**1. Introduction**

Conventional aircrafts have aileron, elevator, and rudder control effectors. Flight control systems are generally developed using one control effector for each rotational degree of freedom. The aileron is utilized to obtain a roll motion, a pitch motion is obtained by using the elevator, and the rudder effector controls the yaw motion of the aircraft. The control problem is the determination of the deflections of control effectors that produce the desired motion specified by a flight controller that transfers the pilot's command given by a control stick. Three control effectors can generate desired motions. However, modern aircrafts have more control effectors than conventional aircrafts [1]. The design of reliable flight control systems is difficult for modern aircrafts because these aircrafts are becoming more complicated. Also, the performance of flight control systems must be very high and the stability of the aircraft has demanded the development of different control systems [2]. In recent years, linear control systems have been developed assuming that flight dynamics are linear timeinvariant about the operation points and the longitudinal dynamics are decoupled

from lateral ones. Zhang et al. [3] proposed a mixed *H*2*=H*<sup>∞</sup> flight controller using enhanced linear matrix inequality, which stabilizes the aircraft system in case of actuator loss. A gain scheduled linear quadratic regulator method is designed in [4] for vehicle dynamics where the flight period is divided into different intervals because flight condition varies during the flight. A proportional-integral-derivative (PID) flight control system is investigated in ref. [5] whose performance is not satisfactory due to uncertainties and nonlinearities of vehicle dynamics. A flight controller law is designed based on optimal control theory in ref. [6] ensuring the reliability of aircraft for pilot's commands in case of all operating conditions. A resilient linear controller is proposed by Bouvier et al. [7] for the dynamic of aircraft in the presence of a loss of control authority. Offline reference regulators and robust control allocation flight controllers were developed in ref. [8] for aerodynamic nonlinearities and parametric uncertainties. Besides, nonlinear controller methods have been proposed by researchers. For example, a nonlinear dynamic inversion control law is proposed by Da Costa et al. [9] where the nonlinear dynamics are transformed into linear dynamics using state or output feedback assuming timescale separation between attitude and altitude rates. Nonlinear dynamic inversion controllers require precise knowledge of all nonlinearities that is not possible for modern fighter aircraft [10]. Sliding mode differentiator [11], disturbance observer-based sliding mode control [12], and disturbance observer-based dynamic surface controller [13] are developed considering nonlinearities and external disturbances.

A backstepping control based on fuzzy logic system is designed in ref. [14] for vehicle dynamics with state constraints and actuator fault. A fuzzy tracking controller [15] was proposed to satisfy the properties of disturbance rejection in aircraft vehicles. Takagi-Sugeno fuzzy robust controller was developed by Luan et al. [16] for the problem of part transportation. An adaptive fuzzy controller [17] was designed for a vehicle dynamic with input saturation.

In this chapter, we develop a control approach based on a switching control with a fuzzy logic rule, which is evaluated in a nonlinear ADMIRE aircraft model. Combined switching control with fuzzy logic has better tracking performance and strong robustness for the nonlinear model of ADMIRE aircraft.
