Sensorless Sliding-Mode Rotor Speed Observer of Induction Machines Based on Magnetizing Current Estimation

Vieira, Rodrigo Padilha; Gastaldini, Cristiane Cauduro; Azzolin, Rodrigo Zelir; Gründling, Hilton Abílio

doi:10.1109/tie.2013.2290759

Cited by 79 publications

(28 citation statements)

References 34 publications

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“…A PID sliding mode control using saturated function has been suggested by some researchers in literature [36][37][38]. The advantage of this method can be rapidly convergence, but the system stability has not been proved and chattering still exists.…”

Section: Remarkmentioning

confidence: 99%

Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller

Trong

Duc

2017

Energies

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Based on Lyapunov theory, this research demonstrates the stability of the sliding surface in the consensus problem of multi-agent systems. Each agent in this system is represented by the dynamically uncertain robot, unstructured disturbances, and nonlinear friction, especially when the dynamic function of agent is unknown. All system states use neural network online weight tuning algorithms to compensate for the disturbance and uncertainty. Each agent in the system has a different position, and their trajectory approach to the same target is from each distinct orientation.In this research, we analyze the design of the sliding surface for this model and demonstrate which type of sliding surface is the best for the consensus problem. Lastly, simulation results are presented to certify the correctness and the effectiveness of the proposed control method.

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Section: Remarkmentioning

confidence: 99%

Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller

Trong

Duc

2017

Energies

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“…ˆˆ( ) dx = Ax + Bu + K y -y dt (7) ˆŷ = Hx (8) where x is the state variable, ŷ is the observation variable, u is the control variable, y is the actual measurement value, A is the state matrix, B is the input matrix, H is the observation matrix, and K is the gain matrix.…”

Section: Ekf Observer For Induction Motorsmentioning

confidence: 99%

“…Therefore, a lot of attention has been paid to rotor speed estimation to achieve sensorless control. Many sensorless methods have been proposed, such as high-frequency signal injection [1], [2], model reference adaptive systems (MRAS) [3], [4], full-order adaptive observers [5]- [7], sliding-mode observers (SMO) [8], [9], artificial neural networks (ANN) [10], and extended Kalman filters (EKF) [11]- [29].…”

Section: Introductionmentioning

confidence: 99%

“…However, the speed fluctuations becomes large as the speed decreases. In [8] and [9], a SMO is used to estimate speed and to identify the stator resistance and overcome the problems of stator resistance variations, particularly for low-speed operation. However, since the scheme is designed based on a mathematical model of an IM, its observability is generally lost at zero magnetic field frequency, and the SMO experiences chattering.…”

Section: Introductionmentioning

confidence: 99%

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An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors

Li¹,

Du²,

Sun³

et al. 2017

Journal of Power Electronics

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To improve the performance of sensorless induction motor (IM) drives, an adaptive speed estimation method based on a strong tracking extended Kalman filter with a least-square algorithm (LS-STEKF) for induction motors is proposed in this paper. With this method, a fading factor is introduced into the covariance matrix of the predicted state, which forces the innovation sequence orthogonal to each other and tunes the gain matrix online. In addition, the estimation error is adjusted adaptively and the mutational state is tracked fast. Simultaneously, the fading factor can be continuously self-tuned with the least-square algorithm according to the innovation sequence. The application of the least-square algorithm guarantees that the information in the innovation sequence is extracted as much as possible and as quickly as possible. Therefore, the proposed method improves the model adaptability in terms of actual systems and environmental variations, and reduces the speed estimation error. The correctness and the effectiveness of the proposed method are verified by experimental results.

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“…In this context, the sliding mode methods are characterized by the its simplicity, disturbance rejection and strong robustness, and they are used in several process for control and estimation, including the rotor speed estimation of the electrical machines and current, torque or rotor speed control [10]- [14]. The paper [13] proposes a sliding mode control method with non-linear disturbance observer applied to systems with mismatched uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Sensorless decoupled IM current control by sliding mode control and disturbance observer

Vieira

Gabbi

Gründling

2014

IECON 2014 - 40th Annual Conference of the IEEE Industrial Electronics Society

Self Cite

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This paper presents a decoupled current control system applied to induction machine drives via sliding mode control and disturbance observer. The current control of induction motor is carried out with indirect field orientation rotating at dq synchronous reference frame. The coupling terms are modeled as disturbance and the observed disturbance is used in the control law. Moreover, an algorithm aiming the rotor speed estimation based on magnetizing current estimation and sliding mode approach is presented. As result, a sensorless control system with decoupling of stator current is proposed. Experimental results are presented aiming to verify the system performance.

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Sensorless Sliding-Mode Rotor Speed Observer of Induction Machines Based on Magnetizing Current Estimation

Cited by 79 publications

References 34 publications

Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller

Sliding Surface in Consensus Problem of Multi-Agent Rigid Manipulators with Neural Network Controller

An Adaptive Speed Estimation Method Based on a Strong Tracking Extended Kalman Filter with a Least-Square Algorithm for Induction Motors

Sensorless decoupled IM current control by sliding mode control and disturbance observer

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