Figures
Abstract
In this paper, control of uncertain fractional-order financial chaotic system with input saturation and external disturbance is investigated. The unknown part of the input saturation as well as the system’s unknown nonlinear function is approximated by a fuzzy logic system. To handle the fuzzy approximation error and the estimation error of the unknown upper bound of the external disturbance, fractional-order adaptation laws are constructed. Based on fractional Lyapunov stability theorem, an adaptive fuzzy controller is designed, and the asymptotical stability can be guaranteed. Finally, simulation studies are given to indicate the effectiveness of the proposed method.
Citation: Wang C (2016) Adaptive Fuzzy Control for Uncertain Fractional-Order Financial Chaotic Systems Subjected to Input Saturation. PLoS ONE 11(10): e0164791. https://doi.org/10.1371/journal.pone.0164791
Editor: Jun Ma, Lanzhou University of Technology, CHINA
Received: July 15, 2016; Accepted: October 2, 2016; Published: October 26, 2016
Copyright: © 2016 Chenhui Wang. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper.
Funding: This work was supported by the National Natural Science Foundation of China (Grant No. 11302184).
Competing interests: The author has declared that no competing interests exist.
1 Introduction
Thirty years ago, Stutzer, an economist, first obtained the chaotic behavior in financial system [1]. Up to now, it has been shown that financial systems can exhibit complicated behavior, for example, chaos [2–5]. Besides, financial crisis can be seen as a kind of chaotic phenomenon [2, 6]. Meanwhile, researchers have found that system uncertainties in the economic development, for example, the abrupt change variety of economy in frequency and the influence of non-financial elements, are increasing [7–10]. Accordingly, taking the chaotic behavior as well as system uncertainties into consideration, to solve financial crisis and some relevant problems, it is advisable to study the chaos control methods for financial systems.
Fractional calculus is an old topic which has a history more than 300 years. Now, it can be seen in many domains, ranging from life sciences and materials engineering to secret communication and control theory [11–21]. One of a major merits of fractional-order systems, compared with the integer ones, is that the fractional-order ones have memory, and they have been proven to be a powerful technique to describe the hereditary and memory properties of a lot of materials and processes. It is known that financial variables possess memories, so fractional-order models can be well used to describe dynamical behaviors in economic systems. Up to now, some work has been done to control or discuss the dynamical behavior of fractional-order financial systems [4, 9, 10, 22–28]. Generally speaking, the control and synchronization for fractional-order financial chaotic systems is becoming a hot research area.
On the other hand, most of actual physical systems may be inflicted by input saturation because there exist a limited size of actuators, sensors, and interfacing system devices [29–33]. These input constraints usually damage the performance of the system or cause system’s ultimate instability if it is not well handled. With respect to integer-order systems with input saturation, many control methods have been given (for example, see, [29–36]). The usually used idea to handle input saturation is that the sector bounded conditions are associated with input nonlinearities, thus the stability of the system can be discussed based on Lyapunov stability criterion. However, as a generalization of integer-order system, fractional-order systems have many very different properties. Thus, these methods which are effective for integer-order systems can not be used to control fractional-order system directly. Up to now, there are only very few literatures which consider the controller design for fractional-order nonlinear systems with input saturation [37, 38]. In above prior work, the input saturation is handled by some linear inequality, which contains a restricted condition. As far as financial systems are considered, the input saturation phenomenon may occur naturally. So, how to design effective controllers for fractional-order financial chaotic systems is a challenging and meaningful work.
Motivated by above discussions, in this paper, we will study the control for uncertain fractional-order financial chaotic system subjected to input saturation with system uncertainty and external disturbance. Our contributions consist in: (1) control of fractional-order financial chaotic system subjected to input saturation is considered in this paper, and the input saturation is handled by a kind of transformation; (2) Fuzzy logic systems are used to approximate nonlinear functions which contain both system uncertainties and input saturation, and an adaptive fuzzy controller is proposed by using the fractional Lyapunov method; (3) to update the fuzzy parameters, fractional-order adaptation laws, which have one more free degree compared with the conventional integer-order ones, are designed.
