Mathematical Problems in Engineering

Volume 2013 (2013), Article ID 180589, 9 pages

http://dx.doi.org/10.1155/2013/180589

Research Article

Sliding Mode Control for a Class of Uncertain MIMO Nonlinear Systems with Application to Near-Space Vehicles

Mou Chen,¹ Rong Mei,² and Bin Jiang¹

¹College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China

²Criminal Investigation Department, Nanjing Forest Police College, Nanjing 210042, China

Received 27 January 2013; Accepted 8 March 2013

Academic Editor: Yu Kang

Copyright © 2013 Mou Chen 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.

Abstract.

We propose a robust sliding mode control (SMC) scheme for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems with the unknown external disturbance, the system uncertainty, and the backlash-like hysteresis. To tackle the continuous system uncertainty, the radial basis function (RBF) neural network is employed to approximate it. And then, combine the unknown external disturbance, and the unknown neural network approximation error with the affection caused by backlash-like hysteresis as a compounded disturbance which is estimated using the developed nonlinear disturbance observer. The robust sliding mode control based on the nonlinear disturbance observer and RBF neural network is presented to track the desired system output in the presence of the unknown system uncertainty, the external disturbance, and the backlash-like hysteresis. Finally, the designed robust sliding mode control strategy is applied to the near-space vehicle (NSV) attitude dynamics, and simulation results are given to illustrate the effectiveness of the proposed sliding mode control approach.

1. Introduction

Recently, the robust adaptive control has been extensively studied for uncertain MIMO nonlinear systems due to most control plants with multichannels, system uncertainties, and unknown external disturbances [1–10]. Sliding mode control is one of the most important approaches which is particularly suited to the deterministic control of systems with large uncertainties, nonlinearities, and bounded external disturbances [11–16]. However, it is usually difficult to directly extend the SMC design to the uncertain MIMO nonlinear system, and a few research results are directly available. In [17], dynamic SMC and higher-order sliding mode were studied for MIMO systems. Generalized SMC was proposed for multi-input nonlinear systems in [18]. In [19], robust MIMO water level control was developed for interconnected twin-tanks using second-order SMC. In general, the robust SMC needs to be further developed for the uncertain MIMO nonlinear system.

Usually, the unknown system uncertainty and the unknown external disturbance will increase the SMC design difficulty of uncertain MIMO nonlinear systems. In many existing works, universal function approximators (such as fuzzy systems and neural networks) are employed to tackle the unknown system uncertainty in nonlinear systems. On the basis of the output of universal function approximators, lots of robust adaptive control schemes were designed for the uncertain MIMO nonlinear system [20–24]. In [17], robust adaptive sliding mode control was proposed using fuzzy modelling for a class of uncertain MIMO nonlinear systems. Fuzzy adaptive sliding-mode control was presented for MIMO nonlinear systems in [25]. In [26], robust adaptive neural network control was studied for a class of uncertain MIMO nonlinear systems with input nonlinearities using the variable structure control technique. In this paper, the RBF neural network is introduced to handle the unknown system uncertainty. However, the unknown time-varying external disturbance of nonlinear systems cannot be efficiently handled via neural networks and the unknown approximation error of the neural network is hard to be tackled. To improve the antidisturbance ability of control systems, the disturbance-observer-based control strategy would provide a very promising approach where a disturbance observer is adopted to estimate unknown external disturbances.

In the recent decade, many disturbance observer design techniques have been developed to fully utilize the information of external disturbances [27, 28]. The general framework for disturbance-observer-based control (DOBC) of nonlinear systems subject to disturbances was presented in [29]. In [30], the direct adaptive neural control was developed for a class of uncertain nonaffine nonlinear systems based on disturbance observer. Nonlinear predictive control was proposed via using disturbance observers in [31]. In [32], a nonlinear disturbance observer was proposed for multivariable minimum-phase systems with arbitrary relative degrees. A nonlinear disturbance observer was developed for robotic manipulators in [33]. For uncertain structural systems [34] and nonlinear systems [35] with disturbances, disturbance-observer-based control using terminal sliding mode technique was studied. The disturbance attenuation and rejection problem was investigated for a class of MIMO nonlinear systems in the DOBC framework in [36]. Combined with disturbance observers, the robust synchronization control was proposed for uncertain chaotic systems in [37, 38]. In [39], robust DOBC was presented for time-delay uncertain systems. However, the disturbance observers need to be further developed for the robust sliding mode control of uncertain MIMO systems with backlash-like hysteresis.

