Skip to main content

Theory and Modern Applications

Adaptive dynamic surface control of parametric uncertain and disturbed strict-feedback nonlinear systems


The construction of backstepping control input needs the derivative of the virtual controller to be available. However, this requirement usually makes the implementation of the controller very difficult and complicated. To overcome this problem, in this paper, an adaptive dynamic surface control (ADSC) is proposed for a class of strict-feedback nonlinear systems with parametric uncertainty and external disturbance. In each step of the backstepping control design, the virtual control input is estimated by an auxiliary signal which is generated by a proposed dynamic surface. This signal’s derivative is easy to obtain, so it is not necessary to achieve the derivative of the virtual control input. By using the Lyapunov stability theorem, an ADSC has been established to guarantee the boundedness of all signals and the convergence of the tracking errors. Finally, a simulation example is given to indicate the effectiveness of our control approach.

1 Introduction

It is well known that tremendous success has been obtained in controlling nonlinear systems based on the development of adaptive backstepping control (ABC) and feedback linearization (FBL) methods [1, 2]. The main idea of FBL consists in transforming a strict-feedback nonlinear system (SFNS) that satisfies some matching conditions into a linear one. That is to say, this method cannot deal with the nonlinear term directly. To overcome this limitation, the ABC method establishes a systematic framework for controlling SFNSs, whose main idea is using some intermediate variables recursively as pseudo-control signals. If the SFNSs have minimum phase and have known relative degrees, the stability of the closed-loop system can be guaranteed by using the backstepping design method. For ABC of an SFNS without parametric uncertainty or external disturbance, some control methods have been presented, for example, in [2,3,4,5]. Based on the main idea of the ABC approach, more complicated conditions are considered in designing ABC for SFNS in [2, 6,7,8]. However, the above literature did not consider parametric uncertainties and disturbance in the ABC design. Thus, the ABC design needs to be studied further to achieve better robust performance.

As is well known, most physical systems usually suffer from system uncertainty, for example, parametric uncertainty, modeling error, or external disturbance, which will decrease the control performance or even leads to instability of the controlled system [9,10,11,12,13,14,15,16,17,18,19,20,21]. Thus, the ABC method usually requires cancelation of the nonlinearities. Sometimes exact knowledge of the system nonlinearities is not available, or these terms change along with time. Therefore, we need to model the nonlinear uncertainties with parametric uncertainties. Some robust control methods have been given to handle these kinds of system uncertainties [19, 22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39]. ABC methods have been studied recently in [40,41,42]. In [2], an ABC method combined with adaptive fuzzy control was investigated. A fuzzy ABC for SFNSs in the presence of both sampled and delayed measurements was studied in [40]. A command filtered ABC method for unknown SFNSs was proposed in [41], where a first-order filter was introduced to handle the virtual control input. However, in the above literature, external disturbance and/or parametric uncertainty was not considered.

The ABC method suffers from the “explosion of complexity” which is produced by repeatedly differentiating the virtual control input. Then, many control methods have been given to solve this problem. Among these, the adaptive dynamic surface control (ADSC) aims to enhance the drawback of ABC by driving the control input passing through a first-order filter [23, 43,44,45,46]. This method not only solve the problem of “explosion of complexity”, but also reduce the requirement of the system model as well as the referenced signal. In [23], by using the ADSC method, an ABC method was proposed for SFNSs with parametric uncertainties. Reference [47] provides a command filtered backstepping control for SFNSs, where the adaptive control method was not considered. Then, based on the work of [47], Ref. [22] provided an adaptive command filtered backstepping control method, and a compensated tracking error was also considered. The above method is based on procedures like nonlinear damping and variable structure as well as their variations, which commonly need prior knowledge of the system uncertainty, for example, utilizing some constant or nonlinear function known in advance as the bounds of the estimated nonlinearity. Consequently, the applications of these kinds of methods may be limited if there is no such prior knowledge.

In this paper, we address the ADSC method for SFNSs with mismatched parametric uncertainties and disturbances. The proposed method described herein needs only the signals \(x_{d}(t)\) and the state variables of the controlled system to be available. The dynamic surface idea is used to obtain a practical generalization of the conventional backstepping technique. A main motivation of this work is to simplify the process of determining the command derivatives required in the backstepping procedure. Our controller ensures the boundedness of all signals and the convergence of the tracking errors. The main contributions of this work can be presented as follows. (1) In this paper, an ADSC is proposed for SFNSs with parametric uncertainties and disturbances. Note that it is very difficult to obtain exact analytical expressions for the dynamic surface, the proposed ADSC method works well even in the presence of parametric uncertainty as well as of an external disturbance. An effective first-order dynamic surface, which is easy to use, has been proposed. (2) Based on the proposed dynamic surface, the conventional “explosion of complexity” will not occur in our work. The virtual control input’s derivative is replaced by an auxiliary variable which can track the virtual control input in arbitrary degree of accuracy. Compared with the method proposed in [48, 49], our method combined with adaptation laws has a more concise construction and is easier to implement.

2 Problem formulation

Consider the following nth parametric uncertain SFNSs:

$$ \textstyle\begin{cases} \dot{x}_{i}(t) = x_{i+1}(t)+ f_{i}(\bar{\pmb{x}}_{i}(t))+ \pmb{\varphi }_{i}^{T}(\bar{\pmb{x}}_{i}(t)) \pmb{\vartheta }, \quad i=1,2,\ldots, n-1,\\ \dot{x}_{n}(t) = f_{n}(\pmb{x}(t))+d(t)+u(t)+ \pmb{\varphi }_{n}^{T}(\bar{\pmb{x}}(t)) \pmb{\vartheta }, \end{cases} $$

where \(x_{1}(t)\in \mathcal{R}\) is the output variable, \(u(t)\in \mathcal{R}\) is the control input, \(\bar{\pmb{x}}_{i}(t)=[x_{1}(t),\ldots, x_{i}(t)]^{T} \in \mathcal{R}^{i}\) (note that \({\pmb{x}}(t)= \bar{\pmb{x}}_{n}(t)\in \mathcal{R}^{n}\)) represents the system state vector, \(f_{i}(\bar{\pmb{x}}_{i}(t))\): \(\mathcal{R}^{i} \mapsto \mathcal{R}\) is a nonlinear function, \(d(t)\in \mathcal{R}\) denotes the bounded unknown external disturbance, \(\pmb{\varphi }_{i}(\bar{ \pmb{x}}_{i}(t))\): \(\mathcal{R}^{i} \mapsto \mathcal{R}^{m}\) is a known basis function, and \(\pmb{\vartheta }=[\vartheta _{1},\ldots, \vartheta _{m}] \in \mathcal{R}^{m}\) represents an unknown constant vector.

Define \(x_{d}(t) \in \mathcal{R}\) as a desired signal. The main objective of this paper is to design an ADSC such that the output variable \(x_{1}(t)\) follows the desired signal \(x_{d}(t)\). Let the tracking error be

$$ e_{1}(t)=x_{1}(t)-x_{d}(t). $$

Throughout this paper, \(\mathcal{R}\), \(\mathcal{R}^{i}\) represent the spaces of real numbers and real i-vectors, respectively. \(\Vert x(t) \Vert \) is the standard 2-norm of \(x(t)\), \(\operatorname{sgn}(\cdot )\) denotes the signum function, \(\mathcal{L}_{\infty }\) represents the space of the bounded variable, \(\varOmega _{c}\) is the ball of radius c, and \(\mathcal{C}^{i}\) is the space of functions for which all ith-order derivatives exist and are continuous.

