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**6** 

Adel Merabet

*Canada* 

**Nonlinear Model Predictive Control** 

*Division of Engineering, Saint Mary's University, Halifax, NS,* 

The induction motor (IM) is widely used in industry because of its well known advantages such as simple construction, less maintenance, reliability and low cost. However, it is highly nonlinear, multivariable, time-varying system and, contrary to DC motor, requires more complex methods of control. Therefore, this machine constitutes a theoretically challenging

One of the most important development in control area for induction motor has been field oriented control (FOC) established firstly by (Blaschke, 1972). However, the performance of this technique is affected by the motor parameter variations and unknown external disturbances. To improve the dynamic response and reduce the complexity of FOC methods, an extension amount of work has been done to find new methods, such as direct torque control (DTC), sliding mode and nonlinear control (Barut et al., 2005; Chen &

Model based predictive control (MPC) is one of the most promising control methods for both linear and nonlinear systems. The MPC formulation integrates optimal control, multivariable control, and the use of future references. It can also handle constraints and nonlinear processes, which are frequently found in industry. However, the computation of the MPC requires some mathematical complexities, and in the way of implementing and tuning this kind of controller, the computation time of the MPC may be excessive for the sampling time required by the process. Therefore, several MPC implementations were done for slow processes (Bordons & Camacho, 1998; Garica et al., 1989; Richalet, 1993). However, the explicit formulation of MPC allows its implementation in fast linear systems (Bemporad et al. 2002). A review of fast method for implementing MPC can be found in (Camacho & Bordons, 2004). In case of nonlinear systems, where the mathematical packages are available in research control community, and thanks to the advancement of signal processing technology for control techniques, it becomes easy to implement these control schemes. Many works have been developed in nonlinear model predictive control (NMPC) theory (Ping, 1996; Chen et al., 1999; Siller-Alcala, 2001; Feng et al., 2002). A nonlinear PID model predictive controller developed in (Chen et al., 1999), for nonlinear control process, can improve some desirable features, such as, robustness to parameters variations and external disturbance rejection. The idea is to develop a nonlinear disturbance observer, and by

**1. Introduction** 

control problem.

Dunnigan, 2003; Chiasson, 1996; Marino et al. 1993).

 **for Induction Motor Drive** 

(Zuritz, 2005) Zuritz C.A., Muñoz E., Mathey H.H., Pérez E.H., Gascón A., Rubio L.A., Carullo C.A., Chemikoff R.E., Cabeza M.S., *"Density, viscosity and soluble solid concentration and temperatures".* Journal of Food Engineering, *Vol. 71, pp. 143 - 149.*
