L1 neural network adaptive fault-tolerant controller for unmanned aerial vehicle attitude control system

2021 ◽  
pp. 095441002098409
Author(s):  
Yan Zhou ◽  
Huiying Liu ◽  
Huijuan Guo ◽  
Jing Li
Keyword(s):  
Neural Network ◽  
Control System ◽  
Unmanned Aerial Vehicle ◽  
Attitude Control ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Attitude Control System ◽  
Attitude Tracking ◽  
Nonlinear Uncertainties ◽  
Aerial Vehicle

In this article, a L1 neural network adaptive fault-tolerant controller is exploited for an unmanned aerial vehicle attitude control system in presence of nonlinear uncertainties, such as system uncertainties, external disturbances, and actuator faults. A nonlinear dynamic inversion controller with sliding mode control law is designed as the outer-loop controller to track the attitude angles quickly and accurately which reduces dependence on model accuracy. A L1 neural network adaptive controller of the inner loop is introduced to compensate the nonlinear uncertainties and have a good attitude tracking. The radial basis function neural network technique is introduced to approximate a lumped nonlinear uncertainty and guarantee the stability and transient performance of the closed-loop system, instead of converting it to a half-time linear system by the parametric linearization method. Simulation results demonstrate the effectiveness of the proposed controller.

scholarly journals Adaptive augmentation of the attitude control system for a multirotor unmanned aerial vehicle

2018 ◽  
Vol 234 (10) ◽  
pp. 1587-1596
Author(s):  
G Bressan ◽  
A Russo ◽  
D Invernizzi ◽  
M Giurato ◽  
S Panza ◽  
...  
Keyword(s):  
Control System ◽  
Unmanned Aerial Vehicle ◽  
Attitude Control ◽  
Adaptive Controller ◽  
Attitude Control System ◽  
Loop Dynamics ◽  
The Standard Model ◽  
Aerial Vehicle ◽  
Adaptation Law ◽  
Model Reference Adaptive

In this paper, the adaptive augmentation of the attitude control system for a multirotor unmanned aerial vehicle is considered. The proposed approach allows to combine a baseline controller with an adaptive one and to disable or enable the adaptive controller when needed, in order to take the advantages of both the controllers. To improve transient performance with respect to the standard model reference adaptive controller, an observed-based approach is exploited. The adaptation law is based on the error between the output of an observer of the nominal closed-loop dynamics and the actual output of the system with uncertainties. Experimental results obtained by testing the proposed approach on a quadrotor unmanned aerial vehicle are presented to compare the performance, in terms of disturbance rejection, with respect to the baseline controller and to a [Formula: see text] adaptive augmentation scheme.

Adaptive fault-tolerant tracking control of flying-wing unmanned aerial vehicle with system input saturation and state constraints

2021 ◽  
pp. 014233122110270
Author(s):  
Zhen Li ◽  
Xin Chen ◽  
Mingyang Xie ◽  
Zhenhua Zhao
Keyword(s):  
Reinforcement Learning ◽  
Unmanned Aerial Vehicle ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Input Saturation ◽  
Control Technique ◽  
Attitude Tracking ◽  
Aerial Vehicle ◽  
Fast Dynamic ◽  
Dynamic Subsystem

In this paper, an adaptive fault-tolerant attitude tracking controller based on reinforcement learning is developed for flying-wing unmanned aerial vehicle subjected to actuator faults and saturation. At first, the attitude dynamic model is separated into two dynamic subsystems as slow and fast dynamic subsystems based on the principle of time scale separation. Secondly, backstepping technique is adopted to design the controller. For the purpose of attitude angle constraints, the control technique based on Barrier Lyapunov is used to design controller of slow dynamic subsystem. Considering the optimization of the fast dynamic subsystem, this paper introduces an adaptive reinforcement learning control method in which neural network is used to approximate the long-term performance index and lumped fault dynamic. It is shown that this control algorithm can satisfy the requirements of attitude tracking subjected to the control constraints and the stability of the system is proved from Lyapunov stability theory. The simulation results demonstrate that the developed fault-tolerant scheme is useful and has more smooth control effect compared with fault-tolerant controller based on sliding mode theory.

