Current-Loop Control for the Pitching Axis of Aerial Cameras via an Improved ADRC

Liu, BingYou; Zhu, ChangAn; Guo, XingZhong

doi:https://doi.org/10.1155/2017/6162194

Mathematical Problems in Engineering

On this page

Abstract Introduction Discussion Conclusions Acknowledgments References Copyright Related Articles

Research Article | Open Access

Volume 2017 | Article ID 6162194 | https://doi.org/10.1155/2017/6162194

Current-Loop Control for the Pitching Axis of Aerial Cameras via an Improved ADRC

BingYou Liu,^1,2ChangAn Zhu,¹and XingZhong Guo²

Academic Editor: Rafael M. Herrera

Received13 Jul 2016

Revised09 Oct 2016

Accepted04 Dec 2016

Published06 Feb 2017

Abstract

An improved active disturbance rejection controller (ADRC) is designed to eliminate the influences of the current-loop for the pitching axis control system of an aerial camera. The improved ADRC is composed of a tracking differentiator (TD), an improved extended state observer (ESO), an improved nonlinear state error feedback (NLSEF), and a disturbance compensation device (DCD). The TD is used to arrange transient process. The improved ESO is utilized to observe the state extended by nonlinear dynamics, model uncertainty, and external disturbances. Overtime variation of the current-loop can be predicted by the improved ESO. The improved NLSEF is adopted to restrain the residual errors of the current-loop. The DCD is used to compensate the overtime variation of the current-loop in real time. The improved ADRC is designed based on a new nonlinear function . This function exhibits enhanced continuity and smoothness compared to previously available nonlinear functions. Thus, the new nonlinear function can effectively decrease the high-frequency flutter phenomenon. The improved ADRC exhibits improved control performance, and disturbances of the current-loop can be eliminated by the improved ADRC. Finally, simulation experiments are performed. Results show that the improved ADRC displayed better performance than the proportional integral (PI) control strategy and traditional ADRC.

1. Introduction

The pitching axis control system of an aerial camera adopts a servo control system with three loops, namely, current-loop, speed-loop, and position-loop. As an inner loop, the current-loop plays an important role in the pitching axis control system of an aerial camera [1, 2]. This loop should be controlled in real time to achieve good performance of the pitching axis control system. Therefore, the current-loop needs an improved control strategy. The current-loop control strategy has been extensively investigated globally, and several developments have been achieved. For example, Radwan and Mohamed [3] examined an improved vector control strategy, but the vector control strategy needs accurately known system parameters. Liu et al. [4] introduced current control strategy with an optimal variable structure that exhibits improved robustness. However, this control strategy cannot solve the jitter problem. Novak and Riener [5] examined an artificial intelligence control strategy, which does not rely on the mathematical model of the controlled object. However, this control strategy has a complex algorithm. Liu et al. [6] examined an adaptive current control technique with constant frequency based on clocked signal. This control strategy exhibits good static performance but weak restrain capacity for external disturbance. Several studies investigated a traditional active disturbance rejection control strategy, which exhibits certain anti-interference ability. In [7], Han introduced the evolution from PID to ADRC and the advantages of ADRC. In [8–10], Guo used active disturbance rejection control (ADRC) and sliding mode control (SMC) in the stabilization of the Euler-Bernoulli beam equation, one-dimensional and multidimensional antistable wave equations with boundary input disturbance. In [11], Sira-Ramírez et al. presented an ADRC scheme for the angular velocity trajectory tracking task on a substantially perturbed, uncertain, and permanent magnet synchronous motor. An ADRC was designed to solve the trajectory tracking problem of a Delta robot with uncertain dynamical model [12]. The stabilization problem of a class of nonlinear systems with actuator saturation is investigated via ADRC [13]. An adaptive extended state observer- (AESO-) based ADRC is proposed to deal with the uncertainties and applied to the air-fuel ratio (AFR) control of gasoline engine, which has large nonlinear uncertainties due to the unknown speed change, fuel film dynamics, and so on [14]. The absolute stability of nonlinear ADRC for single-input-single-output systems is analyzed by the circle criterion method [15]. In [16], an ADRC was applied to stabilization for lower triangular nonlinear systems with large uncertainties. Guo et al. [17] generalized the ADRC to uncertain nonlinear systems subject to external bounded stochastic disturbance described by an uncertain stochastic differential equation driven by white noise. Liu et al. [18] proposed a two-layer ADRC method with the compensation of estimated equivalent input disturbances (EID) for load frequency control (LFC) of multiarea interconnected power system. However, the high-frequency flutter phenomenon is not eliminated, and the anti-interference ability needs to be enhanced. The current-loop of the pitching axis control system of an aerial camera is controlled under weightlessness. Therefore, a small interference will result in a serious error. The control system requires an improved active disturbance rejection controller (ADRC) with strong anti-interference ability.

