A New Control Method for Second-Order Multiple Models Control System Based on Global Sliding Mode
A New Control Method for Second-Order Multiple Models Control System Based on Global Sliding Mode
Dan-xu Zhang,1 Yang-wang Fang,1 Peng-fei Yang,2 You-li Wu,1 and Tong-xin Liu1
1School of Aeronautics Engineering, Air Force Engineering University, Xi’an, China
2Research Center for Evaluation and Argumentation, Academy of Military Sciences, Beijing 100091, China
Correspondence should be addressed to Dan-xu Zhang; moc.361@8189891xdz
Received 11 January 2018; Revised 23 May 2018; Accepted 28 June 2018; Published 30 July 2018
Academic Editor: Andrzej Swierniak
Copyright © 2018 Dan-xu Zhang 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.
This paper proposed a finite time convergence global sliding mode control scheme for the second-order multiple models control system. Firstly, the global sliding surface without reaching law for a single model control system is designed and the tracking error finite time convergence and global stability are proved. Secondly, we generalize the above scheme to the second-order multimodel control system and obtain the global sliding mode control law. Then, the convergent and stable performances of the closed-loop control system with multimodel controllers are proved. Finally, a simulation example shows that the proposed control scheme is more effective and useful compared with the traditional sliding mode control scheme.
Many control systems work in complicated conditions in reality and are faced with the uncertainty brought by parameter variety [1, 2]. For instance, the controller of hypersonic vehicle works in complicated environment caused by fast speed [3–6]. Furthermore, the controller demands highly robustness to uncertainty due to the varying parameters brought by large envelope, hypersonic velocity, and strong coupling [7–9]. Approximating control system via multimodel is one scheme to reduce uncertainty. Many control schemes, such as adaptive control , variable structure control , and robust control  were applied to the multimodel system.
Sliding mode control (SMC) [13–17] has gained widely spread due to its characteristics of robustness to uncertainties. However, the chattering problem and matching condition (the relative degree of the sliding system must be 1) limit its applications in practice .
In order to attenuate the chattering effect, a high-order sliding mode control (HOSMC)  scheme was proposed and the controller obtained less chattering and finite time convergence performances. Nevertheless, designing the controller based on HOSMC for multimodel is a heavy work.
Particularly, slow reaction and convergence, poor transient response, and unstable system behaviours always occur at the switching instant between submodels due to the parameters’ boundary changes or large external disturbance . Thus, the robustness to the switching instant is more important for the overall closed-loop control system. However, the robustness of SMC and HOSMC is meaningful only in the sliding phase , which means that the reaching phase reduces the robust performance of the closed-loop system .
Choi Seung-Bok  presented a time-varying sliding surface for second-order system and reduces the reaching phase. Chang and Hurmuzlu  improved the time-varying sliding surface and designed a new sliding control scheme without reaching phase. The global sliding mode control (GSMC) [24–28] has been proposed and ensures the existence of sliding mode throughout the entire control response. Furthermore, for multimodel control problem, GSMC can ensure that state variables move along the surface and reduce the convergence time at switching instant due to eliminating reaching phase. It is robust to matched perturbations in both sliding phase and switching instant. Thus, the GSMC has advantages on finite time convergence for multimodel control problem.
In this paper, the GSMC is introduced to design a controller for the second-order multimodel system. The paper is organized as follows: Section 2 states the multimodel control problem. In Section 3, the GSMC is derived and the closed-loop control system finite time convergence and global stability in both single model and multimodel are proved. The performance of GSMC controller is verified via computer simulations compared with SMC in Section 4.
2. Problem Statement
Suppose that a nonlinear system can be linearization at a series of feature points as second-order multimodel systems with uncertainties and one of the submodels is described by  Then the second-order systems constitute the multimodel to approximate the original nonlinear system and can be expressed aswhere , , and are continuous sufficiently smooth functions, respectively, and ; is the disturbance with boundary .
The purpose is to design a valid control scheme that can achieve fast stable control performance for each model and be robust to the switching instant.
Particularly, the switching between submodels discussed here is in order. For instance, the flight control system of aircraft can be approximated by some submodels that divided by height range and when the height changes from the range of one submodel to another, the submodel switches. As height changes continuously, the multimodel switches in order. Thus, it is assumed that the multimodel switches in order with state changing and the switching order is . Furthermore, the multimodel can be rewritten aswhere and are the initial and terminal state value of the submodel corresponding range.