To facilitate the controller design, we need the following assumptions.

Assumption 1

The nonlinear functions \(f_{i}(\bar{\pmb{x}}(t))\), \(\pmb{\varphi } _{n}(\bar{\pmb{x}}(t))\) are of \(\mathcal{C}^{1}\).

Assumption 2

The desired signal \(x_{d}(t)\) and its derivative are continuous functions of \(\mathcal{L}_{\infty }\).

Assumption 3

The external disturbance \(d(t)\) is bounded, i.e., there exists a positive constant \(d^{*}\) such that \(\vert d(t) \vert \leq d^{*}\).

Assumption 4

Assume that \(\varOmega _{c}\) is an open set which includes the referenced signal, the origin and the initial conditions of the system 1. Suppose that, for \(i=1,\ldots,n\), \(\frac{\partial ^{j} f _{i}(\bar{\pmb{x}}_{i}(t))}{\partial t^{j}}\), \(\frac{\partial ^{j} x _{d}(t)}{\partial t^{j}}\), where \(j=1,\ldots,n\), are all bounded on \(\bar{\varOmega }_{c}\).

Remark 1

It is worth mentioning that above four assumptions are in line with the practical situation. Firstly, the Assumption 1 is satisfied in most real-world systems. Secondly, in most literature, the referenced signal is a smooth function, that is to say, Assumption 2 is reasonable. Thirdly, in this paper, it is assumed that the unknown external disturbance \(d(t)\) is bounded. In fact, lots of common disturbance functions are bounded. In addition, the upper bound \(d^{*}\) is assumed to be unknown. Finally, Assumption 4 is needed in the stability analysis, and this assumption is common in much related literature, for example, [22, 23, 47].

3 Controller design and stability analysis

3.1 The ADSC design

For convenience, this section will design the controller based on the backstepping procedure which has n steps.

Step 1.

It follows from (1) and (2) that

$$ \begin{aligned}[b] \dot{e}_{1}(t) & = \dot{x}_{1}(t) -\dot{x}_{d}(t) \\ & =x_{2}(t)+ f _{1}\bigl(\bar{\pmb{x}}_{1}(t) \bigr)+ \pmb{\varphi }_{1}^{T}\bigl({ x}_{1}(t) \bigr) \pmb{\vartheta }-\dot{x}_{d}(t) \\ &=\nu _{1}(t)+x_{2}(t)-\nu _{1}(t)+ f _{1}\bigl(\bar{\pmb{x}}_{1}(t)\bigr)+ \pmb{\varphi }_{1}^{T}\bigl({ x}(t)\bigr) \pmb{\vartheta }- \dot{x}_{d}(t) \\ &=\nu _{1}(t)+x_{2}(t)-\nu _{1}^{c}(t)+ \nu _{1}^{c}(t)-\nu _{1}(t)+ f_{1}\bigl({ x}_{1}(t)\bigr)+ \pmb{\varphi }_{1}^{T}\bigl( { x}_{1}(t)\bigr) \pmb{\vartheta }-\dot{x}_{d}(t) \\ &=\nu _{1}(t)+e_{2}(t)+ \tilde{\nu }_{1}(t)+ f_{1}\bigl({ x}_{1}(t)\bigr)+ \pmb{\varphi }_{1}^{T}\bigl({ x} _{1}(t)\bigr) \pmb{\vartheta }- \dot{x}_{d}(t), \end{aligned} $$

where \(\nu _{1}(t)\) represents the virtual control input that will be defined later,

$$ e_{2}(t)=x_{2}(t)-\nu _{1}^{c}(t) $$

is the filtered tracking error of \(x_{2}(t)\), and \(\tilde{\nu }_{1}(t)= \nu _{1}^{c}(t)-\nu _{1}(t)\) denotes the virtual control input estimation error. Thus, the virtual control input \(\nu _{1}(t)\) can be given as

$$ \nu _{1}(t)=-k_{1}e_{1}(t)-f_{1} \bigl({ x}_{1}(t)\bigr)-\pmb{\varphi }_{1}^{T}\bigl( { x}_{1}(t)\bigr) \hat{\pmb{\vartheta }}(t) +\dot{x}_{d}(t), $$

where \(k_{1}>0\) is a design parameter, and \(\hat{\pmb{\vartheta }}(t)\) is the estimation of the unknown constant vector \(\pmb{\vartheta }\).

The auxiliary variable \(\nu _{1}^{c}(t)\) is updated by

$$ \dot{\nu }_{1}^{c}(t)=-\sigma _{1} \nu _{1}^{c}(t)+ \sigma _{1} \nu _{1}(t),\quad\quad \nu _{1}^{c}(0)=\nu _{1}(0), $$

where \(\sigma _{1}>1\) is a design parameter.

Substituting (5) into (3) gives

$$ \begin{aligned}[b] \dot{e}_{1}(t) &=-k_{1}e_{1}(t)+e_{2}(t)+\tilde{\nu }_{1}(t)+ \pmb{\varphi }_{1}^{T}\bigl({ x}_{1}(t)\bigr) \pmb{\vartheta }-\pmb{\varphi } _{1}^{T} \bigl({ x}_{1}(t)\bigr) \hat{\pmb{\vartheta }}(t) \\ &=-k_{1}e_{1}(t)+e _{2}(t)+\tilde{\nu }_{1}(t) -\pmb{\varphi }_{1}^{T}\bigl({ x}_{1}(t)\bigr) \tilde{\pmb{\vartheta }}(t), \end{aligned} $$

where \(\tilde{\pmb{\vartheta }}(t)= \hat{\pmb{\vartheta }}(t)\)- \(\pmb{\vartheta }\) is the estimation error.

Step 2.

It follows from (1) and (4) that

$$ \begin{aligned}[b] \dot{e}_{2}(t) & = \dot{x}_{2}(t) -\dot{\nu }_{1}^{c}(t) \\ & =x_{3}(t)+ f_{2}\bigl(\bar{\pmb{x}}_{2}(t) \bigr)+ \pmb{\varphi }_{2}^{T}\bigl(\bar{ \pmb{x}}_{2}(t)\bigr) \pmb{\vartheta }- \dot{\nu }_{1}^{c}(t) \\ &=\nu _{2}(t)+x_{3}(t)-\nu _{2}^{c}(t)+ \nu _{2} ^{c}(t)-\nu _{2}(t)+ f_{2} \bigl(\bar{\pmb{x}}_{2}(t)\bigr)+ \pmb{\varphi }_{2} ^{T}\bigl(\bar{\pmb{x}}_{2}(t)\bigr) \pmb{\vartheta }-\dot{ \nu }_{1}^{c}(t) \\ &= \nu _{2}(t)+e_{3}(t)+\tilde{\nu }_{2}(t)+ f_{2}\bigl(\bar{\pmb{x}}_{2}(t)\bigr)+ \pmb{\varphi }_{2}^{T}\bigl(\bar{\pmb{x}}_{2}(t)\bigr) \pmb{ \vartheta }- \dot{\nu }_{1}^{c}(t), \end{aligned} $$

