An ISS-modular approach for adaptive neural control of pure-feedback systems

Wang, Cong; Hill, David J.; Ge, Shuzhi Sam; Chen, Guanrong

doi:10.1016/j.automatica.2006.01.004

Cited by 458 publications

(273 citation statements)

References 20 publications

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“…The closedloop control system has been theoretically shown to be SGUUB using Lyapunov synthesis method. 47th IEEE CDC, Cancun, Mexico, Dec. [9][10][11]2008 TuA03.3…”

Section: Resultsmentioning

confidence: 99%

“…In [9] and [10], much simpler pure-feedback systems where the last one or two equations were assumed to be affine, were discussed. In [11], an "ISS-modular" approach combined with small gain theorem was presented for adaptive neural control of the completely non-affine purefeedback system. In this paper, we also consider a class of unknown nonlinear systems in pure-feedback form.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive neural control for uncertain nonlinear systems in pure-feedback form with hysteresis input

Ren

Lee

et al. 2008

2008 47th IEEE Conference on Decision and Control

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Abstract-In this paper, adaptive neural control is investigated for a class of unknown nonlinear systems in purefeedback form with the generalized Prandtl-Ishlinskii hysteresis input. The non-affine problem both in the pure-feedback form and in the generalized Prandtl-Ishlinskii hysteresis input function is solved by adopting the Mean Value Theorem. By utilizing Lyapunov synthesis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small neighborhood of zero. Simulation results are provided to illustrate the performance of the proposed approach.

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“…The closedloop control system has been theoretically shown to be SGUUB using Lyapunov synthesis method. 47th IEEE CDC, Cancun, Mexico, Dec. [9][10][11]2008 TuA03.3…”

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive neural control for uncertain nonlinear systems in pure-feedback form with hysteresis input

Ren

Lee

et al. 2008

2008 47th IEEE Conference on Decision and Control

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“…이러한 pure-feedback 비선형 계통은 기 존에 고려되던 계통에 비해 더 일반적인 계통이며 가상 제 어 항으로 쓰일 상태 변수와 제어 입력이 모든 식에서 음함 수형태로 나타나는 비어파인(nonaffine) 계통이다. 참고문헌 [15]에서 제안된 제어기가 유사한 계통을 다루는 기존의 논 문들 [16][17][18][19][20] …”

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Adaptive Output-feedback Neural Control of uncertain pure-feedback nonlinear systems

Park¹,

Kim²,

Jang³

et al. 2013

Journal of Korean Institute of Intelligent Systems

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Based on the state-feedback adaptive neuro-control algorithm for a SISO nonaffine pure-feedback nonlinear system proposed in [15], an output-feedback controller is proposed in this paper. The output-feedback adaptive neural-net controller for the considered nonlinear system has not been previously proposed in any other literatures yet. The proposed output-feedback controller inherits all the advantages of [15] such that it does not adopt backstepping and this results in relatively simple control and adapting laws. Only one neural network is required for the proposed adaptive controller. The proposed neural-net control scheme expands the applicable class of nonlinear systems.

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“…In [14,15] affine pure-feedback systems were investigated by an adaptive NN-based control method. Adaptive neural backstepping control of completely nonaffine pure-feedback systems was presented in [16] using input-to-state stability analysis and the small gain theorem. In [14][15][16] the time derivatives of the virtual control inputs were either approximated by the NNs, resulting in a complicated controller design, or ignored completely, leading to poor tracking performance.…”

Section: Introductionmentioning

confidence: 99%

Adaptive control of pure-feedback systems in the presence of parametric uncertainties

Asadi¹,

Shandiz²

2017

Turk J Elec Eng & Comp Sci

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Abstract:The purpose of this study is to investigate an adaptive control approach for completely nonaffine pure-feedback systems with linear/nonlinear parameterization. In this approach, the parameter separation technique and the idea of the positive function of linearly connected parameters are coupled effectively with the combination of backstepping and time-scale separation. Subsequently, a fast dynamical equation is derived from the original subsystem, where the solution is sought to approximate the corresponding ideal virtual/actual control inputs. Furthermore, the adaptation law of unknown parameters can be derived based on Lyapunov theory in the backstepping technique and there is no need to design a state predictor for this purpose. Therefore, it results in higher accuracy and avoids complexity. The closed-loop stability and the state regulation of these systems are proved. Finally, the simulation results are provided to demonstrate the effectiveness of the proposed approach.

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An ISS-modular approach for adaptive neural control of pure-feedback systems

Cited by 458 publications

References 20 publications

Adaptive neural control for uncertain nonlinear systems in pure-feedback form with hysteresis input

Adaptive neural control for uncertain nonlinear systems in pure-feedback form with hysteresis input

Adaptive Output-feedback Neural Control of uncertain pure-feedback nonlinear systems

Adaptive control of pure-feedback systems in the presence of parametric uncertainties

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