Flutter Suppression for Underactuated Aeroelastic Wing Section: Nonlinear Gain-Scheduling Approach

2017 ◽  
Vol 40 (8) ◽  
pp. 2102-2109 ◽  
Author(s):  
H. Lhachemi ◽  
Y. Chu ◽  
D. Saussié ◽  
G. Zhu
Keyword(s):  
Gain Scheduling ◽  
Nonlinear Gain ◽  
Flutter Suppression ◽  
Wing Section

A Nonlinear Gain-Scheduling Compensation Approach Using Parameter-Dependent Lyapunov Functions

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Fen Wu ◽  
Xun Song ◽  
Zhang Ren
Keyword(s):  
Lyapunov Functions ◽  
Gain Scheduling ◽  
Control Synthesis ◽  
Linear Matrix ◽  
Nonlinear Gain ◽  
Control Approach ◽  
Lpv Control ◽  
Gain Scheduling Control ◽  
Output Information ◽  
Parameter Dependent

This paper addresses the gain-scheduling control design for nonlinear systems to achieve output regulation. For gain-scheduling control, the linear parameter-varying (LPV) model is obtained by linearizing the plant about zero-error trajectories upon which an LPV controller is based. A key in this process is to find a nonlinear output feedback compensator such that its linearization matches with the designed LPV controller. Then, the stability and performance properties of LPV control about the zero-error trajectories can be inherited when the nonlinear compensator is implemented. By incorporating the exosystem, nominal input, and measured output information into the LPV model, the LPV control synthesis problem is formulated as linear matrix inequalities (LMIs) using parameter-dependent Lyapunov functions (PDLFs). Moreover, explicit formulae for the construction of the nonlinear gain-scheduled compensator have been derived to meet the linearization requirement. Finally, the validity of the proposed nonlinear gain-scheduling control approach is demonstrated through a ball and beam example.

scholarly journals Humanoid Walking Robot: Modeling, Inverse Dynamics, and Gain Scheduling Control

2010 ◽  
Vol 2010 ◽  
pp. 1-19 ◽  
Author(s):  
Elvedin Kljuno ◽  
Robert L. Williams
Keyword(s):  
Inverse Dynamics ◽  
Reference Model ◽  
Tracking Error ◽  
Gain Scheduling ◽  
Bipedal Walking ◽  
Walking Robot ◽  
Concentrated Mass ◽  
Mass Model ◽  
Nonlinear Gain ◽  
Joint Torques

This article presents reference-model-based control design for a 10 degree-of-freedom bipedal walking robot, using nonlinear gain scheduling. The main goal is to show concentrated mass models can be used for prediction of the required joint torques for a bipedal walking robot. Relatively complicated architecture, high DOF, and balancing requirements make the control task of these robots difficult. Although linear control techniques can be used to control bipedal robots, nonlinear control is necessary for better performance. The emphasis of this work is to show that the reference model can be a bipedal walking model with concentrated mass at the center of gravity, which removes the problems related to design of a pseudo-inverse system. Another significance of this approach is the reduced calculation requirements due to the simplified procedure of nominal joint torques calculation. Kinematic and dynamic analysis is discussed including results for joint torques and ground force necessary to implement a prescribed walking motion. This analysis is accompanied by a comparison with experimental data. An inverse plant and a tracking error linearization-based controller design approach is described. We propose a novel combination of a nonlinear gain scheduling with a concentrated mass model for the MIMO bipedal robot system.

Intelligent supervision approach based on multilayer neural PCA and nonlinear gain scheduling

2017 ◽  
Vol 31 (4) ◽  
pp. 1153-1163 ◽  
Author(s):  
H. Chaouch ◽  
S. Charfedine ◽  
K. Ouni ◽  
H. Jerbi ◽  
L. Nabli
Keyword(s):  
Gain Scheduling ◽  
Nonlinear Gain
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