A COMPARISON BETWEEN ROBUST AND FUZZY CONTROLLER DESIGN OF A GYRO STABILIZED ELECTROOPTICAL SIGHT SYSTEM | ||||
| The International Conference on Electrical Engineering | ||||
| Article 52, Volume 5, 5th International Conference on Electrical Engineering ICEENG 2006, May 2006, Page 1-10 PDF (249.38 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iceeng.2006.33637 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| G. Elnashar; T. Elbayoumi; A. Eldsoky; M. Hegazy | ||||
| Egyptian Armed Forces. | ||||
| Abstract | ||||
| ABSTRACT In modern fire control systems, Line of Sight (LOS) stabilization plays an essential and crucial part. LOS stabilization systems have a wide range of military and civilian applications. Their importance arises from the critical applications that employ these systems. Two techniques are used for the LOS stabilization systems, passive and active. The passive LOS stabilization systems are easy to design and are manufactured at a relatively low cost to be interfaced with different types of electro optical systems. Hence, it can be used to increase the efficiency of many armored vehicles serving in the armed forces, where it may be used for constructing fire control systems. The passive LOS stabilization systems are multi-input multi-output (MIMO) systems that are highly nonlinear and possess a strong coupling effect between their states. It presents a challenging system to control. In this paper the analysis of the passive LOS stabilization system with the development of its nonlinear mathematical model is derived. Two different types of control algorithms are presented. The first controller is a Linear Quadratic Gaussian controller (LQG). The controller presents a conventional control technique that proves to be stable with high transient and tracking performances. The controller is applied to the LOS stabilization system and the simulation results are introduced. Next, an intelligent fuzzy controller is introduced. The fuzzy control presents a nonlinear control technique that compensates the system's nonlinearity; hence, it is more appropriate to stabilize and control the system under consideration. The fuzzy controller is designed to decouple the relationship between the system state variables. The controller's performance is verified through simulations and results. Finally, comparative analysis between the two developed controllers is conducted. It discusses the advantages and disadvantages of each control algorithm. | ||||
| Keywords | ||||
| Multi input multi output (MIMO); Fuzzy Model reference learning control (FMRLC); LOS stabilized system; non linear control; LQG/LTR. | ||||
|
Statistics Article View: 126 PDF Download: 216 |
||||
