Adaptive control of uncertain systems with gain scheduled reference models and constrained control inputs

Pakmehr, Mehrdad; Yucelen, Tansel

doi:10.1109/acc.2014.6859326

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…where

K_{x i}^{normalT} false(ρ, t false) = false[k_{x i 1} false(ρ, t false), k_{x i 2} false(ρ, t false), \dots, k_{x i n} false(ρ, t false) false] \in R^{m \times n}

and

K_{r i} false(ρ, t false) = false[k_{r i 1} false(ρ, t false), k_{r i 2} false(ρ, t false), \dots, k_{r i m} false(ρ, t false) false] \in R^{m \times m}

are the estimates of

K_{x i}^{* T} false(ρ false), K_{r i}^{*} false(ρ false)

, respectively. Remark 3 When

ρ false(t false) = t

, that is

u false(t false) = K_{x σ}^{normalT} false(t false) x false(t false) + K_{r σ} false(t false) r false(t false),

then the controller (13) degenerates into the controller in [21]. …”

Section: Resultsmentioning

confidence: 99%

“…In addition, Assumptions 1 and 3 are standard assumptions and have been widely used when the system matrices are scheduling parameter‐independent [24]. Remark 2 Assumption 2 presents a mild boundedness condition on the related parameters, which has been used in [21]. Definition 1 [10] A switching signal has an average dwell time τ a if there exist two positive numbers N 0 and τ a such that

N_{σ} false(T, t false) \leq N_{0} + false(T - t false) / τ_{a}, \forall T \geq t \geq 0,

where

N_{σ} false(T, t false)

is the number of switchings occurring in the interval [ t , T ) and N 0 is the chatter bounds. Definition 2 [22] The system (2) is said to be bounded input–bounded state stable if there exist positive constants η 0 and η 1 such that for any continuous bounded input

r false(t false)

falsefalse| falsefalse| x_{m} false(t false) falsefalse| falsefalse| \leq η_{1} \sup_{t \geq t_{0}} r false(t false) + η_{0} falsefalse| falsefalse| x_{m} false(t_{0} false) falsefalse| falsefalse|, t \geq t_{0} .

…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

“…It is worth noting that in these contributions, scheduling parameters are unknown, while the system matrices are known, then the controller is designed to directly adapt the estimating of the unknown scheduling parameters. Pakmehr and Yucelen [21] developed a new state feedback MRAC approach for uncertain systems with gain scheduled reference models recently, in which the unknown system matrices are dependent on measurable endogenous scheduling parameters, and a scheduling parameter‐independent Lyapunov function was constructed, and then a scheduling parameter‐independent adaptive controller was designed. Obviously, such a controller is often quite conservative.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Model reference adaptive control for switched LPV systems and its application

Xie

Zhao

2016

IET Control Theory & Applications

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“…where

K_{x i}^{normalT} false(ρ, t false) = false[k_{x i 1} false(ρ, t false), k_{x i 2} false(ρ, t false), \dots, k_{x i n} false(ρ, t false) false] \in R^{m \times n}

and

K_{r i} false(ρ, t false) = false[k_{r i 1} false(ρ, t false), k_{r i 2} false(ρ, t false), \dots, k_{r i m} false(ρ, t false) false] \in R^{m \times m}

are the estimates of

K_{x i}^{* T} false(ρ false), K_{r i}^{*} false(ρ false)

, respectively. Remark 3 When

ρ false(t false) = t

, that is

u false(t false) = K_{x σ}^{normalT} false(t false) x false(t false) + K_{r σ} false(t false) r false(t false),

then the controller (13) degenerates into the controller in [21]. …”

Section: Resultsmentioning

confidence: 99%

N_{σ} false(T, t false) \leq N_{0} + false(T - t false) / τ_{a}, \forall T \geq t \geq 0,

where

N_{σ} false(T, t false)

r false(t false)

falsefalse| falsefalse| x_{m} false(t false) falsefalse| falsefalse| \leq η_{1} \sup_{t \geq t_{0}} r false(t false) + η_{0} falsefalse| falsefalse| x_{m} false(t_{0} false) falsefalse| falsefalse|, t \geq t_{0} .

…”

Section: Problem Statement and Preliminariesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Model reference adaptive control for switched LPV systems and its application

Xie

Zhao

2016

IET Control Theory & Applications

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“…Generally, this phenomenon is described and controlled utilising methods to achieve more accurate results. An adaptive control leads to higher efficiency and performance compared to conventional control [46][47][48][49].…”

Section: Astesj Issn: 2415-6698mentioning

confidence: 99%

Advanced Control Strategies on Nonlinear Testbench Dynamometer System for Simulating the Fuel Consumption

Fanesi¹,

Scaradozzi²

2020

Adv. sci. technol. eng. syst. j.

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The adoption of Engine-in-the-loop technology shows real behaviour. This study presents a test runs simulation platform with real engine data. In addition, a test bench model is a demand approach that offers a significant potential to provide an excellent reproducibility of test runs. The platform includes the data integration to upgrade tests run and a comparison with previous results using the advancing control techniques designed. The dynamometer system presents significantly non-linearity. The adaptive control approach, integrated into the Model Predictive Control on the vehicle, allows increasing the tests run performance. The results show how the real data can improve performance and the validation of the system integrating the updated driving cycle and maintaining EiL approach. The conclusion showed the significant benefits regarding the control methods used.

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Model Reference Adaptive Control

Yucelen

2019

Wiley Encyclopedia of Electrical and Electronics Engineering

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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.

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Adaptive control of uncertain systems with gain scheduled reference models and constrained control inputs

Cited by 6 publications

References 15 publications

Model reference adaptive control for switched LPV systems and its application

Model reference adaptive control for switched LPV systems and its application

Advanced Control Strategies on Nonlinear Testbench Dynamometer System for Simulating the Fuel Consumption

Model Reference Adaptive Control

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