Hysteresis as a special input nonlinearity exists in a wide range of practical systems, such as biology optics, mechanical actuators, electromagnetism, and electronic relay circuits [40]. The existence of the hysteresis is challenging problem for the control of uncertain nonlinear systems. For backlash hysteresis, several adaptive control schemes have recently been proposed. In [41], robust adaptive control was developed for a class of nonlinear systems with unknown backlash-like hysteresis. Decentralized adaptive stabilization was developed for the interconnected systems in the presence of unknown backlash-like hysteresis in [42]. In [43], identification method was studied for Hammerstein systems in presence of hysteresis-backlash and hysteresis-relay nonlinearities. Stable adaptive fuzzy control was proposed for nonlinear systems preceded by unknown backlash-like hysteresis in [44]. However, the SMC scheme should be further investigated for MIMO nonlinear systems with the unknown external disturbance, the system uncertainty, and the backlash-like hysteresis.

To show the effectiveness of the proposed sliding mode control approach, it is used to design the robust attitude control law for the NSV. Due to the potential military and civilian dual-use value, the near space has caused much concern around the world in recent years, and the flight control of NSVs becomes a hot research topic [45, 46]. In [47], robust attitude control was developed for NSVs with time-varying disturbances. Adaptive functional link network control was studied for the NSV with dynamical uncertainties in [48]. In [49], adaptive fault-tolerant tracking control was proposed for the NSV using Takagi-Sugeno fuzzy models. Fault tolerant control was presented for a class of nonlinear systems with application to NSVs in [50]. However, the robust sliding mode attitude control needs to be further developed for the NSV with the backlash-like hysteresis.

This work is motivated by the robust sliding mode control of uncertain MIMO nonlinear systems with the unknown external disturbance, the system uncertainty, and the backlash-like hysteresis. The organization of the paper is as follows. Section 2 details the problem formulation. The robust sliding mode control based on the disturbance observer is proposed in Section 3. Simulation results of near space vehicle attitude dynamics are presented in Section 4 to demonstrate the effectiveness of the developed sliding mode control of the uncertain MIMO nonlinear systems, followed by some concluding remarks in Section 5.

2. Problem Statement

To develop the robust sliding mode control, consider the following uncertain MIMO nonlinear system in the form of

where is the state vector of the uncertain nonlinear system, is the system output, and is the control input vector. is the known function vector, is the known control gain matrix, is the continuous unknown system uncertainty, and is the unknown time-varying external disturbance.

Usually, the actuator of the practical control system has a backlash-like hysteresis nonlinearity, and is the output of such hysteresis described as [41]:

where , , is the input of the hysteresis for the th actuator and denotes a backlash hysteresis operator.

In this paper, we consider a class of hysteresis which is expressed as the following continuous-time dynamic model [41]:

where , , and are known constants with .

Considering the solution properties of the dynamic model (3), it can be solved explicitly for piecewise monotone as [41]:

where is defined as

Considering (4), the uncertain MIMO nonlinear system (1) can be rewritten as

where , , and .

Define with the matrix . Then, (6) can be written as

Since the unknown function vector , the function vector is also unknown. To efficiently handle the unknown , the RBF neural network is employed to approximate it. The approximation output of the RBF neural network can be written as

where is a optimal weight value vector of the RBF neural network, is the basis function and , and and are the center and width of the radial basis function, respectively. is the smallest approximation of the RBF neural network.