where \(e_{3}(t)=x_{3}(t)-\nu _{2}^{c}(t)\), \(\tilde{\nu }_{2}(t)=\nu _{2}^{c}(t)-\nu _{2}(t)\),

$$ \dot{\nu }_{2}^{c}(t)=-\sigma _{2} \nu _{2}^{c}(t)+ \sigma _{2} \nu _{2}(t), \quad\quad \nu _{2}^{c}(0)=\nu _{2}(0) $$

and the virtual control input \(\nu _{2}(t)\) can be constructed as

$$ \nu _{2}(t)=-k_{2}e_{2}(t)-e_{1}(t)-f_{2} \bigl(\bar{\pmb{x}}_{2}(t)\bigr)- \pmb{\varphi }_{2}^{T} \bigl(\bar{\pmb{x}}_{2}(t)\bigr) \hat{\pmb{\vartheta }}(t)+ \dot{\nu }_{1}^{c}(t), $$

where \(k_{2}>0\) and \(\sigma _{2}>1\) are two design parameters.

Substituting (10) into (8) yields

$$ \dot{e}_{2}(t) =-k_{2}e_{2}(t) +e_{3}(t)-e_{1}(t)+\tilde{\nu }_{2}(t)- \pmb{ \varphi }_{2}^{T}\bigl(\bar{\pmb{x}}_{2}(t)\bigr) \tilde{\pmb{\vartheta }}(t). $$

Step i, \(3\leq i\leq n-1\).

These steps are very similar to Step 2. It is easy to see that

$$ \begin{aligned}[b] \dot{e}_{i}(t) & = \dot{x}_{i}(t) -\dot{\nu }_{i-1}^{c}(t) \\ & =x_{i+1}(t)+ f_{i}\bigl(\bar{\pmb{x}}_{i}(t) \bigr)+ \pmb{\varphi }_{i}^{T}\bigl(\bar{ \pmb{x}}_{i}(t)\bigr) \pmb{\vartheta }-\dot{\nu }_{i-1}^{c}(t) \\ &=\nu _{i}(t)+x _{i+1}(t)-\nu _{i}^{c}(t)+ \nu _{i}^{c}(t)-\nu _{i}(t)+ f_{i}\bigl( \bar{ \pmb{x}}_{i}(t)\bigr)+ \pmb{\varphi }_{i}^{T} \bigl(\bar{\pmb{x}}_{i}(t)\bigr) \pmb{\vartheta }-\dot{\nu }_{i-1}^{c}(t) \\ &=\nu _{i}(t)+e_{i+1}(t)+ \tilde{\nu }_{i}(t)+ f_{i}\bigl(\bar{\pmb{x}}_{i}(t)\bigr)+ \pmb{\varphi }_{i} ^{T}\bigl(\bar{\pmb{x}}_{i}(t)\bigr) \pmb{ \vartheta }-\dot{\nu }_{i-1}^{c}(t), \end{aligned} $$

where \(e_{i+1}(t)=x_{i+1}(t)-\nu _{i}^{c}(t)\), \(\tilde{\nu }_{i}(t)=\nu _{i}^{c}(t)-\nu _{i}(t)\),

$$ \dot{\nu }_{i}^{c}(t)=-\sigma _{i} \nu _{i}^{c}(t)+ \sigma _{i} \nu _{2}(t),\quad\quad \nu _{i}^{c}(0)=\nu _{i}(0) $$

and the virtual control input \(\nu _{i}(t)\) can be constructed as

$$ \nu _{i}(t)=-k_{i}e_{i}(t)-e_{i-1}(t)-f_{i} \bigl(\bar{\pmb{x}}_{i}(t)\bigr)- \pmb{\varphi }_{i}^{T} \bigl(\bar{\pmb{x}}_{i}(t)\bigr) \hat{\pmb{\vartheta }}(t)+ \dot{\nu }_{i-1}^{c}(t), $$

where \(k_{i}>0\) and \(\sigma _{i}>1\) are two design parameters.

Substituting (14) into (12) yields

$$ \dot{e}_{i}(t) =-k_{i}e_{i}(t) +e_{i+1}(t)-e_{i-1}(t)+\tilde{\nu } _{i}(t)- \pmb{ \varphi }_{i}^{T}\bigl(\bar{\pmb{x}}_{i}(t)\bigr) \tilde{\pmb{\vartheta }}(t). $$

Step n.

In the final step, the control input \(u(t)\) will be constructed. Based on the discussion in Step \({n-1}\), we have

$$ \begin{aligned}[b] \dot{e}_{n}(t) & = \dot{x}_{n}(t)-\dot{\nu }_{n-1}^{c}(t) \\ & =f_{n}\bigl( \pmb{x}(t)\bigr)+d(t)+u(t)+\pmb{\varphi }_{n}^{T}\bigl(\bar{\pmb{x}}(t)\bigr) \pmb{\vartheta }-\dot{ \nu }_{n-1}^{c}(t). \end{aligned} $$

Then the control input can be given as

$$ u(t)=-k_{n}e_{n}(t)-f_{n}\bigl( \pmb{x}(t)\bigr)-\pmb{\varphi }_{n}^{T}\bigl(\bar{ \pmb{x}}(t) \bigr) \hat{\pmb{\vartheta }}(t)+\dot{\nu }_{n-1}^{c}(t)- \hat{d}^{*}(t) \operatorname{sign}\bigl(e_{n}(t) \bigr)-e_{n-1}, $$

where \(k_{n}>0\) is a design parameter, \(\hat{d}^{*}(t)\) is the estimation of the unknown positive constant \(d^{*}\). Then, (16) and (17) implies

$$ \dot{e}_{n}(t) =-k_{n}e_{n}(t)+d(t)- \hat{d}^{*}(t) \operatorname{sign}\bigl(e _{n}(t)\bigr)-\pmb{\varphi }_{n}^{T}\bigl(\bar{\pmb{x}}(t)\bigr) \tilde{\pmb{\vartheta }}(t)-e_{n-1}. $$

3.2 Stability analysis

The proposed filters, i.e., (6), (9), (13) can guarantee that \(\tilde{\nu }_{i}(t)\) is sufficiently small eventually. To prove this result, we give the following lemma first.

Lemma 1

Suppose that \(z(t)\) satisfies \(\vert z(t) \vert \leq \beta \), \(\vert \dot{z}(t) \vert \leq \gamma \) and

$$ \dot{y}(t)=-w\bigl(y(t)-z(t)\bigr), \quad\quad y(0)=z(0), $$

where \(w>0\). Then we have \(\vert y(t)-z(t) \vert \leq \frac{\gamma }{w}\).


