scholarly journals Nonlinear Modeling and Control Design of Active Helicopter Blades

2006 ◽  
Author(s):  
Matthias Althoff ◽  
Mayuresh Patil ◽  
Johannes Traugott
Keyword(s):  
Nonlinear Modeling ◽  
Control Design ◽  
Modeling And Control ◽  
Helicopter Blades ◽  
And Control

scholarly journals Nonlinear Modeling and Control Design of Active Helicopter Blades

2012 ◽  
Vol 57 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Matthias Althoff ◽  
Mayuresh J. Patil ◽  
Johannes P. Traugott
Keyword(s):  
Structural Model ◽  
Simulation Analysis ◽  
Nonlinear Modeling ◽  
Control Design ◽  
Beam Model ◽  
Linear Quadratic ◽  
Cross Sectional ◽  
Modeling And Control ◽  
Helicopter Blades ◽  
And Control

This paper presents the theoretical basis for the simulation and control of active helicopter blades. The analysis is based on a model that considers the structural dynamics, the aerodynamics, as well as the integrated blade actuation and sensing. The effect of the integral actuation enters the beam model via an active beam cross-sectional analysis. A two-dimensional incompressible, inviscid, quasi-steady aerodynamic model is coupled to the active structural model. For simulation, analysis, and control design, the blade model is discretized in space using a Galerkin approach. The resulting nonlinear model of high order is reduced using the aeroelastic modes of the blade. Finally, the usefulness of a reduced-order model is demonstrated by designing an energy optimal linear-quadratic-Gaussian (LQG) control.

scholarly journals A Novel Data-Driven Modeling and Control Design Method for Autonomous Vehicles

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 517
Author(s):  
Dániel Fényes ◽  
Balázs Németh ◽  
Péter Gáspár
Keyword(s):  
Autonomous Vehicles ◽  
Design Method ◽  
Nonlinear Modeling ◽  
Autonomous Vehicle ◽  
Control Design ◽  
Path Following ◽  
Simulation Software ◽  
Model Parameters ◽  
Modeling And Control ◽  
And Control

This paper presents a novel modeling method for the control design of autonomous vehicle systems. The goal of the method is to provide a control-oriented model in a predefined Linear Parameter Varying (LPV) structure. The scheduling variables of the LPV model through machine-learning-based methods using a big dataset are selected. Moreover, the LPV model parameters through an optimization algorithm are computed, with which accurate fitting on the dataset is achieved. The proposed method is illustrated on the nonlinear modeling of the lateral vehicle dynamics. The resulting LPV-based vehicle model is used for the control design of path following functionality of autonomous vehicles. The effectiveness of the modeling and control design methods through comprehensive simulation examples based on a high-fidelity simulation software are illustrated.

Nonlinear Modeling and Control of Hysteresis in Active Material Systems

1999 ◽  
Author(s):  
Glenn V. Webb ◽  
Dimitris C. Lagoudas ◽  
Andrew J. Kurdila
Keyword(s):  
Shape Memory ◽  
Nonlinear Modeling ◽  
Active Material ◽  
Electrorheological Fluids ◽  
Adaptive Model ◽  
Structural System ◽  
Modeling And Control ◽  
Significant Challenge ◽  
Hysteresis Nonlinearity ◽  
And Control

Abstract Active material actuators present a significant challenge to researchers interested in applying them to aerospace structures. Materials such as shape memory alloys, piezo-ceramcs and electrorheological fluids exhibit hysteresis to varying degrees. Not only do they exhibit hysteresis, but in some cases the hysteresis is non-stationary. We present a methodology that allows for design of controllers for the structural system from linear system theory. This is accomplished by compensating, or linearizing, the hysteresis nonlinearity using an adaptive model of hysteresis. Experimental results for adaptive control of shape memory alloy actuators with non-stationary hysteresis are provided.

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