Finite-time Control of Complex Systems and Their ApplicationsView this Special Issue
Research Article | Open Access
Qiangqiang Zhu, Ben Niu, Shengtao Li, Peiyong Duan, Dong Yang, "Finite-Time Adaptive Tracking Control for a Class of Pure-Feedback Nonlinear Systems with Disturbances via Decoupling Technique", Complexity, vol. 2020, Article ID 1354340, 11 pages, 2020. https://doi.org/10.1155/2020/1354340
Finite-Time Adaptive Tracking Control for a Class of Pure-Feedback Nonlinear Systems with Disturbances via Decoupling Technique
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.
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 ). 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 ). 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 ). For real variables and and any positive constants , and , the following relation holds:
Lemma 3. (see ). 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 ). 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 , 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:
Remark 3. As can be seen from formulas (28) and (43), we used Yang’s inequality to obtain in advance such that only one adaptive parameter should be estimated in each step of the controller design. However, multidimensional vectors (weight vectors) are directly estimated in some literatures such as in , which makes the design of the adaptive rate more difficult. Therefore, the method we adopt can reduce the number of adaptive parameters compared to the previous method in . Step : define , where is the ideal weight vector, and is the parameter estimation error. Choose the Lyapunov function candidate for system (13) as follows: where is a design positive constant. It can be seen from the previous step that the virtual control signal can be constructed such that the following inequality can be obtained: As we all know, the dynamic equation of is as follows: with From (49), one can get the time derivative of along (51) as Similar to the processing in the above steps, we define a new function as , where is a design constant. Then, (53) can be rewritten as We designed the actual controller and the adaptation law as follows: where and are two design constants. Like (38)–(48), it is easy to obtain where and .
4. Stability Analysis
In this section, the main result will be summarized in Theorem 1.
Theorem 1. Consider system (1) satisfying Assumptions 1–2, and suppose that the finite-time adaptive controller (55) and the adaptive law (46) as well as (56) are constructed based on the decoupling technology. As long as the design parameters are properly selected, it can be ensured that the system output follows the desired trajectory , and at the same time all the signals of the pure-feedback nonlinear systems (1) are SGUFB.
Proof. For the Lyapunov function candidate , define . Then, it follows from (57) thatFrom the definition of , we can get ; further rewrite (58) asFor Lemma 2, we choose the appropriate parameters for , and . Then, one can obtainSubstituting (60) into (59) and using the zoom method of (6), the following inequality holds:Applying Lemma 3, we can further simplify the time derivative of aswhereNow, define with , , and . Based on Lemma 1, we can get for . So, the solution of is bounded in a finite time and all the signals in the nonlinear system (1) are SGUFB. To be more precise, the finite-time controller proposed by us can converge the tracking error to a small neighborhood of zero and remains there after the finite time . In order to be more intuitive, we will confirm the research results through a simulation example.
5. A Simulation Example
In this section, we will demonstrate the effectiveness of the proposed scheme through the following simulation example.
Let us consider a two-dimensional nonaffine pure-feedback nonlinear system with disturbance as follows:where , are the system state and output and are the system control input, respectively. The reference signal of the system output is given as . According Lemma 3, we construct virtual control signal and actual controller as well as the adaptation law for system (64) as follows:
Then, the initial conditions are given as , and . We choose the design parameter in the simulation as follows: . Finally, we get Figures 1–6. Figure 1 denotes the responses of the system out and reference signal of the example. The tracking error of the example between the output of the system and the reference signal converges to a small neighborhood, which can be observed intuitively in Figure 2. Figure 3 shows the response of the state variable. From the trends in Figures 4 and 5, it is clear to see that the boundedness of the adaptive rate and . It can be seen from these results that even though the nonaffine function of our simulation system is not differentiable, it has achieved excellent control performance. Finally, the response of the control law is shown in Figure 6.
