Mathematical Problems in Engineering

Volume 2015 (2015), Article ID 727162, 13 pages

http://dx.doi.org/10.1155/2015/727162

## Predictive Sliding Mode Control for Attitude Tracking of Hypersonic Vehicles Using Fuzzy Disturbance Observer

College of Aerospace Science and Engineering, National University of Defense Technology, Changsha 410073, China

Received 7 July 2014; Accepted 29 October 2014

Academic Editor: Chunyu Yang

Copyright © 2015 Xianlei Cheng 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 predictive sliding mode control (PSMC) scheme for attitude control of hypersonic vehicle (HV) with system uncertainties and external disturbances based on an improved fuzzy disturbance observer (IFDO). First, for a class of uncertain affine nonlinear systems with system uncertainties and external disturbances, we propose a predictive sliding mode control based on fuzzy disturbance observer (FDO-PSMC), which is used to estimate the composite disturbances containing system uncertainties and external disturbances. Afterward, to enhance the composite disturbances rejection performance, an improved FDO-PSMC (IFDO-PSMC) is proposed by incorporating a hyperbolic tangent function with FDO to compensate for the approximate error of FDO. Finally, considering the actuator dynamics, the proposed IFDO-PSMC is applied to attitude control system design for HV to track the guidance commands with high precision and strong robustness. Simulation results demonstrate the effectiveness and robustness of the proposed attitude control scheme.

#### 1. Introduction

Near space is the airspace of Earth altitudes from 20 km to 100 km, which has shown strategy and spatial superiority with the prosperous development of aerospace technology [1]. The near space hypersonic vehicle (HV) which has many outstanding advantages such as large flight envelope, high maneuverability, and fast response ability compared with the ordinary aircraft has attracted a growing worldwide interest [2, 3]. Due to the hypersonic velocity and changeable flight environment, the HV possesses some distinct dynamic characters of highly coupled control channels, serious nonlinearity, and strong uncertainty, which all contribute to the design difficulty of the attitude control system with remarkable precision and strong robustness. Due to the poor performance of the traditional control approaches in addressing the nonlinear and uncertain problem, advanced control methods should be employed in the attitude control system design for the HV, which is still an open problem.

The sliding mode control (SMC) is insensitive to system uncertainties and disturbances. It is one of the most important approaches to the control of systems with modeling imprecision, and it has been widely applied to the flight control system design [4–11]. References [12, 13] incorporate the sliding mode surface into the quadratic performance index of the predictive control and then the predictive sliding mode control law which has an explicitly analytical form is derived by minimizing the performance index. By using this method, the undesirable chattering phenomenon is attenuated and the large online computational issue of the predictive control is avoided. The predictive sliding mode control (PSMC) takes merits of the strong robustness of sliding model control and the outstanding optimization performance of predictive control, which appears to be a very promising control method in control engineering.

For the design of control system for the HV, many advanced control methods mainly focus on the stability or robust stability rather than considering the system uncertainties and disturbances explicitly in the controller design [14–18]. They may encounter some unexpected problems and the flight control system may even become unstable in the presence of strong disturbances. Thus, it is of profound significance to adopt new strategies to eliminate the influence of the composite disturbances and improve the precision and robustness of the flight control system. Fuzzy disturbance observer (FDO) is a particular type of disturbance observer, which combines the distinct merits of fuzzy control with disturbance observer technology. These advantages make it an appropriate candidate for robust control of uncertain nonlinear systems [19–25]. Nevertheless, the robust flight control scheme based on FDO needs to be further researched for the HV to improve the control ability, which needs to duel with the changeable flight environment and problems caused by system uncertainties.

Motivated by the precise and robust attitude control demand of the HV with uncertain model and external disturbances, this paper considers the composite disturbances in flight control system design of the HV to improve the robust control performance. Three major contributions are presented as follows.(1)We propose a predictive sliding mode control based on fuzzy disturbance observer (FDO-PSMC) method for a class of uncertain affine nonlinear systems with system uncertainties and external disturbances. The composite disturbances are considered, which result in poor performance and instability of the control system.(2)To address the problems which are brought by the composite disturbances, an improved FDO (IFDO) is proposed through utilizing the special properties of a hyperbolic tangent function to compensate for the approximate error of FDO. By using IFDO, the composite disturbances can be approximated effectively.(3)Considering the actuator dynamics, we apply the improved fuzzy disturbance observer based predictive sliding mode control (IFDO-PSMC) scheme to the attitude control system design for the HV. Numerical simulation verifies the surpassing performance of the proposed control scheme.

The rest of this paper is organized as follows. In Section 2, the control-oriented hypersonic flight model during cruise phase is presented. In order to approximate the composite disturbances, Section 3 presents a FDO-PSMC method and the FDO is improved for a better performance for a class of uncertain affine nonlinear systems with external disturbances. In Section 4, the proposed control scheme is applied to the attitude control system design for the HV in consideration of the actuator dynamics. Simulation results are presented in Section 5 to validate the effectiveness of the designed flight control system. Finally, Section 6 provides some conclusions of this paper.

