Adaptive Self-Learning Fuzzy Autopilot Design for Uncertain Bank-To-Turn Missiles

This chapter presents an adaptive self-learning fuzzy autopilot design for uncertain bank-to-turn missiles due to external disturbances and system errors from the variations of the aerodynamic coefficients and control surface loss. In the autopilot design, the ESAFISs are utilized to compensate for these uncertainties in an adaptive Backstepping architecture. Based on the Lyapunov stability theory, adaptation laws are derived to update the consequent parameters of fuzzy rules, which guarantees both tracking performance and stability. The robust control terms with the adaptive bound-estimation schemes are also designed to compensate for modeling errors of the ESAFISs by augmenting the self-learning fuzzy autopilot control laws. The proposed autopilot is validated under the control surface loss, aerodynamic parameter perturbations and external disturbances. Simulation study is also compared with a conventional Backstepping autopilot and a neural autopilot in terms of the tracking ability. The results illustrate that the designed fuzzy autopilot can obtain better steady state and transient performance with the dynamically self-learning ability.