Reduced-Order Observer Based Sliding Mode Control for a Quad-Rotor Helicopter

2016 ◽  
Vol 28 (3) ◽  
pp. 304-313 ◽  
Author(s):  
Reesa Akbar ◽  
◽  
Bambang Sumantri ◽  
Hitoshi Katayama ◽  
Shigenori Sano ◽  
...  

[abstFig src='/00280003/05.jpg' width=""230"" text='Quadcopter for repeated control verification' ] The reduced-order observer design we present estimates the velocity states of a quadrotor helicopter, or quadcopter, based on sampled measurements of position and attitude states. This observer is based on the forward-differentiation Euler model. The observer is robust enough against observation noise that the gain of a closed-loop controller is high enough to improve control performance. A sliding-mode controller stabilizes and implements quadcopter tracking control effectively, as is verified experimentally when compared to a conventional backward-difference method.

2014 ◽  
Vol 39 (8) ◽  
pp. 6521-6530 ◽  
Author(s):  
Ahmed Mustafa ◽  
Khalid Munawar ◽  
Fahad Mumtaz Malik ◽  
Mohammad Bilal Malik ◽  
Muhammad Salman ◽  
...  

Author(s):  
Khaled Laib ◽  
Minh Tu Pham ◽  
Xuefang LIN-SHI ◽  
Redha Meghnous

Abstract This paper presents an averaged state model and the design of nonlinear observers for an on/off pneumatic actuator. The actuator is composed of two chambers and four on/off solenoid valves. The elaborated averaged state model has the advantage of using only one continuous input instead of four binary inputs. Based on this new model, a high gain observer and a sliding mode observer are designed using the piston position and the pressure measurements in one of the chambers. Finally, their closed-loop performances are verified and compared on an experimental benchmark.


2020 ◽  
Vol 65 (1) ◽  
pp. 434-441 ◽  
Author(s):  
Zhihua Zhang ◽  
Thomas Leifeld ◽  
Ping Zhang

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 657 ◽  
Author(s):  
Uyen Tu Thi Hoang ◽  
Hai Xuan Le ◽  
Nguyen Huu Thai ◽  
Hung Van Pham ◽  
Linh Nguyen

The paper addresses the problem of effectively and robustly controlling a 3D overhead crane under the payload mass uncertainty, where the control performance is shown to be consistent. It is proposed to employ the sliding mode control technique to design the closed-loop controller due to its robustness, regardless of the uncertainties and nonlinearities of the under-actuated crane system. The radial basis function neural network has been exploited to construct an adaptive mechanism for estimating the unknown dynamics. More importantly, the adaptation methods have been derived from the Lyapunov theory to not only guarantee stability of the closed-loop control system, but also approximate the unknown and uncertain payload mass and weight matrix, which maintains the consistency of the control performance, although the cargo mass can be varied. Furthermore, the results obtained by implementing the proposed algorithm in the simulations show the effectiveness of the proposed approach and the consistency of the control performance, although the payload mass is uncertain.


2008 ◽  
Vol 53 (11) ◽  
pp. 2602-2614 ◽  
Author(s):  
Dimitrios Karagiannis ◽  
Daniele Carnevale ◽  
Alessandro Astolfi

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