The improved ADRC is composed of a tracking differentiator (TD), an improved extended state observer (ESO), an improved nonlinear state error feedback (NLSEF), and a disturbance compensation device (DCD). In this study, the previously available nonlinear function of a traditional ADRC is improved to a new nonlinear function with better continuity and smoothness. Thus, the improved ADRC based on the new nonlinear function exhibits better anti-interference performance. On the one hand, the total disturbances are sufficiently estimated by the improved ESO, and the state of the system does not require direct observation. Therefore, the controllability of the system can be enhanced greatly. On the other hand, control efficiency is enhanced using the improved NLSEF to conduct nonlinear calculation of the proportion signal, differential signal, and integral signal of the error [19, 20]. Extensive studies on the improved ADRC have resulted in important progress. Qi et al. [21] constructed a continuous and smooth ESO and disturbance rejection cascade decoupling control technology. An improved ADRC has been applied in a hypersonic vehicle and in the straight course error modeling of an electromagnetically controlled gyrocompass [22]. In [23], an improved ADRC based on the nonlinear arctangent function is used to reduce the observation noise in the traditional extended state observation of a system with output measurement noise. An improved ADRC is proposed to improve the tracking performance of the electromechanical actuator [24]. In [25], an improved ADRC is used to control an autonomous underwater vehicle. In [26], an enhanced ADRC is presented for a twin-rotor multi-input multioutput system with two degrees of freedom. However, few reports have focused on the use of improved ADRC in the current-loop control system of the pitching axis of an aerial camera.

The present study proposes an improved ADRC based on a new nonlinear function . Then, the controller is applied to the current-loop control system of an aerial camera pitching axis. Simulations are conducted for cases adopting the PI control strategy, traditional ADRC, and improved ADRC. Finally, the simulation results are discussed.

2. Mathematical Model

The pitching axis of an aerial camera adopts a permanent magnet synchronous motor (PMSM) as an actuator. A control strategy of is used in the current-loop control system, where is the current of the direct axis. Therefore, the controlled object of the current-loop is , where is the current of the quadrature axis. The current equation of the quadrature axis in a () two-phase rotating coordinate system is given as follows:where is the stator phase resistance, is the quadrature axis inductance, is the magnetic chain produced by the rotor permanent magnet, is the equivalent voltage of the quadrature axis, and is the angular speed of the motor rotor. The mathematical model of PMSM in a () two-phase rotating coordinate system shows that the electromagnetic torque is directly controlled by . Therefore, the precise control of the electromagnetic torque can be realized by controlling the current-loop. The expression represents a coupling effect in the current equation of the quadrature axis. The current-loop is a nonlinear system under the influence of the coupling effect. Several definitions are given as follows:Then, the current equation of the quadrature axis can be expressed as follows:where and are the internal and external disturbances of the current-loop, respectively. The total disturbance of the current-loop is expressed as . Then, the current equation of quadrature axis is as follows:Equation (4) is a typical equation used to design the improved ADRC.