A novel finite-time adaptive controller has been presented for the considered pure-feedback nonlinear system in this paper. The first design difficulty in this paper is to decouple the pure feedback system without using the median value theorem. The second design difficulty is the extremely complicated formula derivation when designing the finite-time controller, in order to make the system variables converge to the equilibrium quickly in a limited time. Compared with the existing results, the developed method addressed the finite-time adaptive tracking control problem for the pure feedback system whose nonaffine functions may not be differentiable. Furthermore, the decoupling technology has been used in our design frame to eliminate the restrictive assumption of partial derivatives of nonaffine functions, which makes the method more widely used. It is worth noting that the finite-time controller constructed by us can not only ensure that the system state variables quickly converge to equilibrium within a limited time but also improve the robustness of the closed-loop system. In the future, the finite-time adaptive control of various types of complex switched nonlinear systems can be further discussed, such as multiple input multiple output stochastic switched nonlower triangular systems and stochastic switched nonlower triangular pure feedback nonlinear systems.
The data used to support the findings of this study are included within the article.
Conflicts of Interest
The author declares that there are no conflicts of interest.
This work was supported in part by the National Natural Science Foundation of China, under Grants 61873151, 61773192, 61773246, and 61803225, Shandong Provincial Natural Science Foundation, China, under Grant ZR2019MF009, Taishan Scholar Project of Shandong Province of China, under Grant tsqn20190-9078, and Major Program of Shandong Province Natural Science Foundation, under Grant ZR2018ZB0419.
- G. C. Walsh, O. Beldiman, and L. G. Bushnell, “Asymptotic behavior of nonlinear networked control systems,” IEEE Transactions on Automatic Control, vol. 46, no. 7, pp. 1093–1097, 2001.
- B. Niu, P. Zhao, J. D. Liu, H. J. Ma, and Y. J. Liu, “Global adaptive control of switched uncertain nonlinear systems: an improved MDADT method,” Automatica, vol. 115, Article ID 108872, 2020.
- B. Xian, D. M. Dawson, M. S. deQueiroz, and J. Chen, “A continuous asymptotic tracking control strategy for uncertain nonlinear systems,” IEEE Transactions on Automatic Control, vol. 49, no. 7, p. 1206, 2004.
- B. Niu, M. Liu, and A. Li, “Global adaptive stabilization of stochastic high-order switched nonlinear non-lower triangular systems,” Systems Control Letters, vol. 136, Article ID 104596, 2020.
- X. Li, J. Shen, H. Akca, and R. Rakkiyappan, “LMI-based stability for singularly perturbed nonlinear impulsive differential systems with delays of small parameter,” Applied Mathematics and Computation, vol. 250, pp. 798–804, 2015.
- A. R. Teel, J. Peuteman, and D. Aeyels, “Semi-global practical asymptotic stability and averaging,” Systems & Control Letters, vol. 37, no. 5, pp. 329–334, 1999.
- D. Angeli, “Intrinsic robustness of global asymptotic stability,” Systems & Control Letters, vol. 38, no. 4-5, pp. 297–307, 1999.
- E. Panteley and A. Loria, “On global uniform asymptotic stability of nonlinear time-varying systems in cascade,” Systems & Control Letters, vol. 33, no. 2, pp. 131–138, 1998.
- J. Sun, Y. Zhang, and Q. Wu, “Less conservative conditions for asymptotic stability of impulsive control systems,” IEEE Transactions on Automatic Control, vol. 48, no. 5, pp. 829–831, 2003.
- B. Niu, Y. Liu, W. Zhou, H. Li, P. Duan, and J. Li, “Multiple lyapunov functions for adaptive neural tracking control of switched nonlinear nonlower-triangular systems,” IEEE Transactions on Cybernetics, vol. 50, no. 5, pp. 1877–1886, 2020.
- P. Dorato, “Short-time stability in linear time-varying systems,” Proceedings of IRE International Convention Record, pp. 83–87, 1961.
- L. Weiss and E. Infante, “Finite time stability under perturbing forces and on product spaces,” IEEE Transactions on Automatic Control, vol. 12, no. 1, pp. 54–59, 1967.
- J. Wu, D. Yang, X. Y. He, and X. D. Li, “Finite-time stability for a class of underactuated systems subject to time-varying disturbance,” Complexity, vol. 2020, Article ID 8704505, 7 pages, 2020.