Substituting (8) into (7) yields

Define . Then, (9) can be written as

Define and is the reference tracking signal of the uncertain MIMO nonlinear system. Considering (10), we obtain

In this paper, the control objective is that the robust sliding mode control is designed to follow a given desired output of the uncertain MIMO nonlinear system in the presence of the unknown time-varying external disturbance and the backlash-like hysteresis. For the desired system output , the robust sliding mode control scheme is proposed such that all closed-loop signals are uniformly asymptotically convergent. Furthermore, the developed robust sliding mode control strategy is applied to NSV attitude dynamics to illustrate its effectiveness.

To proceed the design of robust sliding mode control scheme for the uncertain MIMO nonlinear system (1), the following assumptions are required:

Assumption 1 (see [51]). For all , there exist known constants and such that and .

Assumption 2. For the time-varying unknown compounded disturbance , there exists an unknown positive constant such that .

3. Robust Sliding Mode Control Based on Disturbance Observer

In this section, we consider the robust sliding mode control design for the uncertain MIMO nonlinear system (1). Since is unknown, it cannot be directly used to design the robust sliding mode controller. To efficiently handle it, the nonlinear disturbance observer is proposed to estimate it.

The nonlinear disturbance observer is proposed as

where is a design parameter of the nonlinear disturbance observer and is the estimate of the optimal weight value .

Considering (11) and (12), we obtain

Define and . Considering (13) yields

Invoking (14), we obtain

Considering Assumption 2 and the following fact

yields

where , which is unknown due to the unknown constant and is a design parameter.

Considering Assumption 1, and yields

Invoking (17) and (18), we have

So far, the nonlinear disturbance observer design has been completed. In the following, the robust sliding mode control scheme will be proposed. Let the sliding surface be

where is a design matrix.

The sliding condition becomes

Invoking (11), we have

In accordance with the definitions of matrices and , using the output of the designed nonlinear disturbance observer, the robust sliding control law is proposed as

where , is a design matrix with , ,   is a small design parameter, and is the estimate of the unknown constant .

Substituting (23) into (22) yields

Consider the adaptive law for as

where and .

The parameter updated law of is designed as

where and .

The above robust sliding mode control design procedure for a class of uncertain MIMO nonlinear systems (1) can be summarized in the following theorem, which includes the result for robust sliding mode control based on the nonlinear disturbance observer of the uncertain MIMO nonlinear system (1) with the unknown external disturbance and the backlash hysteresis.

Theorem 3. Considering the uncertain MIMO nonlinear system (1) with the unknown system uncertainty, the unknown disturbance, and the backlash hysteresis, the disturbance observer is designed as (12) and the parameter updated laws are chosen as (25) and (26). Then, the robust sliding mode control law is proposed as (23). Under the developed robust sliding mode control scheme, the tracking error of the uncertain MIMO nonlinear system (1) is convergent.

Proof. Consider the Lyapunov function candidate

where .

Invoking (19) and (24), the time derivative of is

Since and , invoking the weight value adaptation law (25) and the parameter updated law (26), (28) can be written as

Considering the following facts

we obtain

where

To ensure the closed-loop system stability, the corresponding design matrices , , , , and should be chosen to make , , and . Considering (31), it may directly show that the signals , , , and are semiglobally uniformly bounded. According to (31), we have

From (33), we can know that is exponentially convergent; that is, . Hence, the sliding mode surface and the approximation errors , , and of the closed-loop system are bounded. From the convergence of the sliding mode surface , we can know that the tracking errors is convergent. Namely, the control objective is achieved. This concludes the proof.

Remark 4. To the developed robust sliding mode control strategy, the reach condition of the sliding mode surface is always satisfied. Since the error signals , , and are convergent, is held if the design parameter matrix is correctly chosen on the basis of (24). On the other hand, to adjust the dynamic control performance of the developed robust sliding mode control, the design parameter in the control term should be chosen as a small positive constant.

Remark 5. In this paper, combining the external time-varying disturbance with the effect of approximation error of backlash hysteresis and the NN approximation error is treated as a compounded disturbance which is estimated via the nonlinear disturbance observer. For the proposed nonlinear disturbance observer, we know that the estimate error with suitable approximation performance can be obtained via choosing the appropriate disturbance observer gain matrix . For example, the estimate error could be decreased by increasing the value of . Due to the introduction of the nonlinear disturbance observer, the disturbance rejection ability is enhanced by the closed-loop control system under the proposed robust sliding mode control approach.

