Chaos control in networked permanent magnet synchronous motor using Lyapunov-based model predictive subject to data loss

This study investigates the chaos control in the permanent magnet synchronous motor (PMSM) using a Lyapunov-based model predictive approach subject to data loss. PMSM, as a rich nonlinear dynamic, can demonstrate chaotic behavior when its parameters are in a certain area. Thus, the performance of the system will degrade in this condition. Moreover, in some networked applications, especially in the modern automation industry, data can be lost at the sensor-controller and controller-actuator links. It will lead the system to illustrate the chaotic behavior. Thus, by assuming a PMSM under data loss, a Lyapunov-based predictive model is applied to control the chaos in the PMSM in the presence of data loss. It shows that the PMSM system also be effective if the states of the system are not available or the system faces data loss. Sufficient conditions are discussed for the control of chaos in these conditions. Finally, the influence of the method on the control performance is evaluated via simulations on a nonlinear model for the PMSM.