Adaptive Dynamic Surface Control for Uncertain Nonlinear Systems With Interval Type-2 Fuzzy Neural Networks

Chang, Chia-Wen; Chan, Wei-Shou

doi:10.1109/tcyb.2013.2253548

Cited by 82 publications

(8 citation statements)

References 40 publications

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“…This controller was applied to unknown dead zones of a system by combining both the back-stepping technique and the small-gain method. By combining fuzzy and neural control, a model of interval type 2 fuzzy neural networks with an adaptive dynamic surface method was presented [12]. In this research, the robust stability was guaranteed by the Lyapunov theorem, and all error signals were uniformly bounded.…”

Section: Introductionmentioning

confidence: 98%

“…It has been observed from experiments that the computation time of all methods in these three categories is faster than the Karnik-Mendel algorithm alone [17]. From the literature mentioned above, it is evident that in adaptive fuzzy control research, there are basically three methods popularly used for controller design; the ∞ H tracking technique [2][3][4][5]13], output tracking [14], and the Lyapunov technique [6][7][8][9][10][11][12]. In addition, feedback/feedforward controllers have been popularized in control study [20,[22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

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Design of a new adaptive fuzzy controller and its implementation for the damping force control of a magnetorheological damper

Phu

Shah

Choi

2014

Smart Mater. Struct.

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This paper presents a new adaptive fuzzy controller and its implementation for the damping force control of a magnetorheological (MR) fluid damper in order to validate the effectiveness of the control performance. An interval type 2 fuzzy model is built, and then combined with modified adaptive control to achieve the desired damping force. In the formulation of the new adaptive controller, an enhanced iterative algorithm is integrated with the fuzzy model to decrease the time of calculation (D Wu 2013 IEEE Trans. Fuzzy Syst. 21 80–99) and the control algorithm is synthesized based on the tracking technique. In addition, for the verification of good control performance of the proposed controller, a cylindrical MR damper which can be applied to the vibration control of a washing machine is designed and manufactured. For the operating fluid, a recently developed plate-like particle-based MR fluid is used instead of a conventional MR fluid featuring spherical particles. To highlight the control performance of the proposed controller, two existing adaptive fuzzy control algorithms proposed by other researchers are adopted and altered for a comparative study. It is demonstrated from both simulation and experiment that the proposed new adaptive controller shows better performance of damping force control in terms of response time and tracking accuracy than the existing approaches.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

Design of a new adaptive fuzzy controller and its implementation for the damping force control of a magnetorheological damper

Phu

Shah

Choi

2014

Smart Mater. Struct.

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“…In this article, we refer to the model reduction method proposed in References 34–37 to obtain the system's ultimate output:

\begin{matrix} alignleft \\ rightalign-odd y & align-even = ς \frac{\sum_{k_{1} = 1}^{r_{1}} \dots \sum_{k_{n} = 1}^{r_{n}} {\underline{θ}}_{k_{1} k_{2} \dots k_{n}} \prod_{j = 1}^{n} {\underline{μ}}_{N_{j}^{k_{j}}} (ζ_{j})}{\sum_{k_{1} = 1}^{r_{1}} \dots \sum_{k_{n} = 1}^{r_{n}} \prod_{j = 1}^{n} {\underline{μ}}_{N_{j}^{k_{j}}} (ζ_{j})} \\ rightalign-odd & align-even + (1 - ς) \frac{\sum_{k_{1} = 1}^{r_{1}} \dots \sum_{k_{n} = 1}^{r_{n}} {\overset{true‾}{θ}}_{k_{1} k_{2} \dots k_{n}} \prod_{j = 1}^{n} {\overset{true‾}{μ}}_{N_{j}^{k_{j}}} (ζ_{j})}{\sum} \end{matrix}

…”

Section: Preliminariesmentioning

confidence: 99%

Fuzzy adaptive containment control for fractional‐order heterogeneous nonlinear multi‐agent systems with mixed time‐varying delays

Xia

2024

Adaptive Control & Signal

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SummaryThis study investigates the issue of containment control (CC) of multi‐agent systems (MASs) that are heterogeneous, nonlinear, and fractional‐order while taking practical scenarios into account, such as uncertainties, unknown nonlinearities, time‐varying state delays, and distributed time‐varying delays. A fully distributed adaptive observer is created for each follower in accordance with the communication topology information among agents. This observer is designed to estimate the convex combination information of the leaders. The approach utilizes interval type‐2 fuzzy set theory and adaptive methods to design a distributed containment control strategy that only uses the self‐information of the followers and neighbor nodes' information. Sufficient conditions for implementing containment control are given. A new Lyapunov‐Krasovskii functional (LKF) is constructed, and the stability of the system with feedback loop is proven using fractional‐order theory and inequality methods. Lastly, a simulation example is presented to illustrate the efficacy of the proposed approach.

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“…In this combination, a classical control plays the main control function and the others are used as main components to improve the control function simultaneously. These advantages are clearly pointed out in previous works [11][12][13][14][15][16][17][18][19]. In these works, fuzzy or fuzzy neural network models are frequently used for evaluating uncertain variables, while sliding mode control and the H infinity technique are used for controlling dynamic parameters of the system.…”

mentioning

confidence: 97%

“…A direct adaptive fuzzy control for an MR damper was also studied in which the H infinity technique and the interval type 2 fuzzy model were adopted [17]. An adaptive dynamic surface control with the fuzzy neural model was presented by integrating the interval type 2 fuzzy model in order to reduce uncertainty errors [18] and a combination control technique of the H infinity with the sliding mode control was investigated via the adaptive fuzzyneural model [19]. It is noted here that there are many types of fuzzy models such as the Takagi-Sugeno and interval type 2 models.…”

mentioning

confidence: 99%

Design of a new adaptive fuzzy controller and its application to vibration control of a vehicle seat installed with an MR damper

Phu

Shin

Choi

2015

Smart Mater. Struct.

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This paper presents a new adaptive fuzzy controller featuring a combination of two different control methodologies: H infinity control technique and sliding mode control. It is known that both controllers are powerful in terms of high performance and robust stability. However, both control methods require an accurate dynamic model to design a state variable based controller in order to maintain their advantages. Thus, in this work a fuzzy control method which does not require an accurate dynamic model is adopted and two control methodologies are integrated to maintain the advantages even in an uncertain environment of the dynamic system. After a brief explanation of the interval type 2 fuzzy logic, a new adaptive fuzzy controller associated with the H infinity control and sliding mode control is formulated on the basis of Lyapunov stability theory. Subsequently, the formulated controller is applied to vibration control of a vehicle seat equipped with magnetorheological fluid damper (MR damper in short). An experimental setup for realization of the proposed controller is established and vibration control performances such as acceleration at the driver's seat are evaluated. In addition, in order to demonstrate the effectiveness of the proposed controller, a comparative work with two existing controllers is undertaken. It is shown through simulation and experiment that the proposed controller can provide much better vibration control performance than the two existing controllers.

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Adaptive Dynamic Surface Control for Uncertain Nonlinear Systems With Interval Type-2 Fuzzy Neural Networks

Cited by 82 publications

References 40 publications

Design of a new adaptive fuzzy controller and its implementation for the damping force control of a magnetorheological damper

Design of a new adaptive fuzzy controller and its implementation for the damping force control of a magnetorheological damper

Fuzzy adaptive containment control for fractional‐order heterogeneous nonlinear multi‐agent systems with mixed time‐varying delays

Design of a new adaptive fuzzy controller and its application to vibration control of a vehicle seat installed with an MR damper

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