4. Simulation Study

In this section, the designed robust sliding mode control strategy is applied to NSV attitude dynamics, and simulation results are given to illustrate the effectiveness of the proposed sliding mode control scheme. The considered attitude control model of the NSV is derived from the six-degree-of-freedom and twelve-state kinematic equations which can be simplified as the affine nonlinear equation as follows [46]:

where is a vector of attitude angles which are angle of attack, sideslip angle, and flight-path roll angle, respectively; is the body-axis angular rates which are the fast-loop states; is the control input vector which is the deflection vector of control surfaces. and are the unknown system uncertainties. is the unknown time-varying external disturbance. To the attitude dynamic, and ; matrices and can be written as [48]

where , , and are moments of inertia of the NSV. Here, the detailed expressions of , , , , , , , , and are omitted. In this simulation study, we assume that the actuator of the NSV has a backlash-like hysteresis nonlinearity and , , is the output of such hysteresis described by

where , , is the input of the hysteresis for the th actuator of NSV and denotes a backlash hysteresis operator. When , , , and with , the hysteresis curves are shown in Figure 1 [41].

Figure 1: Hysteresis curves given by (37).

Suppose that the NSV fight lies in the cruise flight phase with the velocity  m/s and flight altitude  km. The initial attitude and attitude angular velocity conditions are arbitrarily chosen as , , , and  rad/s. The desired flight attitude states are chosen as

The system uncertainties and are assumed as and variation of aerodynamic coefficients and aerodynamic moment coefficients, respectively. On the other hand, the following unknown time-varying disturbance moments are considered in the simulation [48]:

In this simulation, the parameters of the backlash-like hysteresis should be chosen according to the used actuator of the near-space vehicle. All parameters of the nonlinear disturbance observer and the robust sliding mode attitude controller are chosen as , , , , , and, . The disturbance observer is designed as (12) and the parameter updated laws are designed as (25) and (26). The robust sliding mode attitude controller is designed in accordance with (23) for the NSV. The attitude control results, the control input, and the sliding mode surface are shown in Figures 2–5 under the designed robust sliding mode attitude control approach.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 2: Tracking control results.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 3: Control input response.

Figure 4: Sliding mode surface of attitude angular.

Figure 5: Sliding mode surface of attitude angular rates.

The tracking control results of the NSV under the designed robust sliding mode control scheme are shown in Figure 2. From Figure 2, we can see that the small tracking errors of attitude angles are obtained and the expected control performances of the body-axis angular rate are guaranteed. The control inputs are presented in Figure 3 which show the convergence of the control inputs. The plots of the sliding mode surfaces are given in Figures 4 and 5 which are convergent.

In accordance with the above simulation results, the satisfactory attitude tracking control performances are obtained under the developed robust sliding mode attitude control scheme of the NSV. Thus, the developed robust sliding mode control scheme is valid for the uncertain MIMO nonlinear system.

5. Conclusion

Robust sliding mode control approach has been developed for a class of uncertain MIMO nonlinear systems in the presence of the unknown system uncertainty, the external time-varying disturbance, and backlash-like hysteresis in this paper. To enhance the robust control performance, the RBF neural network has been introduced to approximate the unknown continuous system uncertainty. At the same time, we treated the unknown external disturbance, the unknown neural network approximation error with the approximation error of backlash-like hysteresis as a compounded disturbance, and the nonlinear disturbance observer has been developed to estimate it. Then, the robust sliding mode control has been developed for the uncertain MIMO nonlinear system using the outputs of the RBF neural network and the developed nonlinear disturbance observer. Via Lyapunov analysis, the uniformly asymptotical convergence of all closed-loop signals has been guaranteed. Finally, the designed robust sliding mode control strategy has been applied to NSV attitude dynamics, and simulation results have been given to illustrate the effectiveness of the proposed sliding mode control approach.