Denote \(\tilde{y}(t)=y(t)-z(t)\). It is easy to see that \(\varpi (0)=0\). Then we have

$$ \dot{\tilde{y}}(t)=-w \tilde{y}(t)-\dot{z}(t). $$

The solution of (20) can be given as

$$ \tilde{y}(t)=- \int _{0}^{t} \dot{z}(t) e^{-w(\tau -t)}\,d\tau . $$

Thus, we have

$$ \begin{aligned}[b] \bigl\vert \tilde{y}(t) \bigr\vert &= \biggl\vert \int _{0}^{t} \dot{z}(t) e^{-w(\tau -t)}\,d\tau \biggr\vert \\ &\leq \int _{0}^{t} \bigl\vert \dot{z}(t) \bigr\vert e^{-w(\tau -t)}\,d\tau \\ & \leq \gamma \int _{0}^{t} e^{-w(\tau -t)}\,d\tau \\ & = \frac{\gamma }{w}\bigl(1-e ^{-wt}\bigr). \end{aligned} $$

Thus, \(\vert y(t)-z(t) \vert \leq \frac{\gamma }{w}\), and this ends the proof of Lemma 1. □

Based on above result, we can easily obtain the following theorem.

Theorem 1

Under Assumptions 3 and 4, we see that, for any \(\mu _{i}>0\), there exists a constant \(T>0\), for all \(t>T\), \(\vert \tilde{\nu }_{i}(t) \vert \leq \mu _{i}\) if sufficiently large parameters \(\sigma _{i}\) are chosen.


Based on (6), (9), (13), Assumptions 3 and 4, and Lemma 1, we know that for \(i=1,\ldots,n\) and any \(\mu _{i}>0\)

$$ \bigl\vert \tilde{\nu }_{i}(t) \bigr\vert = \bigl\vert \nu _{i}^{c}(t)-\nu _{i}(t) \bigr\vert \leq \mu _{i} $$

if sufficiently large \(\sigma _{i}\) is chosen. □

Now, let us give the following main results.

Theorem 2

Consider system (1) based on Assumptions 14. Let the virtual control inputs be (5), (10) and (14), the dynamic surface be (6), (9), and (13). If the adaptation laws are designed as

$$ \dot{\hat{\pmb{\vartheta }}}(t)= \lambda _{11} \sum _{i=1}^{n} e_{i}(t) \pmb{\varphi }_{i}\bigl(\bar{\pmb{x}}_{i}(t)\bigr)- \lambda _{11}\lambda _{12} {\hat{\pmb{\vartheta }}}(t) $$


$$ \dot{\hat{d}}^{*}(t)=\lambda _{21} \bigl\vert e_{i}(t) \bigr\vert -\lambda _{21}\lambda _{22} {\hat{d}}^{*}(t), $$

where \(\lambda _{11}\), \(\lambda _{12}\), \(\lambda _{21}\), \(\lambda _{22}\) are positive design parameters, then the proposed controller (17) guarantees that: (1) all signals in the closed-loop system keep bounded and (2) \(e_{i}(t)\) and \(\tilde{\nu }_{i}(t)\), \(i=1,2, \ldots , n\), converge to a sufficiently small region under proper controller design parameters.


It follows from (7) that

$$ e_{1}(t)\dot{e}_{1}(t) =-k_{1}e_{1}^{2}(t)+e_{1}(t)e_{2}(t)+e_{1}(t) \tilde{\nu }_{1}(t) -e_{1}(t)\pmb{\varphi }_{1}^{T}\bigl({ x}_{1}(t)\bigr)\tilde{\pmb{ \vartheta }}(t). $$

Based on (11) and (15), we know that, for \(i=2,3,\ldots,n\),

$$ e_{i}(t)\dot{e}_{i}(t) =-k_{i}e_{i}^{2}(t) +e_{i}(t)e_{i+1}(t)-e_{i-1}(t)e _{i}(t)+e_{i}(t) \tilde{\nu }_{i}(t)- e_{i}(t)\pmb{\varphi }_{i}^{T} \bigl(\bar{ \pmb{x}}_{i}(t)\bigr) \tilde{\pmb{\vartheta }}(t). $$

On the other hand, (18) implies

$$ e_{n}(t)\dot{e}_{n}(t) =-k_{n}e_{n}^{2}(t)+e_{n}(t)d(t)- \hat{d}^{*}(t) \bigl\vert e_{n}(t) \bigr\vert -e_{n}(t)\pmb{\varphi }_{n}^{T}\bigl({\pmb{x}}(t) \bigr) \tilde{\pmb{\vartheta }}(t)-e_{n-1}(t)e_{n}(t). $$

Thus, it follows from (26), (27), (28) and Assumption 3 that

$$\begin{aligned} \sum_{i=1}^{n} e_{i}(t)\dot{e}_{i}(t) &=-k_{1}e_{1}^{2}(t)+e_{1}(t)e _{2}(t)+e_{1}(t)\tilde{\nu }_{1}(t) -e_{1}(t)\pmb{\varphi }_{1}^{T}\bigl( { x}_{1}(t)\bigr) \tilde{\pmb{\vartheta }}(t) \\ & \quad {} + \sum_{i=2}^{n-1} \bigl[ -k_{i}e_{i}^{2}(t) +e_{i}(t)\tilde{\nu }_{i}(t)+e_{i}(t)e_{i+1}(t)- e_{i}(t) \pmb{\varphi }_{i}^{T}\bigl(\bar{\pmb{x}}_{i}(t) \bigr) \tilde{\pmb{\vartheta }}(t) \bigr] \\ &\quad {} -k_{n}e_{n}^{2}(t)+e_{n}(t)d(t)-e_{n}(t) \hat{d}^{*}(t) \operatorname{sign}\bigl(e_{n}(t) \bigr)-e_{n}(t)\pmb{\varphi }_{n}^{T}\bigl(\bar{ \pmb{x}}(t)\bigr) \tilde{\pmb{\vartheta }}(t) \\ & \quad {} -\sum_{i=1}^{n} e_{i-1}(t)e_{i}(t)-e _{n-1}e_{n}(t) \\ &= -\sum_{i=1}^{n} k_{i} e_{i}^{2}(t) + \sum_{i=1} ^{n} e_{i}(t)\tilde{\nu }_{i}(t)+e_{n}(t)d(t) -\hat{d}^{*}(t) \bigl\vert e_{n}(t) \bigr\vert \\ & \quad {} - \sum_{i=1}^{n}e_{i}(t) \pmb{\varphi }_{i}^{T}\bigl(\bar{ \pmb{x}}_{i}(t) \bigr) \tilde{\pmb{\vartheta }}(t) \\ &\leq -\sum_{i=1}^{n} k _{i} e_{i}^{2}(t) + \sum_{i=1}^{n} e_{i}(t)\tilde{\nu }_{i}(t)+d^{*} \bigl\vert e _{n}(t) \bigr\vert -\hat{d}^{*}(t) \bigl\vert e_{n}(t) \bigr\vert \\ &\quad {} - \sum_{i=1}^{n}e_{i}(t) \pmb{\varphi }_{i}^{T}\bigl(\bar{\pmb{x}}_{i}(t) \bigr) \tilde{\pmb{\vartheta }}(t) \\ &= - \sum_{i=1}^{n} k_{i} e_{i}^{2}(t) + \sum_{i=1}^{n} e_{i}(t) \tilde{\nu }_{i}(t)-\tilde{d}^{*}(t) \bigl\vert e_{n}(t) \bigr\vert - \sum_{i=1}^{n}e_{i}(t) \pmb{\varphi }_{i}^{T}\bigl(\bar{\pmb{x}}_{i}(t) \bigr) \tilde{\pmb{\vartheta }}(t), \end{aligned}$$

where \(\tilde{d}^{*}(t)= \hat{d}^{*}(t)-d^{*}\) is the estimation error of the unknown constant \(d^{*}\).