The remainder of this paper is organized as follows: Section 2 lists some preliminaries. In Section 3, adaptive fuzzy control algorithm is given and stability of the closed-loop system is analyzed. Simulation studies are presented in Section 4. Finally, Section 5 gives the conclusions of this work.
2 Preliminaries
2.1 Preliminaries
The fractional-order integrodifferential operator can be seen as a extended concept of the integer-order one. The mostly commonly utilized definitions in literature are Grünwald-Letnikov, Riemann-Liouville, and Caputo definitions. The main reason why Caputo’s derivative is introduced for engineering applications consists in that, just like in integer-order systems, its Laplace transform requires integer-order derivatives for the initial conditions. On the contrary, the Laplace transform of the Riemann-Liouville definition contains fractional-order derivatives that are difficult to be physically interpreted. The Caputo’s derivative will be used in this paper. The q-th fractional integral can be given as
(1)
where Γ(⋅) stands for the Gamma function.
The q-th fractional-order derivative is given as
(2)
where n − 1 ≤ q < n (
). In this paper, only the case 0 < q ≤ 1 is included.
To facilitate the controller design, let us give the following results first.
Definition 1 [11]. The Mittag-Leffler function can be given as
(3)
where α1 and α2 are positive constants, and
.
The Laplace transform of Eq (3) is [11]
(4)
Lemma 1 [11]. Let . If 0 < α1 < 2 and
, then, when |ζ|→∞ and ι ≤ |arg(ζ) ≤ π, we have:
(5)
Lemma 2 [11]. Let 0 < α1 < 2 and . If
, then we can obtain
(6) where C > 0, ι ≤ |arg(ζ)| ≤ π and |ζ| ≥ 0.
Lemma 3 [12]. Suppose that x(t) = 0 is an equilibrium point of the following system (7) If there exist a Lyapunov function V(t, x(t)) and a class-K function gi, i = 1, 2, 3 such that
(8)
(9) then system Eq (7) is asymptotically stable.
Lemma 4 [13, 15]. Let be a smooth function and
be a positive definite matrix. Then it holds that
(10)
2.2 Description of a fuzzy logic system
A fuzzy inference system contains four parts, i.e., the fuzzifier, the knowledge base, the fuzzy inference and the defuzzifier [17, 39–49]. The fuzzy rules are used by the fuzzy inference to construct a mapping from which is the input vector to an output
. Suppose that there are
fuzzy rules are used. The ith rule can be expressed as
(11)
where
represent fuzzy sets and gi corresponds to the output of this fuzzy rule. The above fuzzy inference can be modeled by
(12)
where
is the value of membership of xj(t) to
. Denote
and ϕ(x(t)) = [q1(x(t)), q2(x(t)), ⋯, qN(x(t))]T, where qj is defined by
(13)
then, we can rewrite Eq (12) as
(14)
In fact, Eq (12) is the most frequently utilized in literatures. Based on the universal approximation theorem [39], we can use the fuzzy logic system Eq (12) to approximate any continuous function f(t) which is defined on some compact set Ω to an arbitrary degree of accuracy.
