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.

Adaptive Robust Control of Circular Machining Contour Error Using Global Task Coordinate Frame

2013 ◽  
Author(s):  
Tyler A. Davis ◽  
Yung C. Shin ◽  
Bin Yao
Keyword(s):  
Robust Control ◽  
Control Problem ◽  
Research Work ◽  
Approximation Error ◽  
Adaptive Robust Control ◽  
Contour Error ◽  
Coordinate Frame ◽  
Tool Deflection ◽  
Machining Processes ◽  
Highly Nonlinear

The contour error of machining processes is defined as the difference between the desired and actual produced shape. Two major factors contributing to contour error are axis position error and tool deflection. A large amount of research work formulates the contour error in convenient locally defined task coordinate frames that are subject to significant approximation error. The more accurate global task coordinate frame (GTCF) can be used, but transforming the control problem to the GTCF leads to a highly nonlinear control problem. An adaptive robust control (ARC) approach is designed to control machine position in the GTCF, while directly accounting for tool deflection, to minimize the contour error. The combined GTCF/ARC approach is experimentally validated by applying the control to circular contours on a three axis milling machine. The results show that the proposed approach reduces contour error in all cases tested.

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

An Adaptive Robust Control for Displacement-Controlled End-Effectors

2014 ◽  
Author(s):  
Enrique Busquets ◽  
Monika Ivantysynova
Keyword(s):  
Robust Control ◽  
Range Of Motion ◽  
Control Strategies ◽  
Open Loop ◽  
Adaptive Robust Control ◽  
System Stability ◽  
Linear Control ◽  
Parameter Adaptation ◽  
Plant Behavior ◽  
Wide Range

Cascade linear control strategies with output feedback have been studied at the Maha Fluid Power Research Center to demonstrate robust control for displacement-controlled rotary actuation. These strategies have been mainly investigated for closed-loop actuator control where the operator specifies the actuator position to close the loop. This paper presents an extension of the work developed for this kind of actuation by introducing a non-linear control strategy for open-loop applications (i.e. the operator closes the loop via a joystick). The test bench, a 1.5 ton hydraulically-operated end-effector with a range of motion of 270° is utilized to validate the obtained control law. The proposed control scheme, an adaptive robust control (ARC) law, ensures system stability and robustness for a wide range of motion while eliminating the linear controller approach limitations. Furthermore, changes in the plant behavior are taken into account through online parameter adaptation. To emphasize on the advantages of ARC, a deterministic robust control (DRC) law has been derived from the ARC. Results show that the advantages of online parameter adaptation lead to a dramatic increase on the actuator position accuracy. In addition, the ARC results are compared to the cascade controller developed by Grabbel in 2004.

scholarly journals Control of spacecraft formation flying around asteroids

2021 ◽  
Author(s):  
Arthur Kar Leung Lin
Keyword(s):  
Robust Control ◽  
System Dynamics ◽  
Formation Flying ◽  
Adaptive Robust Control ◽  
Asteroid Belt ◽  
System Stability ◽  
Control Law ◽  
Nonlinear Controller ◽  
Multi Agent ◽  
Control Methodology

There exist thousands of different minerals and other possible resources out in space. To exploit these resources and to further expand our knowledge of the universe, planetary exploration has opened new gates towards mankind. There are more than one hundred thousand designated asteroids located inside the asteroid belt. Some of these asteroids are as old as the Big Bang itself. Tracking of astronomical bodies such as asteroids is the new stream of research that has attracted a lot of attention. However, due to environmental constraints around asteroids, monolithic spacecraft missions seem challenging. Multi-agent systems, on the other hand, provide significant advantages when it comes to orbiting around asteroids. In this study, novel consensus algorithms are applied to regulate the multi-agent decentralized formation flying for increased system flexibility and reliability. A nonlinear controller is developed to control the decentralized formation flying system of interest. Faults are evaluated and reduced to a minimum when planning a mission. However, the performance of the controller should not be affected when faults occur. For this reason, sensor and actuator faults are examined in this thesis in conjunction with actuator limitations which is commonly referred to as saturation. The proposed control law is not only able to control the system while faults occur, but rather it is capable of maintaining system stability in the presence of time variant external disturbances. Uncertainty in parameters and dynamic models are inevitable due to the complexity of the relatively new mission and lack of experimental data about the system dynamics. As such, a novel adaptive robust control methodology is developed that does not require full knowledge of the system dynamics. Moreover, the adaptive robust control law is combined with a Chebyshev neural network to overcome system uncertainties. Numerical simulations results along with stability analyses show that the proposed control methodology is capable of reducing the system state error close to zero within 1 orbit when maximum thrust of 5 mN with bounded external disturbance of 3 mN is applied for formation reconfiguration scenarios; these results will be useful for the future formation flying missions around asteroids.

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