This paper is concerned with adaptive neural control of nonlinear strict-feedback systems with nonlinear uncertainties, unmodeled dynamics, and dynamic disturbances. To overcome the difficulty from the unmodeled dynamics, a dynamic signal is introduced. Radical basis function (RBF) neural networks are employed to model the packaged unknown nonlinearities, and then an adaptive neural control approach is developed by using backstepping technique. The proposed controller guarantees semiglobal boundedness of all the signals in the closed-loop systems. A simulation example is given to show the effectiveness of the presented control scheme.

1. Introduction

In the past decades, much attention has been paid on the control design of complex nonlinear systems [111]. Many remarkable control approaches in this area have been developed, including adaptive backstepping technique [13], fault tolerant control [1217], and and fuzzy control [1829]. In particular, adaptive backstepping approach has played an important role in the control of strict-feedback nonlinear systems. Generally, adaptive backstepping provides a systematic control approach to solve the tracking or regulation control problems of uncertain nonlinear systems, in which the classic adaptive control is applied to deal with the unknown parameter and backstepping technique is used to construct controller. The main feature of adaptive backstepping control is that it can handle the control problems of nonlinear systems without the requirement of matching condition. Adaptive backstepping technique was provided in [1] to obtain global stability and asymptotic tracking performance for parametric strict-feedback systems with overparameterization, and the overparameterization was overcome by applying the tuning functions in [2]. Then, a backstepping-based design was extensively utilized to control different types of nonlinear systems [3035]. All the above control methods, however, assume that the nonlinear functions of the control systems are either known or bounded by known functions multiplying uncertain parameters. This restriction makes the aforementioned methods inapplicable to the control of the systems with unknown continuous nonlinear functions.

On the other hand, approximation-based adaptive neural (or fuzzy) backstepping control has received increasing attention in recent years. In general, approximation-based adaptive backstepping technique is an effective control approach for handling the control problem of highly uncertain complex nonlinear strict-feedback systems, in which neural networks or fuzzy systems are utilized to model the unknown nonlinear functions. So far, there exist some elegant results; see, for example, [3654] and the references therein. By applying adaptive neural control together with backstepping, in [3643], many control approaches are developed for single-input and single-output (SISO) nonlinear systems or multi-input and multioutput (MIMO) nonlinear systems. Alternatively, several fuzzy adaptive control strategies [19, 4455] were developed to deal with the control problem of uncertain nonlinear systems with strict-feedback form. However, the above adaptive neural or fuzzy backstepping control approaches required the controlled strict-feedback nonlinear systems to be free of the unmodeled dynamics and dynamic disturbances. As stated in [56, 57], the unmodeled dynamics and dynamic disturbances often appear in practical systems [58, 59] due to the measurement noise, modeling errors, external disturbances, modeling simplifications, or changes with time variations, and they are also the resources of the instability of the considered systems. Therefore, some researchers have concentrated on the problem of control design for nonlinear systems with unmodeled dynamics and dynamic disturbances. In [56, 57], the problem of adaptive backstepping control was investigated for a class of nonlinear systems with dynamics uncertainties, in which the nonlinear functions were assumed to be linear combinations of the known functions with unknown parameters. Furthermore, by using the approximation properties of fuzzy logic systems, Tong et al. [58, 59] developed several fuzzy adaptive control approaches for nonlinear systems in strict-feedback form, where the number of adaptation laws depends on the number of fuzzy base functions. The more fuzzy rules are applied to improve approximation accuracy, the more adaptive parameters will be needed, and, in this way, the online learning time may be very large.

