Direct Adaptive Fuzzy Control of SISO Nonlinear Systems with Input–Output Nonlinear Relationship

Zhang, Faxiang; Li, Yimin; Hua, Jing

doi:10.1007/s40815-017-0414-y

Cited by 13 publications

(7 citation statements)

References 19 publications

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“…Because the T-S fuzzy model has great advantages in guaranteeing universal approximation and system stability, in recent years, studies on the type-2 T-S fuzzy model control achieved rich results [13][14][15][16][17][18][19][20][21][22][23][24][25]. In this paper, the type-2 direct adaptive fuzzy control of Mamdani and T-S type is used for addressing nonlinear systems, i.e., [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Type-2 Combined T-S Adaptive Fuzzy Control

Hao

Xia

et al. 2020

Mathematical Problems in Engineering

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For a class of nonlinear systems with a nonlinear relationship between input and output, a fuzzy control method combining interval type-2 and T-S fuzzy controller is proposed based on type-2 fuzzy system theory. In order to ensure its stability, anti-interference ability, and minimum approximation error, this design combines direct, indirect, supervised, and compensation control types to construct the controller. In this way, the structure of the controller not only has the characteristics of the type-2 fuzzy set, which can reduce the uncertainty of rules, but also has a T-S fuzzy model with linear combination of input variables, which can improve the modeling accuracy and reduce the number of rules of the system. By using the Lyapunov synthesis method, the global stability and the convergence of the closed-loop system under the condition that all variables are uniformly bounded are analyzed, and the adaptive laws of the system parameters are given as well. Finally, the effectiveness and superiority of the proposed method are verified by simulation.

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Section: Introductionmentioning

confidence: 99%

Type-2 Combined T-S Adaptive Fuzzy Control

Hao

Xia

et al. 2020

Mathematical Problems in Engineering

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“…In most of the practical control systems, the uncertainty or nonlinearity is inevitable. 1–3 Many existing works were focused on the controller design for the unknown nonlinear dynamical systems. 4–6 It is significant for real-life applications to design a controller with high accuracy and robustness, especially for the systems with complex dynamic characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…18 The adaptive SMC methods have been widely studied recently in order to reduce the chattering and improve the control performance. 1 , 19–21 An adaptive SMC is able to apply some system identification techniques to acquire a model of the process and its environment by using the input and output values, and use this information to control the system online. 22 Therefore, the adaptive SMC method has excellent adaptability to the unknown nonlinear dynamical systems.…”

Section: Introductionmentioning

confidence: 99%

“…22 Therefore, the adaptive SMC method has excellent adaptability to the unknown nonlinear dynamical systems. It is worth mentioning here that, the adaptive control scheme can be divided into the indirect adaptive control 23 , 24 in which the functions of the system model are estimated, and the direct adaptive control 1 , 25 , 26 where the control input is approximated directly. In addition, the second-order SMC method is another way to mitigate the chattering and enhance the control performance.…”

Section: Introductionmentioning

confidence: 99%

“…In some works, 34 , 35 the FLSs were applied to state space modeling, which could cope with the problem of system uncertainty and nonlinearity. Since the adaptive fuzzy sliding mode control (AFSMC) methods carry the advantages of both the fuzzy system and the sliding mode control simultaneously, many existing works were focused on the indirect AFSMC 23 , 36 and the direct AFSMC, 1 , 37 which showed the excellent control performance of the AFSMC methods for the systems with uncertainty and nonlinearity. With respect to the systems with unmeasured states, there are many recent works focused on the observer based adaptive control scheme.…”

Section: Introductionmentioning

confidence: 99%

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Observer based direct adaptive fuzzy second-order-like sliding mode control for unknown nonlinear systems

Liu

Cheng

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part E:

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This work proposes a novel observer based direct adaptive fuzzy second-order-like sliding mode control (SMC) method for a certain class of high order unknown nonlinear dynamical systems with unmeasurable states. An observer is firstly developed to estimate the tracking error vector directly, and the stability of the observer is analyzed based on Meyer-Kalman-Yakubovich (MKY) lemma. Based on the observer, the equivalent control law is approximated by a double-input single-output fuzzy logic system (FLS), in which the observation of the sliding surface and its derivative are applied as the inputs. In addition, an adaptive switching control law is added to mitigate the system chattering and improve the stability of the system. The free parameters of the controller are adjusted online by the adaptive laws that are derived from the Lyapunov stability analysis. Finally, the convergence of the overall closed-loop system is demonstrated, and the simulation examples illustrate the efficacy of the proposed control method.

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