# Blended Filter-Based Detection for Thruster Valve Failure and Control Recovery Evaluation for RLV
## 1 Introduction  
The successful flight of Colombia in 1981, the first space shuttle of the United States, pioneered the partial reuse of a space launch vehicle [1]. However, it had high launch costs and a long preparation period. Applying the learning experience from this shuttle mission [2], NASA implemented the second-generation reusable launch vehicle (RLV) research program. At present, X-37B has been successfully launched several times, with the longest orbiting flight time of 718 days [3], demonstrating a breakthrough in RLV technology. As a new type of aerospace flight vehicle, the effects of system failure caused by various actuators, sensors, or system components are of more concern to researchers. Statistics from the Federal Aviation Authority (FAA) and the National Transport Safety Board (NTSB) show that among the flight accidents over the past decade, hardware and system failure were the main cause of loss-of-control during flight [4]. Considering the harsh aerospace environment and the complexity of aerospace tasks, there is a call for intelligent fault tolerance control that can handle component faults and does not increase hardware complexity, cost, and mass [5]. RLV reentry usually relies on the thruster to supplement the aerodynamic actuator for attitude control. Specifically, during orbit, deorbit, and initial reentry phase, the thruster is the main actuator for the attitude control of the RLV. Currently, the pulse width modulation (PWM) method [6; 7] is mainly used for thruster control in spacecraft. When a duty cycle of a thruster is modulated using this method, the thruster can be considered as a continuous actuator. Its advantage lies in the application of the continuous multivariablecontrol method to the RLV's attitude control. However, the PWM method requires a thruster that is capable of high-frequency operation, which may lead to excessive wear of the thruster valve. In addition, the PWM duty cycle of the thruster system is subject to a certain practical limit [8]. One common approach to detect and isolate spacecraft thruster faults is to install pressure, temperature, and electrical sensors on the thrusters. The use of these additional sensors makes the fault detection and isolation quite simple and robust, as these sensors can assist in the direct detection of thrust. However, the need for additional sensors adds to the complexity, mass, and volume requirements of the spacecraft. This type of system is used, for example, on NASA's space shuttle orbiter [9]. When such extensive sensing is unavailable, an online fault diagnosis approach for thrusters can play an emergency role in the cases of hardware sensor failure or work as a supplementary reference for online monitoring [10]. A maximum-likelihood-based approach was proposed in the literature [11; 12] to detect leaking thrusters for the space shuttle orbiter's reactive control system jets; the soft failure was used to designate a fuel or oxidizer leak in a vehicle reactive control system jet. For the fuel leak, it only described the state in which one valve was open and the jet did not fire, but it did not consider the fuel leak after valve wear and other valve failure cases. In the literature [13; 14], fault detection and isolation methods were developed based on the exponentially weighted recursive least squares estimation using an accelerometer and angular rate sensors. A neural network then provided adaptive control reconfiguration to multiple destabilizing hard and soft thruster faults. However they did not provide an analysis of the failure caused by thruster valves, or, more particularly, the impact of thruster failure caused by valve performance degradation on the RLV or spacecraft. A nonlinear fault detection observer was designed in literature [15] to realize active fault tolerance tracking and RLV attitude control, combined with adaptive sliding mode technology, by the actuator redundancy. However, the fault detection observer was assumed to be idealized and less affected by the sensor noise and disturbances.