This thesis presents the design of various controllers for a highly maneuverable, high performance aircraft, namely the modified F-18. The aircraft was required to perform high angle-of-attack maneuvers, for which the aircraft behaves in as a highly nonlinear system. An adaptive PID controller was used to control the aircraft through these high angle-of-attack maneuvers. Several nonlinear controllers were then developed based on the adaptive PID control, and were tested for robustness. This thesis also looks at an improvement in the aircraft which may improve performance in high angle-of-attack maneuvers. The contributions of this thesis are in the areas of control, in general, and specifically in the area of aircraft control. Successful application of linear adaptive control and nonlinear control were presented. In the area of aircraft control, controllers were presented which produce good performance for high angle-of-attack maneuvers, while maintaining implementability. Also, some insight is gained into what aircraft changes could improve performance. / Graduation date: 1995

This research presents a solution to the problem of tuning a PID controller for a nonlinear system. Many systems in industrial applications use a PID controller to control a plant or the process. Conventional PID controllers work in linear systems but are less effective when the plant or the process is nonlinear because PID controllers cannot adapt the gain parameters as needed. In this research we design a Nonlinear PID (NPID) controller using a fuzzy logic system based on the Mamdani type Fuzzy Inference System to control three different DC motor systems. This fuzzy system is responsible for adapting the gain parameters of a conventional PID controller. This fuzzy system's rule base was heuristically evolved using an Evolutionary Algorithm (Differential Evolution). Our results show that a NPID controller can restore a moderately or a heavily under-damped DC motor system under consideration to a desired behavior (slightly under-damped).