Acknowledgments

This work is partially supported by the National Natural Science Foundation of China (Grant no.: 61174102), Program for New Century Excellent Talents in University of China (Grant no.: NCET-11-0830), Jiangsu Natural Science Foundation of China (Grant no.: SBK2011069), project-sponsored by SRF for ROCS, SEM, and project funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions.

References

B. M. Mirkin and P. O. Gutman, “Output feedback model reference adaptive control for multi-input-multi- output plants with state delay,” Systems and Control Letters, vol. 54, no. 10, pp. 961–972, 2005.

X. D. Tang, G. Tao, and S. M. Joshi, “Adaptive actuator failure compensation for nonlinear MIMO systems with an aircraft control application,” Automatica, vol. 43, no. 11, pp. 1869–1883, 2007.

Y. Kang, H. Xi, D. Zhang, and H. Ji, “Robust adaptive control design for a class of uncertain nonlinear Markovian jump systems,” Asian Journal of Control, vol. 9, no. 1, pp. 73–79, 2007.

B. Yao and M. Tomizuka, “Adaptive robust control of MIMO nonlinear systems in semi-strict feedback forms,” Automatica, vol. 37, no. 9, pp. 1305–1321, 2001.

Y. Q. Wu, G. L. Ju, and X. Y. Zheng, “Adaptive output feedback control for nonholonomic systems with uncertain chained form,” International Journal of Systems Science, vol. 41, no. 12, pp. 1537–1547, 2010.

Z. J. Li and Y. N. Zhang, “Robust adaptive motion/force control for wheeled inverted pendulums,” Automatica, vol. 46, no. 8, pp. 1346–1353, 2010.

Z. J. Li and Y. Kang, “Dynamic coupling switching control incorporating Support Vector Machines for wheeled mobile manipulators with hybrid joints,” Automatica, vol. 46, no. 5, pp. 832–842, 2010.

C. Yang, Y. Li, S. S. Ge, and T. H. Lee, “Adaptive control of a class of discrete-time MIMO nonlinear systems with uncertain couplings,” International Journal of Control, vol. 83, no. 10, pp. 2120–2133, 2010.

Z. J. Li, J. X. Li, and Y. Kang, “Adaptive robust coordinated control of multiple mobile manipulators interacting with rigid environments,” Automatica, vol. 46, no. 12, pp. 2028–2034, 2010.

M. Chen, S. S. Ge, V. E. How, and Y. S. Choo, “Robust adaptive position mooring control for marine vessels,” IEEE Transactions on Control Systems Technology, vol. 21, no. 2, pp. 395–409, 2013.

X. Yu, B. Wang, and X. Li, “Computer-controlled variable structure systems: the state of the art,” IEEE Transactions on Industrial Informatics, vol. 8, no. 2, pp. 197–205, 2012.

N. Luo, J. Rodellar, M. De La Sen, and J. Vehi, “Output feedback sliding mode control of base isolated structures,” Journal of the Franklin Institute, vol. 337, no. 5, pp. 555–577, 2000.

B. Wang, X. Yu, and G. Chen, “ZOH discretization effect on single-input sliding mode control systems with matched uncertainties,” Automatica, vol. 45, no. 1, pp. 118–125, 2009.

X. Y. Lu and S. K. Spurgeon, “Robust sliding mode control of uncertain nonlinear systems,” System & Control Lettter, vol. 32, no. 2, pp. 75–90, 1997.

G. Bartolini, A. Ferrara, and L. Giacomini, “A robust control design for a class of uncertain non-linear systems featuring a second-order sliding mode,” International Journal of Control, vol. 72, no. 4, pp. 321–331, 1999.

Y. Q. Wu and X. H. Yu, “Variable structure control design for uncertain dynamic systems with disturbances in input and output channels,” Automatica, vol. 35, no. 2, pp. 311–319, 1999.

W. S. Lin and C. S. Chen, “Robust adaptive sliding mode control using fuzzy modelling for a class of uncertain MIMO nonlinear systems,” IEE Proceedings: Control Theory and Applications, vol. 149, no. 3, pp. 193–202, 2002.

