Adaptive Fuzzy Backstepping Dynamic Surface Control of Strict-Feedback Fractional-Order Uncertain Nonlinear Systems

Ma, Zhiyao; Ma, Hongjun

doi:10.1109/tfuzz.2019.2900602

Cited by 176 publications

(86 citation statements)

References 47 publications

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“…Recalling the condition (25), and applying the same method in Theorem 1, the condition (26) and (27) can be derived. Therefore, the model (14) is asymptotically stable with an H ∞ norm bound .…”

Section: Stability Analysis With Improved Lyapunov Function and Integmentioning

confidence: 99%

Robust load frequency control for networked power system with renewable energy via fractional‐order global sliding mode control

Sun

et al. 2021

IET Renewable Power Gen

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Owing to random load changes and transmission time delays in interconnected power systems with renewable energy, the load frequency control scheme has become one of the main methods to keep stability and security of power systems. To relieve communication burden and increase network utilisation, an adaptive event-triggered scheme is explored. Then, a new fractional-order global sliding mode control scheme comprising the fractional-order term in the sliding surface is adopted to improve robustness of load frequency control. The fractional-order term generates a new degree of freedom and more adjustable parameters to improve control performance. Furthermore, the Markov theory is applied in the modelling process to better describe the uncertainty of parameters and external disturbances. The stability and stabilisation criteria for multi-area power systems load frequency control are put forward by employing the improved Lyapunov function and integral inequalities with auxiliary functions. Finally, two simulation examples containing a two-area power system and modified IEEE 39-bus New England test power system with three wind farms are presented to investigate the effectiveness of the proposed method. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Stability Analysis With Improved Lyapunov Function and Integmentioning

confidence: 99%

Robust load frequency control for networked power system with renewable energy via fractional‐order global sliding mode control

Sun

et al. 2021

IET Renewable Power Gen

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“…This assumption is adapted from [20]. Assumptions 3 and 4 are similar to those in [34], [35]. Assumption 2 implies that the unknown interactions f i,j are bounded by a relaxed nonlinear growth condition.…”

Section: Assumptionmentioning

confidence: 99%

“…Consider the fractional-order interconnected system (6) with unknown control directions satisfying Assumptions 1-4, the control input(35), and the parameter updating laws(25),(26),(30), and(39). Then, all the signals in the closed-loop system are bounded.…”

mentioning

confidence: 99%

Adaptive Control for Fractional-order Interconnected Systems

Liang

Zheng

Yang

et al. 2019

2019 Chinese Control Conference (CCC)

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This paper concentrates on the study of the decentralized fuzzy control method for a class of fractional-order interconnected systems with unknown control directions. To overcome the difficulties caused by the multiple unknown control directions in fractional-order systems, a novel fractional-order Nussbaum function technique is proposed. This technique is much more general than those of existing works since it not only handles single/multiple unknown control directions but is also suitable for fractional/integer-order single/interconnected systems. Based on this technique, a new decentralized adaptive control method is proposed for fractional-order interconnected systems. Smooth functions are introduced to compensate for unknown interactions among subsystems adaptively. Furthermore, fuzzy logic systems are utilized to approximate unknown nonlinearities. It is proven that the designed controller can guarantee the boundedness of all signals in interconnected systems and the convergence of tracking errors. Two examples are given to show the validity of the proposed method. Index Termsunknown control directions, fractional-order system, Nussbaum function, adaptive backstepping control, interconnected system, fuzzy logic systems.

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“…Due to their inherent global property and historical dependence, fractional order models (FOMs) or fractional differential equations (FDEs) exhibit great advantages in describing the dynamic behaviors of real-world systems such as viscoelastic systems [1], the electrical characteristics of a solid oxide fuel cell (SOFC) [2], lithium-ion batteries [3], and anomalous diffusion [4]. Therefore, many researchers have been working on methods for building FOMs for realworld systems by using various identification methods and controlling FOSs [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

Parameter Identification of Fractional Order Systems Using a Hybrid of Bernoulli Polynomials and Block Pulse Functions

et al. 2021

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Block pulse functions (BPFs) are piecewise constant and not sufficiently smooth. Therefore, their accuracy is limited when it comes to identifying the parameters of fractional order systems (FOSs). This in turn means that BPFs are incapable of offering highly accurate parameter identification results. However, using a great number of BPFs would significantly increase the dimension of the operational matrix and thereby adds to the computational complexity and burden. To overcome this problem, we present here a hybrid function method for identifying FOSs. The method utilizes a hybrid of Bernoulli polynomials and block pulse functions (HBPBPFs) as the base functions to approximate input and output signals. The fractional integral operational matrix of HBPBPFs is derived and used to convert an FOS to an algebraic system. The parameters of the FOS are successfully identified by minimizing the mean square error between the output of the true system and that of the algebraic representation of the FOS. The simulation experiment verifies that our proposed HBPBPFs method is effective and can generate more accurate identification results than existing BPFs methods.

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Adaptive Fuzzy Backstepping Dynamic Surface Control of Strict-Feedback Fractional-Order Uncertain Nonlinear Systems

Cited by 176 publications

References 47 publications

Robust load frequency control for networked power system with renewable energy via fractional‐order global sliding mode control

Robust load frequency control for networked power system with renewable energy via fractional‐order global sliding mode control

Adaptive Control for Fractional-order Interconnected Systems

Parameter Identification of Fractional Order Systems Using a Hybrid of Bernoulli Polynomials and Block Pulse Functions

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