The Lyapunov function is defined as

$$ V(t)=\frac{1}{2}\sum_{i=1}^{n} e_{i}^{2}(t)+ \frac{1}{2 \lambda _{11}} \tilde{\pmb{\vartheta }}_{i}^{T}(t)\tilde{\pmb{\vartheta }}_{i}(t)+ \frac{1}{2 \lambda _{21}} \tilde{d}^{*2}(t). $$

By using (24), (25), (29) and Theorem 1, the derivative of (30) with respect to time can be given as

$$ \begin{aligned}[b] \dot{V}(t) &=\sum _{i=1}^{n} e_{i}(t)\dot{e}_{i}(t)+ \frac{1}{ \lambda _{11}}\tilde{\pmb{\vartheta }}_{i}^{T}(t) { \dot{\tilde{\pmb{\vartheta }}}} _{i}(t)+ \frac{1}{\lambda _{21}} \tilde{d}^{*}(t) \dot{\tilde{d}}^{*}(t) \\ &= - \sum_{i=1}^{n} k_{i} e_{i}^{2}(t) + \sum_{i=1}^{n} e_{i}(t) \tilde{\nu }_{i}(t)-\tilde{d}^{*}(t) \bigl\vert e_{n}(t) \bigr\vert - \sum_{i=1}^{n}e_{i}(t) \pmb{\varphi }_{i}^{T}\bigl(\bar{\pmb{x}}_{i}(t) \bigr) \tilde{\pmb{\vartheta }}(t) \\ & \quad {} + \frac{1}{ \lambda _{11}}\tilde{\pmb{\vartheta }}_{i}^{T}(t) {\dot{\hat{\pmb{\vartheta }}}}_{i}(t)+ \frac{1}{\lambda _{21}} \tilde{d}^{*}(t) \dot{\hat{d}}^{*}(t) \\ &=- \sum_{i=1}^{n} k_{i} e _{i}^{2}(t) + \sum_{i=1}^{n} e_{i}(t)\tilde{\nu }_{i}(t)- \lambda _{12} \tilde{\pmb{\vartheta }}_{i}^{T}(t) {\hat{\pmb{\vartheta }}}_{i}(t)- {\lambda _{22}} \tilde{d}^{*}(t) { \hat{d}}^{*}(t) \\ & \leq - \sum_{i=1} ^{n} k_{i} e_{i}^{2}(t) + \sum_{i=1}^{n} \sqrt{k_{i}} \bigl\vert e_{i}(t) \bigr\vert \frac{ \mu _{i}}{\sqrt{k_{i}}}- \lambda _{12}\tilde{\pmb{\vartheta }}_{i} ^{T}(t) {\hat{\pmb{{\vartheta }}}}_{i}(t)-{\lambda _{22}} \tilde{d} ^{*}(t) {\hat{d}}^{*}(t) \\ &\leq - \sum_{i=1}^{n} \frac{k_{i}}{2} e _{i}^{2}(t) + \sum_{i=1}^{n} \frac{\mu _{i}^{2}}{2{k_{i}}}- \lambda _{12}\tilde{\pmb{\vartheta }}_{i}^{T}(t) {\hat{\pmb{\vartheta }}} _{i}(t)-{ \lambda _{22}} \tilde{d}^{*}(t) {\hat{d}}^{*}(t) \\ &=- \sum_{i=1}^{n} \frac{k_{i}}{2} e_{i}^{2}(t) + \sum_{i=1}^{n} \frac{\mu _{i}^{2}}{2{k_{i}}}- \lambda _{12}\tilde{\pmb{\vartheta }}_{i}^{T}(t) \bigl( {\tilde{\pmb{\vartheta }}}_{i}(t)+\pmb{\vartheta }\bigr)-{\lambda _{22}} \tilde{d}^{*}(t) \bigl( {\tilde{d}}^{*}(t)+ d^{*} \bigr) \\ & \leq - \sum_{i=1} ^{n} \frac{k_{i}}{2} e_{i}^{2}(t) + \sum_{i=1}^{n} \frac{\mu _{i}^{2}}{2 {k_{i}}} - \frac{\lambda _{12}}{2}\tilde{\pmb{\vartheta }}_{i}^{T}(t) \tilde{\pmb{\vartheta }}_{i}(t) -\frac{{\lambda _{22}}}{2} \tilde{d} ^{*2}(t)+ \frac{\lambda _{12}}{2} \Vert \pmb{\vartheta } \Vert ^{2} +\frac{ \lambda _{22}}{2} d^{*2} \\ &\leq -\frac{k}{2} \sum_{i=1}^{n} e_{i}^{2}(t)- \lambda _{12}\lambda _{11} \frac{1}{2\lambda _{11}} \tilde{\pmb{\vartheta }}_{i}^{T}(t) \tilde{\pmb{\vartheta }}_{i}(t)- \lambda _{21}\lambda _{22}\frac{1}{2\lambda _{21}} \tilde{d}^{*2}(t)+ \alpha, \end{aligned} $$

where \(k=\min \{k_{1},\ldots,k_{n}\}\), and \(\alpha = \sum_{i=1}^{n} \frac{\mu _{i}^{2}}{2{k_{i}}}+ \frac{\lambda _{12}}{2} \Vert \pmb{\vartheta } \Vert ^{2} +\frac{\lambda _{22}}{2} d^{*2}\) is a positive constant. According to the Lyapunov stability criterion, we know that (31) implied that \(\Vert \pmb{e}(t) \Vert \leq \sqrt{ \frac{2\alpha }{k}}\), \(\Vert \tilde{\pmb{\vartheta }}(t) \Vert \leq \sqrt{\frac{ \alpha }{\lambda _{11}\lambda _{12}}}\), and \(\Vert \tilde{d}^{*}(t) \Vert \leq \sqrt{\frac{ \alpha }{\lambda _{21}\lambda _{22}}}\). That is to say, \(e_{i}(t)\), \(\tilde{\pmb{\vartheta }}(t)\), \(\tilde{d}^{*}(t)\) are bounded. Further, we know that \(x_{i}(t)\), \(\hat{\pmb{\vartheta }}(t)\), \(\hat{d}^{*}(t)\), \(\nu _{i}(t)\), \(u(t)\) are all bounded. As a result, we know that all signals are bounded.

On the other hand, we know that if \(\mu _{i}\), \(\lambda _{12}\) and \(\lambda _{22}\) are small enough, then α will be small enough, too. Thus, the tracking errors \(e_{i}(t)\) will eventually converge to a small region at zero if α is small enough. □

Remark 2

From the results of Theorem 2 we know that to drive the tracking errors \(e_{i}(t)\) small enough, we should choose small enough \(\mu _{i}\), \(\lambda _{12}\) and \(\lambda _{22}\). We know that \(\mu _{i}\) will be arbitrarily small if \(\sigma _{i}\) are chosen large enough. With respect to \(\lambda _{12}\) and \(\lambda _{22}\), in some literature, these parameters are set to be zero, for example, in [50,51,52,53,54]. However, we know in this case that the boundedness of the updated parameters cannot be guaranteed. Thus, in our method, these parameters are introduced for the purpose to guarantee the boundedness of all signals in the closed-loop system. In the simulation, to achieve good control performance, we can set these parameters sufficiently small.