Inspired by previous works, this paper focuses on the problem of adaptive neural control for nonlinear strict-feedback systems with unmodeled dynamics and dynamic disturbances. During the controller design, a dynamic signal is introduced to handle the unmodeled dynamics and RBF neural networks are used to approximate the unknown nonlinearities, and then an adaptive neural control scheme is systematically derived via backstepping. The proposed controller guarantees that all the signals in the closed-loop systems are semiglobally uniformly ultimately bounded (SGUUB) in the sense of mean square. Compared with the control approaches [58, 59], the main contributions of this paper are summarized as follows: the strict limitation to the dynamic disturbances is relaxed, which can refer to Remark 3; by estimating the norm of the weight vector of neural networks basis functions, the number of adaptive parameters is not more than the order of the considered nonlinear system. As a result, the burdensome computation is significantly alleviated, which makes our control design more suitable for the practical applications.

The remainder of the paper is organized as follows. Section 2 begins with the problem formulation and some preliminaries. A backstepping-based adaptive control scheme is design in Section 3. In Section 4, a numerical example is given. Finally, the conclusion of this paper is shown in Section 5.

2. Problem Formulation and Preliminaries

In this paper, we consider a class of nonlinear strict-feedback systems described by where , , and are the system state, control input, and system output, respectively, , , and are unknown smooth functions, and () are nonlinear dynamic disturbances. The -dynamics in (1) denotes the unmodeled dynamics.

Remark 1. It is worth noting that many practical systems such as the electromechanical system [59] transformable into (1) have been investigated extensively during the last decades from both theoretical and practical viewpoints; see, for example, [5659].

In order to facilitate the control design later, the below assumptions are imposed on the system (1).

Assumption 2. For the dynamic disturbances () in (1), there exist unknown nonnegative smooth functions and , such that

Remark 3. This assumption is similar to the one in [58, 59] in which and are known. Assumption 2, however, does not require them to be known. Therefore, Assumption 2 relaxes the restriction in the existing results.

Assumption 4 (see [59]). The unmodeled dynamics in (1) is exponentially input-to-state practically stable (exp-ISpS); that is, for the system , there exists an exp-ISpS Lyapunov function such that where , , and are of class -functions and and are known positive constants.

Assumption 5 (see [50]). For , the signs of are known, and there exists unknown positive constant such that

Remark 6. Equation (4) implies that are either strictly positive or negative. Without loss of generality, it is supposed that . In addition, since is not required in the designed controller, its true value is not required to be known.

Lemma 7 (see [59]). If is an exp-ISpS Lyapunov function for a control system, that is, (3) hold, then, for any constants in , any initial condition , and any function , there exists a finite time , a nonnegative function defined for all , and a signal described by such that for all , For all , the solutions are defined. Without loss of generality, this paper takes as , where is a nonnegative smooth function. Therefore, the dynamical defined by (5) becomes where is a nonnegative smooth function.

Throughout this paper, RBF neural networks are applied to model the unknown continuous nonlinear functions. In [60], it has been indicated that, with enough node number , the RBF neural networks can model the continuous function within a compact set to arbitrary accuracy as in which denotes the ideal weight vector and is specified as depicts the approximation error satisfying , is the weight vector, and is the basis function vector with being the number of the neural networks nodes and . The basis function is taken as the Gaussian function in the below form: where and are the center of the receptive field and the width of the Gaussian function, respectively.

3. Adaptive Neural Control Design

In this section, the adaptive backstepping control design for system (1) is proposed. As usual, in the backstepping approach, the following coordinate transformation is made: where , is the virtual control signal and will be constructed at Step i, and the actual controller will be designed at Step n. Now, we begin the controller design procedure.

Step 1. Consider the following subsystem: Based on , then choose Lyapunov functions as where , and are positive design parameters, and is the parameter error with being the estimation of which is defined later.

By taking (12) into account, we have By Assumption 2, it follows that Then, we will deal with the third and fourth terms in (15), respectively. By using , for , we have where and is a smooth function.

By using the same derivations as [58], one has where , , and .

Subsequently, substituting (16) and (17) into (15) gives where the function is defined as where .

Since the smooth function is unknown, it cannot be implemented in practice. By employing RBF neural network in to approximate , we have where denotes an approximation error and is a given positive constant.

Next, the following result can be obtained by substituting (20) into (18): By using one has with being an unknown parameter.

Construct the virtual control signal as where and are positive design constants.

