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Unformatted text preview: Abstract — The purpose of this paper is to provide a practical example to the Linear Quadratic Gaussian (LQG) controller. This method includes a description and some discussion of the discrete Kalman state estimator. One aspect of this optimality is that the estimator incorporates all information that can be provided to it. It processes all available measurements, regardless of their precision, to estimate the current value of the variables of interest, with use of knowledge of the system and measurement device dynamics, the statistical description of the system noises, measurement errors, and uncertainty in the dynamics models. Since the time of its introduction, the Kalman filter has been the subject of extensive research and application, particularly in the area of autonomous or assisted navigation. For example, to determine the velocity of an aircraft or sideslip angle, one could use a Doppler radar, the velocity indications of an inertial navigation system, or the relative wind information in the air data system. Rather than ignore any of these outputs, a Kalman filter could be built to combine all of this data and knowledge of the various systems’ dynamics to generate an overall best estimate of velocity and sideslip angle. Keywords — Aircraft motion, Kalman filter, LQG control, Lateral stability, State estimator. I. INTRODUCTION HE Feedback control systems are widely used in manufacturing, mining, automobile and military hardware applications. In response to demands for increased efficiency and reliability, these control systems are being required to deliver more accurate and better overall performance in the face of difficult and changing operating conditions. In order to design control systems to meet the demands of improved performance and robustness when controlling complicated processes, control engineers will require new design tools and better underlying theory. In particular, a standard method of improving the performance of a control system is to add extra sensors and actuators. This necessarily leads to a multi-input multi-output control system. Thus, it is a requirement for any Manuscript submitted July 20, 2008. A. Maddi is with the LATSI Laboratory at Electronics Department, University of Blida, Road of Soumaa, PB 270, Blida, Algeria (e-mail: [email protected]). A. Guesssoum is with the Electronics Department, University of Blida, Road of Soumaa, PB 270, Blida, Algeria (e-mail: [email protected]). D. Berkani is with the Electrical Engineering Department, Polytechnic School of El Harrach, Road of Hassan Badi, PB 182, Algiers, Algeria (e-mail: [email protected]). modern feedback control system design methodology that it be able to handle the case of multiple actuators and sensors....
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This note was uploaded on 02/04/2012 for the course ECE 222 taught by Professor Goengi during the Spring '11 term at Maryland.
- Spring '11