Integral State Feedback Control Using Linear Quadratic Gaussian in DC-drive System

Abstract

The electric motor is one of the technological developments which can support the production process. DC motor has some advantages compared to AC motor especially on the easier way to control its speed or position as well as its widely adjustable range. The main issue in the DC motor is controlling the angular speed with uncertainty and disturbance. The alternative solution of a control method with simple, easy to design, and implementable in a multi-input multi-output system is integral state feedback such as linear quadratic Gaussian (LQG). It is a combination between linear quadratic regulator and Kalman filter. One of the advantages of this method is the usage of fewer sensors compared with the original linear quadratic regulator method which uses sensors as many as the state in the system model. The design, simulation, and experimental study of the application of LQG as state feedback control in a DC-drive system have been done. Both performance and energy were analyzed and compared with conventional proportional integral derivative (PID). The gain of LQG was determined by trial whereas the PID gain is determined from MATLAB autotuning without fine-tuning. The load test and tracking test were carried out in the experiment. Both simulation and hardware tests showed the same result which LQG is superior in integral absolute error (IAE) by up to 74.37 % in loading test compared to PID. On the other side, LQG needs more energy, it consumes higher energy by 6.34 % in the load test.

A Method of Anti-Windup PID Controller for a BLDC-Drive System

“…2, the fitting between input and output facilitated the extraction of the transfer function presented in Equation (1). The fit to estimation yielded a value of 92.99%, surpassing the threshold of 90%, thus validating the usability of the derived transfer function [14]. Eq.…”

“…[13]. In contrast, the black-box, or experimental approach, relies on input-output data of the system when there is no accessible physical insight [12], [14]. On the other hand, the gray-box model combines elements of both black-box and white-box modeling [15].…”

Real-Time Implementation and Control of Multi-Source Electric Vehicle Traction Motor under Various Drive Conditions

“…In addition, the switching action of DC-DC converters further contributes to the non-linear behavior and damped dynamics of the overall system. Furthermore, many factors, such as sensor noise, process noise, and model errors, may affect the dynamic transient behavior of the system [28,29]. Thus, it is significant to design the speed and torque controller of the traction motor considering non-linearity and other unpredictable disturbances in EV applications.…”