Kalman State Estimation and LQR Assisted Adaptive Control Of a Variable Loaded Servo System

Abstract

This study actualized a new hybrid adaptive controller design to increase the control performance of a variable loaded time-varying system. A structure in which LQR and adaptive control work together is proposed. At first, a Kalman filter was designed to estimate the states of the system and used with the LQR control method which is one of the optimal control servo system techniques in constant initial load. Then, for the variable loaded servo (VLS) system, the Lyapunov based adaptive control was added to the LQR control method which was inadequate due to the constant gain parameters. Thus, it was aimed to eliminate the variable load effects and increase the stability of the system. In order to show the effectiveness of the proposed method, a Quanser servo module was used in Matlab-Simulink environment. It is seen from the experimental results and performance measurements that the proposed method increases the system performance and stability by minimizing noise, variable load effect and steady-state error.

Kalman Filtresi ile Ayrık Zamanlı Durum Tahmini ve Zamanla Değişen Doğrusal Bir Sistemin Adaptif LQR Kontrolü

“…If the gain matrix value at discrete LQR output and also the adaptively produced feedback gain value are respectively defined as   u outputs [21]. As is understood from study in [22], the Lyapunov function needs to be higher than zero for the system to be stable.…”

Sliding Mode Observer and Event Triggering Mechanism Co-design

“…In addition, some SMO have attractive properties similar to those of the Kalman filter (i.e. noise resilience) [5], but with a simpler implementation [6]. Sometimes this design can be performed by applying an equivalent control method [7,8], allowing the proposal of robust to noise observers, since the equivalent control is slightly affected by noisy measurements.…”

Unscented Kalman Filter based State of Charge Estimation for the Equalization of Lithium-ion Batteries on Electrical Vehicles

“…The model performance strongly depends on the data set used [21,22]. Kalman filter (KF) which can solve initial SoC and cumulative error problems is widely used as an accurate SoC estimator, but it is only suitable for linear systems [23,24]. Extended Kalman filter (EKF) known as the nonlinear extension of the conventional KF, is the most commonly used filter to estimate SoC.…”