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Sub-optimal Autopilot Design based on Dynamic Neural Unit Network for Tactical Missile
Dynamic Neural Unit (DNU) network based sub-optimal autopilot design for a tactical missile is investigated. An autopilot is expressed as two-loop nonlinear control phenomena and delivers an approximate solution to value function of State Dependent Riccati Equation (SDRE). The autopilot, not only tracks, stabilizes all the state vectors simultaneously but also, compensates the austere aerodynamic coupling and nonlinearities with an angle-of-attack. The body rates are transformed by outer-loop autopilot using state vectors command (total-angle-of-attack, bank angle), and fin deflections are transformed by inner-loop autopilot using state vectors command (body rates). Reduced cost computation is the novelty of the proposed autopilot. Outcomes are demonstrated using 6-DOF simulations and performance compared with sub-optimal Theta-D autopilot.
Autopilot, Sub-optimal, DNU, SDRE, 6-DOF, SDC.
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