Sensorless stator field-oriented digital control of induction motor based on LQR theory with augmented integral error

Swargiary, Manoj; Dey, Joykrishna; Saha, Tapas Kumar

doi:10.1109/icpcsi.2017.8392050

Cited by 2 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The derivation of the proposed model-reference Adaptive LQI (ALQI) controller is presented as follows [28]. Consider the linear system described by (14).…”

Section: Model Reference Adaptive Lqi Controllermentioning

confidence: 99%

“…The waveform of HSF is smooth, bounded, symmetrical, and differentiable [20]. Correspondingly, the smooth transition of adaptation gains with respect to the variations in e ω renders superior damping and negligible oscillations in the response under rapidly changing operating conditions [14]. Moreover, the control effort can be further harnessed by appropriately selecting the nonlinearity-index of the function.…”

Section: Nonlinear Self-tuning Model-reference Adaptive Lqi Controllermentioning

confidence: 99%

“…The model-based linear-quadratic-integral (LQI) controllers are also preferred for speed control applications because of their optimal control yield [13]. The LQI controller introduces a weighted integral control term that eliminates the steady-state errors and enables the system to track time-varying trajectories [14]. However, unlike the conventional optimal regulators, the integral damping introduced by LQI slows down the system's response, which deteriorates its asymptotic convergence rate and the disturbance-attenuation capability for the same set of state-and control-weighting matrices [15].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

“…The derivation of the proposed model-reference Adaptive LQI (ALQI) controller is presented as follows [28]. Consider the linear system described by (14).…”

Section: Model Reference Adaptive Lqi Controllermentioning

confidence: 99%

Section: Nonlinear Self-tuning Model-reference Adaptive Lqi Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The scalar control and vector control are essentially torquing control; hence speed control requires a separate speed control mechanism. There are numerous speed control algorithms used in the speed control of induction motors, including PID [17], Linear Quadratic Regulator (LQR) [18,19], Linear Quadratic Gaussian (LQG) [20][21][22], fuzzy logic controllers [23,24], Sliding Mode Controllers (SMC) [25,26], backstepping control [2], Artificial Neural Networks (ANNs) [27,28] and some combination methods as in the studies [29,30].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Linear Quadratic Gaussian Speed Control of Induction Motor Using Fuzzy Logic

Maghfiroh,

Ma’arif,

Adriyanto

et al. 2023

JESA

View full text Add to dashboard Cite

An induction motor's speed can be managed in a variety of ways using a Variable Frequency Drive (VFD). In this study, the speed control of an induction motor will be controlled by applying Indirect Field Oriented Control (IFOC) combined with Linear Quadratic Gaussian (LQG). Conventional LQG control is a linear controller; therefore, if the system's dynamic is high and over the linear boundary, the LQG performance will not be optimal. Therefore, Adaptive LQG (ALQG) is proposed. Fuzzy logic is used as an adaptive algorithm with low complexity and ease of implementation. The significance of this study lies in its endeavor to tackle the challenges associated with nonlinearities and high dynamics in induction motor control. The average performance of speed variation and load variation tests proves that ALQG is superior in terms of settling time and undershooting than PID and LQG. PID has the highest overshoot with the smallest Integral Absolute Error (IAE). In comparison, ALQG is superior to conventional LQG in terms of IAE with 3.59% lower.

show abstract

Sensorless stator field-oriented digital control of induction motor based on LQR theory with augmented integral error

Cited by 2 publications

References 16 publications

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

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

Adaptive Linear Quadratic Gaussian Speed Control of Induction Motor Using Fuzzy Logic

Contact Info

Product

Resources

About