## 1 Introduction  
For the civil aircraft, the automatic control system plays an important role in assisting the pilot in all flight phases. Usually, there are three phases in one flight process, that is, takeoff, cruise, and landing. The landing phase is the most challenging phase, during which many variables need to be controlled simultaneously and high airworthiness must be met. The altitude and velocity are rather low during the aircraft landing stage. Hence, accidents are more likely to happen in this phase. BAE firstly developed the Automatic Landing System (ALS) for commercial aircraft in 1965 to increase the safety of the landing maneuver [1]. It has been widely used since then, as it can provide safe and comfortable landing. However, the ALS is far from being robust to strong environmental disturbances (e.g., windshear, turbulence, etc.). Typically, there are four segments in the landing maneuver [2] (see Figure 1): alignment, glide slope, flare, and taxiing. The glide slope and flare segments are mainly studied in this paper, as they are the most challenging segments. During landing, the aircraft needs to track the desired trajectory precisely; this desired trajectory is provided by the Instrumental Landing System (ILS). The ALS works in harmony with the ILS, which is commonly available in a rapidly growing list of airports. The ILS contains two beam transmitters to guide the aircraft during the landing procedure. One is for localizer and the other one is for glide slope. An aircraft aligns and localizes itself to a befitting altitude and approach angle, according to these transmitters. Subsequently, it starts landing and decreasing the altitude [3]. Hence, the autolanding control is inherently a trajectory tracking control problem. Over the past two decades, numerous research results of autolanding control design have been developed to cope with the civil aircraft landing problem. Classical control methods are popular methods due to their simplicity and reliability. [PERSON] et al. [4] carried out a linear quadratic regulator (LQR) feedforward closed-loop controller, which was simulated on both the longitudinal and lateral-directional channels through different loops simultaneously. [PERSON] et al. [5] proposed a control design approach that was based on eigenstructure assignment while using dynamic feedback, which had two advantages, first was that it could be regarded as an efficient controller order reduction, second was the design methodology that could be initialized by \(H_{\infty}\) or \(\mu\) synthesis. With the development of digital flight computers, modern control theories have been progressively developed in an autolanding system. [PERSON] et al. [6] developed a multivariable model reference adaptive control method that was implemented with state feedback to enable a safety landing, despite the parametric variations. An ALS was designed using sliding mode control (SLC) technique in [7]. Lyapunov stability criteria was used to force the sliding function to reach the solution and converge the aircraft's landing path to the desired trajectory. [PERSON] et al. [8] also used the SLC integrated with the cerebellar model articulation controller to improve the ability of disturbance rejection of landing system, but the parameters of the SLC were adjusted by different methods, such as the genetic algorithm, particle swarm optimization, and chaotic particle swarm optimization. Fuzzy-logic dynamic inversion was used to suppress hard landing and roll oscillation in turbulent air condition to increase the adaption of the landing system to different environments [9]. On the other hand, [PERSON] et al. [10] proposed a controller that was based on a multi-layered fuzzy neural network structure, which provided the control signals during landing procedure. The backpropagation algorithm was used to train the network. In the meantime, \(H_{\infty}\) control has been widely used in the ALS. [PERSON] et al.