A discrete model reference adaptive control system for a plant with input amplitude constraints

Ohkawa, Fujio; Yonezawa, Yoh

doi:10.1080/00207178208932927

Cited by 55 publications

(22 citation statements)

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“…However, signal boundedness invokes such an assumption (Theorem 2). The plant stability assumption widely used in the literature (Ohkawa andYonezawa 1982, Payne 1986) is not required.…”

Section: Theoremmentioning

confidence: 99%

“…Thus 10) In this case, the tracking objective y == y*, or its asymptotic version y(t)~y*(t) as t~00, cannot be automatically guaranteed without extra conditions. For instance, a condition required by Ohkawa and Yonezawa (1982) is that…”

Section: C=as+q-drmentioning

confidence: 99%

“…The first work in the field was due to Monopoli (1975), who did not give an analytical stability study. Ohkawa and Yonezawa (1982) assumed the stability and inverse stability of the plant to prove the closed loop stability. Perfect asymptotic input and output matching properties were proved, provided the nominal linear input for known parameters satisfied the amplitude constraint for the linear case.…”

Section: Introductionmentioning

confidence: 99%

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Stable adaptive control for not-necessarily stable discrete linear plants with bounded non-linear inputs

Sen¹,

Martínez-Esnaola²,

Obieta³

1991

International Journal of Control

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This paper deals with the design of stable adaptive control schemes for discrete plants under non-linear bounded inputs. Two algorithms are studied, one of them being a generalization of the d-step-ahead standard algorithm, while the second one involves the use of a time-decreasing adaptation gain. The class of inputs for which the algorithms perform in a stable fashion include saturations, relays and saturated dead-zones. The plant stability assumption is not requested to prove standard (non-asymptotic) stability results. The mechanism to achieve scheme's stability is the use of an extra weighted additive term in the adaptation error which takes into account the output deviation with respect to that associated with linear inputs. Nomenclature y(t), y*(t), .9(t) {z(t), t~O} UL(t), u(t) e. (I) e(t) e2(t) e(t) cP(t), cP;'(t) 0, O(t), 8'(t) O;"O;'(t) plant output, reference model output and estimated output through the estimated parameters, respectively z-sequence over the discrete set t = 0, 1, ... linear and non-linear controls, respectively =y(t) -y*(t) is the tracking or regulation (i.e. y* = 0) error = y(t) -y(t) is the prediction error = y*(t) -y(t) is the error due to the input non-linearity adaptation error in the parameter adaptive algorithm regression vector and regression vector deleting the component u(t) vectors of true and updated parameters parametrizing the corresponding control laws; O'(t) is intermediate steps in the adaptation of O(t) when projections are used subvectors of 0 and O(t) where the parameter which affects u(t)has been deleted. sets of integers, positive integers and positive integers including zero, respectively maximum and minimum eigenvalues of the ( . )-matrix, respectively

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“…However, signal boundedness invokes such an assumption (Theorem 2). The plant stability assumption widely used in the literature (Ohkawa andYonezawa 1982, Payne 1986) is not required.…”

Section: Theoremmentioning

confidence: 99%

Section: C=as+q-drmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Stable adaptive control for not-necessarily stable discrete linear plants with bounded non-linear inputs

Sen¹,

Martínez-Esnaola²,

Obieta³

1991

International Journal of Control

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“…These effects are even more pronounced for adaptive controllers which continue to adapt when the feedback loop has been severed due to the presence of actuator saturation causing unstable controller modes to drift, which in turn leads to severe windup effects and unacceptable transients after saturation. Direct adaptive controllers for adaptive tracking of multivariable nonlinear uncertain dynamical systems with amplitude saturation constraints have been developed in the literature [1]- [10].…”

Section: Introductionmentioning

confidence: 99%

A neuroadaptive control architecture for nonlinear uncertain dynamical systems with input actuator constraints

Yucelen

Haddad

Calise

2010

Proceedings of the 2010 American Control Conference

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This paper presents a new model reference neuroadaptive control architecture for multivariable nonlinear uncertain dynamical systems with input actuator constraints. In particular, we consider both linear and nonlinear in the parameters neural network approximations to design neuroadaptive controllers for stabilization and command following in the presence of actuator dynamics that can effectively account for actuator amplitude and rate saturation constraints. An illustrative numerical example is provided to demonstrate the efficacy of the proposed approach.

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“…In that paper, it is shown that it is only necessary to know a nominal trajectory, within a Downloaded by [University of California Santa Barbara] at 19:33 14 June 2016 bound, so that boundedness properties of the overall adaptive system be preserved by using bias signals under some (rather weak) conditions on the nonlinearities. The stability related to some separate nonlinearities like relays and saturations has been also investigated (de la Sen 1989, Ohkawa and Yonezawa 1982, Payne 1986. A common assumption has been the plant stability.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Robust Adaptive Control for Continuous-Time Linear Plants

Sen

1991

Cybernetics and Systems

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Results on robustness of continuous-time decentralized adaptive control using a relative deadzone are given for the situation of many adaptive systems interacting through inputs and outputs subject lo static memoryless nonlinear actuators. Stability is preserved by the use of a deadzone function in each adaptation loop. Augmented adaptation error signals taking account of the input deviations from linearity may be used to adapt the adaptive algorithm deadzone to the relative changes of the adaptation error with respect to the estimated unmodeled dynamics contribution.

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A discrete model reference adaptive control system for a plant with input amplitude constraints

Cited by 55 publications

References 0 publications

Stable adaptive control for not-necessarily stable discrete linear plants with bounded non-linear inputs

Stable adaptive control for not-necessarily stable discrete linear plants with bounded non-linear inputs

A neuroadaptive control architecture for nonlinear uncertain dynamical systems with input actuator constraints

Decentralized Robust Adaptive Control for Continuous-Time Linear Plants

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