Robust adaptive control: a unified approach

Ioannou, Pétros; Sun, Jing

doi:10.1109/cdc.1989.70486

Cited by 98 publications

(214 citation statements)

References 7 publications

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“…Using (17), we can rewrite the closed-loop performance measure dynamics(14) as(18)where the functional estimation error is defined as…”

mentioning

confidence: 99%

Adaptive critic neural network for feedforward compensation

Campos

Lewis²

1999

Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251)

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This paper is concerned with the application of adaptive critic techniques to feedback control of nonlinear systems using neural networks (NN). No initial model for the nonlinear system is necessary. This work shows how to cope with nonlinearities through adaptive critics with no preliminary off-line learning required. The proposed scheme consists of a feedforward action generating NN that compensates for the unknown system nonlinearities. The learning of this NN is performed on-line based on a signal from a second neural network acting as a critic. The critic NN tunes itself online using the performance measure of the system. Both NN tuning algorithms are based on backpropagation, which must be modified to guarantee closed-loop performance. The two algorithms are derived using nonlinear stability analysis, so that both system tracking stability and error convergence can be guaranteed in the closed-loop system.

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“…Using (17), we can rewrite the closed-loop performance measure dynamics(14) as(18)where the functional estimation error is defined as…”

mentioning

confidence: 99%

Adaptive critic neural network for feedforward compensation

Campos

Lewis²

1999

Proceedings of the 1999 American Control Conference (Cat. No. 99CH36251)

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show abstract

“…for t E [ k T , (k + 1) 2) where all the superscripts 'hat' denote estimates, and = fi(D,t)u,(t)+i[k]+ t ( t ) + e ( t ) (12) fort …”

Section: A) Estimation Schemementioning

confidence: 99%

“…The role of such a gain is to avoid a large forcing term depending of the filtered plant output at sampling instants which is generated by the couplings from the dynamics at sampling instants to the dynamics within the intersample period as reflected in Lemma 1 . Note from (12), (16) and (17) that the closed-loop system at sampling instants is described, subject to (13) …”

Section: A) Estimation Schemementioning

confidence: 99%

Robust adaptive control of dynamic hybrid systems

Sen

Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171)

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This paper deals with the problem of synthesizing a robust adaptive controller for a class of single-input singleoutput time-invariant hybrid plant which can operate under bounded disturbances and / or unmodelled dynamics. The hybrid plant dealt with is composed of two subsystems one being of continuous-time type while the other is digital. The system is simultaneously driven by the standard continuous-time input plus an extra signal. A relative adaptation dead zone is used in the parameter estimation scheme whose role is the robust adaptive stabilization in the presence of uncertainties.

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“…The first researches for these problems had been formulated to deal with the control approaches which assume the given plants to be described by a class of time-invariant linear system with system uncertainties such as unknown constant parameters and/or modeling errors, and apply adaptive and/or robust theoretical advancements to the linear systems in References [7] [15] [14] [2].…”

Section: Introductionmentioning

confidence: 99%

“…However, in contrast to the exact tracking algorithms of Papers [9] [8] [I] [11] [12] requiring some restrictive conditions and exact cancellation of nonlinear terms, we present robust adaptive scheme constructed with robust adaptive parameter identification [7] [14] [Ill [12], approximate linearization [3] [4] [6] [lo], and robust control theory [5] [7].…”

Section: Introductionmentioning

confidence: 99%

Adaptive regulation for nonlinear systems with switching parameters

Lee

Abe²

Proceedings of the 36th SICE Annual Conference. International Session Papers

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We consider a design of the output tracking for a class of singleinput, single-output nonlinear systems with constant parameter switching at low frequency in this paper. The control law presented in this note follows a robust adaptive scheme of nonlinear systems via approximate linearization derived from geometric nonlinear control systems. We show that this control approach satisfies stability of the closed-loop system and achieves robust adaptive tracking with bounds on all the signals.

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Robust adaptive control: a unified approach

Cited by 98 publications

References 7 publications

Adaptive critic neural network for feedforward compensation

Adaptive critic neural network for feedforward compensation

Robust adaptive control of dynamic hybrid systems

Adaptive regulation for nonlinear systems with switching parameters

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