3 Main results
3.1 Description of fractional-order financial chaotic systems
The mathematical model of the fractional-order financial system to be considered in this paper is [9, 22, 23]
(15)
where α corresponds to the saving amount, β represents the cost per investment, γ is the elasticity of demand of commercial market, and q ∈ (0, 1]. The first state variable x1(t), which represents the interest rate, can be effected by the surplus between investment and savings as well as structural adjustments of the prices. The second state variable x2(t) corresponds to the rate of investment, and inversely proportional to the cost of investment and the interest rate. The third state variable x3(t) depends on the difference between supply and demand in the market, and it can also be affected by the inflation rate. The equilibrium points of Eq (15) are:
(16)
With respect to the equilibrium point
, the Jacobian matrix is
(17)
Suppose that α = 1, β = 0.1 and γ = 1, then the eigenvalues of the equilibrium Q1 = (0.000; 10.000; 0.000) are ρ1 = 8.8990, ρ2 = −0.8990, and ρ3 = −0.1000. As a result, it is a saddle point. For equilibrium points Q2 = (0.8944; 2.000; −0.8944) and Q3 = (−0.8944; 2.000; 0.8944), they are: ρ1 = −0.7609 and ρ2,3 = 0.3304 ± 1.4112i. Since they are unstable equilibriums, the condition for chaos is satisfied. It is easy to get the minimal commensurate order of the system is q > 0.8537. Let the initial conditions be x1(0) = 1, x2(0) = 2, x3(0) = −0.5. The chaotic attractor of the fractional-order financial system Eq (15) is shown in Figs 1 and 2 when q = 0.86 and q = 0.97, respectively.
3.2 Adaptive fuzzy controller design
Taking the system uncertainties and external disturbance into consideration, and let and
, the controlled fractional-order financial chaotic system Eq (15) can be rewritten as
(18)
where x(t) = [x1(t), x2(t), x3(t)]T is the state vector,
is a constant control gain matrix,
represents the control input to be designed,
represents the input saturation, and △f(x(t)) and
are the system uncertainty and external disturbance, respectively. In this paper, the input saturation is defined as
(19)
where ui,max > 0, ui,min < 0, i = 1, 2, 3 are known constants. Define
(20)
Let ω(t) = [ω1(t), ω2(t), ω3(t)]T, then it follows from Eqs (19) and (20) that
(21)
To proceed, the following Assumptions are needed.
Assumption 1 The control gain matrix G is an unknown positive definite matrix.
Assumption 2 There exists an unknown positive constant
such that
.
Multiplying G−1 to both sides of Eq (18), denoting P = G−1 and using Eq (21), we have
(22)
where
is an unknown nonlinear function. Denote
.
Noting that the continuous nonlinear function is fully unknown, we can approximate it, by using the fuzzy logic system Eq (14), as
(23)
The ideal parameter of ϑi(t) can be defined as
(24)
It is worth mentioning that the parameter
is given only for theoretical analysis purpose. In fact, in the controller design procedure, we will not need its exact value. Let the parameter estimation error and the fuzzy system approximation error be
(25)
and
(26)
respectively. According to the results in [40, 42, 50], we can suppose that there exists an unknown positive constant
such that
(27)
Denote ε(t) = [ε1(t), ε2(t), ε3(t)]T, ϑ(t) = [ϑ1(t), ϑ2(t), ϑ3(t)], ,
,
, we have
(28)
To proceed, we will present the following two Lemmas.
Lemma 5 Let
be a smooth function. If
, then the function z(t) is monotone decreasing.
Proof 1 It is easy to get that (29) where
is a non-negative function. The Laplace transform of Eq (29) is
(30) where Z(s) and G(s) represent the Laplace transforms of z(t) and g(t), respectively. The solution of Eq (30) can be given as
(31) Noting that g(t) ≥ 0 for all t > 0, according to Eq (1) we have
. Thus, it follows Eq (31) that z(t) ≤ z(0), and the function z(t) is monotone decreasing.
Lemma 6 Let
, where
and
are smooth functions. Suppose that
(32) where κ > 0. Thus, it holds that
(33)
Proof. Taking the q-th fractional integral Eq (32) gives
(34)
Then Eq (34) implies
(35)
Thus we know that we can find a function h(t) ≥ 0 such that
(36)
Then the Laplace transform (
) of Eq (36) is
(37)
Based on Eq (4), we can solve Eq (37) as
(38)
where * represents the convolution operator. It is easy to know that both Eq, 0(−2ktq) and t−1 are nonnegative functions, thus we have that Eq (33) holds. This ends the proof of Lemma 6.
According to Lemma 6, we can obtain the following results.
