Improving transient performance of adaptive control architectures using frequency-limited system error dynamics

Yucelen, Tansel; Torre, Gerardo De La; Johnson, Eric N.

doi:10.1080/00207179.2014.922702

Cited by 38 publications

(77 citation statements)

References 20 publications

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“…These benefits are made possible by our new global strict barrier Lyapunov functions that were not present in the work of Mazenc et al and that contain a new coupling of state components and unknown parameters. These new Lyapunov function constructions (rather than trajectory tracking mechanisms for the systems in this work, which were reported by Yucelen et al in the special case of time‐invariant systems) are the focus of this work. Unlike in the work of Mazenc et al the unknown parameters in our dynamics enter nonlinearly (through products of entries of unknown weight and control effectiveness matrices) which also puts our work outside the scope of works such as that of Mazenc et al Also, we do not restrict the dimensions of the systems, so our work can cover higher‐order systems; see Section 5.…”

Section: Introductionmentioning

confidence: 92%

“…In addition to the preceding benefits, the systems in this work are motivated by frequency‐limited model reference adaptive control methods from the work of Yucelen et al, which improved on basic adaptive control by ensuring better transient performance. However, while Yucelen et al used nonstrict Lyapunov functions and so did not ensure parameter convergence, here, we combine barrier Lyapunov functions with a different class of update laws from those in the work of Yucelen et al to overcome the obstacles that prevented from being applicable to model reference adaptive control with unknown weight matrices. We use penalty terms in our update laws, and known intervals containing the unknown parameter component values.…”

Section: Introductionmentioning

confidence: 99%

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Tracking and parameter identification for model reference adaptive control

Malisoff

2019

Intl J Robust & Nonlinear

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Summary We provide barrier Lyapunov functions for model reference adaptive control algorithms, allowing us to prove robustness in the input‐to‐state stability framework and to compute rates of exponential convergence of the tracking and parameter identification errors to zero. Our results ensure identification of all entries of the unknown weight and control effectiveness matrices. We provide easily checked sufficient conditions for our relaxed persistency of excitation conditions to hold. Our illustrative numerical example demonstrates the performance of the control methods.

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Section: Introductionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Tracking and parameter identification for model reference adaptive control

Malisoff

2019

Intl J Robust & Nonlinear

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“…In the second example, the proposed controller has been compared with MMAC 5 and the MRAC proposed in the work of Yucelen et al 20 It is important to note that the plant considered in the work of Narendra and Balakrishnan 5 did not satisfy the assumptions A1 and A3 in Section 2. In the first example, the proposed controller has been compared with the standard MRAC.…”

Section: Simulation Studymentioning

confidence: 99%

“…

In this paper, a resetting mechanism is proposed to enhance the transient performance of model reference adaptive control. [18][19][20][21] In the work of Yucelen and Haddad, 18 an adaptive controller has been proposed in which the transient and steady-state bounds in terms of L 1 -norms of the error dynamic are obtained. Whenever the transient specification is not satisfying, there is a jump in the controller parameters.

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confidence: 99%

Transient performance improvement of model reference adaptive control: LMI‐based resetting

Davanipour

Khayatian

Dehghani

2017

Adaptive Control & Signal

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In this paper, a resetting mechanism is proposed to enhance the transient performance of model reference adaptive control. While the suggested method has a simple structure, it is capable of taking into account both the desired steady-state behavior and the transient response, simultaneously. Whenever the transient specification is not satisfying, there is a jump in the controller parameters. This jump is determined by designing an optimal reset law. At the reset times, the after-reset values of parameters are calculated based on a minimization problem. The considered cost function is a mixed H 2 /H ∞ criterion, which minimizes the tracking error. The optimization problem is converted to an LMI formulation, and the reset law is designed by solving this LMI at certain reset times. To verify the effectiveness of the proposed approach, simulation results are presented. KEYWORDSlinear matrix inequality, model reference adaptive control, reset control, transient performance improvement 390 control acts as a disturbance that is in conflict with the control objective. There are also some research studies that use intelligent algorithms in MRAC to improve transient performance; however, they do not involve any analytical support for their statement. 16,17 There are also some research studies in robust adaptive control that propose some methods to improve transient performance. [18][19][20][21] In the work of Yucelen and Haddad, 18 an adaptive controller has been proposed in which the transient and steady-state bounds in terms of L 1 -norms of the error dynamic are obtained. In fact, the final bound of the system response can be determined independent of the system adaptation rate. In another work of Yucelen and Haddad, 19 to achieve fast adaptation, an adaptive control architecture with a high-gain learning rate has been presented. The controller includes an update law with a modification term that filters out the high-frequency content and makes the reduction of the high-frequency oscillations. Yucelen et al 20 proposed a new adaptive controller in which the reference system is modified by a mismatch term containing the high-frequency content of the closed-loop error dynamic. The suggested reference system causes the frequency content of the error dynamic to be restricted and allows fast adaptation without occurring high-frequency oscillations. In the work of Yucelen and Johnson, 21 a command governor-based adaptive controller that can achieve fast dominance of system uncertainties just by adjusting a given command was proposed.On the other hand, recently, a reset controller, which is a specific class of hybrid controllers, was introduced as a strong approach capable of overwhelming fundamental limitations of linear systems in transient performance. [22][23][24] The first reset controller named Clegg integrator was presented in 1958. 25 Clegg showed that, by using a reset mechanism in an integrator, it has the same magnitude as a pure integrator but with 38.1 • phase lag compared with the phase lag of a standard integra...

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“…Then, the controller adapts feedback gains to suppress this error using the information received from the update law. From a practical standpoint, the output (resp., state) of the uncertain dynamical system can be far different from the output (resp., state) of the reference model during transient time (learning phase) and this difference can lead to poor transient performance, although a model reference adaptive control scheme can guarantee that this system error vanishes asymptotically 3,4 .…”

Section: Introductionmentioning

confidence: 98%

A Direct Uncertainty Minimization Framework in Model Reference Adaptive Control

Yucelen

Gruenwald

Muse

2015

AIAA Guidance, Navigation, and Control Conference

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This paper considers stabilization and command following of uncertain dynamical systems and presents a new adaptive control approach with improved system performance. The proposed framework consists of a novel architecture involving modification terms in the adaptive controller and the update law. Specifically, these terms are activated when the system error between an uncertain dynamical system and a given reference model, which captures a desired closed-loop dynamical system behavior, is nonzero and vanishes as the system reaches its steady-state. This key feature of our framework allows to suppress the effect of system uncertainty on the transient system response through a gradient minimization procedure, and hence, leads to improved system performance. We further show that by automatically adjusting the design parameter of the added terms in response to system variations, we can enforce system error to approximately stay in a priori given, userdefined error performance bounds. Several illustrative numerical examples are provided to demonstrate the efficacy of the proposed approach.

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Improving transient performance of adaptive control architectures using frequency-limited system error dynamics

Cited by 38 publications

References 20 publications

Tracking and parameter identification for model reference adaptive control

Tracking and parameter identification for model reference adaptive control

Transient performance improvement of model reference adaptive control: LMI‐based resetting

A Direct Uncertainty Minimization Framework in Model Reference Adaptive Control

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