MULTIVARIABLE CONTINUOUS TIME GENERALIZED PREDICTIVE CONTROL (CGPC) BASED INTERNAL MODEL CONTROL (IMC) APPLIED TO MOTOR DRIVES

When traditional regulator PID does not give possibility to obtain desired performances, the Internal Model Control (IMC) proves to be an interesting approach. Generalized Predictive Control (GPC) based on the Internal Model Control (IMC) structure appears to provide good performances in terms of disturbances and errors cancellation. The GPC depends on predicting the plants output over several steps based on the assumptions about future control actions. One assumption is that “there is a control horizon beyond which all control increments become zero”, is shown to be beneficial both in terms of robustness and for providing simplified calculations. Hence GPC can be used wither to control a simple plant (e.g. open-loop stable) with little prior knowledge or a more complex plant such as nonminimum phase, openloop unstable and having variable dead time. For continuous time version of the GPC i.e., CGPC, the future control is implicitly included where as in the original GPC it is explicitly included. Here the controller design is a Multivariable Continuous time Generalized Predictive control with IMC structure, based on the state space plant model obtained from the nominal values of the parameters and a specific operating point. In this project Multivariable CGPC based on IMC approach is applied to Induction machine. Induction motor is modeled in d-q synchronously rotating reference frame and the controller is designed with multivariable CGPC based IMC approach. CGPC based IMC approach is effective with system whose model is over parameterized bye the estimation scheme. This is verified with the simulation study. It is shown that the CGPC/IMC design is straightforward and the resulting controller is simple to implement.