Lemma 7 Suppose that
, where
and
are smooth functions, and G1 and
are two positive definite matrices. Then, if there exists a positive definite matrix G3 and such that
(39) then we have ‖x(t)‖ and ‖y(t)‖ will converge to the origin asymptotically (i.e.
).
The main results can be concluded as the following Theorem.
Theorem 1 Consider the controlled fractional-order financial chaotic system Eq (18) under the Assumptions 1 and 2. Suppose that the controller is designed as (40) where K1 = diag(k1i, k12, k13) and
(k1i > 0 is positive design parameter and
is adjustable parameter which is the estimation of the unknown constant
) and the fractional adaptation laws are given as
(41) and
(42) where δ1i and δ2i are positive design parameters, then we have that the state variables will converge to the origin asymptotically and all signals involved will keep bounded.
Proof 2 From Eqs (22), (28) and (40) we have (43)
Multiplying xT(t) to both sides of Eq (43), and using Assumptions 1 and 2, we can obtain (44) where
(45) is the estimation error.
Noting that
and
. Thus, Lemma 4, Eqs (41), (42) and (44) imply that
(47)
Noting that
, according to Lemma 5 we know that x(t),
and
will keep bounded, and as a result, ϑi(t) and
are all bounded for all t ≥ 0. Thus, we know that all signals involved will remain bounded. Besides, it follows from Lemma 7 and Eq (47) that x(t) will converge to zero asymptotically. And this completes the proof of Theorem 1.
Remark 1 It is worth mentioning that in the controller design, the model of the fractional-order financial chaotic systems is not needed (see, Eq (40)). Besides, the proposed method can be generalized to control many other uncertain fractional-order nonlinear systems.
Remark 2 In Eq (40), the sign function, which may result in the chattering phenomenon, is used. To solve this problem, we can use some continuous function to replace it.
Remark 3 It should be noted that the input saturation in fractional-order systems is also considered in [37] and [38]. In [37], stability and stabilization for a class of fractional-order linear system is discussed. To handle the effect of the input saturation, a memoryless nonlinearity is used, thus the input saturation can be written as a linear matrix inequality. This method is very interesting and easy to be realized. However, the results of this work can be guaranteed only is a small region of the initial condition. In [38], a fractional-order nonlinear model is considered. Just like the work of [37], a memoryless nonlinearity is also used. Besides, to discuss the stability of the closed-loop system, a assumption, is used to impose restrictions on the system nonlinear function. However, this is a very restrictive condition, which is very hard to be satisfied in real physical systems. As a comparison, the above mentioned problems will not occur in this paper.
4 Simulation studies
In the simulation, the system parameters are chosen as that in Section 3, i.e., α = 1, β = 0.1 and γ = 1. The fractional-order q is selected as q = 0.91, and the initial condition is chosen as x(0) = [2, −3, 3]T. The design parameters are k1i = 0.8, δ1i = δ2i = 1, i = 1, 2, 3, and the initial condition for is
. The control gain matrix is G = diag(0.7, 1.1, 1), which is positive definite. The system uncertainty is
, and the external disturbance is d(t) = [0.1sin(t), 0.1cos(t), 0.15sin(3t)].
For the fuzzy logic systems, we choose four Gaussian membership functions distributed on [−3, 3]. As a results, there are 4 × 4 × 4 = 64 fuzzy rules are involved in the controller design. The initial condition is .
The simulations are presented in Figs 3–8. The state variables, which is indicated in Fig 3, tend to the origin rapidly. To eliminate the chattering phenomenon which is produced by the sign(⋅) function in Eq (40), we use the continuous function arctan(10) to replace it. The control inputs and their input saturations are shown in Figs 4–6. The norm of the fuzzy parameters, and the estimations of the unknown constants, are given in Figs 7 and 8, respectively. It can be seen that the state variables have a rapid convergence, and the adaptive fuzzy controller works well even in a noisy environment with input saturation as well as a fully unknown system model. Yet, the state variables cannot stop at the origin but have some tiny fluctuations near the origin. The reasons for this phenomenon are: (1) with respect to a fractional-order nonlinear system, whenever the system trajectories reach the equilibrium point, they can not stay there thereafter because there are no finite-time stable equilibria in fractional-order systems [51]; (2) sign(⋅) is replaced by arctan(10) in this paper so that asymptotical convergence of the tracking error cannot be guaranteed.