- Y. P. Luo and Y. J. Yao, “Finite-time synchronization of uncertain complex dynamic networks with nonlinear coupling,” Complexity, vol. 2019, Article ID 9821063, 14 pages, 2019.
- J. Q. Lu, Y. Q. Wang, X. C. Shi, and J. D. Cao, “Finite-time bipartite consensus for multi-agent systems under detail-balanced antagonistic interactions,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2019.
- Y. Li, S. Tong, and T. Li, “Hybrid fuzzy adaptive output feedback control design for uncertain MIMO nonlinear systems with time-varying delays and input saturation,” IEEE Transactions on Fuzzy Systems, vol. 24, no. 4, pp. 841–853, 2015.
- Y. H. Zhang, J. Sun, H. J. Liang, and H. Y. Li, “Event-triggered adaptive tracking control for multiagent systems with unknown disturbances,” IEEE Transactions on Cybernetics, vol. 50, no. 3, pp. 890–901, 2020.
- Y. Li, S. Tong, and S. C. Tong, “Adaptive fuzzy output-feedback stabilization control for a class of switched nonstrict-feedback nonlinear systems,” IEEE Transactions on Cybernetics, vol. 47, no. 4, pp. 1007–1016, 2017.
- B. Niu, D. Wang, N. D. Alotaibi, and F. E. Alsaadi, “Adaptive neural state-feedback tracking control of stochastic nonlinear switched systems: an average dwell-time method,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 4, pp. 1076–1087, 2019.
- Z. L. Xiong, S. C. Qu, and J. Luo, “Adaptive multi-switching synchronization of high-order memristor-based hyperchaotic system with unknown parameters and its application in secure communication,” Complexity, Article ID 3827201, 18 pages, 2019.
- Y. T. Wen and X. M. Ren, “Neural networks-based adaptive control for nonlinear time-varying delays systems with unknown control direction,” IEEE Press, vol. 22, no. 10, pp. 1599–1612, 2011.
- B. Niu, D. Wang, M. Liu, X. M. Song, H. Q. Wang, and P. Y. Duan, “Adaptive neural output feedback controller design of switched non-lower triangular nonlinear systems with time-delays,” IEEE Transactions on Neural Networks and Learning Systems, 2019.
- C. F. Hsu, C. M. Lin, and T. T. Lee, “Wavelet adaptive backstepping control for a class of nonlinear systems,” IEEE Transactions on Neural Networks, vol. 17, no. 5, pp. 1175–1183, 2006.
- L. Ma, X. Huo, X. Zhao, B. Niu, and G. Zong, “Adaptive neural control for switched nonlinear systems with unknown backlash-like hysteresis and output dead-zone,” Neurocomputing, vol. 357, pp. 203–214, 2019.
- S. Xing, F. a. Liu, Q. Wang, X. Zhao, and T. Li, “A hierarchical attention model for rating prediction by leveraging user and product reviews,” Neurocomputing, vol. 332, pp. 417–427, 2019.
- L. Zhu, Z. Huang, Z. Li, L. Xie, and H. T. Shen, “Exploring auxiliary context: discrete semantic transfer hashing for scalable image retrieval,” IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 11, pp. 5264–5276, 2018.
- Y. Tan, M. Xiong, B. Niu, J. Liu, and S. Fei, “Distributed hybrid-triggered H∞ filter design for sensor networked systems with output saturations,” Neurocomputing, vol. 315, pp. 261–271, 2018.
- Q. Wang, Y. Zheng, G. Yang, W. Jin, X. Chen, and Y. Yin, “Multiscale rotation-invariant convolutional neural networks for lung texture classification,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 1, pp. 184–195, 2018.
- X. W. Zheng, B. Hu, D. J. Lu, and H. Liu, “A multi-objective virtual network embedding algorithm in cloud computing,” Journal of Internet Technology, vol. 17, no. 4, pp. 633–642, 2016.
- X. Li, D. O’Regan, and H. Akca, “Global exponential stabilization of impulsive neural networks with unbounded continuously distributed delays,” IMA Journal of Applied Mathematics, vol. 80, no. 1, pp. 85–99, 2015.
