Finite-Time Adaptive Tracking Control for a Class of Pure-Feedback Nonlinear Systems with Disturbances via Decoupling Technique

Abstract

This paper addresses the finite-time adaptive tracking control problem for a class of pure feedback nonlinear systems whose nonaffine functions may not be differentiable. By properly modeling the nonaffine function, the design difficulty of the pure feedback structure is overcome without using the median value theorem. In our design procedure, an finite-time adaptive controller is elaborately developed using the decoupling technology, which eliminates the limitation assumption on the partial derivatives of nonaffine functions. Furthermore, the constructed controller can stabilize the system within a finite-time so that all signals in the closed-loop system are semiglobally uniformly finite-time bounded (SGUFB), while ensuring the tracking performance. Finally, the simulation results prove the effectiveness of the proposed method.

1. Introduction

In the past few decades, there have been various research results on the nonfinite time stability of nonlinear systems [1–10], and these results are widely applied to practical systems. However, in actual engineering, the control goal is always expected to be achieved within a finite time. Nonfinite time stable schemes cannot accomplish such control objective because nonfinite time stable control often requires a long transient response. Therefore, the definition of finite-time stability was first proposed in [11, 12] and has received great attention. The finite-time stability can ensure that the system state variables quickly converge to equilibrium within a limited time. At present, the finite-time control of nonlinear systems has become a new research hot spot [13–15]. At the same time, there are many challenging problems which needs hard work to overcome.

On the contrary, the rapid development of computer technology has made great progress in the research of adaptive control [16–23]. It is worth mentioning that when there exist completely unknown nonlinear functions in a nonlinear system with a strict feedback structure, radial basis function neural networks (RBF NNs) and fuzzy logic systems play an important role in its adaptive control [24–35]. Using the approximation ability of RBF NNs or fuzzy logic systems, there have been many meaningful research results on adaptive intelligent control for strict feedback nonlinear systems [36–39]. Despite great success in the research on adaptive intelligent control for strict feedback nonlinear systems have been achieved, the research on finite-time control for the nonlinear system is not fully considered [40–42].

In recent years, the finite-time adaptive control schemes for strict-feedback nonlinear systems have been developed in [43–47]. However, the finite-time control strategies in [43–47] are only applicable to the strict feedback nonlinear systems, but not applicable to the pure feedback nonlinear systems. It is worth noting that the study on the finite-time tracking control of pure feedback systems has achieved some results [48, 49], but almost all the results are obtained based on the use of differential median theorem, which can convert the pure feedback structure into the strict feedback structure. This requires us to make restrictive assumptions for the partial derivatives of nonaffine functions. However, it is well known that nonsmooth nonlinearities, such as dead zones and hysteresis, exist in a wide range of practical control systems. Thus, not all system functions are differentiable in actual control, which requires exploring a new design technique to deal with the pure feedback structure.

In order to meet the actual requirements better, we consider the finite-time adaptive control problem for a class of nonlinear systems with the pure-feedback structure as well as external disturbances. A finite-time adaptive control method based on the decoupling technology is proposed to make the system have better transient response. By selecting the design parameters appropriately, the generated tracking error can converge to a smaller neighborhood of the origin so that the system output follows the desired trajectory within a limited time. The main contributions of this article are as follows. First, we consider a more general class of pure-feedback systems with nonaffine nonlinear functions that may not be differentiable. In order to make our method more practical in industrial control systems than the existing methods [50–59], the limitation of using the differential median theorem in the study of pure feedback systems is eliminated. Second, we construct a suitable controller to stabilize the system in a finite time, which not only ensures that the system state variables quickly converge to equilibrium within a limited time but also improves the robustness of the system and reduces the effects of approximation errors. Third, the appropriate scaling technique is applied to reduce the number of adaptive parameters in the process of designing the controller such that the developed result is more suitable for the actual operation process, which also reduces the complexity of the design procedure. In the end, even if the control direction of the system is unknown, our method can still make all signals in the closed-loop system which are SGUFB.

2. Mathematical Preliminaries

Consider a class of pure-feedback nonlinear systems given bywhere , , and are the system state, output, and control input, respectively, are unknown nonaffine nonlinear functions, and are the unknown external disturbances.

