Indirect M-MRAC for systems with time varying parameters and bounded disturbances

Stepanyan, Vahram; Krishnakumar, Kalmanje

doi:10.1109/isic.2012.6398265

Cited by 8 publications

(7 citation statements)

References 8 publications

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“…This is the case of unconstrained M-MRAC with time variant uncertainties, which was studied in [17].…”

Section: Prediction Modelmentioning

confidence: 99%

“…In this paper, we present an adaptive control method for input constraint multi-input multioutput nonlinear dynamical systems with time varying parametric uncertainties and bounded external disturbances. The method, which was rst presented in [17], uses a prediction model to rapidly generate adaptive estimates of the system's uncertainties. It is shown that the designed controller requires no modication or tuning to guarantee tracking of a given reference model inside a region in the space of initialization errors, design parameters and external commends described by a sucient condition, which is derived based on the adaptive estimation bounds.…”

Section: Introductionmentioning

confidence: 99%

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Input Constrained M-MRAC for Multirotors Operating in an Urban Environment

Stepanyan

Krishnakumar

2019

AIAA Scitech 2019 Forum

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The paper presents a modied model reference adaptive control (M-MRAC) for multiinput multi-output nonlinear dynamical systems with time varying parametric uncertainties and bounded external disturbances. It uses a prediction model to rapidly generate adaptive estimates of the system's uncertainties with adjustable errors that converge to a small neighborhood of the origin. A sucient condition is derived to specify the region of attraction in the space of initialization errors, design parameters and external commends. The approach is applied to thrust controlled multi-rotor air vehicles operating in an urban environment. It is shown that the designed controller can provide a good tracking of a given trajectory in the unknown urban wind eld, assuming that the maximum thrust generated by the rotors is known. The performance

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“…This is the case of unconstrained M-MRAC with time variant uncertainties, which was studied in [17].…”

Section: Prediction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Input Constrained M-MRAC for Multirotors Operating in an Urban Environment

Stepanyan

Krishnakumar

2019

AIAA Scitech 2019 Forum

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“…Therefore, a prediction-identification model based adaptive control method was proposed for uncertain systems with timevarying parameters in the presence of bounded external disturbances, and the desired tracking performance was achieved by feeding back the state prediction error to the identification model. The desired closed-loop properties were obtained when the error feedback gain was proportional to the square root of the adaptation rate [8]. However, an apparent drift of adaptive gains may occasionally arise, eventually leading to closed-loop instability.…”

Section: State Of the Artmentioning

confidence: 99%

Enhanced Model Reference Adaptive Control based on Data-driven Technique for Permanent Magnet Synchronous Motors with Uncertainties

Qiao¹,

Liu²,

Zong³

2017

JESTR

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Precise velocity control of permanent magnet synchronous motors (PMSMs) is challenging due to the uncertainties induced by the mathematical model, unmodeled dynamics, and various disturbances; consequently, the traditional model reference adaptive control (MRAC) for PMSMs becomes questionable. In order to improve the traditional MRAC and lessen the influence of various sources of uncertainty, this study proposes an enhanced MRAC approach based on datadriven technique. First, the approach established an equivalent differential expression linear model for the discrete-time nonlinear description of PMSM and designed a data-driven controller using a model-free adaptive technique. This design depended on the unique bounded parameter referred as pseudo-partial-derivative (PPD) that was a slowly time-varying parameter relating to the system action point or system dynamics. Second, the approach identified the parameter PPD using Popov criterion to ensure the asymptotic stability of the controlled system. Finally, the approach was simulated and applied on a 50 kW PMSM drive of Toyota Prius II hybrid electric vehicles to demonstrate the effects of different control parameters. Results show that the proposed approach does not suffer from the drawback of the modeling process and unmodeled dynamics and provides improved velocity-tracking precision against parameter variations and external disturbances. When the weight factor increases from 0.1 to 1 or the pseudo-orders from 1 to 5, the velocity presents high robustness. Moreover, the approach yields satisfactory disturbance rejection and fault tolerance compared with the traditional MRAC even if some input and output (I/O) data are missing. This study solves the problem of dependence on plant models for the traditional MRAC and achieves better suppression of uncertainties, demonstrating the effective applications of this approach for actual nonlinear motor systems with uncertainties.

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“…The method was extended to several class of systems in state feedback framework. [13][14][15][16] In this paper we apply the M-MRAC method to a class of uncertain single-input single-output (SISO) linear systems in output feedback framework. It is shown that the tracking errors in the output signal as well as in the input signal can be decreased as desired by a proper selection of the design parameters.…”

Section: Introductionmentioning

confidence: 99%

Output Feedback M-MRAC

Stepanyan

Krishnakumar

2013

AIAA Guidance, Navigation, and Control (GNC) Conference

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This paper presents design and performance analysis of a modified reference model MRAC (M-MRAC) architecture in the output feedback framework for a class of singleinput single-output (SISO) uncertain systems. It is shown that the modification of the reference model by the tracking error feedback enables the designer to achieve a desired level of accuracy in the performance of the system's input and output signals by means of fast adaptation. A guideline is given for the choice of the design parameters. The results are consistent in the initial conditions and reference commands. The benefits of the method are demonstrated via a simulation example.

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Indirect M-MRAC for systems with time varying parameters and bounded disturbances

Cited by 8 publications

References 8 publications

Input Constrained M-MRAC for Multirotors Operating in an Urban Environment

Input Constrained M-MRAC for Multirotors Operating in an Urban Environment

Enhanced Model Reference Adaptive Control based on Data-driven Technique for Permanent Magnet Synchronous Motors with Uncertainties

Output Feedback M-MRAC

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