3. Global Sliding Mode Control3.1. Single Model Control
Considering a single model expressed aswe design the global sliding surface asin which is the track error. is the reference command signal. is the forcing function and is 1st-order differentiable with . Moreover, , .
Obviously, due to the additional time-varying term in (5), the global sliding surface can ensure that the state of the closed-loop control system moves along the sliding surface from the beginning. Thus, there is no need for reaching law.
3.1.1. Lyapunov Stability Analysis
According to the equivalent control method, let and, then, we obtain the following equation:
Substitute (4) into (6), the control law is solved aswhere , and . is the supremum of . Additionally, without ambiguity we take , , and as brief form for , , and , respectively.
To prove the stability of the control scheme, we define the Lyapunov function as and the derivative is
If , . The derivative satisfies because of and . If , , and with the same parameter conditions the derivative satisfies . Therefore, the closed-loop control system based on GSMC is globally stable.
3.1.2. Finite Time Convergence Analysis
Consider the sliding mode system (5) and rewrite it as where . To prove that the sliding mode system (9) is convergent in finite time, we can solve the equation to obtain the convergence time .
Let ; the differential equation about is
According to , it is a kind of Bernoulli equation, and is given by where is an arbitrary constant.
To obtain the convergence time , take the natural logarithm of both sides in (11) and is
Obviously, the parameters in (12) must satisfy the following constraints: , , , and .
In particular, is closely related to the parameters , , and . reduces with the increasing of . What is more, the value of is not large due to the character of the function , and, for example, . Therefore, with reasonable design value of , the convergence time is finite.
3.2. Multiple Models Control
In the following section, we will discuss multimodel controller design based on the GSMC. Considering the multimodel , firstly we design sliding surfaces for each model as follows:
Then, we design the corresponding control law where is short for the derivative of and , . Additionally, the time represents switching instants. The other parameters are the same as mentioned before.
Therefore, the controller for a multiple model control system is expressed as follows:
The state variable moves like Figure 1 shows.
Figure 1: Phase trajectory of the state variable.
The state variable moves along sliding surfaces with the order . Suppose that the initial state variable is and moves along the sliding surface . When the state satisfies , submodel switches to at the switching instant , and the state variable moves to the next sliding surface . By parity of reasoning, with the submodel switching from to , the state variable moves along corresponding sliding surfaces from to and until the equilibrium point.
3.2.1. Lyapunov Stability Analysis
For each model in (3), the Lyapunov function is defined as and the process of proving the Lyapunov stability is the same as Section 3.1.1. We can obtain the result , which means the multimodel closed-loop control system based on GSMC is globally stable.
3.2.2. Finite Time Convergence Analysis
Suppose that and are the switching instants where surface switches from to and to , respectively. Thus, the time state variable moving along the sliding surface to switching instant is . According to (11), the state variable at switching instant is expressed as
Let , , ; the equation is transformed to
Furthermore, let , in which is an undetermined parameter. Then, substitute it into (17)
From (12) we can easily obtain that the time is
Now substitute (19) into :
Thus, the parameter satisfies the following equation:Obviously, the solution of (21) exists.
Similarly, the time that the state variable moves along other sliding surfaces can be calculated. Suppose that the last sliding surface is and its finite convergence time is . Thus, the finite convergence time for the multimodel control is expressed as where satisfies (21).
Particularly, because all the sliding surfaces may not be ergodic, the real convergence time should be expressed as follows:
4. Numerical Example and Simulation
Numerical example will be provided in the following section to verify the control performance of proposed GSMC method.
Suppose that the nonlinear system is approximated by three second-order systems expressed as
The switch instant from models A to B occurs at 2s and it is 5s from models B to C. The initial states are and the states at two switching instants are and , respectively. The command signal is . The parameters of the sliding surface are , , , , , and . The simulation results are shown in Figures 2–8.
Figure 2: The position tracking.
Figure 3: The control input.
Figure 4: The sliding dynamic.
Figure 5: The sliding dynamic of model A.
Figure 6: The sliding dynamic of model B.
Figure 7: The sliding dynamic of model C.
Figure 8: The phase trajectory.
Furthermore, we take the method and model in  as a comparison to show the advantage of GSMC.
The system is expressed as follows:
Then, we set the switching instants and parameters of multiple models as  given and list them in Table 1.
Table 1: The parameters of models in .
Using GSMC, the controller is designed as follows:
Then, we obtain the simulation results in Figures 9 and 10.
Figure 9: The position tracking.
Figure 10: The sliding dynamic.
