Control and Estimation of Asynchronous Machines Using Fuzzy Logic

These techniques are traditionally solved using a mathematical model of the machine with fixed parameters. However, in a real machine, the stator and rotor resistances are altered by temperature and the inductances are altered by the magnetizing current values that change for example when the machine is running in the flux weakening region or by an improper detuning between the flux and torque producing currents. For these reasons, the induction machine shows properties of nonlinear and time-varying systems. Parameter variations degrade the system performance over the full range of motor operation and in extreme conditions this can lead to instability (Vas, 1999). To solve this problem the controller parameters have to be continuously adapted. This adaptation can be achieved using different techniques such as MRAC or model reference adaptive control (Zhen & Xu, 1998), sliding mode (Won & Bose, 1992), or self tuning PIDs (Astrom & Hagglung, 1996). For some of these techniques the motor parameters and load inertia must be calculated in real time, so there is a high processing requirement for the used processors.