Output-feedback control of uncertain nonlinear systems using a self-structuring adaptive fuzzy observer

Park, Jang-Hyun; Park, Gwi-Tae; Kim, Seong-Hwan; Moon, Chae-Joo

doi:10.1016/j.fss.2004.07.007

Cited by 69 publications

(85 citation statements)

References 17 publications

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“…According to the work in [27,28,36], in the control design, the filtered signals of FBF vector, ur, and ua are all included in the lumped uncertainty 2. The filter L −1 (s) appearing in the lumped uncertainty 2 is just for analysis purpose.…”

Section: E1 = H(s) B(βζ(ê E E α)mentioning

confidence: 99%

“…To use the Meyer-KalmomYacubovich (MKY) lemma, in these schemes, the strictly positive real (SPR) condition on the observation error dynamics must be satisfied. The need for SPR condition results in the filtering of the fuzzy basis function (FBF), which makes the dynamic order of the observer-controller very large [27] . In addition, as stated in [28,29], these observerbased fuzzy adaptive controllers have not been derived rigorously in mathematics.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, as stated in [28,29], these observerbased fuzzy adaptive controllers have not been derived rigorously in mathematics. Aiming at these disadvantages, the authors in [27,29,30] employed the state variable filters or the tracking error filter to design the update law and the robust control term. In their solutions, no SPR condition is needed and the filtering of FBF can be effectively avoided.…”

Section: Introductionmentioning

confidence: 99%

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Observer-based Variable Universe Adaptive Fuzzy Controller Without Additional Dynamic Order

Guo

Zhang

2014

Int. J. Autom. Comput.

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A high-precision fuzzy controller, based on a state observer, is developed for a class of nonlinear single-input-single-output (SISO) systems with system uncertainties and external disturbances. The state observer is introduced to resolve the problem of the unavailability of state variables. Assisted by the observer, a variable universe fuzzy system is designed to approximate the ideal control law. Being auxiliary components, a robust control term and a state feedback control term are designed to suppress the influence of the lumped uncertainties and remove the observation error, respectively. Different from the existing results, no additional dynamic order is required for the control design. All the adaptive laws and the control law are built based on the Lyapunov synthesis approach, and the signals involved in the closed-loop system are guaranteed to be uniformly ultimately bounded. Simulation results performed on Duffing forced oscillation demonstrate the advantages of the proposed control scheme.

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Section: E1 = H(s) B(βζ(ê E E α)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Observer-based Variable Universe Adaptive Fuzzy Controller Without Additional Dynamic Order

Guo

Zhang

2014

Int. J. Autom. Comput.

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“…However, it can be imagined that such complicated structuring learning may lead to computational load so that they are not suitable for online practical applications. Recently, some intelligent control schemes utilize the SFS approach proposed in Gao and Er (2003), Hsu (2007), Lin et al (2001) and Park et al (2003Park et al ( , 2005. However, some of them use the gradient descent method to derive the parameter learning algorithms which cannot guarantee the system stability (Lin et al 2001).…”

Section: Introductionmentioning

confidence: 99%

“…However, some of them use the gradient descent method to derive the parameter learning algorithms which cannot guarantee the system stability (Lin et al 2001). Some of them derive the parameter learning algorithms in the Lyapunov sense to guarantee system stability, but the structure learning algorithm is too complex (Gao and Er 2003;Hsu 2007;Park et al 2003Park et al , 2005. In Hsu (2007), Lin et al (2001) and Park et al (2005), a self-constructing fuzzy neural network control is proposed to avoid the newly generated membership function being too similar to the existing ones.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-identification-based adaptive backstepping control using a self-organizing fuzzy system

et al. 2008

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In this paper, a fuzzy-identification-based adaptive backstepping control (FABC) scheme is proposed. The FABC system is composed of a backstepping controller and a robust controller. The backstepping controller, which uses a self-organizing fuzzy system (SFS) with the structure and parameter learning phases to on-line estimate the controlled system dynamics, is the principal controller, and the robust controller is designed to dispel the effect of approximation error introduced by the SFS. The developed SFS automatically generates and prunes the fuzzy rules by the proposed structure adaptation algorithm and the parameters of the fuzzy rules and membership functions tunes on-line in the Lyapunov sense. Thus, the overall closed-loop FABC system can guarantee that the tracking error and parameter estimation error are uniformly ultimately bounded; and the tracking error converges to a desired small neighborhood around zero. Finally, the proposed FABC system is applied to a chaotic dynamic system to show its effectiveness. The simulation results verify that the proposed FABC system can achieve favorable tracking performance even with unknown controlled system dynamics.

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Output Feedback Adaptive Neural Control Without Seeking SPR Condition

Er¹,

Chen²,

Huang³

2014

Asian Journal of Control

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For output-feedback adaptive control of affine nonlinear systems based on feedback linearization and function approximation, the observation error dynamics usually should be augmented by a low-pass filter to satisfy a strictly positive real (SPR) condition so that output feedback can be realized. Yet, this manipulation results in filtering basis functions of approximators, which makes the order of the controller dynamics very large. This paper presents a novel output-feedback adaptive neural control (ANC) scheme to avoid seeking the SPR condition. A saturated output-feedback control law is introduced based on a state-feedback indirect ANC structure. An adaptive neural network (NN) observer is applied to estimate immeasurable system state variables. The output estimation error rather than the basis functions is filtered and the filter output is employed to update NNs. Under given initial conditions and sufficient control parameter constraints, it is proved that the closed-loop system is uniformly ultimately bounded stable in the sense that both the state estimation errors and the tracking errors converge to small neighborhoods of zero. An illustrative example is provided to demonstrate the effectiveness of this approach.

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Output-feedback control of uncertain nonlinear systems using a self-structuring adaptive fuzzy observer

Cited by 69 publications

References 17 publications

Observer-based Variable Universe Adaptive Fuzzy Controller Without Additional Dynamic Order

Observer-based Variable Universe Adaptive Fuzzy Controller Without Additional Dynamic Order

Fuzzy-identification-based adaptive backstepping control using a self-organizing fuzzy system

Output Feedback Adaptive Neural Control Without Seeking SPR Condition

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