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.

Desired Compensation Adaptive Robust Control

2009 ◽  
Vol 131 (6) ◽  
Author(s):  
Bin Yao
Keyword(s):  
Robust Control ◽  
Nonlinear Systems ◽  
Experimental Studies ◽  
Adaptive Robust Control ◽  
System Stability ◽  
Parameter Estimates ◽  
Adaptive Model ◽  
State Variables ◽  
Model Uncertainties ◽  
Parameter Adaptation

A desired compensation adaptive robust control (DCARC) framework is presented for nonlinear systems having both parametric uncertainties and uncertain nonlinearities. The paper first considers a class of higher order nonlinear systems transformable to a normal form with matched model uncertainties. For this class of uncertain systems, the desired values of all states for tracking a known desired trajectory can be predetermined and the usual desired compensation concept can be used to synthesize DCARC laws. The paper then focuses on systems with unmatched model uncertainties, in which the desired values of the intermediate state variables for perfect output tracking of a known desired trajectory cannot be predetermined. A novel way of formulating desired compensation concept is proposed and a DCARC backstepping design is developed to overcome the design difficulties associated with unmatched model uncertainties. The proposed DCARC framework has the unique feature that the adaptive model compensation and the regressor depend on the reference output trajectory and on-line parameter estimates only. Such a structure has several implementation advantages. First, the adaptive model compensation is always bounded when projection type adaption law is used, and thus does not affect the closed-loop system stability. As a result, the interaction between the parameter adaptation and the robust control law is reduced, which may facilitate the controller gain tuning process considerably. Second, the effect of measurement noise on the adaptive model compensation and on the parameter adaptation law is minimized. Consequently, a faster adaptation rate can be chosen in implementation to speed up the transient response and to improve overall tracking performance. These claims have been verified in the comparative experimental studies of several applications.

Vibration control of multi-mode rotor-bearing systems

1983 ◽  
Vol 386 (1790) ◽  
pp. 77-94 ◽  
Keyword(s):  
Vibration Control ◽  
Least Squares Method ◽  
Control Strategies ◽  
Open Loop ◽  
External Control ◽  
Wide Range ◽  
Rotor Bearing ◽  
Optimum Response ◽  
Control Forces ◽  
Adaptive Vibration Control

This paper presents a computationally fast and efficient least-squares method to minimize the vibration of any general rotor-bearing system by the application of external control forces. The D-optimality concept is used to optimize the force locations. The proposed method provides a wide range of statistical information, and the sensitivity of the optimum response to changes in the control forces. Magnetic bearings can be applied to implement the open-loop adaptive vibration control strategies outlined in the paper. These components can also be used to inject a multi-frequency test signal as required for identi­fication studies.

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.

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.

scholarly journals Adaptive robust control design to enhance smart grid power system stabilization using wind characteristics in Indonesia

2021 ◽  
Vol 12 (4) ◽  
pp. 2182
Author(s):  
Khamda Herbandono ◽  
Cuk Supriyadi Ali Nandar
Keyword(s):  
Robust Control ◽  
Smart Grid ◽  
Power System ◽  
High Reliability ◽  
Control Design ◽  
Adaptive Robust Control ◽  
System Stability ◽  
Identification System ◽  
Wind Characteristics ◽  
Robust Control Design

<span lang="EN-US">This paper is interested to study power system stability in smart grid power system using wind characteristic in south of Yogyakarta, Indonesia. To overcome the intermittent of wind characteristics, this paper presents adaptive robust control design to enhance power system stabilization. The online identification system is used in this research, which updated whenever the estimated model mismatch exceeds predetermined bounds. Then genetic algorithm (GA) is applied to re-tune parameters controller based on the estimated model. The structure of controller is proportional integral (PI) controller due to the most applicable in industry, simple structure, low cost and high reliability. Robustness of controller is guaranteed by taking system uncertainties into consideration. The performance of the proposed controller has been carried out in a hybrid wind-diesel power system in comparison with previous work controller. Simulation results confirm that damping effect of the proposed controllers are much better that of the conventional controllers against various operating.</span>

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.

ROBUST CONTROL FOR A PNEUMATIC MUSCLE ACTUATOR SYSTEM

2013 ◽  
Vol 37 (3) ◽  
pp. 581-590 ◽  
Author(s):  
Liu-Hsu Lin ◽  
Jia-Yush Yen ◽  
Fu-Cheng Wang
Keyword(s):  
Robust Control ◽  
Transfer Functions ◽  
Control Strategies ◽  
System Stability ◽  
Pneumatic Muscle ◽  
Guarantee System ◽  
Actuator System ◽  
System Uncertainties ◽  
Pneumatic Muscle Actuator ◽  
And Performance

This paper presents the modeling and robust control of a pneumatic muscle actuator system. Due to the inherent nonlinear and time-varying characteristics of this system, it is difficult to achieve excellent performance using conventional control methods. Therefore, we apply identification techniques to model the system as linear transfer functions and regard the un-modeled dynamics as system uncertainties. Because H∞ robust control is well-known for its capability in dealing with system uncertainties, we then apply H∞ robust control strategies to guarantee system stability and performance for the system. From the experimental results, the proposed H∞ robust controller is deemed effective.

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