Speed Control of Electrical Drives Using Classical Control Methods

Harnefors, Lennart; Saarakkala, Seppo E.; Hinkkanen, Marko

doi:10.1109/tia.2013.2244194

Cited by 164 publications

(52 citation statements)

References 41 publications

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“…The simulation parameters of the six-phase IM are listed in Table 1. The effectiveness of the presented control technique for the six-phase IM has been evaluated under a load condition (T L = 2 Nm), the sampling frequency is 8 kHz, V dc is 400 V and the d-axis current reference (i * ds ) has been set in 1 A, while for the gains of the two degree PI controller with a saturation, can be found in [19]. Moreover, for the proposed control, λ xy = 0.1, defined in (15), has been used in order to give more emphasis to the (α-β) stator current tracking.…”

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

“…For the external speed control loop a two degree PI controller has been incorporated, described in [19], which is based on the FOC strategy due to its easiness. Into the FOC strategy, the reference current is generated by the PI speed controller in the dynamic reference frame, known as d-q frame.…”

Section: Speed Controlmentioning

confidence: 99%

“…In the proposed current strategy, three vectors have been considered at every sampling period, composed by two active vectors (taking only into account the largest vectors) and null vector, where their corresponding duty cycles are achieved according to the switching states and a switching pattern has also been used before being applied to VSI in order to generate a fixed switching frequency. Whereas, for the speed control loop, a PI controller has been developed by a technique shown in [19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predictive-Fixed Switching Current Control Strategy Applied to Six-Phase Induction Machine

et al. 2019

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In applications such as multiphase motor drives, classical predictive control strategies are characterized by a variable switching frequency which adds high harmonic content and ripple in the stator currents. This paper proposes a model predictive current control adding a modulation stage based on a switching pattern with the aim of generating a fixed switching frequency. Hence, the proposed controller takes into account the prediction of the two adjacent active vectors and null vector in the ( α - β ) frame defined by space vector modulation in order to reduce the (x-y) currents according to a defined cost function at each sampling period. Both simulation and experimental tests for a six-phase induction motor drive are provided and compared to the classical predictive control to validate the feasibility of the proposed control strategy.

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Section: Simulation and Experimental Resultsmentioning

confidence: 99%

Section: Speed Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predictive-Fixed Switching Current Control Strategy Applied to Six-Phase Induction Machine

et al. 2019

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“…A two-degree PI controller with a saturation stage, introduced in [37], is used as the outer speed control loop, based on the IRFOC method. In this loop, the output of the PI regulator is used to get the dynamic current reference i * sq (k).…”

Section: Outer Speed Control Loopmentioning

confidence: 99%

Current Control of a Six-Phase Induction Machine Drive Based on Discrete-Time Sliding Mode with Time Delay Estimation

et al. 2019

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This paper proposes a robust nonlinear current controller that deals with the problem of the stator current control of a six-phase induction motor drive. The current control is performed by using a state-space representation of the system, explicitly considering the unmeasurable states, uncertainties and external disturbances. To estimate these latter effectively, a time delay estimation technique is used. The proposed control architecture consists of inner and outer loops. The inner current control loop is based on a robust discrete-time sliding mode controller combined with a time delay estimation method. As said before, the objective of the time delay estimation is to reconstruct the unmeasurable states and uncertainties, while the sliding mode aims is to suppress the estimation error, to ensure robustness and finite-time convergence of the stator currents to their desired references. The outer loop is based on a proportional-integral controller to control the speed. The stability of the current closed-loop system is proven by establishing sufficient conditions on the switching gains. Experimental work has been conducted to verify the performance and the effectiveness of the proposed robust control scheme for a six-phase induction motor drive. The results obtained have shown that the proposed method allows good performances in terms of current tracking, in their corresponding planes.

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“…It is superior compared to conventional brushed DC motor due to attractive features such as high efficiency, high torque, great speed, small noise, small volume, and extended life [9]. In the 1990s, several conventional linear controllers such as Proportional Integral (PI), Proportional Derivative (PD), and PID are used to control BLDC motor [10]. However, since the BLDC motor is a multivariable, nonlinear, and strong coupling complex system [11].…”

Section: Introductionmentioning

confidence: 99%

BLDC Motor Control Optimization Using Optimal Adaptive PI Algorithm

Praptodiyono

Maghfiroh

Hermanu

2020

indones.j.electron.telecommun.

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The main problem of using a Proportional Integral (PI) Controller in Brushless Direct Current (BLDC) motor speed control is tuning the PI’s parameter and its performance cannot adapt to the system behavior changes. Particle Swarm Optimization (PSO) has been chosen to optimize the tuning. Fuzzy Logic Controller (FLC) is used to online tuning PI’s parameters to adapt to system conditions. Optimal adaptive PI, which combines the PSO method and FLC method to tune PI, is proposed. It was successfully implemented in the simulation environment. The test was carried out in three conditions: step responses, set-point changes, and disturbance rejection. The proposed algorithm is superior with no overshoot/undershoot. Whereas in terms of settling time is in between PI and PI-PSO. PI controller has the smallest control effort. However, the other parameter is the worst. PI-PSO is superior in terms of settling time and Integral of Absolute Error (IAE) except for the step response test. The proposed method has lower IAE and higher control effort by 78.73 % and 60 % compared to PI control. On the other hand, it has a higher IAE dan lower control effort by 11.82 % and 33.88 % compared to PI-PSO. Therefore, the optimal adaptive PI control can reduce energy consumption compared to optimal PI with better performance than PI control.

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Speed Control of Electrical Drives Using Classical Control Methods

Cited by 164 publications

References 41 publications

Predictive-Fixed Switching Current Control Strategy Applied to Six-Phase Induction Machine

Predictive-Fixed Switching Current Control Strategy Applied to Six-Phase Induction Machine

Current Control of a Six-Phase Induction Machine Drive Based on Discrete-Time Sliding Mode with Time Delay Estimation

BLDC Motor Control Optimization Using Optimal Adaptive PI Algorithm

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