Remark 3

In this paper, the dynamic surfaces (6), (10) and (14) are used to get the estimation of the virtual control inputs and their derivatives. It should be mentioned that the proposed Lemma 1 plays an important role in the stability analysis, which can guarantee the estimation error to be as small as possible. The estimation error can be adjusted by the design parameter \(\sigma _{i}\). In fact, in practical applications, one does not to select too large \(\sigma _{i}\), which is indicated by the following simulation results.

Remark 4

In the conventional ABC method, every middle variable is treated as an input, and by using Lyapunov stability theorems, a virtual control input is designed. In the next step, the derivative of the virtual input is needed. However, as the order increases, it is more and more difficult to get the exact value of the derivative of the virtual input. Thus, the “explosion of complexity” occurs. To overcome this problem, in [2, 40,41,42], the derivative of the virtual input was estimated by using a fuzzy logic system, however, more control energy is needed and more computational burden will be added to the control system. In this paper, the ADSC method was proposed for SFNS with parametric uncertainty. The ADSC is an extension of the ABC, which is effective for handling SFNS. By using the proposed dynamical surface, i.e. (6), (10) and (14), the estimation of the derivatives of the virtual inputs is easy to obtain. As a result, the “explosion of complexity” problem can be solved effectively.

4 Simulation example

To indicate the effectiveness of the proposed control method, the well-known Chua chaotic system will be used in the simulation, which can be described as [20]

$$ \textstyle\begin{cases} \dot{x}_{1}(t) = 11.25 x_{2}(t) -11.25 x_{1}(t) -f(x_{1}(t)),\\ \dot{x}_{2}(t) = x_{3}(t) + x_{1}(t) -x_{2}(t)+ \pmb{\varphi }_{2} ^{T}(\bar{\pmb{x}}_{2}(t)) \pmb{\vartheta },\\ \dot{x}_{3}(t) = 18.6x _{2}(t)+d(t)+u(t), \end{cases} $$


$$ f\bigl(x_{1}(t)\bigr)=-0.68x_{1}(t)-0.545\bigl( \bigl\vert x_{1}(t)+1 \bigr\vert - \bigl\vert x_{1}(t)-1 \bigr\vert \bigr). $$

In system (32), \(\pmb{\varphi }_{1}({ x}_{1}(t))= \pmb{\varphi }_{3}({\pmb{x}}(t))\equiv 0\). The uncontrolled and undisturbed system (32) (i.e., \(u(t)=d(t)=0\), \(\pmb{\varphi }_{2}(\bar{\pmb{x}}_{2}(t))= \pmb{0}\)) shows chaotic behavior, which is depicted in Fig. 1.

Figure 1
figure 1

Chaotic behavior of system (32) under initial condition \(\pmb{x}(0)=[1.6, -1.1, 0.7]\)

In the simulation, let \(\pmb{\vartheta }=[1.5, 2.1, -1.5]^{T}\), \(\pmb{\varphi }_{2}(\bar{\pmb{x}}_{2}(t))=[x_{1}(t), x_{2}(t), x_{1}(t) \sin x_{2}(t)]^{T}\), and the disturbance is set as \(d(t)=0.5\sin t\). The referenced signal \(x_{d}(t)\) and its first-order derivative is produced by the following differential equation:


where \(\pmb{\zeta }(0)=[0, 0]^{T}\), \(\zeta _{c}(t)=\pi /6\) for \(t\in [0, 5]\) and \(\zeta _{c}(t)=0\) for \(t>5\) with \(x_{d}(t)= \zeta _{1}(t)\) and \(\dot{x}_{d}(t)= \zeta _{2}(t)\).

The controller design parameters are chosen as \(k_{1}=k_{2}=k_{3}=1.5\), \(\sigma _{1}=\sigma _{2}=\sigma _{3}=20\), \(\lambda _{11}=\lambda _{21}=10\), \(\lambda _{21}=\lambda _{22}=0.05\). The true value of \(\pmb{\vartheta }\) is \(\pmb{\vartheta }=[-0.5, 0.1, 0.5]^{T}\). The initial conditions for \(\hat{\pmb{\vartheta }}(t) \) and \(\hat{d}^{*}(t)\) are \(\hat{\pmb{\vartheta }}(0)=\pmb{0}\) and \(\hat{d}^{*}(0)=0\), respectively.

Figures 26 depict the simulation results. The tracking performance is indicated in Fig. 2, from which we can see that the output variable \(x_{1}(t)\) of system (32) follows the desired signal \(x_{d}(t)\) generated from system (33) in a very short time, i.e., the proposed method guarantee a fast convergence of the tracking error. The time response of the control input \(u(t)\) is shown in Fig. 3. It should be pointed out that the chattering phenomenon can be seen in Fig. 3 because the sign function is included in the proposed controller (17). In fact, to cancel the chattering, we can replace the sign function with some continuous function, such as the arctan function. The updated parameters \(\hat{\vartheta }_{1}(t)\), \(\hat{\vartheta }_{2}(t)\), \(\hat{\vartheta }_{3}(t)\) and \(\hat{d}^{*}(t)\) are presented in Fig. 4, from which we can see that the boundedness of these parameters can be guaranteed. Finally, the tracking performance between \(\nu _{1}(t)(t)\) and \(\nu _{1}^{c}(t)\), and \(\nu _{2}(t)(t)\) and \(\nu _{2}^{c}(t)\) is given in Fig. 5 and Fig. 6, respectively. It is indicated that the proposed dynamic surfaces have very good estimation ability.

Figure 2
figure 2

\(x_{1}(t)\) and \(x_{d}(t)\)

Figure 3
figure 3

Control input \(u(t)\)

Figure 4
figure 4

The updated parameters

Figure 5
figure 5

Virtual control input \(\nu _{1}(t)\) and its estimation \(\nu _{1}^{c}(t)\)

Figure 6
figure 6

Virtual control input \(\nu _{2}(t)\) and its estimation \(\nu _{2}^{c}(t)\)

5 Conclusions

In this paper, we present a strict stability analysis for a practical generalization of the conventional ABC method. Our main idea consists in designing a backstepping controller by using an auxiliary variable, i.e., out of a dynamic surface, whose derivative is easy to obtain to approximate the virtual controller. The proposed method is feasible even when the controlled system suffers from parametric uncertainty and external disturbances. It has been proven that our method is feasible for a wider range of SFNSs than conventional ABC and the dynamic surface can also be utilized to enhance constraints on the state trajectories. The proposed ADSC guarantees that all signals in the closed-loop system keep bounded and tracking errors converge to a sufficiently small region. The effectiveness of our method has been verified by a simulation results. Our future research direction include: (1) Design an extended controller consider how to deduce the requirement of the system model; (2) Combined our method with some robust control approach, for example, adaptive fuzzy control, adaptive neural network control, sliding mode control.


