A distributed state estimation and control algorithm for plantwide processes

2003 ◽  
Vol 11 (1) ◽  
pp. 119-127 ◽  
Author(s):  
R. Vadigepalli ◽  
F.J. Doyle
Keyword(s):  
State Estimation ◽  
Control Algorithm ◽  
Distributed State Estimation ◽  
Estimation And Control ◽  
And Control

scholarly journals Macroscopic Traffic Flow Control Via State Estimation

2015 ◽  
Vol 15 (5) ◽  
pp. 5-16
Author(s):  
H. Abouaïssa ◽  
H. Majid
Keyword(s):  
Flow Control ◽  
State Estimation ◽  
Traffic Flow ◽  
Traffic Control ◽  
Traffic Density ◽  
Traffic State ◽  
Loop Detectors ◽  
Traffic State Estimation ◽  
Estimation And Control ◽  
And Control

Abstract The studies presented in this paper deal with traffic control in case of missing data and/or when the loop detectors are faulty. We show that the traffic state estimation plays an important role in traffic prediction and control. Two approaches are presented for the estimation of the main traffic variables (traffic density and mean speed). The state constructors obtained are then used for traffic flow control. Several numerical simulations show very promising results for both traffic state estimation and control.

Implementation of State and Disturbance Estimation for Quadrotor Control Using Extended High-Gain Observers

2016 ◽  
Author(s):  
Connor J. Boss ◽  
Joonho Lee ◽  
Charles Carvalho de Aguiar ◽  
Jongeun Choi
Keyword(s):  
Control Algorithm ◽  
High Gain ◽  
Dynamic Inversion ◽  
Model Uncertainties ◽  
External Disturbances ◽  
Disturbance Estimation ◽  
Sample Data ◽  
Estimation And Control ◽  
Control Forces ◽  
And Control

This paper proposes a discrete-time, multi-time-scale estimation and control design for quadrotors in the presence of external disturbances and model uncertainties. Assuming that not all state measurements are available, they will need to be estimated. The sample-data Extended High-Gain Observers are used to estimate unmeasured states, system uncertainties, and external disturbances. Discretized dynamic inversion utilizes those estimates and deals with an uncertain principal inertia matrix. In the plant dynamics, the proposed control forces the rotational dynamics to be faster than the translational dynamics. Numerical simulations and experimental results verify the proposed estimation and control algorithm. All sensing and computation is done on-board the vehicle.

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