Adaptive Robust Control Schemes for a Class of Uncertain Systems with Partial Knowledge of Uncertainty Bounds

1998 ◽  
Vol 31 (25) ◽  
pp. 51-56
Author(s):  
Hansheng Wu ◽  
Shinji Shigemaru ◽  
Kokyu Kawabata
Keyword(s):  
Robust Control ◽  
Uncertain Systems ◽  
Adaptive Robust Control ◽  
Partial Knowledge ◽  
Control Schemes

A computationally efficient adaptive robust control scheme for a quad-rotor transporting cable-suspended payloads

2021 ◽  
pp. 095441002110136
Author(s):  
Nasim Ullah ◽  
Irfan Sami ◽  
Wang Shaoping ◽  
Hamid Mukhtar ◽  
Xingjian Wang ◽  
...  
Keyword(s):  
Robust Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Adaptive Robust Control ◽  
Computationally Efficient ◽  
Control Scheme ◽  
Control Schemes ◽  
Adaptive Laws ◽  
Unknown Disturbances ◽  
The Stability

This article proposes a computationally efficient adaptive robust control scheme for a quad-rotor with cable-suspended payloads. Motion of payload introduces unknown disturbances that affect the performance of the quad-rotor controlled with conventional schemes, thus novel adaptive robust controllers with both integer- and fractional-order dynamics are proposed for the trajectory tracking of quad-rotor with cable-suspended payload. The disturbances acting on quad-rotor due to the payload motion are estimated by utilizing adaptive laws derived from integer- and fractional-order Lyapunov functions. The stability of the proposed control systems is guaranteed using integer- and fractional-order Lyapunov theorems. Overall, three variants of the control schemes, namely adaptive fractional-order sliding mode (AFSMC), adaptive sliding mode (ASMC), and classical Sliding mode controllers (SMC)s) are tested using processor in the loop experiments, and based on the two performance indicators, namely robustness and computational resource utilization, the best control scheme is evaluated. From the results presented, it is verified that ASMC scheme exhibits comparable robustness as of SMC and AFSMC, while it utilizes less sources as compared to AFSMC.

Variable Neural Direct Adaptive Robust Control of Uncertain Systems

2008 ◽  
Vol 53 (11) ◽  
pp. 2658-2664 ◽  
Author(s):  
Jianming Lian ◽  
Yonggon Lee ◽  
Stanislaw H. $\dot {\hbox{Z}}$ak
Keyword(s):  
Robust Control ◽  
Uncertain Systems ◽  
Adaptive Robust Control ◽  
Control Of Uncertain Systems

scholarly journals Adaptive Robust Control for Uncertain Systems via Data-Driven Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-9
Author(s):  
Jun Zhao ◽  
Qingliang Zeng
Keyword(s):  
Robust Control ◽  
Control Problem ◽  
Adaptive Learning ◽  
Uncertain Systems ◽  
Adaptive Robust Control ◽  
System Stability ◽  
Data Driven ◽  
Algebraic Riccati Equation ◽  
Stability And Convergence ◽  
Online Data

Although solving the robust control problem with offline manner has been studied, it is not easy to solve it using the online method, especially for uncertain systems. In this paper, a novel approach based on an online data-driven learning is suggested to address the robust control problem for uncertain systems. To this end, the robust control problem of uncertain systems is first transformed into an optimal problem of the nominal systems via selecting an appropriate value function that denotes the uncertainties, regulation, and control. Then, a data-driven learning framework is constructed, where Kronecker’s products and vectorization operations are used to reformulate the derived algebraic Riccati equation (ARE). To obtain the solution of this ARE, an adaptive learning law is designed; this helps to retain the convergence of the estimated solutions. The closed-loop system stability and convergence have been proved. Finally, simulations are given to illustrate the effectiveness of the method.

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