R. M. Hirschorn, “Generalized sliding-mode control for multi-input nonlinear systems,” IEEE Transactions on Automatic Control, vol. 51, no. 9, pp. 1410–1422, 2006.

M. K. Khan and S. K. Spurgeon, “Robust MIMO water level control in interconnected twin-tanks using second order sliding mode control,” Control Engineering Practice, vol. 14, no. 4, pp. 375–386, 2006.

C. Yang, S. S. Ge, C. Xiang, T. Chai, and T. H. Lee, “Output feedback NN control for two classes of discrete-time systems with unknown control directions in a unified approach,” IEEE Transactions on Neural Networks, vol. 19, no. 11, pp. 1873–1886, 2008.

M. Chen, C. S. Jiang, and Q. X. Wu, “Robust adaptive control of time delay uncertain systems with FLS,” International Journal of Innovative Computing, Information and Control, vol. 4, no. 8, pp. 1995–2004, 2008.

M. Chen, S. S. Ge, and B. Ren, “Robust attitude control of helicopters with actuator dynamics using neural networks,” IET Control Theory & Application, vol. 4, no. 12, pp. 2837–2854, 2010.

Y. Li, C. Yang, S. S. Ge, and T. H. Lee, “Adaptive output feedback NN control of a class of discrete-time MIMO nonlinear systems with unknown control directions,” IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 41, no. 2, pp. 507–517, 2011.

Z. J. Li and C. Yang, “Neural-adaptive output feedback control of a class of transportation vehicles based on wheeled inverted pendulum odels,” IEEE Transactions on Control System Technology, vol. 20, no. 6, pp. 1583–1591, 2012.

S. C. Tong and H. X. Li, “Fuzzy adaptive sliding-mode control for MIMO nonlinear systems,” IEEE Transactions on Fuzzy Systems, vol. 11, no. 3, pp. 354–360, 2003.

M. Chen, S. S. Ge, and B. V. E. How, “Robust adaptive neural network control for a class of uncertain MIMO nonlinear systems with input nonlinearities,” IEEE Transactions on Neural Networks, vol. 21, no. 5, pp. 796–812, 2010.

M. Chen and W. H. Chen, “Sliding mode control for a class of uncertain nonlinear system based on disturbance observer,” International Journal of Adaptive Control and Signal Processing, vol. 24, no. 1, pp. 51–64, 2010.

M. Chen, Q. X. Wu, and R. X. Cui, “Terminal sliding mode tracking control for a class of SISO uncertain nonlinear systems,” ISA Transactions, vol. 52, no. 2, pp. 198–206, 2013.

W. H. Chen, “Disturbance observer based control for nonlinear systems,” IEEE Transaction on Mechatronics, vol. 9, no. 4, pp. 706–710, 2004.

M. Chen and S. S. Ge, “Direct adaptive neural control for a class of uncertain non-affine nonlinear systems based on disturbance observer,” IEEE Transactions on Cybernetics, 2012.

W. H. Chen, D. J. Ballance, P. J. Gawthrop, and J. O'Reilly, “Nonlinear pid predictive controller,” IEE Proceedings Control Theory & Applications, vol. 146, no. 6, pp. 603–611, 1999.

X. K. Chen, C. Y. Su, and T. Fukuda, “A nonlinear disturbance observer for multivariable systems and its application to magnetic bearing systems,” IEEE Transactions on Control Systems Technology, vol. 12, no. 4, pp. 569–577, 2004.

W. H. Chen, D. J. Ballance, P. J. Gawthrop, and J. O'Reilly, “A nonlinear disturbance observer for robotic manipulators,” IEEE Transactions on Industrial Electronics, vol. 47, no. 4, pp. 932–938, 2000.

X. J. Wei, H. F. Zhang, and L. Guo, “Composite disturbance-observer-based control and terminal sliding mode control for uncertain structural systems,” International Journal of Systems Science, vol. 40, no. 10, pp. 1009–1017, 2009.

