FTESO-adaptive neural network based safety control for a quadrotor UAV under multiple disturbances: algorithm and experiments

Abstract

Purpose Quadrotors have been applied in various fields. However, because the quadrotor is subject to multiple disturbances, consisting of external disturbances, actuator faults and parameter uncertainties, it is difficult to control the unmanned aerial vehicle (UAV) to achieve high-precision tracking performance. This paper aims to design a safety controller that uses observer and neural network method to improve the tracking performance of UAV under multiple disturbances. The experiments prove that this method is effective. Design/methodology/approach First, to actively estimate and compensate the synthetic uncertainties of the system, a finite-time extended state observer is investigated, and the disturbances are transformed into the extended state of the system for estimation. Second, an adaptive neural network controller that does not accurately require the dynamic model knowledge is designed based on the estimated value, where the weights of the neural network can be dynamically adjusted by the adaptive law. Furthermore, the finite-time bounded convergence of the proposed observer and the stability of the system are proved through homogeneous theory and Lyapunov method. Findings The figure-“8” climbing flight simulation and real flight experiments illustrate that the proposed safety control strategy has good tracking performance. Originality/value This paper proposes the safety control structure of the UAV, which combines the extended state observer with the neural network method. Numerical simulation results and actual flight experiments demonstrate the effectiveness of the proposed control strategy.

Distributed Lyapunov-Based Model Predictive Control for AUV Formation Systems with Multiple Constraints

“…The control algorithm incorporates nonsingular terminal sliding mode and super-twisting methods. The authors of [21] investigated a safety control based on the FTESO adaptive neural network for unmanned aerial vehicles. The double-power FTESO was utilized to compensate for lumped disturbances.…”

“…To achieve stable velocity tracking, based on the backstepping method and the lumped disturbances compensated by FFTESO, we choose the following control law: (21) where K iv > 0 is a specified gain matrix. Then, (13) becomes the following form:…”