3. Improved ADRC Design for the Current-Loop

3.1. Design of the New Nonlinear Function

The nonlinear function is the core of the ADRC. The following conditions should be fully considered in designing the nonlinear function. First, the nonlinear function should exhibit good convergence around the origin. Second, the value of the nonlinear function is 0 at the origin. Third, the nonlinear function should be continuous around the origin. The traditional ADRC adopts a nonlinear function , which can be expressed as follows:The characteristics of are given as follows. The value of influences the nonlinearity degree of . The value of is usually selected between 0 and 1. The nonlinear function exhibits optimal nonlinearity when . The degree of linearity of is optimal when . The value of is the linear interval width of and related to the error range of the system. When the input is an error signal, the system can achieve rapid stability by adjusting the parameter values of . is a nonderivable function, although this function is continuous. If the value of is too small, will also cause the high-frequency flutter phenomenon. The value of is difficult to adjust, because the control performance of is sensitive to the value of . Thus, a new nonlinear function is designed. The proposed exhibits better convergence and continuity around the origin than . The expression of is calculated by the following steps.

When , is expressed as .

When , the expression of is designed as an interpolation function form by multinomial and trigonometric functions and can be expressed as follows:Part three of the above expression is selected as , but not , because exhibits better convergence than around the origin. Then, can be given as follows:where , , and are the function coefficients and can be calculated by the following steps.

In the range of , the following expression is given to meet the continuous and derivable conditions:The following expression can be obtained by (7) and (8),The values of , , and can be calculated as follows:Thus, the expression of can be obtained as follows:Simulations are performed using the two nonlinear functions under . The curves of the function are shown in Figure 1.

The simulation results demonstrate that exhibits better continuity and derivability than . Thus, is not sensitive to the selection of the value and can avoid the high-frequency flutter phenomenon. In this study, the improved ADRC is designed based on .

3.2. Improved ADRC Design for the Current-Loop of the Pitching Axis of an Aerial Camera

Figure 2 shows the structure of the improved ADRC of the current-loop for the pitching axis control system of an aerial camera.

3.2.1. TD

TD in the improved ADRC is used to arrange the transient process. A continuous and differentiable input signal can be obtained by TD. Therefore, drastic changes in the control signal can be avoided effectively. The TD expression of the current-loop of the pitching axis control system of an aerial camera is designed as follows:where is the current given the quadrature axis, is the tracking signal for , is the velocity factor, is the filter factor, and and are used to arrange the speed of the transient process. is an optimal control synthesis function and can be defined aswhere is the input and is the output of the TD. can track as fast as possible in the case of the acceleration constraint . The larger the value of , the faster the tracking speed. is the differentiation of when is tracking . is used to filter out the high-frequency interferences of . The value of is parameterized as . The greater the value of , the better the filtering effect.

3.2.2. Improved ESO

Improved ESO is the core of the improved ADRC, which is a nonlinear control technology that does not rely on the system model. Nonlinear dynamics, model uncertainty, and external disturbances are extended to a new state and observed by the improved ESO. The overtime variation of system can be predicted by the improved ESO. Therefore, the robustness of the system can be improved greatly. The expression of the improved ESO for the current-loop for the pitching axis control system of an aerial camera is designed aswhere is the tracking signal for , is the tracking error, and is the tracking signal for the total disturbance . If the new nonlinear function is selected appropriately, the state variables and can indicate the state variables and , respectively. Therefore, the improved ESO for the current-loop for the pitching axis of the aerial camera can be obtained. are the gain coefficients of the improved ESO. are the nonlinear factors and satisfy the condition . is a filtering factor with a value of . is the estimation of the compensation factor. The internal structure of the improved ESO of the current-loop for the pitching axis of the aerial camera is shown in Figure 3.

The stability of the improved ESO can be proved as follows. The bandwidth of the current-loop is defined as . The following equation can be obtained according to previous research:The nonlinear parts of the improved ESO are divided into many areas to analyze the stability of the improved ESO. All areas are replaced by piecewise linear functions. The stability of the entire area can be ensured when the stability of each area is ensured. The present study used the linear expression to replace the nonlinear function to analyze the stability of the improved ESO. Therefore, (15) can be written asLaplace transform is carried out for the above equation and the following equation can be obtained:therefore, the following equation can be obtained:The errors of and can be calculated as follows:then, (21) can be calculated:This article selected and as unit step input. Thus, , and . The　steady-state errors can be calculated as follows:therefore, the improved ESO exhibits stability.

