Air target tracking by homing missile using deep learning and fuzzy adaptive control
In the integrated guidance and control approach, the guidance law and the autopilot are traditionally developed and tested separately, assuming the ideality of each other. This paper presents the design and simulation of a deep and fuzzy learning adaptive controller to guide a homing missile in a three-dimensional scenario to minimize collision time and maximize target interception accuracy. A deep learning neural network controller is initially developed offline in the proposed controller design and utilized as a gain table within the adaptive control framework. Subsequently, introducing fuzzy control further enhances the controller's adaptability and performance. The effectiveness of both controllers is evaluated under disturbance conditions. Simulation results demonstrate that the proposed controllers, along with the integrated guidance and control model, achieve reduced final miss distance and collision time compared to conventional PID and LQR controllers.
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Guidance and control of a two-dimensional model of an air defense missile using deep learning and fuzzy adaptive control
Mohammadmahdi Soori, *
Aerospace Science and Technology Journal, Spring-Summer 2024 -
Closed Loop Subject-Independent SSVEP Frequency Detection System Using CCA Features and Ensemble Learning Methods
M. Moein Esfahani, Hosein Najafi, Hossein Sadati
Frontiers in Biomedical Technologies, Summer 2024 -
Integrated guidance and control of the Pitch channel in a homing missile using optimal linear and nonlinear model predictive control
Mohamad Mahdi Soori, Seyed Hosein Sadati *
Journal of Technology in Aerospace Engineering,