  1. Krstic, M., Kanellakopoulos, I., Kokotovic, P.V., et al.: Nonlinear and Adaptive Control Design, vol. 222. Wiley, New York (1995)

    MATH  Google Scholar 

  2. Liu, H., Pan, Y., Li, S., Chen, Y.: Adaptive fuzzy backstepping control of fractional-order nonlinear systems. IEEE Trans. Syst. Man Cybern. Syst. 47(8), 2209–2217 (2017)

    Article  Google Scholar 

  3. Li, H., Wang, L., Du, H., Boulkroune, A.: Adaptive fuzzy backstepping tracking control for strict-feedback systems with input delay. IEEE Trans. Fuzzy Syst. 25(3), 642–652 (2017)

    Article  Google Scholar 

  4. Coron, J.-M., Hu, L., Olive, G.: Finite-time boundary stabilization of general linear hyperbolic balance laws via Fredholm backstepping transformation. Automatica 84, 95–100 (2017)

    Article  MathSciNet  Google Scholar 

  5. Chen, C.P., Wen, G.-X., Liu, Y.-J., Liu, Z.: Observer-based adaptive backstepping consensus tracking control for high-order nonlinear semi-strict-feedback multiagent systems. IEEE Trans. Cybern. 46(7), 1591–1601 (2016)

    Article  Google Scholar 

  6. Kwan, C., Lewis, F.L.: Robust backstepping control of nonlinear systems using neural networks. IEEE Trans. Syst. Man Cybern., Part A, Syst. Hum. 30(6), 753–766 (2000)

    Article  Google Scholar 

  7. Vaidyanathan, S., Volos, C., Pham, V.-T., Madhavan, K., Idowu, B.A.: Adaptive backstepping control, synchronization and circuit simulation of a 3-d novel jerk chaotic system with two hyperbolic sinusoidal nonlinearities. Arch. Control Sci. 24(3), 375–403 (2014)

    Article  MathSciNet  Google Scholar 

  8. Li, Y., Sui, S., Tong, S.: Adaptive fuzzy control design for stochastic nonlinear switched systems with arbitrary switchings and unmodeled dynamics. IEEE Trans. Cybern. 47(2), 403–414 (2017)

    Google Scholar 

  9. Wu, H.: Liouville-type theorem for a nonlinear degenerate parabolic system of inequalities. Math. Notes Acad. Sci. USSR 103(1–2), 155–163 (2018)

    MathSciNet  MATH  Google Scholar 

  10. Hao, X., Zuo, M., Liu, L.: Multiple positive solutions for a system of impulsive integral boundary value problems with sign-changing nonlinearities. Appl. Math. Lett. 82, 24–31 (2018)

    Article  MathSciNet  Google Scholar 

  11. Wang, P., Liu, X., Liu, Z.: The convexity of the level sets of maximal strictly space-like hypersurfaces defined on 2-dimensional space forms. Nonlinear Anal. 174, 79–103 (2018)

    Article  MathSciNet  Google Scholar 

  12. Peng, X., Shang, Y., Zheng, X.: Lower bounds for the blow-up time to a nonlinear viscoelastic wave equation with strong damping. Appl. Math. Lett. 76, 66–73 (2018)

    Article  MathSciNet  Google Scholar 

  13. Sun, W.W.: Stabilization analysis of time-delay Hamiltonian systems in the presence of saturation. Appl. Math. Comput. 217(23), 9625–9634 (2011)

    MathSciNet  MATH  Google Scholar 

  14. Sun, W., Peng, L.: Observer-based robust adaptive control for uncertain stochastic Hamiltonian systems with state and input delays. Nonlinear Anal., Model. Control 19(4), 626–645 (2014)

    Article  MathSciNet  Google Scholar 

  15. Guo, Y.: Exponential stability analysis of travelling waves solutions for nonlinear delayed cellular neural networks. Dyn. Syst. 32(4), 490–503 (2017)

    Article  MathSciNet  Google Scholar 

  16. Xu, Y., Zhang, H.: Positive solutions of an infinite boundary value problem for nth-order nonlinear impulsive singular integro-differential equations in Banach spaces. Appl. Math. Comput. 218(9), 5806–5818 (2012)

    MATH  Google Scholar 

  17. Liu, C., Wu, X.: The boundness of the operator-valued functions for multidimensional nonlinear wave equations with applications. Appl. Math. Lett. 74, 60–67 (2017)

    Article  MathSciNet  Google Scholar 

  18. Lin, X., Zhao, Z.: Iterative technique for third-order differential equation with three-point nonlinear boundary value conditions. Electron. J. Qual. Theory Differ. Equ. 2016, 12 (2016)

    Article  MathSciNet  Google Scholar 

  19. Pan, Y., Yu, H.: Biomimetic hybrid feedback feedforward neural-network learning control. IEEE Trans. Neural Netw. Learn. Syst. 28(6), 1481–1487 (2017)

    Article  Google Scholar 

  20. Liu, H., Li, S., Wang, H., Huo, Y., Luo, J.: Adaptive synchronization for a class of uncertain fractional-order neural networks. Entropy 17(10), 7185–7200 (2015)

    Article  MathSciNet  Google Scholar 

  21. Liu, H., Li, S., Wang, H., Sun, Y.: Adaptive fuzzy control for a class of unknown fractional-order neural networks subject to input nonlinearities and dead-zones. Inf. Sci. 454–455, 30–45 (2018)

    Article  MathSciNet  Google Scholar 

  22. Dong, W., Farrell, J.A., Polycarpou, M.M., Djapic, V., Sharma, M.: Command filtered adaptive backstepping. IEEE Trans. Control Syst. Technol. 20(3), 566–580 (2012)

    Article  Google Scholar 

  23. Pan, Y., Yu, H.: Composite learning from adaptive dynamic surface control. IEEE Trans. Autom. Control 61(9), 2603–2609 (2016)

    Article  MathSciNet  Google Scholar 

  24. Li, F., Gao, Q.: Blow-up of solution for a nonlinear Petrovsky type equation with memory. Appl. Math. Comput. 274, 383–392 (2016)

    MathSciNet  Google Scholar 

  25. Gao, L., Wang, D., Wang, G.: Further results on exponential stability for impulsive switched nonlinear time-delay systems with delayed impulse effects. Appl. Math. Comput. 268, 186–200 (2015)

    MathSciNet  Google Scholar 

  26. He, X., Qian, A., Zou, W.: Existence and concentration of positive solutions for quasilinear Schrödinger equations with critical growth. Nonlinearity 26(12), 3137 (2013)

    Article  MathSciNet  Google Scholar 

  27. Feng, Y.-H., Liu, C.-M.: Stability of steady-state solutions to Navier–Stokes–Poisson systems. J. Math. Anal. Appl. 462(2), 1679–1694 (2018)

    Article  MathSciNet  Google Scholar 

  28. Bai, Y., Mu, X.: Global asymptotic stability of a generalized sirs epidemic model with transfer from infectious to susceptible. J. Appl. Anal. Comput. 8(2), 402–412 (2018)

    MathSciNet  Google Scholar 

  29. Cao, X., Wang, J.: Finite-time stability of a class of oscillating systems with two delays. Math. Methods Appl. Sci. 41(13), 4943–4954 (2018)