It should be pointed out that the proposed methods is valid for all q ∈ (0, 1]. The simulation results for q = 0.98 are given in Figs 9 and 10, from which we can see that the results are the same as that of the case q = 0.91.
5 Conclusion
Most of real world systems are inflicted by input constraints, especially in financial systems. This paper provides an adaptive fuzzy controller for uncertain fractional-order financial chaotic systems subject to input saturation. The saturation is divided into two parts, i.e., an unknown nonlinear function and the control input to be determined. The unknown part together with system uncertainty is approximated by a fuzzy logic system. It is showed that fractional-order adaptation laws can be given to eliminate the fuzzy approximation errors as well as estimation errors. How to design adaptive fuzzy control for fractional-order financial chaotic system (commensurate or incommensurate) with other kinds of input nonlinearities, such as backlash-like hysteresis and dead-zones, is one of our future research directions.
Acknowledgments
This work is supported by the National Natural Science Foundation of China (Grant No. 11302184).
Author Contributions
- Conceptualization: CW.
- Data curation: CW.
- Funding acquisition: CW.
- Investigation: CW.
- Methodology: CW.
- Project administration: CW.
- Software: CW.
- Visualization: CW.
- Writing – original draft: CW.
- Writing – review & editing: CW.
References
- 1. Stutzer MJ. Chaotic dynamics and bifurcation in a macro model. Journal of Economic Dynamics and Control. 1980;2:353–376.
- 2. Wang Z, Huang X, Shen H. Control of an uncertain fractional order economic system via adaptive sliding mode. Neurocomputing. 2012;83:83–88.
- 3.
Liu H, Li S, Cao J, Alsaedi A, Alsaadi F E. Adaptive fuzzy prescribed performance controller design for a class of uncertain fractional-order nonlinear systems with external disturbances Neurocomputing. 2016;
- 4. Pan I, Das S, Das S. Multi-objective active control policy design for commensurate and incommensurate fractional order chaotic financial systems. Applied Mathematical Modelling. 2015;39(2):500–514.
- 5. Xu Y, Xie C, Wang Y, Zhou W, Fang J. Chaos projective synchronization of the chaotic finance system with parameter switching perturbation and input time-varying delay. Mathematical Methods in the Applied Sciences. 2015;38(17):4279–4288.
- 6.
Thirlwall AP, Rampa G, Stella L. Economic dynamics, trade and growth: essays on Harrodian themes. Springer; 2015.
- 7. Lu P, Zhou J, Zhang H, Zhang R, Wang C. Chaotic differential bee colony optimization algorithm for dynamic economic dispatch problem with valve-point effects. International Journal of Electrical Power & Energy Systems. 2014;62:130–143.
- 8. Baskonus HM, Mekkaoui T, Hammouch Z, Bulut H. Active control of a chaotic fractional order economic system. Entropy. 2015;17(8):5771–5783.
- 9. Xin B, Zhang J. Finite-time stabilizing a fractional-order chaotic financial system with market confidence. Nonlinear Dynamics. 2015;79(2):1399–1409.
- 10. Zhao M, Wang J. H∞ control of a chaotic finance system in the presence of external disturbance and input time-delay. Applied Mathematics and Computation. 2014;233:320–327.
- 11.
Podlubny I. Fractional differential equations: an introduction to fractional derivatives, fractional differential equations, to methods of their solution and some of their applications. vol. 198. Academic press; 1998.
