Robust adaptive control for unmatched systems with guaranteed parameter estimation convergence

Yang, Jun; Na, Jing; Gao, Guanbin

doi:10.1002/acs.2982

Cited by 15 publications

(6 citation statements)

References 39 publications

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“…To overcome the mentioned stability issue of the prior class, a novel composite learning technique was suggested [17], which used the online recorded data together with instantaneous data to generate the prediction error. In [18], [19], a new model reference adaptive control (MRAC) framework with modified reference model was proposed to improve the transient response that was originated from the possible uncertainties in the system dynamic. In addition, the stability issue was solved by the use of a leakage term in the adaption law to guarantee the convergence.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Predictive Control for Torque Applying System of High-powered Test Rig

Parvaresh

Mardani

2021

Scientia Iranica

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This paper proposes the implementation of a novel predictive control scheme known as adaptive generalized predictive control (AGPC) in the actuation system of a high-powered test rig. Through the use of actuation system, the required torque for simulating different conditions can be applied to the tested gearboxes. The accurate and precise control of this system is of great importance as it affects the overall performance of the test rig. The considered actuation system in this investigation is electro-hydraulically driven with nonlinear and uncertain characteristics.The performance of the proposed control scheme in different conditions of the parametric uncertainty as well as presence of disturbances are evaluated and the results are discussed. The results confirmed the superior performance of the proposed scheme in different studied conditions.

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Section: Introductionmentioning

confidence: 99%

Adaptive Predictive Control for Torque Applying System of High-powered Test Rig

Parvaresh

Mardani

2021

Scientia Iranica

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“…In References 25,26, both historical and instant data are used to modify the adaptation algorithm to guarantee the parameter convergence by relaxing the persistent excitation condition needed in the parameter estimation. In our previous works, 6,18,27 we have proposed a novel parameter estimation framework based on the parameter estimation error, which can guarantee the exponential convergence of parameter estimation without resorting to observers or predictors.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, when the controlled systems contain large uncertainties or external disturbance, an error feedback term is introduced into the reference model to avoid the compensator being sensitive to the measurement noise and external disturbances. Finally, inspired by our previous work, 6,18,27 a novel adaptive law containing a new leakage term is proposed to online estimate the unknown parameters, which can achieve exponential convergence of both the tracking error and parameter estimation. On the other hand, a new control performance analysis method based on L 2 -norm and Cauchy-Schwartz inequality is presented to illustrate the mechanisms behind these proposed methods, and several possible pathways for improving the transient control performance are summarized to promote the future studies.…”

Section: Introductionmentioning

confidence: 99%

Robust model reference adaptive control for transient performance enhancement

Yang

Gao

2020

Intl J Robust & Nonlinear

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To circumvent the potentially poor transient response induced by nonlinear uncertain dynamics in the adaptive control system, this article proposes a new model reference adaptive control design scheme to improve its transient control response. We first construct a compensator to online extract the undesired dynamics in the online learning, which is incorporated into the reference model and control simultaneously. Then, an error feedback term is incorporated into the reference model to speed up the convergence of both the compensator and tracking error. Moreover, a new leakage term containing the estimation error is constructed and then added in the adaptive law to guarantee the convergence of both the estimation error and tracking error. To further reveal the mechanisms behind these proposed methods, a new methodology to analyze the transient error bounds based on L 2-norm and Cauchy-Schwartz inequality is also developed. Based on the analysis results, we find that the proposed methods can effectively reduce the bound of the tracking error and thus achieve an improved transient control performance without violating the system stability even with high-gain adaptation. In addition, the frequency-domain analysis is resorted to show the comparative responses of different adaptive laws, which indicate that the proposed adaptive law can maintain the stability margin even with a high-gain learning rate. A numerical example is given to demonstrate improved control responses of these proposed schemes.

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“…A system model for MRAC design is derived and guidelines on controller parameters selection are proposed. Yang et al [24] provide a modified MRAC scheme to achieve better transient control performance for systems with unknown unmatched dynamics, where an adaptive law with guaranteed convergence is introduced.…”

Section: Introductionmentioning

confidence: 99%

A new model reference adaptive control with the disturbance observer‐based adaptation law for the Nonlinear Servomechanisms: SISO and MIMO Systems

Chen¹

2020

Asian Journal of Control

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A disturbance observer (DOB) is proposed to replace the external disturbance adaptation law in model reference adaptive control (MRAC) to perform robust tracking control performance for nonlinear servomechanisms. Lyapunov stable theory is implemented to combine the DOB with MRAC to simultaneously converge the estimated states and tracking errors toward null. Then, the disturbance observer‐based MRAC (DOBMRAC) is proposed and realized. The single‐input single‐output (SISO) mass‐spring‐damper system and multi‐input multi‐output (MIMO) two‐link manipulator robot system are both demonstrated as the control examples. However, in comparisons of tracking control performance between MRAC and DOBMRAC, it is found that the proposed controller reveals better robust tracking control performance for SISO and MIMO servomechanisms with unknown time‐varying disturbances. The contributions of this paper are: i) the proposed DOBMRAC is successfully implemented in both the SISO and MIMO systems; ii) the proposed controller performs better tracking control performance than MRAC for unknown time‐varying disturbances; iii) the DOBMRAC cannot only perform robust tracking control performance, but also shows disturbance estimation ability well; and iv) the proposed control methodology can also be widely applied to other MRAC systems with unknown time‐varying disturbance.

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Robust adaptive control for unmatched systems with guaranteed parameter estimation convergence

Cited by 15 publications

References 39 publications

Adaptive Predictive Control for Torque Applying System of High-powered Test Rig

Adaptive Predictive Control for Torque Applying System of High-powered Test Rig

Robust model reference adaptive control for transient performance enhancement

A new model reference adaptive control with the disturbance observer‐based adaptation law for the Nonlinear Servomechanisms: SISO and MIMO Systems

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