Adaptive neural control for a class of pure-feedback nonlinear time-delay systems with asymmetric saturation actuators

Yu, Zhensheng; Li, Shugang; Yu, Zhaosheng

doi:10.1016/j.neucom.2015.09.020

Cited by 33 publications

(44 citation statements)

References 29 publications

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“…Many references have been given ignoring uncertain parameter perturbation and external disturbances (Lu et al, 2016;Wang et al, 2016;Yu et al, 2016). In the actual system construction, considering device parameter constraints and other factors, parameter mismatch cannot be completely avoided.…”

Section: Introductionmentioning

confidence: 99%

Hybrid adaptive function projective synchronization of a hyper Lüchaotic system

2017

Revista de la Facultad de Ingeniería

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In this paper, a hybrid adaptive function projective synchronization of a hyper Lü chaotic system is investigated. Firstly, according to the Lyapunov stability theory and Pecora-Carroll synchronization criterion, the controller and adaptive rate of these two kinds of synchronization methods are analyzed and designed. Then a new hybrid adaptive projective synchronization method is proposed. Finally, in order to prove the effectiveness of the proposed scheme, the comparison between those controllers is studied by simulation example.

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Section: Introductionmentioning

confidence: 99%

Hybrid adaptive function projective synchronization of a hyper Lüchaotic system

2017

Revista de la Facultad de Ingeniería

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“…In the control literature, strict-feedback, pure-feedback, and stochastic nonlinear systems are frequently encountered [1][2][3][4][5]. In addition, there exist several real systems described by non-integer-order differential equations such as [6] regular variation in thermodynamics, viscoelastic systems, dielectric polarization, electrical circuits, biological and financial systems, electromagnetic waves, heat conduction in a semi-infinite slab, robotics, biophysics, and so on.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, it has been proved that the fractional calculus is an excellent mathematical tool for accurate descriptions of memory and hereditary properties of several materials and processes [6]. Compared with integer-order systems, there is very little research dealing with multi-input-multi-output (MIMO) fractional-order systems [1,. This fact can be explained by the specificity of MIMO systems and the difficulties with the extension of the approaches employed for integer-order systems to fractional ones [10,22,37,41,46,50,56].…”

Section: Introductionmentioning

confidence: 99%

“…In the recent research works [6,9], nonlinear fractional-order systems with affine structures have been also investigated. • Nonaffinity: However, there exist several nonlinear nonaffine systems in practice, such as chemical reactors, biochemical processes, some aircraft and pendulum dynamical models, and so on [1,10,12,19,20,23,25,26,44,50,56,58]. Due to the great efforts devoted by researchers, remarkable adaptive control approaches have been developed for non-affine systems [19,44,50,56].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the great efforts devoted by researchers, remarkable adaptive control approaches have been developed for non-affine systems [19,44,50,56]. It is worth noting that affine systems are a special case of non-affine systems [1,10,12]. In [10,19,20], it has been shown that the non-affine problem can be traditionally addressed by five approaches, namely: (1) approach based on Taylor series expansion, (2) approach based on implicit function theorem, (3) approach based on the mean value theorem, (4) approach based on differentiating the original system equation, and (5) approach based on a local inversion of the TakagiSugeno (TS) fuzzy affine model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

Zouari

Boulkroune

Ibeas

et al. 2016

Neural Comput & Applic

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This paper focuses on the adaptive neural output feedback control of a class of uncertain multi-input-multioutput nonlinear time-delay non-integer-order systems with unmeasured states, unknown control direction, and unknown asymmetric saturation actuator. Thus, the mean value theorem and a Gaussian error function-based continuous differentiable model are used in the paper to describe the unknown asymmetric saturation actuator and to get an affine model in which the control input appears in a linear fashion, respectively. The design of the controller follows a number of steps. Firstly, based on the semigroup property of fractional-order derivative, the system is transformed into a normalized fractional-order system by means of a state transformation in order to facilitate the control design. Then, a simple linear state observer is constructed to estimate the unmeasured states of the transformed system. A neural network is incorporated to approximate the unknown nonlinear functions while a Nussbaum function is used to deal with the unknown control direction. In addition, the strictly positive real condition, the Razumikhin Lemma, the frequency-distributed model, and the Lyapunov method are utilized to derive the parameter adaptive laws and to perform the stability proof. The main advantages of this work are that:(1) it can handle systems with constant, time-varying, and distributed time-varying delays, (2) the considered class of systems is relatively large, (3) the number of adjustable parameters is reduced, (4) the tracking errors converge asymptotically to zero and all signals of the closed-loop system are bounded. Finally, some simulation examples are provided to demonstrate the validity and effectiveness of the proposed scheme.

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Adaptive neural prescribed performance output feedback control of pure feedback nonlinear systems using disturbance observer

Chen

Yang

2020

Adaptive Control & Signal

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Summary In this study, an adaptive output feedback control with prescribed performance is proposed for unknown pure feedback nonlinear systems with external disturbances and unmeasured states. A novel prescribed performance function is developed and incorporated into an output error transformation to achieve tracking control with prescribed performance. To handle the unknown non‐affine nonlinearities and avoid the algebraic loop problem, the radial basis function neural network (RBFNN) is adopted to approximate the unknown non‐affine nonlinearities with the help of Butterworth low‐pass filter. Based on the output of the RBFNN, the coupled design between sate observer and disturbance observer is presented to estimate the unmeasured states and compounded disturbances. Then, the adaptive output feedback control scheme is proposed for unknown pure feedback nonlinear systems, where a first‐order filter is introduced to tackle with the issue of “explosion of complexity” in the traditional back‐stepping approach. The boundedness and convergence of the closed‐loop system are proved rigorously by utilizing the Lyapunov stability theorem. Finally, simulation studies are worked out to demonstrate the effectiveness of the proposed scheme.

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Adaptive neural control for a class of pure-feedback nonlinear time-delay systems with asymmetric saturation actuators

Cited by 33 publications

References 29 publications

Hybrid adaptive function projective synchronization of a hyper Lüchaotic system

Hybrid adaptive function projective synchronization of a hyper Lüchaotic system

Observer-based adaptive neural network control for a class of MIMO uncertain nonlinear time-delay non-integer-order systems with asymmetric actuator saturation

Adaptive neural prescribed performance output feedback control of pure feedback nonlinear systems using disturbance observer

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