On transient performance improvement of adaptive control architectures

Gruenwald, Benjamin C.; Yucelen, Tansel

doi:10.1080/00207179.2015.1041001

Cited by 28 publications

(34 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the same conclusion is also true when Equation (33) is compared with (31)- (33) of [11], where the latter has an extra differential equation in addition to the modification terms. Thus, the approach proposed here is much less computationally expensive than [9][10][11].…”

Section: Remarksupporting

confidence: 49%

“…Note that the structure of Equation (26) is much simpler than the structure of (44) in [11], in that the former does not involveŴ(t) dependence and additional integration terms. Furthermore, the same conclusion is also true when Equation (33) is compared with (31)- (33) of [11], where the latter has an extra differential equation in addition to the modification terms.…”

Section: Remarkmentioning

confidence: 99%

“…In addition, this new framework directly allows one to suppress the effect of system uncertainty on the transient system response through a gradient minimization procedure and, hence, leads to improved system performance. Furthermore, unlike the approaches in [9][10][11], the proposed framework is computationally less expensive, and it can enforce the system error to approximately stay in an a priori given, user-defined error performance bound. The proposed framework is first developed for adaptive control laws with linear reference models and then generalized to adaptive control laws with nonlinear reference models.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, the work in [1][2][3] uses filtered versions of the control input and state; [4][5][6] uses a moving time window of the system uncertainty; and [7,8] uses recorded and instantaneous data concurrently. In contrast to these approaches, the authors of [9][10][11] present an approach called artificial basis functions that adds modification terms not only to the update law, but also to the adaptive controller and show that the system error can be suppressed during the transient system response. The common denominator of the approaches in [1][2][3][4][5][6][7][8][9][10][11] is that they introduce additional mechanisms to model reference adaptive control laws that capture a form of the system uncertainty in order to suppress its effect.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Direct Uncertainty Minimization Framework for System Performance Improvement in Model Reference Adaptive Control

2017

Self Cite

View full text Add to dashboard Cite

Abstract:In this paper, a direct uncertainty minimization framework is developed and demonstrated for model reference adaptive control laws. The proposed framework consists of a novel architecture involving modification terms in the adaptive control law and the update law. In particular, these terms are constructed through a gradient minimization procedure in order to achieve improved closed-loop system performance with adaptive control laws. The proposed framework is first developed for adaptive control laws with linear reference models and then generalized to adaptive control laws with nonlinear reference models. Two illustrative numerical examples are included to demonstrate the efficacy of the proposed framework.

show abstract

Section: Remarksupporting

confidence: 49%

Section: Remarkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Direct Uncertainty Minimization Framework for System Performance Improvement in Model Reference Adaptive Control

2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…Employing the relations (14) and scaling the command with the command coefficient c(t) = αc 0 (t) scalability for adaptive control architectures with modified reference models is obtained. 1…”

Section: Reference Model Modificationmentioning

confidence: 99%

Application of a Novel Scalability Notion in Adaptive Control to Various Adaptive Control Frameworks

Schatz

Yucelen

Gruenwald

et al. 2015

AIAA Guidance, Navigation, and Control Conference

Self Cite

View full text Add to dashboard Cite

In adaptive control theory it is a well-known phenomena that nonidentical command profiles entail nonidentical closed-loop responses of these adaptive systems. While adaptive controllers provide a viable methodology to control uncertain dynamical systems, this lack of predictability is a significant disadvantage, in particular in terms of certification of such control methods. Consequently, achieving predictable closed-loop responses of adaptivelycontrolled systems is of grand practical interest. For this purpose, we recently introduced a method 1 to scale the learning rates of the adaptive weight update laws in order to achieve predictable closed-loop performances for nonidentical, but scalable command profiles. This paper applies the proposed methodology to a model of the longitudinal motion of a Boeing 747 aircraft and simulations for diverse adaptive control schemes illustrate the efficiacy of the proposed scalability notion, which may be a further step towards validation and verification of these adaptive control frameworks.

show abstract

Model Reference Adaptive Control

Yucelen

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

View full text Add to dashboard Cite

Adaptive control methods present effective system‐theoretical tools in order to achieve closed‐loop system stability and performance in the presence of exogenous disturbances and system uncertainties, where they are generally classified as either direct or indirect. A well‐known class of direct adaptive control methods is model reference adaptive control architectures. In particular, these architectures employ two major components – a reference model and a parameter adjustment mechanism. A desired closed‐loop dynamical system response is captured by the reference model for which its response is compared with the response of the uncertain dynamical system. The system error signal resulting from this comparison drives the parameter adjustment mechanism. This mechanism then adjusts the controller parameters in a real‐time (i.e., online) manner for driving the trajectories of the uncertain dynamical system to the trajectories of the reference model. This article discusses these components in a basic state feedback setting in order to provide an introduction to the model reference adaptive control design procedure. The article also makes connections to several other, relatively advanced model reference adaptive control methods for interested readers.

show abstract

On transient performance improvement of adaptive control architectures

Cited by 28 publications

References 15 publications

Direct Uncertainty Minimization Framework for System Performance Improvement in Model Reference Adaptive Control

Direct Uncertainty Minimization Framework for System Performance Improvement in Model Reference Adaptive Control

Application of a Novel Scalability Notion in Adaptive Control to Various Adaptive Control Frameworks

Model Reference Adaptive Control

Contact Info

Product

Resources

About