- B. Hu, H. Wang, X. Yu, W. Yuan, and T. He, “Sparse network embedding for community detection and sign prediction in signed social networks,” Journal of Ambient Intelligence and Humanized Computing, vol. 10, no. 1, pp. 175–186, 2019.
- C. Luo, C. Tan, X. Wang, and Y. Zheng, “An evolving recurrent interval type-2 intuitionistic fuzzy neural network for online learning and time series prediction,” Applied Soft Computing, vol. 78, pp. 150–163, 2019.
- X. Zheng, J. Tian, X. Xiao, X. Cui, and X. Yu, “A heuristic survivable virtual network mapping algorithm,” Soft Computing, vol. 23, no. 5, pp. 1453–1463, 2019.
- H. Zhang, H. Ji, and X. Wang, “Transfer learning from unlabeled data via neural networks,” Neural Processing Letters, vol. 36, no. 2, pp. 173–187, 2012.
- C. Huang, J. Q. Lu, G. S. Zhai, J. D. Cao, G. P. Lu, and M. Perc, “Stability and stabilization in probability of probabilistic boolean networks,” IEEE Transactions on Neural Networks and Learning Systems, 2020.
- B. Niu, D. Wang, N. D. Alotaibi, and F. E. Alsaadi, “Adaptive neural state-feedback tracking control of stochastic nonlinear switched systems: an average dwell-time method,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 4, pp. 1076–1087, 2018.
- H. Ma, H. J. Liang, Q. Zhou, and C. K. Ahn, “Adaptive dynamic surface control design for uncertain nonlinear strict-feedback systems with unknown control direction and disturbances,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 3, pp. 506–515, 2018.
- S. Tong and Y. Li, “Observer-based fuzzy adaptive control for strict-feedback nonlinear systems,” Fuzzy Sets and Systems, vol. 160, no. 12, pp. 1749–1764, 2009.
- T. Zhang, S. S. Ge, and C. C. Hang, “Adaptive neural network control for strict-feedback nonlinear systems using backstepping design,” Automatica, vol. 36, no. 12, pp. 1835–1846, 2000.
- S. Tong, Y. Li, Y. M. Li, and Y. Liu, “Observer-based adaptive fuzzy backstepping control for a class of stochastic nonlinear strict-feedback systems,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 41, no. 6, pp. 1693–1704, 2011.
- D. Wang and J. Huang, “Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form,” IEEE Transactions on Neural Networks, vol. 16, no. 1, pp. 195–202, 2005.
- B. Chen, X. Liu, K. Liu, and C. Lin, “Direct adaptive fuzzy control of nonlinear strict-feedback systems,” Automatica, vol. 45, no. 6, pp. 1530–1535, 2009.
- X. Peng, H. Q. Wu, and J. D. Cao, “Global nonfragile synchronization in finite time for fractional-order discontinuous neural netorks with nonlinear growth activations,” IEEE Transactions on Neural Networks and Learning Systems, vol. 30, no. 7, pp. 2123–2137, 2018.
- Z. Wang and H. Wu, “Global synchronization in fixed time for semi-Markovian switching complex dynamical networks with hybrid couplings and time-varying delays,” Nonlinear Dynamics, vol. 95, no. 3, pp. 2031–2062, 2019.
- M. Liu and H. Wu, “Stochastic finite-time synchronization for discontinuous semi-Markovian switching neural networks with time delays and noise disturbance,” Neurocomputing, vol. 310, pp. 246–264, 2018.
- Z. Wang and H. Wu, “Projective synchronization in fixed time for complex dynamical networks with nonidentical nodes via second-order sliding mode control strategy,” Journal of the Franklin Institute, vol. 355, no. 15, pp. 7306–7334, 2018.
- X. Peng, H. Wu, K. Song, and J. Shi, “Global synchronization in finite time for fractional-order neural networks with discontinuous activations and time delays,” Neural Networks, vol. 94, pp. 46–54, 2017.
- Y. Wang and Y. Song, “Fraction dynamic-surface-based neuroadaptive finite-time containment control of multiagent systems in nonaffine pure-feedback form,” IEEE Transactions on Neural Networks and Learning Systems, vol. 28, no. 3, pp. 678–689, 2017.
