Azeddine Kaddouri

Speed tracking control of a permanent-magnet synchronous motor with state and load torque observer

This paper is concerned with the speed tracking control problem for a permanent-magnet synchronous motor (PMSM) in the presence of an unknown load torque disturbance. After a brief review of the mathematical model of the PMSM, a speed tracking control law using the exact linearization methodology is introduced. The tracking control algorithm is completed by adding an extended observer which provides, on the one hand, the motor speed and acceleration and, on the other hand, estimates the unknown load torque. The stability of the closed-loop system composed of a nonlinear speed tracking controller and an observer is studied by the way of Lyapunov theory. Furthermore, the decoupling of the state observer and the load torque observer is discussed. Finally, a real-time implementation and the experimental results of the proposed control strategy are presented.

A nonlinear state observer for the sensorless control of a permanent-magnet AC machine

This paper presents a sensorless speed regulation scheme for a permanent-magnet synchronous motor (PMSM) based solely on the motor line currents measurements. The proposed scheme combines an exact linearization-based controller with a nonlinear state observer which estimates the rotor position and speed. Moreover, the stability of the closed-loop system, including the observer, is demonstrated through Lyapunov stability theory. The proposed observer has the advantage of being insensitive to rotation direction. It is shown how a singularity at zero velocity appears in the scheme and how it can be avoided by switching smoothly from the observer-based closed-loop control to an open-loop control at low velocity. The system performance is tested with an experimental setup consisting of a PMSM servo drive and a digital-signal-processor-based controller for both unidirectional and bidirectional speed regulation.

Loss minimization control of induction motor drives based on genetic algorithms

In this paper, a new approach using a floating point genetic algorithm (GA) is applied to minimize induction motor (IM) electric losses. The maximum efficiency is obtained by the online evaluation of the optimal magnetization flux. The loss model approach used is based on the steady state mathematical model of the IM. Some simulation results are given to validate this new approach. It is shown that this approach gives good results both in the steady state and dynamic operations.

An adaptive fuzzy controller gain scheduling for power system load-frequency control

In this paper, an adaptive fuzzy controller gain scheduling scheme for power system load-frequency control is designed to damp the frequency oscillations and to track its error to zero at steady state. A Sugeno type inference system is used in the proposed controller to adapt the scaling gains of a single fuzzy controller through a classical on-line monitoring of the most sensitive parameters of the system. The proposed controller avoids excessive patterns and training time compared to neural network based adaptive schemes. A typical single-area non reheat power system is considered. Simulation results indicate that the proposed controller is insensitive to parameter changes in a wide range of operating condition, and to the generation rate constraints. Furthermore, it is simple to implement.

Adaptive nonlinear control for speed regulation of a permanent-magnet synchronous motor

This paper presents an adaptive nonlinear controller (ANC) for a three-phase permanent-magnet synchronous motor (PMSM). The designed ANC controller combines the nonlinear input-output linearization technique with linear adaptive control techniques. It takes into account the uncertainties in the stator inductance and the rotor moment of inertia which are difficult to measure with accuracy. A good speed tracking performance with no error is obtained. Real-time implementation based on a floating-point DSP TMS320C31 is used to test the controllers performance.

Multiobjective genetic estimation of DC motor parameters and load torque

In order to simplify the offline identification of motor parameters, a new method based on optimization using a multiobjective elitist genetic algorithm is proposed. The non-dominated sorting genetic algorithm (NSGA-II) is used to minimize the error between the current and velocity responses of data and an estimated model. The robustness of the method is shown by estimating parameters of a DC motor in four different cases. Simulation results show that the method successfully estimates the motor parameters and is also capable of identifying a load torque simultaneously.

Adaptive Backstepping Speed Control for a Permanent Magnet Synchronous Motor

The application of the backstepping feedback design technique to the speed control of a PMSM is investigated. Using backstepping method, both feedback laws and Lyapunov based designs are applied in the controller design. The proposed stabilizing feedback law for the PMSM is shown to be globally asymptotically stable in the context of Lyapunov theory. It takes into account nonlinearities of the system and uncertainties of the parameters. To validate the contribution of this control, the performances are analyzed and highlighted by simulation results.

Nonlinear Control by Input-Output Linearization Scheme for EV Permanent Magnet Synchronous Motor

This paper presents a modern approach of speed control for permanent magnet synchronous motor (PMSM) applied for electric vehicle using a nonlinear control. The regulation algorithms are based on the input-output feedback linearization technique. The direct component of the current is controlled to be zero which insures the maximum torque operation. The near unity power factor operation is also achieved. Moreover, among EVs motor electric propulsion features, the energy efficiency is a basic characteristic that is influenced by vehicle dynamics and system architecture. For this reason, the EV dynamics are taken into account. Simulation tests have been carried out on a 19.8-kW EV PMSM drive to evaluate the consistency and the performance of the proposed control approach.

Nonlinear Controller design for a Permanent Magnet Synchronous Motor

This paper present a speed control technique for a permanent magnet synchronous motor (PMSM) based on an adaptive backstepping technique. The proposed stabilizing feedback law for the PMSM is shown to be globally asymptotically stable in the context of Lyapunov theory. It takes into account nonlinearities of the system and uncertainties of the parameters. Computer simulations have been carried out in order to validate the effectiveness of the proposed controller. The results show that accurate tracking performance of the PMSM has been achieved.

A Two-Layered Self-Tuning Fuzzy Controller For Interconnected Power Systems

In this paper, a two-layered load-frequency controller with a fuzzy pre-compensator and a self-tuning stabilizer is designed. The proposed controller is able to damp and to track the steady-state error of the frequency, the output power and the tie-line power deviations. A two-area reheat thermal power system is considered to verify the effectiveness of the controller. Simulations results indicate that the proposed controller is insensitive to parameter changes and to speed-governor dead-zone in a wide range of operation conditions, with and without generation rate constraints (GRC). In addition, the proposed scheme requires less training time and patterns compared to a neural network adaptive scheme