Extended state observer based robust control of wing rock motion

2014 ◽  
Vol 33 (1) ◽  
pp. 107-117 ◽  
Author(s):  
Deepak Kumar Kori ◽  
Jaywant P. Kolhe ◽  
S.E. Talole
Author(s):  
Kun Cheng ◽  
DaTong Qin ◽  
Junhang Jian ◽  
Bangzhi Wu

The clutch characteristics of dual clutch transmission (DCT) will change as the service time increases, which will lead to the deterioration of gearshift performance. To reduce the influence of the change in clutch characteristics on the gearshift performance, an adaptive gearshift control method based on the extended state observer and H∞ robust control is proposed. First, the gearshift problem of the DCT is transformed into the reference trajectory tracking problem, and the gearshift reference trajectory is designed using the minimum principle. The uncertain term related to the change in clutch characteristics in the DCT gearshift dynamic model is defined, and an extended state observer is designed to estimate the uncertain term. On this basis, the gearshift controller is designed using the backstepping method, and H∞ robust control is introduced to further improve the adaptation effect of the controller, then the adaptive control laws of the clutch pressure and engine torque are obtained. Finally, the adaptation effect of the proposed method was verified by both simulation and experiment. The results show that the proposed adaptive gearshift control method can effectively avoid the gearshift delay caused by the change in clutch characteristics, and the gearshift jerk in the simulation and experiment is reduced by 55.01% and 34.8%, respectively.


Author(s):  
Sushant N Pawar ◽  
Rajan H Chile ◽  
Balasaheb M Patre

This paper describes a predictive extended state observer-based robust control for uncertain process control applications. The technique discussed in the article uses the extended state observer (ESO) that can estimate the dynamics of the system as well as total disturbance encountered in the system. The disturbances, parametric uncertainties associated with the processes are treated as an extended state variable to be estimated in real-time using ESO. With the implementation of a predictive algorithm with an ESO, the proposed control structure extends its applicability to time-delayed higher-order processes. The proposed control technique utilizes the simple first-order modified predictive ESO even in the case of higher-order processes. The novel predictive ESO is able to obtain a delay less estimation of total disturbance as compared with existing normal ESO. Also, novel predictive ESO maintains its stability margin in presence of time delay as well provides better response as compared with normal ESO. Numerical simulations show that the proposed scheme provides a significant improvement in transient response as compared with internal model control-based proportional-integral-derivative (IMC-PID) control. The proposed scheme requires less knowledge of the process as compared with the IMC-PID structure. The implementation of the proposed control is tested on a real-life single tank level control system. Because of its merit, the suggested technique can be used as automatic for online tuning, as it is less reliant on the process model.


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