Output-feedback control of nonlinear systems using direct adaptive fuzzy-neural controller

Wang, Wei Yen; Leu, Yih Guang; Lee, Tsu Tian

doi:10.1016/s0165-0114(02)00519-5

Cited by 54 publications

(37 citation statements)

References 27 publications

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“…, k c 1 ] ∈ R n i is the feedback gain matrix to ensure that the characteristic polynomial of A − BK T c is Hurwitz. If the functions F (x) and G(x) are known and the system is free of external disturbance d, then control law of the certainly equivalent controller is obtained as [20] …”

Section: Assumptionmentioning

confidence: 99%

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Adaptive fuzzy controller design for a class of uncertain nonlinear MIMO systems

Kang

Wang

2009

Nonlinear Dyn

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A combined adaptive fuzzy output feedback control scheme is presented for a class of multipleinput-multiple-output (MIMO) nonlinear systems with the unmeasured states. A state observer is used to estimate the unmeasured states of the systems. The resulting closed-loop system consists of the state observer and four control components, where the control components are the combined adaptive fuzzy controller, supervisory controller, H ∞ robust control item and auxiliary compensation item. Compared with the existing results in the observer design, the main advantages of this scheme are that (1) the combined adaptive fuzzy control item, which can trade off the fuzzy descriptions for controlled object rules and control action rules, is designed in the controller; (2) the proposed controller has the supervisory control performance. The stability of the closed-loop system is analyzed by using Lyapunov analysis method. Simulation results validate the effectiveness of the proposed control scheme.

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Section: Assumptionmentioning

confidence: 99%

“…Thus, the problem has been converted to the classical problem of designing a state observer for estimating the state vector e in (20).…”

Section: Remarkmentioning

confidence: 99%

Adaptive fuzzy controller design for a class of uncertain nonlinear MIMO systems

Kang

Wang

2009

Nonlinear Dyn

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“…However, this assumption is not sufficient for many practical situations, because it is difficult to construct a nonlinear plant by known nonlinear functions precisely. Observer design-based adaptive fuzzy control has been a very active field and has obtained many significant efforts during the last decade [16][17][18][19][20][21][22][23][24]. The direct [18,19,24] and the indirect [16,17,[20][21][22][23] adaptive fuzzy design schemes are studied.…”

Section: Introductionmentioning

confidence: 99%

“…Observer design-based adaptive fuzzy control has been a very active field and has obtained many significant efforts during the last decade [16][17][18][19][20][21][22][23][24]. The direct [18,19,24] and the indirect [16,17,[20][21][22][23] adaptive fuzzy design schemes are studied. These results proposed in [16][17][18][19][20][21][22][23][24] can guarantee that the corresponding closed-loop systems are stable based on the Lyapunov stability theory.…”

Section: Introductionmentioning

confidence: 99%

Observer-based direct adaptive fuzzy control of uncertain nonlinear systems and its applications

Liu

Tong

Wang

et al. 2009

Int. J. Control Autom. Syst.

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A direct adaptive fuzzy control algorithm is developed for a class of uncertain SISO nonlinear systems. In this algorithm, it doesn't require to assume that the system states are measurable. Therefore, it is needed to design an observer to estimate the system states. Compared with the numerous alternative approaches with respect to the observer design, the main advantage of the developed algorithm is that on-line computation burden is alleviated. It is proven that the developed algorithm can guarantee that all the signals in the closed-loop system are uniformly ultimately bounded and the tracking error converges to a small neighborhood around zero. The simulation examples validate the feasibility of the developed algorithm.

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“…Based on the universal approximation theorem and by incorporating fuzzy logic systems into adaptive control schemes, a stable fuzzy adaptive controller was first developed to control unknown nonlinear systems [1] . Afterwards, various adaptive fuzzy control approaches have been introduced for controlling nonlinear systems were introduced in [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16]. References [2][3][4][5][6] studied the single-input and single-output (SISO) nonlinear systems; the references [7][8][9][10] addressed multiple-input and multipleoutput (MIMO) nonlinear systems; and references [11][12][13][14][15][16] discussed nonlinear systems with states unmeasured.…”

Section: Introductionmentioning

confidence: 99%

Adaptive backstepping output feedback control for SISO nonlinear system using fuzzy neural networks

Tong

2009

Int. J. Autom. Comput.

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In this paper, a new fuzzy-neural adaptive control approach is developed for a class of single-input and single-output (SISO) nonlinear systems with unmeasured states. Using fuzzy neural networks to approximate the unknown nonlinear functions, a fuzzyneural adaptive observer is introduced for state estimation as well as system identification. Under the framework of the backstepping design, fuzzy-neural adaptive output feedback control is constructed recursively. It is proven that the proposed fuzzy adaptive control approach guarantees the global boundedness property for all the signals, driving the tracking error to a small neighbordhood of the origin. Simulation example is included to illustrate the effectiveness of the proposed approach.

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Output-feedback control of nonlinear systems using direct adaptive fuzzy-neural controller

Cited by 54 publications

References 27 publications

Adaptive fuzzy controller design for a class of uncertain nonlinear MIMO systems

Adaptive fuzzy controller design for a class of uncertain nonlinear MIMO systems

Observer-based direct adaptive fuzzy control of uncertain nonlinear systems and its applications

Adaptive backstepping output feedback control for SISO nonlinear system using fuzzy neural networks

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