Intelligent reference learning techniques for pitch control of an aircraft

This paper presents a comparative analysis of two intelligent reference learning techniques to achieve better performance of pitch control of an aircraft. Fuzzy Model Reference Learning Controller (FMRLC) and Radial Basic Function Neural Controller (RBFNC) are designed for pitch control of a FOXTROT fighter aircraft. These controllers utilize a learning mechanism, which observes the plant output and adjusts the configuration in the direct controller, so that the overall system behaves like a reference model which characterizes the desired behavior. The performance of the pitch control system is demonstrated by simulation for various conditions with change in the aircraft dynamics caused due to change in speed of the aircraft and sensor noise. The simulation results establish the superiority of RBFNC over FMRLC with respect to rise time, settling time and overshoot.