- 12. Li Y, Chen Y, Podlubny I. Mittag–Leffler stability of fractional order nonlinear dynamic systems. Automatica. 2009;45(8):1965–1969.
- 13. Liu H, Li SG, Sun YG, Wang HX. Adaptive fuzzy synchronization for uncertain fractional-order chaotic systems with unknown non-symmetrical control gain. Acta Physica Sinaca. 2015;64(7):070503.
- 14. Liu H, Li SG, Sun YG, Wang HX. Prescribed performance synchronization for fractional-order chaotic systems. Chinese Physics B. 2015;24(9):090505.
- 15. Aguila-Camacho N, Duarte-Mermoud MA, Gallegos JA. Lyapunov functions for fractional order systems. Communications in Nonlinear Science and Numerical Simulation. 2014;19(9):2951–2957.
- 16. Hartley TT, Lorenzo CF, Qammer HK. Chaos in a fractional order Chua’s system. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications. 1995;42(8):485–490.
- 17. Liu H, Li S, Wang H, Huo Y, Luo J. Adaptive synchronization for a class of uncertain fractional-order neural networks. Entropy. 2015;17(10):7185–7200.
- 18. Safarinejadian B, Asad M, Sadeghi MS. Simultaneous state estimation and parameter identification in linear fractional order systems using coloured measurement noise. International Journal of Control. 2016;14:1–20.
- 19. Gallegos JA, Duarte-Mermoud MA. On the Lyapunov theory for fractional order systems. Applied Mathematics and Computation. 2016;287:161–170.
- 20. Wang Y, Sun K, He S, Wang H. Dynamics of fractional-order sinusoidally forced simplified Lorenz system and its synchronization. The European Physical Journal Special Topics. 2014;223(8):1591–1600.
- 21. Wang Y, He S, Wang H, Sun K. Bifurcations and synchronization of the fractional-order simplified Lorenz hyperchaotic systems. Journal of Applied Analysis and Computation. 2015;5(2):210–219.
- 22. Chen L, Chai Y, Wu R. Control and synchronization of fractional-order financial system based on linear control. Discrete Dynamics in Nature and Society. 2011;2011:958393.
- 23. Chen WC. Nonlinear dynamics and chaos in a fractional-order financial system. Chaos, Solitons & Fractals. 2008;36(5):1305–1314.
- 24. Tacha O, Volos CK, Kyprianidis I, Stouboulos I, Vaidyanathan S, Pham VT. Analysis, adaptive control and circuit simulation of a novel nonlinear finance system. Applied Mathematics and Computation. 2016;276:200–217.
- 25. Kocamaz UE, Göksu A, Taşkın H, Uyaroğlu Y. Synchronization of chaos in nonlinear finance system by means of sliding mode and passive control methods: a comparative study. Information Technology And Control. 2015;44(2):172–181.
- 26. Zhang XD, Liu XD, Zheng Y, Liu C. Chaotic dynamic behavior analysis and control for a financial risk system. Chinese Physics B. 2013;22(3):030509.
- 27. Yu H, Cai G, Li Y. Dynamic analysis and control of a new hyperchaotic finance system. Nonlinear Dynamics. 2012;67(3):2171–2182.
- 28.
Uyaroğlu Y, Kocamaz UE, Goksu A, Can E. Sliding mode control of hyperchaotic finance system. In: 1st International Work-Conference on Time Series (ITISE); 2014. p. 260–268.
- 29. Grimm G, Hatfield J, Postlethwaite I, Teel AR, Turner MC, Zaccarian L. Antiwindup for stable linear systems with input saturation: an LMI-based synthesis. IEEE Transactions on Automatic Control. 2003;48(9):1509–1525.
- 30. Kapoor N, Teel AR, Daoutidis P. An anti-windup design for linear systems with input saturation. Automatica. 1998;34(5):559–574.
