently it has become a trend in the study of the control system, including optimizing the advanced algorithms on current compensating control and DC side voltage tracking control.
The beginning of the research works of APF control system is at the point that the concept of “active power filter” was proposed in the 70th last century, when the “instantaneous reactive power theory” was posed by Akagi et al. [1]. Researchers have done much work on the advanced control algorithms to improve the current compensation and voltage tracking respectively [2–7]. Hou et al. [8] combined adaptive fuzzy control with neural network control on the active power filter to eliminate harmonics. Fei et al. [9] used an adaptive sliding mode controller to combine neural network control in order to eliminate harmonics. Zhu et al. [10] adopted fuzzy sliding mode control on PV Grid Connected Inverter creatively to ensure the good robustness of the system.
The Active Disturbance Rejection Control (ADRC) algorithm firstly was proposed by Han et al. [11–13] and Sun [14]. It consists of Tracking Differentiator (TD), Extended State Observer (ESO), and Nonlinear Error State Feedback law (NLSEF). This control method inherits good qualities from PID [13]. It is a nonlinear control structure based on the process error instead of the plant model. This feature brings the ADRC to wider fields of the engineering application than the traditional PID control method. Xia et al. [15] presented a tracking control method for the lateral motion of an autonomous land vehicle based on ADRC scheme and differential flatness theory. Zhu et al. [16] designed an airship horizontal trajectory tracking control based on ADRC. ADRC method has been discussed for hypersonic reentry vehicle and uncertain nonlinear systems [17, 18].
Meanwhile, the fractionalorder calculus is also mentioned in this paper. Within the last decades, fractionalorder calculus has been used in many scientific fields, especially in control engineering. It is a branch of mathematics in which the integer order of the conventional differential equation is extended to a noninteger (fractional) number. Li et al. [19] verified that a fractionalorder proportional and derivative controller is suitable for the closedloop system that can enhance the system robustness. Tseng et al. [20] designed a fractionalorder FIR differentiators. Doye et al. [21] proposed a state observer design for constructing a chaotically synchronized systems and fractionalorder direct Lyapunov theorem is used to ensure the closedloop asymptotic stability. Adaptive fuzzyneural fractionalorder controller using a finitetime sliding mode method was developed in [22–27].
As a result, the ADRC and fractionalorder PID compound controller are combined together to be applied in the APF to make use of both advantages of the two algorithms, which include the strong robust and fast response of ADRC and flexibility of fractionalorder PID. The stability of the ADRC was investigated in [28]. The contributions of the work compared with existing works can be concluded as follows.
It is firstly proposed that the combined algorithm of ADRC and fractionalorder PID is applied for the current compensation and voltage tracking on APF. ADRC is such a control method that considers the uncertainties as a whole, which means the working principle of APF can be simpler without harmonic detection module.
The APF control system has two control loops: the reference current tracking loop with ADRC and the voltage tracking loop with the fractionalorder PID controller. The fractionalorder PID can improve the flexibility and the robustness of the system.
In this paper, the control theory of ADRC control is discussed in Section 2. The control theory of fractionalorder PID control is introduced in Section 3. The control system is then given in Section 4. The simulation study is presented in Section 5. Finally conclusion is given in Section 6.
2. Active Disturbance Rejection Control (ADRC) Theory
Normally, an ADRC system contains three parts, including Tracking Differentiator (TD), Extended State Observer (ESO), and Nonlinear State Error Feedback Law (NLSEF). The framework of this controller is shown in Figure 1, where are the tracking signals of derivatives of the reference input through TD. are the state variables of plant estimated in ESO. is the realtime function value. By subtracting and , we can obtain the control law through the process of NLSEF.
2.1. Extended State Observer (ESO)
For an uncertain system,
It can be rewritten in another form aswhere is unknown, is an uncertain external disturbance, is a known parameter. Then we can design an extended state observer (ESO) as Then we set ; can be seen as extended state variables. are the output variables of the observer. Let output variables track extended variables, described as
Choose appropriate nonlinear functions to achieve the track target. Then, the equation can be written as
In fact, when and are unknown, can be seen as the estimated value of . It can compensate the uncertain external disturbance. Thus, ESO is actually such a process; when the system output is calculated in ESO, the derivative signals and the estimated signal of the system disturbance can be obtained for the convenience of the next step.
2.2. Nonlinear State Error Feedback Law (NLSEF)
According to the outputs of ESO and TD, the error between them can be obtained as
Then, the nonlinear error feedback of the system can be written as where are adjustable parameters.
Generally, is such a kind of nonlinear functions as
Just as we can see , it is actually a nondifferentiable function. It will avoid the influence of oscillation when the error changes in linear range. While is very small, the saltation of derivate could make the property of system worse and create larger saltation. So due to the fact that the control performance is sensitive to , a continuous and smooth nonlinear function is more suitable for this control system, as follows:
Then, we can get the system control law as
3. FractionalOrder PID Control
The transfer function of fractionalorder controller iswhere denotes integral order and denotes differential order. According to the performance index of the control system, we can optimize the parameters of the controller, such as .