#### 2. HV Modeling

Suppose that the fuel slosh is not considered and the products of inertia are negligible [26]. Based on the hypothesis of an inverse-square-law gravitational model, the centripetal acceleration for the nonrotating Earth, the mathematical model of HV during the cruise phase can be described as where , and are the angle-of-attack, sideslip angle, and bank angle, respectively; , and are the pitch, yaw, and rolling angles, respectively; , and are the flight velocity, flight path angle, and trajectory angle, respectively; , and are pitch, yaw, and roll rates, respectively; are the three position coordinates in the ground coordinate frame; are radial distance from the Earth’s center and the mean radius of the spherical Earth, respectively; , and are control moments including roll, yaw, and pitch control moments; , and are the lift, side, and thrust forces, respectively; , and are the main moments of inertia of the three axes, respectively.

*Remark 1. *For the convenience of control system design, is selected as the state variables. In view of the aim of attitude control system design for HV which is to track the guidance commands precisely, it is necessary to transform into by means of (3) which is obtained by the coordinate system transformation.

#### 3. Predictive Sliding Mode Control Based on Fuzzy Disturbance Observer

##### 3.1. Predictive Sliding Mode Control for Systems with Uncertainties and Disturbances

To develop the predictive sliding mode control, we consider the following MIMO uncertain affine nonlinear system with external disturbances: where is the state vector of the uncertain nonlinear system; is the control input vector; is the output vector of the uncertain nonlinear system; and are the given function vector and control gain matrix, respectively; and are the internal uncertainty and modeling error; and is the external time-varying disturbance.

Define where denotes the system uncertainties and external disturbances.

Without loss of generality, the equilibrium of uncertain nonlinear system (5) is supposed to be . In accordance with the Lie derivative operation [27] in differential geometry theory, the vector relative degree of MIMO system can be defined as follows.

*Definition 2 (see [27]). *The vector relative degree of system (5) is at under the following conditions, where denote the relative degree of each channel, respectively.(1)For all in a neighborhood of , (, , ).(2)The following matrix is nonsingular at :
where is the* j*th row vector of and is the* i*th output of system (5). Similarly, the disturbance relative degree at can be defined as .

To facilitate the process of control system design, the following reasonable assumptions are required before developing predictive sliding mode control of the uncertain MIMO nonlinear system (5).

*Assumption 3. * and are continuously differentiable, and is continuous.

*Assumption 4. *All states are available; moreover, the output and reference signals are also continuously differentiable.

*Assumption 5. *The zero dynamics are stable.

*Assumption 6. *The vector relative degree is , and the disturbance relative degree of composite disturbances is , where ().

According to Assumption 6, we can obtain

Associate the equations; the nonlinear system (5) can be written as where where , , , and is related to the composite disturbances.

Furthermore, the sliding surface vector is defined as where each sliding surface is the combination of the tracking error and its higher order derivatives, which can be depicted as where () and must make the polynomial (12) Hurwitz stable. Differentiating (12), we have

For the sake of simplified notation, define as

Then the vector can be written as

Consequently, differentiating the sliding surface vector, we obtain where .

Within the moving time frame, the sliding surface at the time is approximately predicted by

For the derivation of the control law, the receding-horizon performance index at the time is given by

The necessary condition for the optimal control to minimize (18) with respect to is given by

According to (19), substitute (17) into (18); then, the predictive sliding mode control law can be proposed as

*Remark 7. *If , the control law given by (20) is the nominal predictive sliding mode control law. If , cannot be directly obtained due to the immeasurable unknown composite disturbances . The performance of flight control system may become worse, even unstable, when is big enough. To handle this problem, a fuzzy observer would be designed to estimate the composite disturbances.

*Remark 8. *For the convenience of notation, would be denoted by in the rest of this paper.

##### 3.2. Fuzzy Disturbance Observer

###### 3.2.1. Fuzzy Logic System

The fuzzy inference engine adopts the fuzzy if-then rules to perform a mapping from an input linguistic vector to an output variable . The* i*th fuzzy rule can be expressed as [19]
where are fuzzy sets characterized by fuzzy membership functions and is a singleton number.