    Article  MathSciNet  Google Scholar 

  30. Shen, T., Xin, J., Huang, J.: Time–space fractional stochastic Ginzburg–Landau equation driven by Gaussian white noise. Stoch. Anal. Appl. 36(1), 103–113 (2018)

    Article  MathSciNet  Google Scholar 

  31. Li, M., Wang, J.: Exploring delayed Mittag-Leffler type matrix functions to study finite time stability of fractional delay differential equations. Appl. Math. Comput. 324, 254–265 (2018)

    MathSciNet  Google Scholar 

  32. Liu, S., Wang, J., Zhou, Y., Fečkan, M.: Iterative learning control with pulse compensation for fractional differential systems. Math. Slovaca 68(3), 563–574 (2018)

    Article  MathSciNet  Google Scholar 

  33. Zhang, J., Wang, J.: Numerical analysis for Navier–Stokes equations with time fractional derivatives. Appl. Math. Comput. 336, 481–489 (2018)

    Article  MathSciNet  Google Scholar 

  34. Zhang, J., Lou, Z., Ji, Y., Shao, W.: Ground state of Kirchhoff type fractional Schrödinger equations with critical growth. J. Math. Anal. Appl. 462(1), 57–83 (2018)

    Article  MathSciNet  Google Scholar 

  35. Wang, Y., Jiang, J.: Existence and nonexistence of positive solutions for the fractional coupled system involving generalized p-Laplacian. Adv. Differ. Equ. 2017(1), 337 (2017)

    Article  MathSciNet  Google Scholar 

  36. Feng, Q., Meng, F.: Traveling wave solutions for fractional partial differential equations arising in mathematical physics by an improved fractional Jacobi elliptic equation method. Math. Methods Appl. Sci. 40(10), 3676–3686 (2017)

    Article  MathSciNet  Google Scholar 

  37. Hao, X.: Positive solution for singular fractional differential equations involving derivatives. Adv. Differ. Equ. 2016(1), 139 (2016)

    Article  MathSciNet  Google Scholar 

  38. Diblík, J., Feckan, M., Pospíšil, M.: On the new control functions for linear discrete delay systems. SIAM J. Control Optim. 52(3), 1745–1760 (2014)

    Article  MathSciNet  Google Scholar 

  39. Diblík, J., Khusainov, D.Y., Baštinec, J., Sirenko, A.: Exponential stability of linear discrete systems with constant coefficients and single delay. Appl. Math. Lett. 51, 68–73 (2016)

    Article  MathSciNet  Google Scholar 

  40. Wang, T., Zhang, Y., Qiu, J., Gao, H.: Adaptive fuzzy backstepping control for a class of nonlinear systems with sampled and delayed measurements. IEEE Trans. Fuzzy Syst. 23(2), 302–312 (2015)

    Article  Google Scholar 

  41. Wang, Y., Cao, L., Zhang, S., Hu, X., Yu, F.: Command filtered adaptive fuzzy backstepping control method of uncertain non-linear systems. IET Control Theory Appl. 10(10), 1134–1141 (2016)

    Article  MathSciNet  Google Scholar 

  42. Sadek, U., Sarjaš, A., Chowdhury, A., Svečko, R.: Improved adaptive fuzzy backstepping control of a magnetic levitation system based on symbiotic organism search. Appl. Soft Comput. 56, 19–33 (2017)

    Article  Google Scholar 

  43. Zhai, D., Xi, C., An, L., Dong, J., Zhang, Q.: Prescribed performance switched adaptive dynamic surface control of switched nonlinear systems with average dwell time. IEEE Trans. Syst. Man Cybern. Syst. 47(7), 1257–1269 (2017)

    Article  Google Scholar 

  44. Singh, U.P., Jain, S., Singh, R., Parmar, M., Makwana, R., Kumare, J.: Dynamic surface control based ts-fuzzy model for a class of uncertain nonlinear systems. Int. J. Control Theory Appl. 9(2), 1333–1345 (2016)

    Google Scholar 

  45. Uyen, H.T.T., Tuan, P.D., Van Tu, V., Quang, L., Minh, P.X.: Adaptive neural networks dynamic surface control algorithm for 3 dof surface ship. In: System Science and Engineering (ICSSE), 2017 International Conference on, pp. 71–76. IEEE (2017)

    Chapter  Google Scholar 

  46. Semprun, K.A., Yan, L., Butt, W.A., Chen, P.C.: Dynamic surface control for a class of nonlinear feedback linearizable systems with actuator failures. IEEE Trans. Neural Netw. Learn. Syst. 28(9), 2209–2214 (2017)

    MathSciNet  Google Scholar 

  47. Farrell, J., Sharma, M., Polycarpou, M.: Backstepping-based flight control with adaptive function approximation. J. Guid. Control Dyn. 28(6), 1089–1102 (2005)

    Article  Google Scholar 

  48. Pan, Y., Yu, H.: Dynamic surface control via singular perturbation analysis. Automatica 57, 29–33 (2015)

    Article  MathSciNet  Google Scholar 

  49. Ma, J., Zheng, Z., Li, P.: Adaptive dynamic surface control of a class of nonlinear systems with unknown direction control gains and input saturation. IEEE Trans. Cybern. 45(4), 728–741 (2015)

    Article  Google Scholar 

  50. Zhang, X., Liu, L., Wu, Y.: The uniqueness of positive solution for a fractional order model of turbulent flow in a porous medium. Appl. Math. Lett. 37, 26–33 (2014)

    Article  MathSciNet  Google Scholar 

  51. Wang, J., Yuan, Y., Zhao, S.: Fractional factorial split-plot designs with two-and four-level factors containing clear effects. Commun. Stat., Theory Methods 44(4), 671–682 (2015)

    Article  MathSciNet  Google Scholar 

  52. Zhang, L., Zheng, Z.: Lyapunov type inequalities for the Riemann–Liouville fractional differential equations of higher order. Adv. Differ. Equ. 2017(1), 270 (2017)

    Article  MathSciNet  Google Scholar 

  53. Liu, H., Pan, Y., Li, S., Chen, Y.: Synchronization for fractional-order neural networks with full/under-actuation using fractional-order sliding mode control. Int. J. Mach. Learn. Cybern. 9(7), 1219–1232 (2018)

    Article  Google Scholar 

  54. Liu, H., Li, S., Li, G., Wang, H.: Adaptive controller design for a class of uncertain fractional-order nonlinear systems: an adaptive fuzzy approach. Int. J. Fuzzy Syst. 20(2), 366–379 (2018)

    Article  MathSciNet  Google Scholar 

Download references


Not applicable.


This work is supported by the National Natural Science Foundation of China (Grant No. 11302184) and the Young and Middle-aged Teacher Education and Science Research Foundation of Fujian Province of China (Grant No. JAT170423).

Author information

Authors and Affiliations



All authors contributed equally to the writing of this paper. All authors conceived of the study, participated in its design and coordination, read and approved the final manuscript.

Corresponding author

Correspondence to Chenhui Wang.

Ethics declarations

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Wang, C. Adaptive dynamic surface control of parametric uncertain and disturbed strict-feedback nonlinear systems. Adv Differ Equ 2019, 29 (2019).

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI:


  • Strict-feedback nonlinear system
  • Adaptive backstepping control
  • Adaptive dynamic surface control
  • Mismatched uncertainty