Observer-based adaptive neural control for a class of nonlinear pure-feedback systems

Wang, Honghong; Chen, Bing; Lin, Chong; Sun, Yumei

doi:10.1016/j.neucom.2015.07.103

Cited by 30 publications

(20 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that the scheme of filtering u is inspired by [32]. The difference is that the proposed filter in this paper adopts stabilizing terms of −cw i for i = 1, • • • , n in (12). If c = 0, the filter (12) becomes simple connected integrators that is used in [32] and all the p i (w)'s become zeros.…”

Section: Lemma 2 ([31]mentioning

confidence: 99%

“…d are also smooth functions and they are continuously differentiable. The w is evidently differentiable and continuous by (13) which results that the p n is also continuously differentiable since p n is the polynomial of w. The control input u is determined as (29) which is also continuously differentiable considering (3), (12). Gathering all these facts together, it is evident that a (n) has a Lipschitz constant.…”

Section: Control Law and Stability Anlaysismentioning

confidence: 99%

“…Designing a controller for nonlinear systems containing unstructured uncertainties has been considerably advanced and performed in recent years. Conventionally, many adaptive controllers using universal approximators (UAs) such as neural networks (NNs) or fuzzy logic systems (FLSs) have been proposed (refer to [1][2][3][4][5][6][7][8][9][10][11][12] and references therein). More recently, controllers for nonlinear pure-feedback systems that contain unstructured uncertainties and unmatched disturbances have been actively proposed.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, controllers for nonlinear pure-feedback systems that contain unstructured uncertainties and unmatched disturbances have been actively proposed. Adaptive backstepping with UAs or dynamic surface control (DSC) algorithm is typically adopted to induce control laws that deal with unstructured uncertainties and unmatched disturbances [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24]. The pure-feedback nonlinear systems are more general than strict-feedback systems [8,[25][26][27][28] because they have the nonaffine appearance of the states which are chosen as virtual controls in each intermediate design steps.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Approximation-Free Output-Feedback Non-Backstepping Controller for Uncertain SISO Nonautonomous Nonlinear Pure-Feedback Systems

Park

Kim

2019

Mathematics

View full text Add to dashboard Cite

A novel differentiator-based approximation-free output-feedback controller for uncertain nonautonomous nonlinear pure-feedback systems is proposed. Using high-order sliding mode observer, which is a finite-time exact differentiator, the time-derivatives of the signal generated using tracking error and filtered input are directly estimated. As a result, the proposed non-backstepping control law and stability analysis are drastically simple. The tracking error vector is guaranteed to be exponentially stable in finite time regardless of the nonautonomous property in the considered system. It does not require neural networks or fuzzy logic systems, which are typically adopted to capture unstructured uncertainties intrinsic in the controlled system. As far as the authors know, there are no research results on the output-feedback controller for the uncertain nonautonomous pure-feedback nonlinear systems. The results of the simulation show clearly the performance and compactness of the control scheme proposed.

show abstract

Section: Lemma 2 ([31]mentioning

confidence: 99%

Section: Control Law and Stability Anlaysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Approximation-Free Output-Feedback Non-Backstepping Controller for Uncertain SISO Nonautonomous Nonlinear Pure-Feedback Systems

Park

Kim

2019

Mathematics

View full text Add to dashboard Cite

show abstract

“…In this work, it is extended to a function of the partial state variables. Therefore, the systems in [46,47] are special situations of the system considered for this work. (ii) Corresponding model is more universal than references [38,42,45].…”

Section: Introductionmentioning

confidence: 99%

Adaptive neural control for a class of stochastic nonlinear systems with unknown parameters, unknown nonlinear functions and stochastic disturbances

et al. 2017

View full text Add to dashboard Cite

In this paper, adaptive neural control (ANC) is investigated for a class of strict-feedback nonlinear stochastic systems with unknown parameters, unknown nonlinear functions and stochastic disturbances. The new controller of adaptive neural network with state feedback is presented by using a universal approximation of radial basis function neural network and backstepping. An adaptive neural network state-feedback controller is designed by constructing a suitable Lyapunov function. Adaptive bounding design technique is used to deal with the unknown nonlinear functions and unknown parameters. It is shown that, the global asymptotically stable in probability can be achieved for the closed-loop system. The simulation results are presented to demonstrate the effectiveness of the proposed control strategy in the presence of unknown parameters, unknown nonlinear functions and stochastic disturbances.

show abstract

A small‐gain approach for adaptive output‐feedback NN control of switched pure‐feedback nonlinear systems

Long

2019

Adaptive Control & Signal

View full text Add to dashboard Cite

Summary This paper investigates the problem of adaptive output‐feedback neural network (NN) control for a class of switched pure‐feedback uncertain nonlinear systems. A switched observer is first constructed to estimate the unmeasurable states. Next, with the help of an NN to approximate the unknown nonlinear terms, a switched small‐gain technique‐based adaptive output‐feedback NN control scheme is developed by exploiting the backstepping recursive design scheme, input‐to‐state stability analysis, the common Lyapunov function method, and the average dwell time (ADT) method. In the recursive design, the difficulty of constructing an overall Lyapunov function for the switched closed‐loop system is dealt with by decomposing the switched closed‐loop system into two interconnected switched systems and constructing two Lyapunov functions for two interconnected switched systems, respectively. The proposed controllers for individual subsystems guarantee that all signals in the closed‐loop system are semiglobally, uniformly, and ultimately bounded under a class of switching signals with ADT, and finally, two examples illustrate the effectiveness of theoretical results, which include a switched RLC circuit system.

show abstract

Observer-based adaptive neural control for a class of nonlinear pure-feedback systems

Cited by 30 publications

References 55 publications

Approximation-Free Output-Feedback Non-Backstepping Controller for Uncertain SISO Nonautonomous Nonlinear Pure-Feedback Systems

Approximation-Free Output-Feedback Non-Backstepping Controller for Uncertain SISO Nonautonomous Nonlinear Pure-Feedback Systems

Adaptive neural control for a class of stochastic nonlinear systems with unknown parameters, unknown nonlinear functions and stochastic disturbances

A small‐gain approach for adaptive output‐feedback NN control of switched pure‐feedback nonlinear systems

Contact Info

Product

Resources

About