Design of MRAS-based adaptive controllers for motion systems using linear motors

Cương, Nguyễn Duy

doi:10.1109/infoseee.2014.6946150

Cited by 1 publication

(3 citation statements)

References 8 publications

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“…This assertion implies that the error, e(t), will eventually converge to zero. Substituting the expression for ϕ in the gain-adjustment law provides the expression in (27).…”

Section: Model Reference Adaptive Lqi Controllermentioning

confidence: 99%

“…T Pe(t) (27) All the states of the system, x(t), are measurable. Integrating both sides of expression (27) provides an iterative adaptation mechanism for the dynamic adjustment of the state-feedback gain vector, given by (28).…”

Section: K C = βX(t)bmentioning

confidence: 99%

“…Wherein, the LQI controller is used as a reference model that provides the desired input-output characteristics of the system. An asymptotically stable parameter adjustment law is derived using the Lyapunov theory [27]. The fixed gains of the parameter adjustment lack robustness in compensating the exogenous disturbances and parametric variations.…”

Section: Introductionmentioning

confidence: 99%

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Performance enhancement of multivariable model reference optimal adaptive motor speed controller using error-dependent hyperbolic gain functions

et al. 2019

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The main contribution of this paper is to formulate a robust-adaptive and stable state-space speed control strategy for DC motors. The linear-quadratic-integral (LQI) controller is utilized as the baseline controller for optimal speed-regulation, accurate reference-tracking and elimination of steady-state fluctuations in the motor's response. To reject the influence of modelling errors, the LQI controller is augmented with a Lyapunov-based model reference adaptation system (MRAS) that adaptively modulates the controller gains while maintaining the asymptotic stability of the controller. To further enhance the system's robustness against parametric uncertainties, the adaptation gains of MRAS online gain-adjustment law are dynamically adjusted, after every sampling interval, using smooth hyperbolic functions of motor's speed-error. This modification significantly improves the system's response-speed and damping against oscillations, while ensuring its stability under all operating conditions. It dynamically re-configures the control-input trajectory to enhance the system's immunity against the detrimental effects of random faults occurring in practical motorized systems such as bounded impulsive-disturbances, modelling errors, and abrupt load-torque variations. The efficacy of the proposed control strategy is validated by conducting credible hardware-in-the-loop experiments on QNET 2.0 DC Motor Board. The experimental results successfully validate the superior tracking accuracy and disturbance-rejection capability of the proposed control strategy as compared to other controller variants benchmarked in this article.

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“…This assertion implies that the error, e(t), will eventually converge to zero. Substituting the expression for ϕ in the gain-adjustment law provides the expression in (27).…”

Section: Model Reference Adaptive Lqi Controllermentioning

confidence: 99%