A terminal guidance algorithm based on ant colony optimization. (July 2019)

• Record Type: Journal Article
• Authors: Nobahari, Hadi; Nasrollahi, Saeed
• Is Part Of: Computers & electrical engineering. Volume 77(2019)
• Page Start: 128
• Page End: 146
• Publication Date: 2019-07
• Subjects: Terminal guidance -- Ant colony optimization -- State estimation -- Target maneuver estimation -- Autopilot dynamic -- Nonlinear model predictive control; 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.2019.05.012
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: In this paper, terminal engagement of a maneuvering target and a pursuer is investigated. A heuristic nonlinear model predictive guidance algorithm is presented. Nonlinear kinematics of the pursuer and the target is utilized to formulate the guidance problem. Also, the target maneuver is assumed to be unknown. The proposed heuristic guidance algorithm uses an ant-based optimization algorithm to estimate simultaneously the states of the pursuer, the maneuver of the target, and the optimal guidance commands. Performance of the new guidance algorithm against maneuvering and non-maneuvering targets is evaluated using numerical simulations. Also, the results of the guidance algorithm are compared to the true proportional navigation, a guidance law based on backstepping, augmented proportional navigation, switched bias proportional navigation, linear quadratic differential game, state dependent Riccati equation-differential game, and proportional navigation-improved particle swarm optimization guidance.