Neural Network Based Finite-Time Attitude Tracking Control of Spacecraft With Angular Velocity Sensor Failures and Actuator Saturation

Observer-Based Adaptive Fuzzy Finite-Time Attitude Control for Quadrotor UAVs

“…However, it is difficult to measure accurately due to sensor failure and measurement noise, and it costs expensive expenses to install extra velocity sensors. Hence far, many works have been deeply studied to obviate this problem [23], [37]- [42]. The motion capture system [37] was used to numerically estimate the velocity signals.…”

Vibration control of a translational coupled double flexible beam system using sliding mode neural network fuzzy control

“…Ling et al (2021) developed an online neural network-based SMC scheme for a class of piezoelectric drive systems to obtain robust adaptive precision motion, in which the nonlinearity of the system is compensated by neural network. Ye et al (2022a) presented a saturated finite-time fast terminal sliding mode controller, while the Legendre polynomial-based neural network was incorporated to approximate the unknown nonlinear dynamics. Long et al (2021) used the improved SMC based on the nominal model as the main controller, and adopted the reinforcement learning controller based on the actor-critic to output the compensation torque to suppress the end vibration of a flexible manipulator with hybrid structure.…”

Observer-Based Predefined-Time Attitude Control for Spacecraft Subject to Loss of Actuator Effectiveness

“…In the field of aircraft control, due to the requirements of spacecraft tasks, the system needs to respond quickly; as such, some scholars have proposed the finite time control method. Reference [9] designed a finite time tracking controller based on fast terminal sliding mode control technology and neural network for a spacecraft attitude control system with sensor failure and actuator saturation. In the stability proof of reference [9], the Lyapunov function satisfies V ≤ −λV α (x), where 0 < α < 1, the system will be stable within a finite time T r , i.e., T r ≤ V 1−α (x 0 ) λ(1−α) , x 0 is the initial value of the system state variable.…”

“…Reference [9] designed a finite time tracking controller based on fast terminal sliding mode control technology and neural network for a spacecraft attitude control system with sensor failure and actuator saturation. In the stability proof of reference [9], the Lyapunov function satisfies V ≤ −λV α (x), where 0 < α < 1, the system will be stable within a finite time T r , i.e., T r ≤ V 1−α (x 0 ) λ(1−α) , x 0 is the initial value of the system state variable. For the rendezvous task of thrust vector spacecraft, a constrained optimal orbit finite time attitude controller design scheme is proposed in [10].…”

“…It can be seen from [9][10][11] that the proof of finite time convergence of the system is related to the initial value of the system. When the initial value of the system changes or uncertain, it is difficult to calculate the convergence time of the system, which limits the application of finite time control.…”