**1. Introduction**

Safe operation and reliability are important performances of unmanned aerial vehicles (UAVs). Unmanned helicopters (UHs) as a kind of UAVs develop quickly because of their ability to hover and low speed flight. Nevertheless, traditional control and planning strategies

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cannot guarantee their safety and reliability in the face of malfunctions such as sensor faults, actuator faults, and component faults. Furthermore, taking physical limits such as actuator constraints and state constraints into account, the situation would be more serious. In order to ensure the reliability and safety of UHs, fault detection and diagnosis (FDD) and fault‐ tolerant control (FTC) approaches become focus of research and many related research results have been presented [1]. However, physical limits are not well considered in conventional FTC approaches which may affect their efficiency. On the other hand, besides UH's dynam‐ ic limits, external environment constraints, such as obstacles, also affect UH's safety. However, traditional planning methods cannot consider environment constraints and vehicles' limits at the same time which makes these methods helpless in the face of faults. In this chapter, a self‐healing control (SHC) framework is proposed against actuator faults and constraints of single‐rotor UHs under external environment constraints. The SHC frame‐ work is shown in **Figure 1** which involves FDD module, reconfigurable controller, trajecto‐ ry (re‐)planning module, and evaluator module.

**Figure 1.** SHC framework.

The tasks of the above modules are as follows:


**4.** Evaluator module: Evaluate system performance of UHs according to AHCs, UH dynamic model, and controller and trajectory information. If the original controller and trajectory are not feasible, this module will ask related modules to reconfigure controller and replan trajectory.

The remaining part of this chapter is organized as follows: single‐rotor UH and AHC models are simply introduced in Section 2; Section 3 investigates FDD approach against actuator faults based on extended Kalman filter (EKF) and linear neural network (LNN). In Section 4, the postfault controller is designed to guarantee the stability of UH system under both actuator faults and constraints; Section 5 presents invariant‐set based planning (ISBP) approach that can compute controller reference according to desired path and UH dynamic model. Evalua‐ tion strategy is introduced in Section 6 and numerical simulations are shown in Section 7. Finally, Section 8 summarizes the chapter with conclusions.
