Gain Scheduling Based PID Control Approaches for Path Tracking and Fault Tolerant Control of a Quad-rotor UAV

Author(s):  
Jing Qiao ◽  
Zhixiang Liu ◽  
Youmin Zhang Zhang
2014 ◽  
Vol 1006-1007 ◽  
pp. 581-585 ◽  
Author(s):  
Lei Wan ◽  
Ying Hao Zhang ◽  
Yu Shan Sun ◽  
Yue Ming Li

An autonomous under vehicle (AUV) should have the ability of self-saving and finishing the certain targets when faults occur, which means that an AUV must have the ability of fault-tolerant control. In order to make it possible, one AUV’s fault-tolerant control strategy is made, which is based on the active disturbance rejection control (ADRC). In this paper, the control method in normal and the one in fault are offered respectively. Besides that, one simulation compared with PID control is made. The simulation results show the AUV’s fault-tolerant control strategy based on ADRC can achieve the goal and has better control results to restrain the shock, overshoot and other phenomena caused by disturbance than the strategy based on PID.


Author(s):  
Jinwei Sun ◽  
JingYu Cong ◽  
Liang Gu ◽  
Mingming Dong

As the possibility of sensor faults in the vehicle chassis system is higher and influences the vehicle stability, this paper deals with active fault-tolerant control for vehicle with vertical and lateral dynamics. It focuses on the combined control of active suspension system and electronic stability control with sensor faults based on the interaction between vehicle with vertical and lateral dynamics. A 9-degree-of-freedom vehicle integrated model is adopted for accurate control. The aim of the controller is to improve riding comfort when the vehicle is driving straight and improve lateral stability when the vehicle is steering in the presence of external disturbances and sensor faults. First, an H∞-based method is introduced to reconstruct the sensor fault signals, and meanwhile, the method can also observe the unmeasured signals. Based on the reconstruction faults and observed signals, a gain scheduling controller is utilized to guarantee the performance of the integrated model under different driving conditions, and the steering input is chosen as the scheduling parameter. Three different conditions, step steering input, single lane change input, and sensor faults, are considered. The main contributions of this study are as follows: (1) an H∞-based observer was designed for sensor fault estimation of the vertical and lateral integrated model and (2) a gain scheduling controller was designed to improve the performance of the integrated system. Simulations results indicated that the active fault-tolerant controller can reconstruct sensor faults and observe the unmeasured states exactly, and the linear parameter varying framework–based gain scheduling controller ensures the system performance adaptively.


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