From Figures 2–8, the results show that the output tracks are fast and accurate. The sliding dynamic converges fast as shown in Figure 4. What is more, it converges to 0 at switching instants immediately when the saltation occurs. From the phase trajectory in Figures 5–8 we can conclude that the error state moves along the sliding surfaces to the equilibrium point. At the two switching instants, the GSMC steers to the next surface both within two steps (0.02s in simulation). Thus, the validity and effectiveness of GSMC for multiple models are verified.
Furthermore, compared with the multimodel robust control method of  in Figures 9 and 10, the convergence characteristic of GSMC provides fast stability at switch instants, which increases the robust to the system mutation.
In this paper, a global sliding mode control method for second-order multimodel system was proposed. The Lyapunov stability and finite time convergence for both single model and multiple model were proved.
Finally, the feasibility and effectiveness of the proposed control method were verified through a numerical simulation. The simulation results conclude that the control law based on GSMC is effective for the multimodel control problem. And also, the closed-loop control system output can track the command signal fast and accurately. Compared with another multimodel control method in , GSMC provides faster convergence at switching instants and promotes the global robust performance of the controller.
All the data used in the article is available and can be downloaded from the network address “https://pan.baidu.com/s/1wYaxh6qnWMwuT_jEFf 350 CWhA" or is available from the corresponding author upon request.
Conflicts of Interest
The authors declare that there are no conflicts of interest regarding the publication of this paper.
This work has been supported by the Major Program of the National Natural Science Foundation of China (no. 61627901).
References X. Lu and H. Dong, “Application of Multi-model Active Fault-tolerant Sliding Mode Predictive Control in Solar Thermal Power Generation System,” Zidonghua Xuebao/Acta Automatica Sinica, vol. 43, no. 7, pp. 1241–1247, 2017. View at Google Scholar · View at ScopusK. Xue and Y. Li, “A Multi-model Modeling Approach to MIMO Nonlinear Systems,” ACTA Electronica Sinica, vol. 33, no. 1, 2005. View at Google ScholarY. Fang, D. Chai, D. Mao, and L. Zhang, “Status and development trend of the guidance and control for air-breathing hypersonic vehicle,” Hangkong Xuebao/Acta Aeronautica et Astronautica Sinica, vol. 35, no. 7, pp. 1776–1786, 2014. View at Google Scholar · View at ScopusH. Duan and P. Li, “Progress in control approaches for hypersonic vehicle,” Science China Technological Sciences, vol. 55, no. 10, pp. 2965–2970, 2012. View at Publisher · View at Google Scholar · View at ScopusB. Xu and Z. K. Shi, “An overview on flight dynamics and control approaches for hypersonic vehicles,” Science China Information Sciences, 2015. View at Publisher · View at Google ScholarL. Wu and B. Meng, “Revies on The Control of Hypersonic Flight Vehicles,” Advance in Mechanics, vol. 39, no. 6, pp. 756–765, 2009. View at Google ScholarC.-Y. Sun, C.-X. Mu, and Y. Yu, “Some control problems for near space hypersonic vehicles,” Zidonghua Xuebao/Acta Automatica Sinica, vol. 39, no. 11, pp. 1901–1913, 2013. View at Publisher · View at Google Scholar · View at ScopusE. J. Cui, “Research Statutes, Development Trends and Key Technical Problems of Near Space Flying Vehicles,” Advance in Mechanics, vol. 36, pp. 658–673, 2009. View at Google ScholarL. Huang, Z.-S. Duan, and J.-Y. Yang, “Challenges of control science in near space hypersonic aircrafts,” Kongzhi Lilun Yu Yingyong/Control Theory and Applications, vol. 28, no. 10, pp. 1496–1505, 2011. View at Google Scholar · View at ScopusM.-G. Gan, M. Zhang, H.