Definition 1. (see [60]). The equilibrium of nonlinear system is semiglobal practical finite-time stable (SGPFS) if for all , there exists and a settling time to make , for all .

Lemma 1. (see [60]). Consider the system . If there is a smooth positive definite function and scalars , , and such thatthen this nonlinear system is SGPFS.

Proof. For , from (2), one hasLet and , if , one yields . By solving differential equations, we can obtain thatSo, is held for , otherwise, the trajectory of does not exceed the set . This means that the time to reach the set is bounded as . In other words, the solution of is bounded in a finite time.

Lemma 2. (see [61]). For real variables and and any positive constants , and , the following relation holds:

Lemma 3. (see [62]). For , the following inequality is true:

Remark 1. It is worth noting that system function is always assumed to satisfy in existing articles [52, 54, 55]. However, it is well known that not all system functions are differentiable in actual control, which requires exploring a new design technique to deal with the pure feedback structures. Next, we will introduce a decoupling technique to deal with the unknown nonaffine nonlinear functions of the pure feedback system (1) rather than the median theorem.

Lemma 4. (see [63]). In order to effectively design the control input of the system, the decoupling technology is utilized to deal with the nonaffine terms. After a series of processing, the following formula can be obtained:where and are defined immediately below.

Proof. Define , where and . We assume that the function satisfieswhere are unknown positive constants and , are unknown constants.
It can be shown that there exist functions and , taking values in the closed interval [0, 1] and satisfyingTo facilitate the controller design, we have defined the following simplified symbols and asWe can infer from the above definition that and are bounded. Then, we can model the nonaffine terms asHence, (7) was established, and we can rewrite (1) asIn backstepping design, the variable is usually taken as the virtual control input for the subsystem. So, the virtual control coefficient function should not pass though the zero point. Therefore, the following assumption is pressed on the system (13).

Assumption 1. The desired trajectory and its derivatives and are continuous and bounded.

Assumption 2. Due to realistic considerations, for , there exist unknown positive constants such that .

Remark 2. Define , , and . It can be inferred from definitions (10) and (11) that the functions and satisfy in the design of this article, the following radial basis function neural networks (RBF NNs) is used to approximate the continuous function :where is the weight vector and the neural network node number . is the basic vector being chosen as the commonly used Gaussian functions, which has the form:where is the input vector, is the center of the respective field, and is the width of the Gaussian function.
As shown in [64], the neural network can approximate any continuous function on the compact set to any desired accuracy as follows:where is the ideal constant weight vector and is the approximation error satisfying , is a very small constant.

3. Adaptive State-Feedback Controller Design

In this section, the finite-time adaptive controller is proposed for the backstepping control of system (13). To start, consider the following change of coordinates:where is the virtual control signal constructed in step and . Step 1: differentiating through the first system of (13), we have Choose Lyapunov function candidate to construct the virtual control signal of this system as By substituting (20), we can get the time derivative of as where . Now, we define a new function as with , where is a natural number and is a constant. Then, (22) can be rewritten as Next, based on (15) and Assumption 2, we can obtain Because the unknown function cannot be used for the controller design, we can infer from (18) that where . For simplicity, we use and instead of and , respectively. Define , and is the parameter estimation error; then, using Yang’s inequality and Remark 2, one yields where is the design positive constant. Substituting (26) and (28) into (24) produces where and . Next, we construct a virtual signal as Substituting (30) into (29) yields Next, we construct the adaptive rate as where and are two design constants. As a result, one can obtain the following formula: Step : from (19), we can know that . Next, we use and instead of and for simplicity. Then, the dynamic equation of is constructed as follows: where Choose a Lyapunov function candidate as Differentiating results in where , , represents the parameter estimation error. Similar to the processing in the first step, we need to define a new function as , with is a design constant. Then, (37) can be rewritten as Now, based on Assumptions 2 and Remark 2, one can obtain According to (18), we can choose the following neural network system: where and . For simplicity, we use and to represent and , respectively. Thus, we can obtain where is the design positive constant. Like the first step, substituting (41)–(43) into (39), the following inequality holds: where and . Next, we construct the virtual signal as well as the adaptive rate as follows: where and are two design constants. As a result, substituting (45) and (46) into (44), one can get the following formula: Comparing (33) and (47), we can get the following formula by mathematical induction: