∓1-optimal feedback control strategy with application to longitudinal autopilots

Author(s):  
L.S. Zhiteckii ◽  
K.V. Melnyk ◽  
A.Yu. Pilchevsky ◽  
I.R. Kvasha
Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 758 ◽  
Author(s):  
Debaprasad Dutta ◽  
Simant Ranjan Upreti

In this work, an optimal state feedback control strategy is proposed for non-linear, distributed-parameter processes. For different values of a given parameter susceptible to upsets, the strategy involves off-line computation of a repository of optimal open-loop states and gains needed for the feedback adjustment of control. A gain is determined by minimizing the perturbation of the objective functional about the new optimal state and control corresponding to a process upset. When an upset is encountered in a running process, the repository is utilized to obtain the control adjustment required to steer the process to the new optimal state. The strategy is successfully applied to a highly non-linear, gas-based heavy oil recovery process controlled by the gas temperature with the state depending non-linearly on time and two spatial directions inside a moving boundary, and subject to pressure upsets. The results demonstrate that when the process has a pressure upset, the proposed strategy is able to determine control adjustments with negligible time delays and to navigate the process to the new optimal state.


Author(s):  
Jongeun Choi ◽  
Dejan Milutinović

This tutorial paper presents the expositions of stochastic optimal feedback control theory and Bayesian spatiotemporal models in the context of robotics applications. The presented material is self-contained so that readers can grasp the most important concepts and acquire knowledge needed to jump-start their research. To facilitate this, we provide a series of educational examples from robotics and mobile sensor networks.


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