Current and speed control of a permanent magnet synchronous motor using PID and LQR

Robin, .; Prasad, M P R

doi:10.1109/icccnt.2017.8204151

Cited by 1 publication

(3 citation statements)

References 13 publications

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“…Therefore, the overshoots can be suppressed, and the amount of energy given to the system can be reduced. The determined Q and R here are similar to that in [38], and there are 0 0…”

Section: K K Ksupporting

confidence: 75%

“…The weighting matrices Q and R are constant symmetric positive definite matrices, and their values have a great influence on the dynamic performance of the system. Usually, q1, q2 and q3 are set to 1 as proposed in [16], [38], and [39], which means that all the state variables are considered with equal importance. Matrix R is related with control signals, and in [16] and [38], it was set to relatively high values, which gives more attention on control signal.…”

Section: K K Kmentioning

confidence: 99%

“…Usually, q1, q2 and q3 are set to 1 as proposed in [16], [38], and [39], which means that all the state variables are considered with equal importance. Matrix R is related with control signals, and in [16] and [38], it was set to relatively high values, which gives more attention on control signal. Therefore, the overshoots can be suppressed, and the amount of energy given to the system can be reduced.…”

Section: K K Kmentioning

confidence: 99%

See 2 more Smart Citations

State Feedback Control for a PM Hub Motor Based on Gray Wolf Optimization Algorithm

Sun

Lei

et al. 2020

IEEE Trans. Power Electron.

176

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This paper presents an optimal control strategy for a permanent-magnet synchronous hub motor (PMSHM) drive using the state feedback control method plus the grey wolf optimization (GWO) algorithm. First, the linearized PMSHM mathematical model is obtained by voltage feedforward compensation. Second, to acquire satisfactory dynamics of speed response and zero d-axis current, the discretized state space model of the PMSHM is augmented with the integral of rotor speed error and integral of d-axis current error. Then, the GWO algorithm is employed to acquire the weighting matrices Q and R in liner quadratic regulator optimization process. Moreover, a penalty term is introduced to the fitness index to suppress overshoots effectively. Finally, comparisons among the GWObased state feedback controller with and without the penalty term, the conventional state feedback controller, and the genetic algorithm enhanced PI controllers are conducted in both simulations and experiments. The comparison results show the superiority of the proposed state feedback controller with the penalty term in fast response.

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“…Therefore, the overshoots can be suppressed, and the amount of energy given to the system can be reduced. The determined Q and R here are similar to that in [38], and there are 0 0…”

Section: K K Ksupporting

confidence: 75%

Section: K K Kmentioning

confidence: 99%