By taking Assumption 5 into account, one has Further, by substituting (25) into (23), we obtain Next, we choose the adaptive law in the following form: where and are design parameters.

By using (27), we can rewrite (26) as Noting then the following inequality holds: where .

Step 2. Based on , then the time derivative of is given by where .

Construct the Lyapunov function

The derivative of is By Assumption 2, we have Similar to the estimation methods in (35), the following results can be obtained: where , , , , and .

Substituting (35) into (33) gives where and the function is specified as with and being some known compact set in .

To compensate for the unknown nonlinear function , a neural network is utilized to model such that, for any given positive constant , where denotes approximation error.

Then, substituting (38) into (36), one has By using it can be easily verified that with being an unknown constant.

Furthermore, the virtual control is constructed as where and are the design constants.

Then, the following result can be easily obtained:

By applying (43), (41) can be rewritten as Define the adaptive law as where , , and are design parameters.

Combining (44) with (45) produces where , , and the result has been used in the above equation.

Step i (). According to , the dynamics of is where .

Consider the Lyapunov function as By using the derivations similar to those used in the former steps, we can obtain Similar to (34), we have Furthermore, the following inequalities can be easily verified by repeating the same arguments as (35): where , , , , and , noting that for all .

Substituting (51) and (52) into (49) results in where the function is defined by where , , and is some known compact set in .

Currently, a neural network is utilized to model such that, for a given , can be expressed as Further, similar to (40), we can obtain where is an unknown constant.

Now, construct the virtual control signal as with and being design constants.

Then, by substituting (55)–(57) into (53), choosing the adaptive law with , and being the design parameters, and then following the same line as the procedures from (43) to (46), we have where , .

Step n. In this step, the actual controller is designed. According to , then we have where . Similarly, choose the following Lyapunov function as From (53) and (54), we have Using the same estimation methods as (42)–(44), we have where , , , , and are defined in (51) or (52) with . By substituting (63) into (62), one has where the function is defined by with and being some known compact set in . Similarly, neural network is employed to model such that , . Then, following the same line as used in (40), we have where denotes an unknown constant and is a design constant.
Subsequently, by combining (64) together with (66), the inequality below holds: At the present stage, construct the real controller and adaptive law in the following forms: where , , , and are design constants.
Then, repeating the similar procedures as (43)–(46), we can obtain where , .

Now, the main result of this research is summarized as follows.

Theorem 8. Consider the system (1) consisting of Assumptions 25, the control input (68), and the adaptive laws (58) and (69). Assume that the packaged unknown functions () could be modeled by neural networks with the bounded approximation errors. Then, for bounded initial values with , all the signals in the closed-loop system are semiglobally boundedness in mean square.

Proof. To give the stability analysis for the closed-loop system, consider the Lyapunov function in the form , and define
Furthermore, we can rewrite (70) as Next, from (72), the following inequality can be easily verified: which means that Therefore, based on the definition of in (61), , (), and are bounded. Because the signal is bounded, the trajectory is bounded. Since are constants, are bounded. Consequently, are also bounded because and are bounded variables. Hence, we conclude that the signals are bounded.

4. Simulation Example

A simulation example is presented to show the effectiveness of the proposed control scheme. Consider the second-order nonlinear system as where , , , and . It can be easily verified that Assumption 2 is satisfied. In order to check Assumption 4 holds for -subsystem in (75), consider , and then By choosing , we have By Defining , , , , and , Assumption 4 is satisfied. Take and define the dynamic signal as follows: By using Theorem 8, the virtual control signal, the real control input, and the adaptive laws are constructed as follows: In the simulation, the design constants are chosen as , , , and . The simulation is carried out with the initial conditions .

The simulation results are shown in Figures 13. Figure 1 shows the trajectories of states and . Figure 2 displays the trajectory of control input . Figure 3 shows the trajectories of adaptive parameters and . From Figures 13, we can see that the proposed control approach can guarantee the boundedness of the variables , , , , and .