In this paper, we choose a fractionalorder as system controller whose transfer function can be written as
The framework of the fractionalorder can be shown in Figure 2. This controller is based on Flat phase theory proposed by Chen [29] and the index of amplitude margin and phase margin can be obtained aswhere and denote the cutoff frequency openloop transfer function respectively.
According to (11), the frequency response of the controller can be obtained as
The phase and amplitude of the controller can be also obtained as
The transfer function of the controlled device can be written asSo the frequency response of the controlled device can be calculated as
The phase and amplitude can be also obtained asThe openloop transfer function of the system is written asThe frequency response, the phase, and amplitude of the system are deduced as
According to (13)(15), it can be deduced aswhere , , is a low pass filter of system.
4. Active Power Filter Control
This proposed APF working principle contains two loops; one is the internal loop of the reference current tracking and the other is the external loop of voltage control for DC side. The internal loop is to compensate the harmonic current by the switches in the main circuit. The PWM can control the conditions of those switches (ON/OFF) when the ADRC gives the modulation signals. The external loop can get the reference current by the fractionalorder PID control and import to the internal loop. The framework of the control system is plotted in Figure 3. For the consideration of the feature that the fractionalorder PID control is not sensitive to the parameter variance and has strong robust, it is applied for the voltage control.
4.1. The Current Control with an ADRC Controller
The basic circuit structure of a singlephase active power filter is shown in Figure 4, where is a grid voltage, is a grid current, is a filter output compensation current, is an AC inductor, is an equivalent resistor, and is a DC capacitor.
APF consists of three parts, including the harmonic current detection module, the current tracking control module, and the compensation current module. However, when the proposed ADRC control method is applied, the harmonic current module can be not necessary any more.
The APF model is shunt singlephase one and the main circuit can be equivalent to the diagram as shown in Figure 5.
According to the circuit theory and Kirchhoff’s voltage law, the mathematic model can be written aswhere , , as switch function is the control value of the system.
Set the firstorder output of TD: where are the parameters to be selected; we can design the ESO aswhere , are the parameters to be selected. Though , as the feedback signal of the system state error, we can get the ADRC law aswhere are also the parameters to be selected. From (29), we could find out that the control law has nothing to do with the model uncertainties and external disturbances, only related to the given input and the system output.
4.2. The Voltage Control with a FractionalOrder PID Controller
When the reference voltage on the DC side , , , it can be derived that and the transfer function of the controller can be written as where is substituted by Oustaloup Filter [30]. The original fractionalorder transfer function can be approached by the integralorder one. As a result, the high order term can be approximated by . It can be written as
5. Simulation Study
In order to verify the feasibility of the proposed control strategy, the simulation has been done in the MATLAB/ Simulink with the APF model (24). The supply voltage , . The nonlinear load is applied in the system, where , . When .1s, another same nonlinear load is added in the system in order to test the proposed controller. According to (24) and (25), , , .
We can see the control effects obviously from two kinds of figures; some are without APF control and the others are with ADRC and fractionalorder PID control method. The simulation results can be seen in the Figures 6–14.
Figure 6 shows the grid current waveform in APF without any control methods. It can be seen that due to the presence of nonlinear load, the grid current waveform has severe distortion that will affect the quality of grid power. As can be seen from the waveform in Figure 7 compared with Figure 6, the grid current distortion has been significantly improved when the proposed controller is used in the system.
Figure 8 shows that nonlinear load leads grid current containing a large number of harmonics. Figures 9–11 plot the harmonic content with ADRC control based on fractionalorder PID control. It is shown that the ADRC and fractionalorder PID control are effective in harmonic suppression of APF.
As a consequence, from Figures 6–11, it is obvious that with the nonlinear load impact increasing asymptotically, ADRC and fractionalorder PID for current control of an APF illustrate good tracking performance, suitability, and robustness at different simulation stage.
Here, in order to demonstrate the advantage of the proposed controller more clearly, a comparative control method that consists of doubleloop PID for current control on the AD side and PID for voltage control on the DC side is proposed. It can be seen in Table 1, which compared the doubleloop PID control and the fractionalorder PID and ADRC control method in their THD in different loads.

From Figures 9–14 and Table 1, it is obvious that with the nonlinear load impact increasing asymptotically, fractionalorder PID and ADRC for current control of an APF illustrate better tracking performance, suitability and robustness at different simulation stage than the one based on doubleloop PID.
6. Conclusion
In this paper, a compound control method consisting of ADRC and fractionalorder PID is used in shunt APF successfully. According to the established mathematical model of APF, the referenced current can be obtained by fractionalorder PID method applied to the voltage control module on DC side, then the control law can be derived by the ADRC method applied to the current compensation module, which can be seen as the PWM signal to control the main circuit to gain the compensation current. Simulation results verified that the presented method has a stronger robustness and higher compensation accuracy, compared to the doubleloop PID method.
Data Availability
The data used to support the findings of this study are included within the article.
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Acknowledgments
The abstract of this paper was presented in 2017 29th Chinese Control and Decision Conference (CCDC). This work is supported by National Science Foundation of China under Grant No. 61873085, Natural Science Foundation of Jiangsu Province under Grant No. BK20171198, and the Fundamental Research Funds for the Central Universities under Grant No. 2017B 20014.
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Copyright © 2019 Nixuan Liu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.