X. J. Wei and L. Guo, “Composite disturbance-observer-based control and terminal sliding mode control for non-linear systems with disturbances,” International Journal of Control, vol. 82, no. 6, pp. 1082–1098, 2009.

L. Guo and W. H. Chen, “Disturbance attenuation and rejection for systems with nonlinearity via DOBC approach,” International Journal of Robust and Nonlinear Control, vol. 15, no. 3, pp. 109–125, 2005.

C. Mou, C. S. Jiang, J. Bin, and Q. X. Wu, “Sliding mode synchronization controller design with neural network for uncertain chaotic systems,” Chaos, Solitons and Fractals, vol. 39, no. 4, pp. 1856–1863, 2009.

M. Chen, Q. X. Wu, and C. S. Jiang, “Disturbance-observer-based robust synchronization control of uncertain chaotic systems,” Nonlinear Dynamics, vol. 70, no. 4, pp. 2421–2432, 2012.

M. Chen and W. H. Chen, “Disturbance-observer-based robust control for time delay uncertain systems,” International Journal of Control, Automation and Systems, vol. 8, no. 2, pp. 445–453, 2010.

J. Zhou, C. Y. Wen, and Y. Zhang, “Adaptive backstepping control of a class of uncertain nonlinear systems with unknown backlash-like hysteresis,” IEEE Transactions on Automatic Control, vol. 49, no. 10, pp. 1751–1757, 2004.

C. Y. Su, Y. Stepanenko, J. Svoboda, and T. P. Leung, “Robust adaptive control of a class of nonlinear systems with unknown backlash-like hysteresis,” IEEE Transactions on Automatic Control, vol. 45, no. 12, pp. 2427–2432, 2000.

C. Y. Wen and J. Zhou, “Decentralized adaptive stabilization in the presence of unknown backlash-like hysteresis,” Automatica, vol. 43, no. 3, pp. 426–440, 2007.

F. Giri, Y. Rochdi, F. Chaoui, and A. Brouri, “Identification of Hammerstein systems in presence of hysteresis-backlash and hysteresis-relay nonlinearities,” Automatica, vol. 44, no. 3, pp. 767–775, 2008.

C. Y. Su, M. Oya, and H. Hong, “Stable adaptive fuzzy control of nonlinear systems preceded by unknown backlash-like hysteresis,” IEEE Transactions on Fuzzy Systems, vol. 11, no. 1, pp. 1–8, 2003.

B. Xu, F. Sun, C. Yang, D. Gao, and J. Ren, “Adaptive discrete-time controller design with neural network for hypersonic flight vehicle via back-stepping,” International Journal of Control, vol. 84, no. 9, pp. 1543–1552, 2011.

L. Chen, C. S. Jiang, and M. Pu, “Online-SVR-compensated nonlinear genealizred predictive control for hypersonic vehicles,” Science China Information Sciences, vol. 54, no. 3, pp. 551–562, 2011.

M. Chen and B. Jiang, “Robust attitude control of near space vehicles with time-varying disturbances,” International Journal of Control, Automation, and Systems, vol. 11, no. 1, pp. 182–187, 2013.

Y. L. Du, Q. X. Wu, and C. S. Jiang, “Adaptive functional link network control of near-space vehicles with dynamical uncertainties,” Journal of System Engineering and Electronics, vol. 21, no. 5, pp. 868–876, 2010.

B. Jiang, Z. F. Gao, P. Shi, and Y. Xu, “Adaptive fault-tolerant tracking control of near-space vehicle using TakagiSugeno fuzzy models,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 5, pp. 1000–1007, 2010.

Y. F. Xu, B. Jiang, G. Tao, and Z. Gao, “Fault tolerant control for a class of nonlinear systems with application to near space vehicle,” Circuits, Systems, and Signal Processing, vol. 30, no. 3, pp. 655–672, 2011.

M. Chen, S. S. Ge, and B. Ren, “Adaptive tracking control of uncertain MIMO nonlinear systems with input saturation,” Automatica, vol. 47, no. 3, pp. 452–465, 2011.