Sliding mode based fault detection, reconstruction and fault tolerant control scheme for motor systems

Mekki, H.; Benzineb, Omar; Boukhetala, Djamel; Tadjine, Mohamed; Benbouzid, Mohamed

doi:10.1016/j.isatra.2015.02.004

Cited by 62 publications

(27 citation statements)

References 36 publications

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“…In the next section, a new sliding mode control of a five phase PMSM is proposed based on the SMC built in [6] for three-phase induction motor. The synthesis of the sliding mode controller occurs in two steps: a.…”

Section: Control Lawmentioning

confidence: 99%

New Sensorless Sliding Mode Control of a Five-phase Permanent Magnet Synchronous Motor Drive Based on Sliding Mode Observer

Hosseyni¹,

Trabelsi²,

Kumar³

et al. 2017

IJPEDS

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This paper proposes a sensorless sliding mode control (SMC) for a five phase permanent magnet synchronous motor (PMSM) based on a sliding mode observer (SMO). The stability of the proposed strategy is proved in the sense of the Lyapunov theory. The sliding mode controller is designed with an integral switching surface and the sliding mode observer is developed for the estimation of rotor position and rotor speed. The proposed sensorless control strategy exhibits good dynamic response to disturbances. Simulation results are provided to prove the effectiveness of the proposed strategy.

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Section: Control Lawmentioning

confidence: 99%

New Sensorless Sliding Mode Control of a Five-phase Permanent Magnet Synchronous Motor Drive Based on Sliding Mode Observer

Hosseyni¹,

Trabelsi²,

Kumar³

et al. 2017

IJPEDS

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“…The occurrence of an output sensor fault and especially the actuator faults which directly affect them, hence the development of fault-tolerant controls is needed [6,11,15].…”

Section: Introductionmentioning

confidence: 99%

Actuator Fault Estimation Based on LPV Unknown Input Observer for Induction Machine

Amrane¹,

Larabi²,

Aïtouche³

2017

STUD INFORM CONTROL

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This machine is not easy to control due to the nonlinearity of its dynamic model. Moreover, its state variables are not all measurable and it is under the influence of parametric variations. Therefore, a good control is necessary to guaranty the stability of its proper functioning. In the literature, most of the works deal with steady state vector control [8-10, 21, 25], Direct Torque Control (DTC) [19], Sliding Mode Control (SMC) [13,27]. In addition, nonlinear control applied to induction machine has been developed such in [14].The advantages of such approaches are their robustness to disturbances and their simplicity of implementation. The major disadvantage of the set-up is that all controls rely on the reliability of the measurement provided by sensors, Abstract: This paper presents an unknown inputs observer (UIO) applied to an induction machine (IM) in order to estimate the actuator faults and the state variables. Knowing that the machine used is highly nonlinear, a model based on LPV (linear parameter varying) system of the machine is used. Indeed, using the induction machine based on LPV model, we develop the structure of an observer where the actuator faults are the unknown inputs. The conditions for convergence are based on the Lyapunov theory that will ensure the stability. Based on the LMI (Linear Matrix Inequalities), the gains of the UIO subject to actuator faults are confirmed and then ensure the efficiency of our approach. The obtained results through simulations demonstrate the effectiveness of the proposed approach.

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“…Modern systems are complex, and new approaches in electric motor control are demanded, having high-precision requirements of speed and position under variable load torque. Consequently, it is necessary to develop new proposals for control and protection of electric motors [2,3].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, a nonlinear dynamic system could be subjected to unknown endogenous and exogenous disturbances affecting the performance indicators specified for the system response. There are several controllers based on sliding modes [2], predictive control [4], conventional proportional-integral-derivative (PID) techniques [5][6][7][8], robust control [9], neural networks [1,10], and fuzzy logic [11]. Some relevant aspects of these contributions are described in the next paragraphs.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Speed Control Approach for DC Shunt Motors

et al. 2016

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A B-spline neural networks-based adaptive control technique for angular speed reference trajectory tracking tasks with highly efficient performance for direct current shunt motors is proposed. A methodology for adaptive control and its proper training procedure are introduced. This algorithm sets the control signal without using a detailed mathematical model nor exact values of the parameters of the nonlinear dynamic system. The proposed robust adaptive tracking control scheme only requires measurements of the velocity output signal. Thus, real-time measurements or estimations of acceleration, current and disturbance signals are avoided. Experimental results confirm the efficient and robust performance of the proposed control approach for highly demanding motor operation conditions exposed to variable-speed reference trajectories and completely unknown load torque. Hence, laboratory experimental tests on a direct current shunt motor prove the viability of the proposed adaptive output feedback trajectory tracking control approach.

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Sliding mode based fault detection, reconstruction and fault tolerant control scheme for motor systems

Cited by 62 publications

References 36 publications

New Sensorless Sliding Mode Control of a Five-phase Permanent Magnet Synchronous Motor Drive Based on Sliding Mode Observer

New Sensorless Sliding Mode Control of a Five-phase Permanent Magnet Synchronous Motor Drive Based on Sliding Mode Observer

Actuator Fault Estimation Based on LPV Unknown Input Observer for Induction Machine

An Adaptive Speed Control Approach for DC Shunt Motors

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