Abstract

In this paper, an adaptive sliding mode tracking control scheme is developed for the medium-scale unmanned autonomous helicopter with system uncertainties and external unknown disturbances. A simplified mathematical model is established, which is divided into position subsystem and attitude subsystem. The uncertainty term of the system is handled by the inherent approximation ability of the neural network. The sliding model control scheme under the backstepping frame is developed for tackling disturbances. The stability of the simplified system is proved by using the Lyapunov theory, and the tracking errors are guaranteed to be uniformly bounded. Numerical simulation results show that the proposed control strategy is effective.

1. Introduction

The unique flight capabilities of unmanned autonomous helicopters (UAHs), such as vertical take-off and landing, hovering, low-altitude flying, make them have an irreplaceable position in military and civil fields. The UAHs are widely used in battlefield reconnaissance, information collection, earthquake relief, and fire detection [1]. Compared with small-scale UAHs, medium-scale UAHs which weigh between 500 kg and 1000 kg have significant and remarkable advantages of high altitude, long cruise, large payload, and strong robustness [2]. However, the dynamics of the UAH are highly nonlinear, strong coupling, and underactuated. Meanwhile, autonomous flight control technology is an extremely complex system engineering, which involves flight dynamics, aerodynamics, microelectronics, image processing, wireless transmission technology, advanced control technology, and other fields. These features make the flight difficult to control. Thus, the design of high-performance flight control system is a very challenging research topic.

Recently, some efficient control methods have been explored and utilized for the UAH [3–5]. Traditional linear control methods are based on a linearized model which cannot accurately describe the actual state of the UAH. Ignoring the nonlinear term will lead to the decrease of control performance and even the instability of the system. Thus, various nonlinear control strategies have been proposed for the helicopters including feedback linearization [6], predictive control [7], backstepping control [8–11], sliding mode control (SMC) [12–16], and neural network (NN) control [17–25]. In [6], a flight controller with the approximate feedback linearization method and active disturbance rejection control was designed for the helicopter, which had good robustness. In [7], a nonlinear model predictive control combined with the disturbance observer was proposed for helicopter tracking control. Among various control methods, backstepping has become a widespread control design technique for nonlinear control systems due to a systematic design process [8, 9]. In [10], the control design based on backstepping provided asymptotic tracking and bounded tracking for a model of the UAH. In [11], the backstepping technique combined dynamic inversion with command filters was designed for the trajectory tracking of an uncertain system. The flight environment of the UAH is always changing, so that modeling uncertainties and external disturbances should be taken into account during the design of flight control.

SMC has the advantages of fast response, insensitivity to system parameter changes, good adaptability to unmodeled dynamics and external disturbances, and the design is simple and easy to implement [12, 13]. In [14], a SMC design approach was adopted to globally stabilize some underactuated systems. Literature [15] designed a sliding mode controller for the stability of the UAH in suspension and forward flight and to ensure that the system can track the expected reference trajectory. In [16], two nonlinear control methods were compared and adaptive SMC can handle sensor noise and model uncertainty better than feedback linearization control.

On the other hand, NNs have been widely used as universal approximators to compensate the unmodeled dynamics of the system [17]. In [18], an adaptive predictive control combined with a high-order NN observer was presented to obtain predictions of future system states, which was applied to avoid input delay and nonlinearity. In [19], an adaptive neural dynamic surface control scheme was designed for a class of time-delay systems with an unknown dead-zone input. A novel high-order NN was adopted to account for unknown nonlinearities. High-order sliding mode observer combined with NNs was applied for nonlinear pure-feedback systems in [20].

In [21], an optimal output feedback controller based on the NN observer was designed for tracking the trajectory of the UAH. In [22], a nonlinear adaptive controller based on the NN was designed under a backstepping framework and tracking errors were restricted within ultimate bounds. In [23], a modified NN dynamic inversion control method for the helicopter has been presented and the inversion model was based on the linearized aerodynamics of hover. In [24, 25], neural control was proposed for strict-feedback systems with unknown dynamics, using the dynamic surface control. The simulation results of hypersonic flight dynamics were presented to demonstrate the effectiveness. The ability of the NN was to reconstruct and cancel the errors caused by simplifications, nonlinearity, and variations in operating conditions. Combining SMC with the NN can improve the control performance.

This paper is motivated by the tracking control of the UAH with the effect of unknown uncertainties and disturbances. The adaptive SMC scheme based on the NN is developed for tracking reference trajectory.

The rest of this paper is organized as follows: in Section 2, the UAH model with uncertainties and disturbances is established; in Section 3, the design processes of the adaptive SMC method with NNs are presented and stability analysis is carried out to guarantee the feasibility of the proposed controller; Section 4 provides simulations to verify the effectiveness of the proposed control method; and Section 5 concludes the paper.

2. Problem Formulation and Preliminaries

2.1. UAH Model

In this paper, the medium-scale UAH is regarded as a rigid-body model. Then, the simplified nonlinear dynamic model of the 6 degrees of freedom (DOF) can be expressed as follows [26]: where represents the positional vector in the inertial coordinate, is velocity vector, is the mass of the helicopter, is the acceleration due to gravity, is the attitude angular velocity vector, is the attitude angle vector in the body-fixed frame, and are external forces and moments acting on the airframe, respectively, , is the thrust generated by the main rotor, is the inertia matrix, is the transformation matrix from the body coordinate to the inertial coordinate, and is the attitude kinematic matrix [27].