3.2.3. Improved NLSEF

Improved NLSEF is used to combine the state variables produced by tracking the TD and improved ESO with the error of estimated value nonlinearly. The expression of the improved NLSEF for the aerial camera pitching axis current-loop is designed aswhere is the gain coefficient of the improved NLSEF, is a nonlinear factor with a value between 0 and 1, and is a filtering factor.

3.2.4. DCD

DCD is used to compensate for the total disturbance of the current-loop for the pitching axis of the aerial camera. The total disturbance is composed of internal disturbance, external disturbance, and coupling influence. The expression of the DCD for the current-loop of the pitching axis of the aerial camera is designed aswhere is the control signal without DCD, is the control signal, and is the compensation component of total disturbance for the aerial camera pitching axis current-loop.

4. Simulation Experiments and Discussion

The improved ADRC is compared with the traditional ADRC and PI control strategy to accurately evaluate its performance. A PMSM is selected as the executing agency of the system. Table 1 shows the PMSM parameters, Table 2 presents the traditional ADRC parameters, and Table 3 presents the improved ADRC parameters.

Simulations are performed in the current-loop control system of the pitching axis of an aerial camera in the following three cases.

First, simulations are performed to verify the response speed and steady-state performance of the improved ADRC. A current of 1 A is applied on the rotor for nonload starting. Figure 4 shows the motor response curves under the PI control strategy, traditional ADRC, and improved ADRC. The control strategy based on improved ADRC exhibits shorter response time and smaller overshoot amount than the PI control strategy and the traditional ADRC control strategy. The simulation results show that the overshoot amount of the control strategy based on improved ADRC is 14.2822% and 9.4183% lower than those of the PI and traditional ADRC control strategies, respectively. The response time of the control strategy based on the improved ADRC is 1.5392 and 0.6459 s shorter than those of the PI and traditional ADRC control strategies, respectively. The current-loop parameters of the overshoot amount and the response times for the three strategies are shown in Table 4.

Second, simulations are performed to verify the anti-interference ability of the improved ADRC. As an external disturbance, a step signal of 1 A is applied on the motor for 3 s. The current response curves are shown in Figure 5. The simulation results show that the improved ADRC control strategy exhibits smaller response curve fluctuation, shorter recovery time, and smaller load disturbance influence than the PI and traditional ADRC control strategies. The simulation results show that the overshoot amount of the improved ADRC control strategy is 7.5911% and 36.4762% lower than those of the traditional ADRC and PI control strategies. The recovery time of the improved ADRC control strategy is 1.4877 and 6.9423 s shorter than those of the traditional ADRC and PI control strategies. Table 5 presents the current-loop parameters of the overshoot amount and recovery time.

Finally, simulations are performed to verify the indicator ability of the improved ADRC. A sinusoidal signal input is applied to the current-loop control system. The current output curves for the traditional ADRC and improved ADRC control strategies are shown in Figure 6. According to the simulation results, the improved ADRC control strategy exhibits better indicator ability than the traditional ADRC control strategy.

Simulations are also performed to verify the performance of tracking the disturbance, and the results are shown in Figure 7. Figure 7 shows that can track the disturbance in real time when a unit step disturbance is applied on the current-loop at the time 3 s.

5. Conclusions

An improved ADRC of the current-loop of the pitching axis control system of an aerial camera is proposed. The new nonlinear function can effectively decrease the high-frequency flutter phenomenon, because it exhibits better continuity and smoothness than the previously available nonlinear functions. Therefore, the improved ADRC designed based on the new nonlinear function exhibits better control performances than the PI control strategy and the traditional ADRC. The nondifferentiable and discontinuous characteristics of the nonlinear function of NLSEF are overcome by the improved ADRC. Simulations are performed, and the results indicate that the system with improved ADRC control strategy exhibits better dynamic performance, static performance, and robustness than the system with PI control strategy and traditional ADRC control strategy. Extending the proposed design technique to the improved ADRC of the position-loop is an interesting topic, and this technique is the focus of our future study.