-X. Ma, and J. Chen, “Adaptive control of a servo system based on multiple models,” Asian Journal of Control, vol. 18, no. 2, pp. 652–663, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at ScopusR. A. DeCarlo, S. H. Zak, and G. P. Matthews, “Variable structure control of nonlinear multivariable systems: a tutorial,” Proceedings of the IEEE, vol. 76, no. 3, pp. 212–232, 1988. View at Publisher · View at Google Scholar · View at ScopusA. E. Ariffin and N. Munro, “Robust control analysis of a gas-turbine aeroengine,” IEEE Transactions on Control Systems Technology, vol. 5, no. 2, pp. 178–188, 1997. View at Publisher · View at Google Scholar · View at ScopusG. Bartolini, A. Ferrara, E. Usai, and V. I. Utkin, “On multi-input chattering-free second-order sliding mode control,” IEEE Transactions on Automatic Control, vol. 45, no. 9, pp. 1711–1717, 2000. View at Publisher · View at Google Scholar · View at MathSciNet · View at ScopusA. Levant, “Principles of 2-sliding mode design,” Automatica, vol. 43, no. 4, pp. 576–586, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at ScopusI. Boiko, L. Fridman, and M. I. Castellanos, “Analysis of second-order sliding-mode algorithms in the frequency domain,” Institute of Electrical and Electronics Engineers Transactions on Automatic Control, vol. 49, no. 6, pp. 946–950, 2004. View at Publisher · View at Google Scholar · View at MathSciNetC. Edwards and S. K. Spurgeon, Sliding Mode Control: Theory And Applications, Francis Systems and Control Book Series, Taylor & Francis, Taylor, 1998. V. I. Utkin, Sliding Modes in Control Optimization, Springer, Berlin, Germany, 1991. View at Publisher · View at Google Scholar · View at MathSciNetB. W. Gao, The Theory And Design Method of Variable Structure Control, Science Press, Beijing, China, 1998. A. Levant, “Chattering analysis,” IEEE Transactions on Automatic Control, vol. 55, no. 6, pp. 1380–1389, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at ScopusG. C. Goodwin, D. J. Hill, and M. Palaniswami, “A perspective on convergence of adaptive control algorithms,” Automatica, vol. 20, no. 5, pp. 519–531, 1984. View at Publisher · View at Google Scholar · View at MathSciNetT. L. Tai and Y. S. Lu, “Global sliding mode control with chatter alleviation for robust eigenvalue assignment,” Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 220, no. 7, pp. 573–584, 2006. View at Google Scholar · View at ScopusS.-B. Choi, C.-C. Cheong, and D.-W. Park, “Moving switching surfaces for robust control of second-order variable structure systems,” International Journal of Control, vol. 58, no. 1, pp. 229–245, 1993. View at Publisher · View at Google Scholar · View at MathSciNet · View at ScopusT.-H. Chang and Y. Hurmuzlu, “Sliding control without reaching phase and its application to bipedal locomotion,” Journal of Dynamic Systems, Measurement, and Control, vol. 115, no. 3, pp. 447–455, 1993. View at Publisher · View at Google Scholar · View at ScopusS. Y. Lu and S. J. Chen, “Design of a global sliding mode controller for robot manipulator with robust tracking capability,” Proceedings of the National Science Council, Republic of China - Part A: Physical Science and Engineering, vol. 18, pp. 463–476, 1994. View at Google ScholarY.-S. Lu and J.-S. Chen, “Design of a global sliding-mode controller for a motor drive with bounded control,” International Journal of Control, vol. 62, no. 5, pp. 1001–1019, 1995. View at Publisher · View at Google Scholar · View at MathSciNet · View at ScopusJ.-Y. Lin, Y.-S. Lu, and J.-S. Chen, “A global sliding-mode control scheme with adjustable robustness for a linear variable reluctance motor,” JSME International Journal Series C Mechanical Systems, Machine Elements and Manufacturing, vol. 45, no. 1, pp. 215–225, 2002. View at Publisher · View at Google Scholar · View at ScopusH. S. Choi, Y.-H. Park, Y. Cho, and M. Lee, “Global sliding-mode control. Improved design for a brushless DC motor,” IEEE Control Systems Magazine, vol. 21, no. 3, pp. 27–35, 2001. View at Publisher · View at Google ScholarK.-K. Shyu and H.-J. Shieh, “A new switching surface sliding-mode speed control for induction motor drive systems,” IEEE Transactions on Power Electronics, vol. 11, no. 4, pp. 660–667, 1996. View at Publisher · View at Google Scholar · View at ScopusL. Shao, H.-M. Lei, and Z.-L. Tang, “Multiple-model composite control based on LQ/VSC,” Xi Tong Gong Cheng Yu Dian Zi Ji Shu, vol. 31, no. 5, pp. 1194–1197, 2009. View at Google Scholar · View at ScopusJ. Yan, F. Y. Yu, and Y. H. Fan, The Control Technology of Air-breathing Hypersonic Vehicle, Northwestern Polytechnic University Press, Xi'an, China, 2015. V. F. Zaitsev and A. D. Polyanin, Handbook of Exact Solutions for Ordinary Differential Equations, CRC Press, 2002. View at MathSciNet