Control of Quadrotor Unmanned Aerial Vehicles Using Exact Linearization Technique with the Static State Feedback

2016 ◽  
pp. 340-346 ◽  
Author(s):  
Yasuhiko Mutoh ◽  
Shusuke Kuribara
Keyword(s):  
Unmanned Aerial Vehicles ◽  
State Feedback ◽  
Exact Linearization ◽  
Static State ◽  
Linearization Technique ◽  
Aerial Vehicles ◽  
Static State Feedback ◽  
Quadrotor Unmanned Aerial Vehicles

scholarly journals Robust linearization scheme by structural state feedback for a quadrotor

2021 ◽  
pp. 13-21
Author(s):  
Luis Ángel Blas-Sánchez ◽  
Margarita Galindo-Mentle ◽  
Adolfo Quiroz-Rodríguez ◽  
Marlon Licona-González
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Dynamic Model ◽  
Feedback Linearization ◽  
State Feedback ◽  
Circular Trajectory ◽  
Linearization Technique ◽  
Aerial Vehicles ◽  
State Observers ◽  
Control Scheme ◽  
Change Of Variable

In this work a feedback linearization technique is proposed, to carry it out to linearize the dynamic model of the quadrotor, a change of variable is introduced that maps the nonlinearities of the system into a nonlinear uncertainty signal contained in the domain of the action of control and is applied to the dynamic model of the quadrotor. To estimate the nonlinear uncertainty signal, the Beard-Jones filter is used, which is based on standard state observers. To verify the effectiveness of the proposed control scheme, experiments are carried out outdoors to follow a circular trajectory in the (x,y) plane. This presented control scheme is suitable for unmanned aerial vehicles where it is important to reject not only non-linearities but also to seek the simplicity and effectiveness of the control scheme for its implementation.

Nonregular Static State Feedback for Linear Bond Graph Models

2001 ◽  
Vol 34 (13) ◽  
pp. 71-76
Author(s):  
C. Sueur ◽  
A. Karim ◽  
G. Dauphin-Tanguy
Keyword(s):  
State Feedback ◽  
Bond Graph ◽  
Graph Models ◽  
Static State ◽  
Static State Feedback

Static-state feedback laws for output regulation of non-linear systems

1996 ◽  
Vol 4 (7) ◽  
pp. 1009-1014
Author(s):  
R. Mahony ◽  
I. Mareels ◽  
G. Bastin ◽  
G. Campion
Keyword(s):  
Linear Systems ◽  
State Feedback ◽  
Output Regulation ◽  
Static State ◽  
Non Linear ◽  
Non Linear Systems ◽  
Feedback Laws ◽  
Static State Feedback

scholarly journals Robust non-fragile LQ controllers: The static state feedback case

2000 ◽  
Vol 73 (2) ◽  
pp. 159-165 ◽  
Author(s):  
Domenico Famularo ◽  
Peter Dorato ◽  
Chaouki T. Abdallah ◽  
Wassim M. Haddad ◽  
Ali Jadbabaie
Keyword(s):  
State Feedback ◽  
Static State ◽  
Static State Feedback

A Two-Step Optimization-Based Iterative Learning Control for Quadrotor Unmanned Aerial Vehicles

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Revant Adlakha ◽  
Minghui Zheng
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Design Problem ◽  
Iterative Learning Control ◽  
Design Method ◽  
Learning Control ◽  
Iterative Learning ◽  
Learning Performance ◽  
Aerial Vehicles ◽  
Quadrotor Unmanned Aerial Vehicles ◽  
Optimization Based Design

Abstract This paper presents a two-step optimization-based design method for iterative learning control and applies it onto the quadrotor unmanned aerial vehicles (UAVs) trajectory tracking problem. Iterative learning control aims to improve the tracking performance through learning from errors over iterations in repetitively operated systems. The tracking errors from previous iterations are injected into a learning filter and a robust filter to generate the learning signal. The design of the two filters usually involves nontrivial tuning work. This paper presents a new two-optimization design method for the iterative learning control, which is easy to obtain and implement. In particular, the learning filter design problem is transferred into a feedback controller design problem for a purposely constructed system, which is solved based on H-infinity optimal control theory thereafter. The robust filter is then obtained by solving an additional optimization to guarantee the learning convergence. Through the proposed design method, the learning performance is optimized and the system's stability is guaranteed. The proposed two-step optimization-based design method and the regarding iterative learning control algorithm are validated by both numerical and experimental studies.

Distributed Static State Feedback Control for DC Microgrids

2021 ◽  
pp. 306-314
Author(s):  
Sifeddine Benahmed ◽  
Pierre Riedinger ◽  
Serge Pierfederici
Keyword(s):  
Feedback Control ◽  
State Feedback ◽  
State Feedback Control ◽  
Static State ◽  
Dc Microgrids ◽  
Static State Feedback

Persistent disturbance rejection via static-state feedback

1995 ◽  
Vol 40 (6) ◽  
pp. 1127-1131 ◽  
Author(s):  
F. Blanchini ◽  
M. Sznaier
Keyword(s):  
Disturbance Rejection ◽  
State Feedback ◽  
Static State ◽  
Static State Feedback
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