By adopting a center-average and singleton fuzzifier and the product inference, the output of the fuzzy system can be written as where is the number of fuzzy rules, is the membership function value of fuzzy variable , is an adjustable parameter vector, and is a fuzzy basis function vector. can be expressed as

Lemma 9 (see [28]). *For any given real continuous function on the compact set and arbitrary , there exists a fuzzy system presented as (22) such that
*

*According to Lemma 9, a fuzzy system can be designed to approximate the composite disturbances by adjusting the parameter vector online. The designed fuzzy system can be written as
where
*

*Let belong to a compact set ; the optimal parameter vector can be defined as
where the optimal parameter matrix lies in a convex region given by
where is the Frobenius norm and is a design parameter with .*

*Consequently, the composite disturbances can be written as
where is the smallest approximation error of the fuzzy system. Apparently, the norm of is bounded with
where is the upper bound of the approximation error .*

*Through adjusting the parameter vector online, the composite disturbances can be approximated by the fuzzy system; hence, the performance of the control system with composite disturbances can be improved. As the performance of the control system is closely related to the selected fuzzy system, the control precision will be reduced when the approximation ability of the selected fuzzy system is dissatisfactory. In order to improve the approximation ability, a hyperbolic tangent function is integrated with the fuzzy disturbance observer to compensate for the approximate error.*

*3.2.2. Fuzzy Disturbance Observer*

*Consider the following dynamic system:
where , is a design parameter, and is utilized to compensate for the composite disturbances.*

*Define the disturbance observation error ; invoking (9) and (31) yields
where is the adjustable parameter error vector.*

*Then, the FDO-PSMC is proposed as
*

*Invoking (16) and (33), we have
*

*Invoking (32) and (34), the augmented system is obtained as
where ,), and .*

*Theorem 10. Assume that the disturbance of (9) is monitored by the system (32), and the system (9) is controlled by (33). If the adjustable parameter vector of FDO is tuned by (36), then the augmented error is uniformly ultimately bounded within an arbitrarily small region:
where
*

*Proof. *Consider the Lyapunov function candidate:

Invoking (35) and (36), the time derivative of is

Considering the fact
and invoking (37), we have

Consequently, we obtain

If or , . Therefore, the augmented error is uniformly ultimately bounded.

*Remark 11. *In order to make sure that the FDO outputs zero signal in case of the composite disturbances , the consequent parameters should be set to be 0:

*Remark 12. *A projection operator (36) is used in the tuning method of the adjustable parameter vector; then, can be guaranteed to be bounded [29].

*Remark 13. *The adjustable parameter vector of the FDO (36) includes the observation error and the sliding surface . In view of the fact that the sliding surface which is the linearization combination of the tracking error is Hurwitz stable, the observation error and tracking error are both uniformly ultimately bounded, which ensures stability of the control system.

*3.3. Improved Fuzzy Disturbance Observer*

*3.3. Improved Fuzzy Disturbance Observer*

*Lemma 14 (see [30]). For all and , the following inequality holds:
where is a constant such that ; that is, .*

*The hyperbolic tangent function is incorporated to compensate for the approximate error and improve the precision of FDO. Consider the following dynamic system:
where , is a design parameter, and is utilized to compensate for the composite disturbances.*

*Define the disturbance observation error ; invoking (9) and (45) yields
where is an adjustable parameter vector.*

*The IFDO-PSMC is proposed as
*

*Invoking (16) and (47), we have
*

*Invoking (46) and (48), the augmented system is obtained as
where , , and .*

*Theorem 15. Assume that the disturbance of the system (9) is monitored by the system (49), and the system (9) is controlled by (47). If the adjustable parameter vector of IFDO is tuned by (50) and the approximate error compensation parameter is tuned by (51), the augmented error is uniformly ultimately bounded within an arbitrarily small region:
where
*

*Proof. *Consider the Lyapunov function candidate:
where . Invoking (49), (50), and (51), the time derivative of is

*According to Lemma 14, we have
*

*Consequently, we obtain
*

*If or , . Therefore, the augmented error is uniformly ultimately bounded.*

*Remark 16. *The composite disturbances can be approximated effectively by adjusting the parameter law (50) and (51). If the fuzzy system approximates the composite disturbances accurately, the approximate error compensation will have small effect on compensating the approximate error, and vice versa. Based on this, the approximate precision of FDO can be improved through overall consideration.

*4. Design of Attitude Control of Hypersonic Vehicle*

*4. Design of Attitude Control of Hypersonic Vehicle**4.1. Control Strategy*

*4.1. Control Strategy**The structure of the IFDO-PSMC flight control system is shown in Figure 1. Considering the strong coupled, nonlinear, and uncertain features of the HV, predictive sliding mode control approach which has distinct merits is applied to the design of the nominal flight control system. To further improve the performance of flight control system, an improved fuzzy disturbance observer is proposed to estimate the composite disturbances. The adjustable parameter law of IFDO can be designed based on Lyapunov theorem to approximate the composite disturbances online. According to the estimated composite disturbances, the compensation controller can be designed and integrated with the nominal controller to track the guidance commands precisely. In order to improve the quality of flight control system, three same command filters are designed to smooth the changing of the guidance commands in three channels. Their transfer functions have the same form as
where and are the reference model states and the external input guidance commands, respectively.*