Flapping Wing Micro Aerial Vehicle Attitude Control with Fuzzy Sliding Mode Controller Based on RBF Neural Network

2012 ◽  
Vol 443-444 ◽  
pp. 177-182
Author(s):  
Sheng Bin Hu ◽  
Wen Hua Lu ◽  
Min Xun Lu
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Attitude Control ◽  
Sliding Mode ◽  
Flapping Wing ◽  
Micro Aerial Vehicle ◽  
Mode Control ◽  
Attitude Tracking ◽  
Aerial Vehicle ◽  
Fuzzy Sliding Mode

A adaptive fuzzy Sliding Mode Control (SMC) scheme based on Radial Basis Function Neural Network (RBFNN) for attitude tracking control of Flapping Wing Micro Aerial Vehicle (FWMAV) is proposed in this paper. A RBFNN is used to compute the equivalent control of sliding mode control, An adaptive algorithm is used for weight adaptation of the RBFNN and A Lyapunov function is selected for the design of the SMC. The simulation results of FWMAV demonstrate that the control scheme is effective.

scholarly journals Sliding Mode Fault Tolerant Control for Unmanned Aerial Vehicle with Sensor and Actuator Faults

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 643 ◽  
Author(s):  
Juan Tan ◽  
Yonghua Fan ◽  
Pengpeng Yan ◽  
Chun Wang ◽  
Hao Feng
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Health Management ◽  
Fault Tolerant ◽  
Sliding Mode ◽  
Control Process ◽  
Fault Tolerant Control ◽  
Accelerometer Sensor ◽  
Actuator Failures ◽  
Aerial Vehicle ◽  
Gyroscope Sensor

The unmanned aerial vehicle (UAV) has been developing rapidly recently, and the safety and the reliability of the UAV are significant to the mission execution and the life of UAV. Sensor and actuator failures of a UAV are one of the most common malfunctions, threating the safety and life of the UAV. Fault-tolerant control technology is an effective method to improve the reliability and safety of UAV, which also contributes to vehicle health management (VHM). This paper deals with the sliding mode fault-tolerant control of the UAV, considering the failures of sensor and actuator. Firstly, a terminal sliding surface is designed to ensure the state of the system on the sliding mode surface throughout the control process based on the simplified coupling dynamic model. Then, the sliding mode control (SMC) method combined with the RBF neural network algorithm is used to design the parameters of the sliding mode controller, and with this, the efficiency of the design process is improved and system chattering is minimized. Finally, the Simulink simulations are carried out using a fault tolerance controller under the conditions where accelerometer sensor, gyroscope sensor or actuator failures is assumed. The results show that the proposed control strategy is quite an effective method for the control of UAVs with accelerometer sensor, gyroscope sensor or actuator failures.

scholarly journals Fault Reconstruction Approach for Distributed Coordinated Spacecraft Attitude Control System

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Mingyi Huo ◽  
Yanning Guo ◽  
Xing Huo
Keyword(s):  
Control System ◽  
Attitude Control ◽  
Large Scale ◽  
Sliding Mode ◽  
Reconstruction Error ◽  
Spacecraft Attitude ◽  
Alignment Error ◽  
Spacecraft Attitude Control ◽  
Attitude Control System ◽  
Fault Reconstruction

This work presents a novel fault reconstruction approach for a large-scale system, that is, a distributed coordinated spacecraft attitude control system. The attitude of all the spacecrafts in this distributed system is controlled by using thrusters. All possible faults of thruster including thrust magnitude error and alignment error are investigated. As a stepping stone, the mathematical model of thruster is firstly established based on the thruster configuration. On the basis of this, a sliding mode observer is then proposed to reconstruct faults in each agent of the coordinated control system. A Lyapunov-based analysis shows that the observer asymptotically converges to the actual faults. The key feature of this fault reconstruction approach is that it can achieve a faster reconstruction of the fault in comparison with the conventional fault reconstruction schemes. It can globally reconstruct thruster faults with zero reconstruction error, and this is accomplished within finite time. The effectiveness of the proposed approach is analytically authenticated via simulation study.

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