- 31. Li Y, Tong S, Li T. Composite adaptive fuzzy output feedback control design for uncertain nonlinear strict-feedback systems with input saturation. IEEE Transactions on Cybernetics. 2015;45(10):2299–2308. pmid:25438335
- 32. He W, Dong Y, Sun C. Adaptive neural impedance control of a robotic manipulator with input saturation. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2016;46(3):334–344.
- 33. Huang X, Zhang C, Lu H, Li M. Adaptive reaching law based sliding mode control for electromagnetic formation flight with input saturation. Journal of the Franklin Institute. 2016;353(11):2398–2417.
- 34.
Ji H, Hou Z, Fan L, Lewis FL. Adaptive iterative learning reliable control for a class of nonlinearly parameterized systems with unknown state delays and input saturation. IET Control Theory & Applications. 2016;.
- 35. Li Y, Tong S, Li T. Adaptive fuzzy output-feedback control for output constrained nonlinear systems in the presence of input saturation. Fuzzy Sets and Systems. 2014;248:138–155.
- 36. Li Y, Tong S, Li T. Hybrid fuzzy adaptive output feedback control design for uncertain MIMO nonlinear systems with time-varying delays and input saturation. IEEE Transactions on Fuzzy Systems. 2016;24(4):841–853.
- 37. Lim YH, Oh KK, Ahn HS. Stability and stabilization of fractional-order linear systems subject to input saturation. IEEE Transactions on Automatic Control. 2013;58(4):1062–1067.
- 38. Luo J. State-feedback control for fractional-order nonlinear systems subject to input saturation. Mathematical Problems in Engineering. 2014;2014:891639.
- 39.
Wang LX. Adaptive fuzzy systems and control: design and stability analysis. Prentice-Hall, Inc.; 1994.
- 40. Boulkroune A, Tadjine M, Saad MM, Farza M. Fuzzy adaptive controller for MIMO nonlinear systems with known and unknown control direction. Fuzzy Sets and Systems. 2010;161(6):797–820.
- 41. Pan Y, Er MJ, Huang D, Wang Q. Adaptive fuzzy control with guaranteed convergence of optimal approximation error. IEEE Transactions on Fuzzy Systems. 2011;19(5):807–818.
- 42. Liu H, Yu HJ, Xiang W. Adaptive fuzzy nonlinear inversion-based control for uncertain chaotic systems. Chinese Physics B. 2012;21(12):120505.
- 43. Tong SC, He XL, Zhang HG. A combined backstepping and small-gain approach to robust adaptive fuzzy output feedback control. IEEE Transactions on Fuzzy Systems. 2009;17(5):1059–1069.
- 44. Pan Y, Yu H, Sun T. Global asymptotic stabilization using adaptive fuzzy PD control. IEEE Transactions on Cybernetics. 2015;45(3):574–582.
- 45. Pan Y, Yu H. Dynamic surface control via singular perturbation analysis. Automatica. 2015;57:29–33.
- 46. Boulkroune A, Bouzeriba A, Hamel S, Bouden T. Adaptive fuzzy control-based projective synchronization of uncertain nonaffine chaotic systems. Complexity. 2015;21(2):180–192.
- 47. Rigatos G, Zhu G, Yousef H, Boulkroune A. Flatness-based adaptive fuzzy control of electrostatically actuated MEMS using output feedback. Fuzzy Sets and Systems. 2016;290:138–157.
- 48. Boulkroune A. A fuzzy adaptive control approach for nonlinear systems with unknown control gain sign. Neurocomputing. 2016;179:318–325.
- 49. Pan Y, Er MJ. Enhanced adaptive fuzzy control with optimal approximation error convergence. IEEE Transactions on Fuzzy Systems. 2013;21:1123–1132.
- 50. Tong S, Li Y. Adaptive fuzzy output feedback control of MIMO nonlinear systems with unknown dead-zone inputs. IEEE Transactions on Fuzzy Systems. 2013;21(1):134–146.
- 51. Shen J, Lam J. Non-existence of finite-time stable equilibria in fractional-order nonlinear systems. Automatica. 2014;50(2):547–551.