Competing Interests

The authors declare no conflict of interests regarding the publication of this paper.

Acknowledgments

This work was supported by the Key Project of Natural Science by Education Department of Anhui Province (no. KJ2015A316), the Outstanding Young Talents at Home Visit the School Training Project (no. gxfxZD2016101), and Natural Science Foundation of Anhui Province of China (nos. 1501021015 and 1408085ME105).

References

M. Wang, Y.-F. Cheng, B. Yang, and X. Chen, “On-orbit calibration approach for star cameras based on the iteration method with variable weights,” Applied Optics, vol. 54, no. 21, pp. 6425–6432, 2015.
View at: Publisher Site | Google Scholar
X. Guan and G.-T. Zheng, “Integrated design of space telescope vibration isolation and attitude control,” Journal of Astronautics, vol. 34, no. 2, pp. 214–221, 2013.
View at: Publisher Site | Google Scholar
A. A. A. Radwan and Y. A.-R. I. Mohamed, “Improved vector control strategy for current-source converters connected to very weak grids,” IEEE Transactions on Power Systems, vol. 31, no. 4, pp. 3238–3248, 2016.
View at: Publisher Site | Google Scholar
B.-L. Liu, Y.-L. Wang, and Z.-J. Ding, “Active power filter optimal variable-structure current control strategy,” Electrical Measurement and Instrumentation, vol. 51, no. 22, pp. 67–71, 2014.
View at: Google Scholar
D. Novak and R. Riener, “Control strategies and artificial intelligence in rehabilitation robotics,” Ai Magazine, vol. 36, no. 4, pp. 23–33, 2015.
View at: Google Scholar
W. Liu, L. Luo, Z. Zhang, J. Xu, and Z. Huang, “An adaptive current control technique with constant frequency based on clocked signal,” Automation of Electric Power Systems, vol. 38, no. 1, pp. 109–120, 2014.
View at: Publisher Site | Google Scholar
J. Han, “From PID to active disturbance rejection control,” IEEE Transactions on Industrial Electronics, vol. 56, no. 3, pp. 900–906, 2009.
View at: Publisher Site | Google Scholar
B.-Z. Guo and F.-F. Jin, “The active disturbance rejection and sliding mode control approach to the stabilization of the Euler-Bernoulli beam equation with boundary input disturbance,” Automatica, vol. 49, no. 9, pp. 2911–2918, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
B.-Z. Guo and F.-F. Jin, “Sliding mode and active disturbance rejection control to stabilization of one-dimensional anti-stable wave equations subject to disturbance in boundary input,” IEEE Transactions on Automatic Control, vol. 58, no. 5, pp. 1269–1274, 2013.
View at: Publisher Site | Google Scholar
B.-Z. Guo and H.-C. Zhou, “The active disturbance rejection control to stabilization for multi-dimensional wave equation with boundary control matched disturbance,” IEEE Transactions on Automatic Control, vol. 60, no. 1, pp. 143–157, 2015.
View at: Publisher Site | Google Scholar | MathSciNet
H. Sira-Ramírez, J. Linares-Flores, C. García-Rodríguez, and M. A. Contreras-Ordaz, “On the control of the permanent magnet synchronous motor: an active disturbance rejection control approach,” IEEE Transactions on Control Systems Technology, vol. 22, no. 5, pp. 2056–2063, 2014.
View at: Publisher Site | Google Scholar
L. A. Castaneda, A. Luviano-Juarez, and I. Chairez, “Robust trajectory tracking of a delta robot through adaptive active disturbance rejection control,” IEEE Transactions on Control Systems Technology, vol. 23, no. 4, pp. 1387–1398, 2015.
View at: Publisher Site | Google Scholar
M. Ran, Q. Wang, and C. Dong, “Stabilization of a class of nonlinear systems with actuator saturation via active disturbance rejection control,” Automatica. A Journal of IFAC, the International Federation of Automatic Control, vol. 63, pp. 302–310, 2016.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
