Neural-fuzzy controller configuration design for an electro-optical line of sight stabilization system. (December 2020)

• Record Type: Journal Article
• Authors: Singh, Ravindra; Khatoon, Shahida; Chaudhary, Himanshu; Pandey, Ashish; Hanmandlu, M.
• Is Part Of: Computers & electrical engineering. Volume 88(2020)
• Publication Date: 2020-12
• Subjects: Line of Sight (LOS) -- Fuzzy Logic Ccontrol (FLC) -- Neural-fuzzy -- Electro-Optical (EO) -- Tracking and Pointing -- Disturbance attenuation; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106837
• Languages: English
• ISSNs: 0045-7906

Abstract

Research highlights: A gimballed Electro-optical Line of Sight Stabilization system is modelled for precision pointing application. Neural fuzzy controller structure is proposed to attenuate angular disturbances. Fuzzy curve approach is used for finding optimum combination of significant rules and initial weights for constructing neural fuzzy controller architecture. Neural fuzzy Control model resulted in ameliorated system dynamic performance by reducing jitter level to 34.23 µrad as compared to Fuzzy (39.22 µrad) and also provides optimized step input command response. Abstract: This paper presents a Neural-fuzzy model structure to improve the disturbance attenuation performance of a gimballed Line of Sight (LOS) stabilization system. Initially, a Fuzzy Logic Controller (FLC) based on prior qualitative information about system dynamics and linguistic performance criteria is developed. Next, proposed Neural-fuzzy model architecture is constructed, which overcome the difficulties and limitations of each isolated methodology. The Neural-fuzzy architecture is developed based on input-output data-sets available from FLC. Fuzzy curve approach is used to determine significant inputs, number of rules, initialization of connecting layers weights, and hence the model structure. Both the controller configurations are tested based on critical performance characteristics such as stability of the loop, responsiveness of the loop and insensitivity to disturbances. Finally, the comparative analysis suggests that the proposed Neural-fuzzy controller completely outperforms the FLC configuration and hence, can be very effective for more precise pointing applications. Graphical abstract: Image, graphical abstract