Predictive Control of Axis Drift in Linear Motion Control Systems

The positional accuracy of a linear motion system used in machine tools can be enhanced by using closed loop feedbackinvolving a positional measurement by means of an encoder.The position error is developed in the linear motion system because of the thermal expansion of the ball screw assembly and also due to the error in encoder measurement values. The traditional error compensation and correction methods used in a linear motion system do not satisfy all the dynamic performance requirements and constraints. In this paper, a Model Predictive Control (MPC) algorithm is proposed to reduce the position error of the linear motion control system at no-load and light load conditions. The future predictions made by the model predictive controller are based on the behaviour of the ball screw motion mechanism and encoder measurements to enhance the position accuracy of the linear motion system. The performance of the proposed model predictive controller is verified for no-load conditions in ball screw based linear motion system, and the results have been shown to outperform the current Proportional, Integral and Derivative (PID) and Fractional Order Proportional, Integral and Derivative(FOPID) control methods

Hybrid parametric islanding detection technique for microgrid system

In microgrid distribution generation (DG) sources are integrated parallelly for the economic and efficient operation of a power system. This integration of DG sources may cause many challenges in a microgrid. The islanding condition is termed a condition in which the DG sources in the microgrid continue to power the load even when the grid is cut off. This islanding situation must be identified as soon as possible to avoid the collapse of the microgrid. This work presents the hybrid islanding detection technique. This technique consists of both active and parametric estimation methods such as slip mode shift frequency (SMS) and exact signal parametric rotational invariance technique (ESPRIT), respectively. This technique will easily distinguish between islanding and non-islanding events even under very low power perturbations. The proposed method also has no power quality impact. The proposed method is tested with UL741 standard test conditions

Predictive Control of Axis Drift in Linear Motion Control Systems

827-836The positional accuracy of a linear motion system used in machine tools can be enhanced by using closed loop feedbackinvolving a positional measurement by means of an encoder.The position error is developed in the linear motion system because of the thermal expansion of the ball screw assembly and also due to the error in encoder measurement values. The traditional error compensation and correction methods used in a linear motion system do not satisfy all the dynamic performance requirements and constraints. In this paper, a Model Predictive Control (MPC) algorithm is proposed to reduce the position error of the linear motion control system at no-load and light load conditions. The future predictions made by the model predictive controller are based on the behaviour of the ball screw motion mechanism and encoder measurements to enhance the position accuracy of the linear motion system. The performance of the proposed model predictive controller is verified for no-load conditions in ball screw based linear motion system, and the results have been shown to outperform the current Proportional, Integral and Derivative (PID) and Fractional Order Proportional, Integral and Derivative(FOPID) control methods