Error State Kalman Filter Estimator Based Input Saturated Controlfor Rotorcraft Unmanned Aerial Vehicle

Regarding the autonomous flight control for unmanned aerial vehicle (UAV) in global positioning system denied environments, an error state Kalman filter (ESKF) framework is designed to fuse the vision and IMU (inertial measurement unit). On this basis, a novel input saturated control approach is proposed to further alleviate those issues due to motion blur and the field of view constraint. Different from the traditional extended Kalman filter (EKF) framework, the designed filter framework updates and corrects the error state rather than directly estimates the system state. Since the error state is a small variable with good linearity, the model error caused by its local linearization is smaller than that of the system state. Hence, the state estimation accuracy can be improved by using the error state. Based on the full state estimation in the ESKF framework, a novel hybrid linear and hyperbolic tangent saturation function is proposed to design the input saturated control approach. And it is shown that the equilibrium of the closed-loop system is asymptotically stable in the Lyapunov sense. Finally, comparative experimental results on the rotorcraft unmanned aerial vehicle (RUAV) demonstrate that the state estimation based on the proposed ESKF approach is more accurate. In addition, the proposed input saturated control method can help to keep the visual features in the field of view, and it has better transient and steady-state performances compared with the bounded integral control method.