- J. Zhang, Q. Zhu, Y. Li, and X. Wu, “Homeomorphism mapping based neural networks for finite time constraint control of a class of nonaffine pure-feedback nonlinear systems,” Complexity, vol. 2019, Article ID 9053858, 11 pages, 2019.
- A.-M. Zou, Z.-G. Hou, and M. Tan, “Adaptive control of a class of nonlinear pure-feedback systems using fuzzy backstepping approach,” IEEE Transactions on Fuzzy Systems, vol. 16, no. 4, pp. 886–897, 2008.
- G. Sun, D. Wang, and Z. Peng, “Adaptive control based on single neural network approximation for non-linear pure-feedback systems,” IET Control Theory and Applications, vol. 6, no. 15, pp. 2387–2396, 2011.
- J. Lian, S. Hou, X. Sui, F. Xu, and Y. Zheng, “Deblurring retinal optical coherence tomography via a convolutional neural network with anisotropic and double convolution layer,” IET Computer Vision, vol. 12, no. 6, pp. 900–907, 2018.
- S. S. Ge and C. Wang, “Adaptive NN control of uncertain nonlinear pure-feedback systems,” Automatica, vol. 38, no. 4, pp. 671–682, 2002.
- M. Wang, X. P. Liu, and P. Shi, “Adaptive neural control of pure-feedback nonlinear time-delay systems via dynamic surface technique,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 41, no. 6, pp. 1681–1692, 2011.
- T. P. Zhang, H. Wen, and Q. Zhu, “Adaptive fuzzy control of nonlinear systems in pure feedback form based on input-to-state stability,” IEEE Transactions on Fuzzy Systems, vol. 18, no. 1, pp. 80–93, 2010.
- C. Wang, D. J. Hill, S. S. Ge, and G. Chen, “An ISS-modular approach for adaptive neural control of pure-feedback systems,” Automatica, vol. 42, no. 5, pp. 723–731, 2006.
- Y. Li, “Impulsive synchronization of stochastic neural networks via controlling partial states,” Neural Processing Letters, vol. 46, no. 1, pp. 59–69, 2017.
- C. Huang, J. Q. Lu, D. W. C. Ho, G. S. Zhai, and J. D. Cao, “Stabilization of probabilistic boolean networks via pinning control strategy,” Information Sciences, vol. 510, pp. 205–217, 2019.
- C. Huang, X. Zhang, H. K. Lam, and S. H. Tsai, “Synchronization analysis for nonlinear complex networks with reaction-diffusion terms using fuzzy-model-based approach,” IEEE Transactions on Fuzzy Systems, 2020.
- F. Wang, B. Chen, X. Liu, and C. Lin, “Finite-time adaptive fuzzy tracking control design for nonlinear Systems,” IEEE Transactions on Fuzzy Systems, vol. 26, no. 3, pp. 1207–1216, 2018.
- C. Qian and W. Lin, “Non-Lipschitz continuous stabilizers for nonlinear systems with uncontrollable unstable linearization,” Systems & Control Letters, vol. 42, no. 3, pp. 185–200, 2001.
- G. H. Hardy, J. E. Littlewood, and G. Polya, “Inequalities, Cambridge University Press, Cambridge, UK, 1952.
- Z. Liu, X. Dong, J. Xue, H. Li, and Y. Chen, “Adaptive neural control for a class of pure-feedback nonlinear systems via dynamic surface technique,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 9, pp. 1969–1975, 2016.
- W. S. Chen, L. C. Jiao, J. Li, and R. H. Li, “Adaptive NN backstepping output-feedback control for stochastic nonlinear strict-feedback systems with time-varying delays,” IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 40, no. 3, pp. 939–950, 2010.
- J. Wu, W. S. Chen, D. Zhao, and J. Li, “Globally stable direct adaptive backstepping NN control for uncertain nonlinear strict-feedback systems,” Neurocomputing, vol. 122, pp. 134–147, 2013.
Copyright © 2020 Qiangqiang Zhu 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.