## 2 Related Works  
### Igc  
Compared with the traditional guidance and control system design, IGC treats the guidance system and control system as a whole system, which eliminates the assumption that the guidance system has a faster response speed than the control system. Therefore, it solves the coordination problem of the spectral characteristics of guidance and control systems. This section presents a brief introduction to IGC. IGC considers the attitude dynamics in the guidance subsystem design and produces the fin deflection commands directly. In early years, channel-separated IGC was widely researched, in which the Six-Degrees-of-Freedom (6 DOF) motion of the flight vehicle is decomposed into longitudinal and lateral motion planes, and IGC is designed independently for each plane. This method reduces the order of the system and the complexity of the control parameter tuning. However, essentially, the spatial motion of the flight vehicle is located in SE(3) space. Hence, the above-mentioned decomposition method does not consider the coupling of the longitudinal and lateral motion planes, which may reduce the system precision. To simplify the problem, in [2], IGC was treated as a linear optimal control problem by ignoring nonlinear items. To consider the nonlinearities, State-Dependent Riccati Equation (SDRE)-based IGC was proposed [9]. In SDRE-based IGC, the 6 DOFmotion of the flight vehicle was rewritten as a pseudo-linear state space equation in which the coefficients are state-dependent. In what follows, by solving an online Riccati Equation (RE), similar to the Linear Quadratic Regulator (LQR) technique, the control input can be obtained. To avoid solving the online RE, the \(\theta\)-\(D\) technique was proposed [10; 11]. Apart from this, feedback linearization is also usually applied to obtain a linear IGC model, and based on that, the LQR method can be applied. By comparison, SDRE, \(\theta\)-\(D\), or feedback linearization-based IGC has weak robustness against uncertainties because the accurate information of the model is required. To improve the robust of the system, an adaptive IGC for pitch channel was designed with a so-called Nussbaum function being applied to deal with the disturbance [12]. In order to improve the performance of the flight vehicle system, in recent years, three-dimensional IGC was proposed, which combines the nonlinear flight vehicle models with the vehicle-object pursuit model in three-dimensional space. Three-dimensional IGC fully describes the 6 DOF motion of the flight vehicle, and channel-separated IGC is a simplification of the three-dimensional IGC by assuming that the angle between the LOS and missile velocity is small or almost constant [13]. Several nonlinear control methods are employed, such as sliding mode control (SMC) [14], the small-gain theorem [15], the backstepping approach [16], and intelligent control [17]. With the backstepping approach, the high-order nonlinear system of the flight vehicle is divided into multiple subsystems with the cascade structure. For each subsystem, SMC, adaptive control, and intelligent control can be applied for command tracking and disturbance rejection. The small-gain theorem-based IGC treats the system of the flight vehicle as an interconnected system, combined with the guidance subsystem and control subsystem. The guidance law and control law are designed separately such that each subsystem is input-to-state-output practically stable with its input and the output of another subsystem [15; 18]. The above-mentioned forms of IGC assume that the actuator (fin) system can respond to the command of IGC. However, in practice, due to some physical constraints, the flight vehicle may suffer from the actuator fault problem, which may degrade system performance and lead to instability of the system. The fault-tolerant ability of IGC will significantly improve the robustness of the system.