W. Xue, W. Bai, S. Yang, K. Song, Y. Huang, and H. Xie, “ADRC with adaptive extended state observer and its application to air-fuel ratio control in gasoline engines,” IEEE Transactions on Industrial Electronics, vol. 62, no. 9, pp. 5847–5857, 2015.
View at: Publisher Site | Google Scholar
J. Li, Y.-Q. Xia, X.-H. Qi, Z.-Q. Gao, K. Chang, and F. Pu, “Absolute stability analysis of on-linear active disturbance rejection control for single-input–single-output systems via the circle criterion method,” IET Control Theory & Applications, vol. 9, no. 15, pp. 2320–2329, 2015.
View at: Google Scholar
Z.-L. Zhao and B.-Z. Guo, “Active disturbance rejection control approach to stabilization of lower triangular systems with uncertainty,” International Journal of Robust and Nonlinear Control, vol. 26, no. 11, pp. 2314–2337, 2016.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
B.-Z. Guo, Z.-H. Wu, and H.-C. Zhou, “Active disturbance rejection control approach to output-feedback stabilization of a class of uncertain nonlinear systems subject to stochastic disturbance,” IEEE Transactions on Automatic Control, vol. 61, no. 6, pp. 1613–1618, 2016.
View at: Publisher Site | Google Scholar | MathSciNet
F. Liu, Y. Li, Y. Cao, J. She, and M. Wu, “A two-layer active disturbance rejection controller design for load frequency control of interconnected power system,” IEEE Transactions on Power Systems, vol. 31, no. 4, pp. 3320–3321, 2016.
View at: Publisher Site | Google Scholar
B. Li, Q. Hu, and G. Ma, “Extended State Observer based robust attitude control of spacecraft with input saturation,” Aerospace Science and Technology, vol. 50, pp. 173–182, 2016.
View at: Publisher Site | Google Scholar
Z.-Q. Pu, R.-Y. Yuan, J.-Q. Yi, and X.-M. Tan, “A class of adaptive ESOs for nonlinear disturbed systems,” IEEE Transactions on Industrial Electronics, vol. 62, no. 9, pp. 5858–5869, 2015.
View at: Google Scholar
N.-M. Qi, C.-M. Qin, and Z.-G. Song, “Improved ADRC cascade decoupling controller design of hypersonic vehicle,” Journal of Harbin Institute of Technology, vol. 43, no. 11, pp. 34–38, 2011.
View at: Google Scholar
W.-X. Xia, X.-D. Yang, and J.-H. Yan, “Application of improved ADRC technique in straight course error modeling of electromagnetic controlled gyrocompass,” Electronics Optics & Control, vol. 17, no. 6, pp. 73–79, 2010.
View at: Google Scholar
L.-Y. Zhou and S.-J. Wang, “An improved ADRC based on nonlinear arctangent function,” Shanghai Jiaotong Daxue Xuebao/Journal of Shanghai Jiaotong University, vol. 47, no. 7, pp. 1043–1048, 2013.
View at: Google Scholar
M.-Y. Zhang, H.-B. Yang, J.-B. Zhang, T.-C. Ding, and H.-G. Jia, “Servo system of harmonic drive electromechanical actuator using improved ADRC,” Optics and Precision Engineering, vol. 22, no. 1, pp. 99–108, 2014.
View at: Publisher Site | Google Scholar
Y. Shen, K. Shao, W. Ren, and Y. Liu, “Diving control of autonomous underwater vehicle based on improved active disturbance rejection control approach,” Neurocomputing, vol. 173, pp. 1377–1385, 2016.
View at: Publisher Site | Google Scholar
X. Yang, J. Cui, D. Lao, D. Li, and J. Chen, “Input Shaping enhanced Active Disturbance Rejection Control for a twin rotor multi-input multi-output system (TRMS),” ISA Transactions, vol. 62, pp. 287–298, 2016.
View at: Publisher Site | Google Scholar

Copyright

Copyright © 2017 BingYou 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.

PDF Download Citation

Download other formats

Order printed copies

Views

810

Downloads

799

Citations