Adaptive neural control of high-order uncertain nonaffine systems: A transformation to affine systems approach

Meng, Wenchao; Yang, Qinmin; Jagannathan, S.; Sun, Youxian

doi:10.1016/j.automatica.2014.03.013

Cited by 118 publications

(61 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lemma 3 [40] For the adaptive law (30), there exists a compact set X w ¼ fWjjjWjj ð ffiffiffiffi N p þ r WÞ=rg such that WðtÞ 2 X w , 8t ! 0 provided thatWð0Þ 2 X w .…”

Section: Controller Designmentioning

confidence: 99%

Single-approximation-based adaptive control of a class of nonlinear time-delay systems

Choi

Yoo

2015

Neural Comput & Applic

View full text Add to dashboard Cite

This paper presents a simple adaptive control approach for uncertain strict-feedback nonlinear systems with unknown time-varying delays. All nonlinear functions and time delays in the systems are assumed to be unknown. Compared with the existing works, the contribution of this study is the design of a simple adaptive control law using single function approximator, without the implementation of virtual controllers derived from the backstepping design procedure. Unlike the existing backstepping methods, virtual controllers are only used as intermediate signals for designing the actual control. Therefore, the proposed control scheme is simpler than the existing methods for strictfeedback time-delay systems because the problems of using multiple approximators and calculating virtual controllers are eliminated. In addition, it is shown that all signals in the closed-loop system are uniformly ultimately bounded.

show abstract

“…Lemma 3 [40] For the adaptive law (30), there exists a compact set X w ¼ fWjjjWjj ð ffiffiffiffi N p þ r WÞ=rg such that WðtÞ 2 X w , 8t ! 0 provided thatWð0Þ 2 X w .…”

Section: Controller Designmentioning

confidence: 99%

Single-approximation-based adaptive control of a class of nonlinear time-delay systems

Choi

Yoo

2015

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…In this paper, the design ofu(t) instead of u(t) will be given in Section III-C. Consequently, u(t) can be considered as a variable of the function N(Y ) by treating it as a new system state [35].…”

Section: B Nn Approximationmentioning

confidence: 99%

Robust Integral of Neural Network and Error Sign Control of MIMO Nonlinear Systems

Yang

Jagannathan

Sun

2015

IEEE Trans. Neural Netw. Learning Syst.

Self Cite

104

View full text Add to dashboard Cite

This paper presents a novel state-feedback control scheme for the tracking control of a class of multi-input multioutput continuous-time nonlinear systems with unknown dynamics and bounded disturbances. First, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback multiplied with an adaptive gain is introduced. The NN in the control law learns the system dynamics in an online manner, while the NN residual reconstruction errors and the bounded disturbances are overcome by the error sign signal. Since both of the NN output and the error sign signal are included in the integral, the continuity of the control input is ensured. The controller structure and the NN weight update law are novel in contrast with the previous effort, and the semiglobal asymptotic tracking performance is still guaranteed by using the Lyapunov analysis. In addition, the NN weights and all other signals are proved to be bounded simultaneously. The proposed approach also relaxes the need for the upper bounds of certain terms, which are usually required in the previous designs. Finally, the theoretical results are substantiated with simulations.

show abstract

“…However, the works described in [4,5,[12][13][14] only focus on the stabilization of interested systems, and the output-constraint problems are rarely considered. Meanwhile, it must be pointed out that violation of the output constraint may lower the system performance or even lead to systems instability [15,16].…”

Section: Introductionmentioning

confidence: 99%

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Shi

2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This paper focuses on a single neural network tracking control for a class of nonlinear strict-feedback systems with input dead-zone and time-varying output constraint via prescribed performance method. To release the limit condition on previous performance function that the initial tracking error needs to be known, a new modified performance function is first constructed. Further, to reduce the computational burden of traditional neural back-stepping control approaches which require all the virtual controllers to be necessarily carried out in each step, the nonlinear items are transmitted to the last step such that only one neural network is required in this design. By regarding the input-coefficients of the dead-zone slopes as a system uncertainty and introducing a new concise system transformation technique, a composite adaptive neural state-feedback control approach is developed. The most prominent feature of this scheme is that it not only owes low-computational property but also releases the previous limitations on performance function and is capable of guaranteeing the output confined within the new form of prescribed bound. Moreover, the closed-loop stability is proved using Lyapunov function. Comparative simulation is induced to verify the effectiveness.

show abstract

Adaptive neural control of high-order uncertain nonaffine systems: A transformation to affine systems approach

Cited by 118 publications

References 19 publications

Single-approximation-based adaptive control of a class of nonlinear time-delay systems

Single-approximation-based adaptive control of a class of nonlinear time-delay systems

Robust Integral of Neural Network and Error Sign Control of MIMO Nonlinear Systems

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Contact Info

Product

Resources

About