Considering the system uncertainties and external disturbances, system (1) will be reformulated. Define , , , , and . The position dynamics of the UAH can be transformed into the following affine nonlinear form: where indicates modeling error and indicates disturbances.

Define , , and . The attitude dynamics of the UAH can be transformed into the following affine nonlinear form: where indicate the control input of the attitude subsystem, indicates modeling error, and indicates disturbances.

2.2. Preliminaries

In view of the problem of handling the UAH robust trajectory tracking control, the relevant assumptions and lemmas are described as follows.

Assumption 1 [28]. The attitude kinematic matrix is considered to be nonsingular, so that the roll angle and pitch angle always change in the range of , .

Assumption 2 [29]. The external disturbances and are unknown and bounded, and there exists positive constants such that and .

Assumption 3 [29]. The desired trajectories are bounded, and their derivatives are bounded. Furthermore, all system states can be measured.

Lemma 1 [30]. In the case of initial bounded conditions, if there exists a continuous and positive definite Lyapunov function which satisfies and , where , : are class functions, , and , then the solution is uniformly bounded.

Lemma 2 [31–33]. The NN has the inherent ability of learning and implements nonlinear mapping through training of sample data. The radical basis function NN (RBFNN) is a class of linearly parameterized NNs, which is widely employed to approximate the continuous unknown function . It can be represented in the form of where is the input vector of the NN, is a weight vector of the NN, is the approximation error which satisfies , is an unknown bound parameter, and is the Gaussian radial basis function which can be chosen as the form where is the center and is the width of the hidden layer neuron.
The continuous function can be smoothly approximated over the compact set to arbitrary precision by RBFNN as where is the least approximation error in the special case of and the optimal weight value can be defined as where is a valid field of the estimate parameter and is a design constant.

3. Adaptive Sliding Mode Tracking Controller Design

Borrowing the backstepping control method, the inner-outer-loop control scheme is adopted in this section. The position loop is considered as an outer loop; then, the attitude loop is considered as an inner loop.

3.1. Scheme of Position Control

In this section, an adaptive SMC based on RBFNN will be established for the UAH position system.

Step 1. Consider (2) and define the position and velocity tracking errors where is the desired position vector and as the virtual control law will be defined in the next step.
Differentiating , we obtain Consider the Lyapunov function candidate Its time derivative is Choose the following virtual control law as where is a design parameter.
Substituting (10) and (14) into (11), we have

Step 2. Consider the second equation in (2). is unknown function, so that the RBFNN is adopted to approximate . Thus, we obtain where is the optimal weight value and is the least approximation error of RBFNN, which satisfies with . Define . The external disturbance is bounded, which satisfies with . Therefore, we have that , is a positive constant.
Substituting (16) into the second equation in (2) results in Differentiating and invoking (20) yield Define the sliding surface as where is a design parameter.
Then, the time derivative of is Choose the Lyapunov function candidate Its time derivative is Choose the Lyapunov function candidate where is a design parameter, is the estimate value of , and is the estimate error.
The time derivative of is Choose the following SMC law as where and are positive constants and .
Substituting (25) into (24), we obtain The time derivative of is Then, we obtain The updated law of the NN is designed as where is a design parameter.
Substituting (29) into (26), we have Consider the following inequality: Substituting (31) into (30), we have

Remark 1. It is well known that the control input is discontinuous due to the use of sign function , which may cause the chattering effect. In order to overcome this shortcoming, is a continuous approximation of the sign function and defined as follows [34]: where is a positive constant. Furthermore, as increases, the approximation error can be reduced. This approximation also allows the control law to be derivable.
Define . From , we have The inequality (32) can be written as where and .

3.2. The Calculation of the Reference Attitude Angle

Considering the underactuated characteristics of the UAH, it is necessary to calculate the reference roll angle and the reference pitch angle of the attitude subsystem with the virtual control input of the position subsystem.

Define as the reference signal of the attitude subsystem, where is directly indicated by the signal generator given and and are obtained by the position loop output.

Considering , then , , and can be expressed as follows [35]:

3.3. Scheme of Attitude Control

Similar to the position system, the control law based on SMC and RBFNN will be designed to achieve the purpose of tracking the reference attitude signal.

Step 3. Consider (3) and define the tracking errors of attitude angular and angular velocity where is the desired attitude vector and will be defined.
Differentiating , we obtain Choose the Lyapunov function candidate Its time derivative is Choose the following virtual control law where and are design parameters.
Substituting (38) and (42) into (39), we have

Step 4. Similar to the position subsystem, is unknown, so the RBFNN is used to approximate . Thus, we obtain where is the optimal weight value and is the least approximation error of RBFNN, which satisfies with . Define . The external disturbance is bounded, which satisfies with . Therefore, we have that , is a positive constant.
Substituting (47) into the second equation in (6) results in Differentiating with respect to time and invoking (48) yield Define the sliding surface as where is a design parameter.
Then, the time derivative of is Choose the Lyapunov function candidate Its time derivative is Consider the Lyapunov function candidate where is a design parameter, is the estimate value of , and is the estimate error.
The time derivative of is Choose the following virtual control law as where and are positive constants and .
Substituting (53) into (52), we obtain The time derivative of is The updated law of the NN is designed as where is a design parameter.
Substituting (56) into (54), we have Considering the following inequality: Substituting (58) into (57), we have Similar to the position loop, the approximation function is used to replace a symbolic function, the proof is the same as before (34). The inequality (59) can be written as where and .
Consider the Lyapunov function candidate of the overall closed-loop system Consider (35) and (60), differentiating yields where and .