scholarly journals Correlations in state space can cause sub-optimal adaptation of optimal feedback control models

2011 ◽  
Vol 32 (2) ◽  
pp. 297-307 ◽  
Author(s):  
Jonathan Aprasoff ◽  
Opher Donchin
Keyword(s):  
Feedback Control ◽  
State Space ◽  
Optimal Feedback Control ◽  
Optimal Feedback ◽  
Control Models ◽  
Optimal Adaptation

scholarly journals State-space optimal feedback control of optogenetically driven neural activity

2020 ◽  
Author(s):  
Michael F Bolus ◽  
Adam A Willats ◽  
Christopher J. Rozell ◽  
Garrett B Stanley
Keyword(s):  
Feedback Control ◽  
State Space ◽  
Neural Activity ◽  
Optimal Feedback Control ◽  
Optimal Feedback

Mixed Strategy Global Sub-Optimal Feedback Control for Chaotic Systems

1997 ◽  
Vol 07 (03) ◽  
pp. 607-623 ◽  
Author(s):  
H. W. J. Lee ◽  
M. Paskota ◽  
K. L. Teo
Keyword(s):  
Feedback Control ◽  
State Space ◽  
Chaotic Systems ◽  
Mixed Strategy ◽  
The State ◽  
Optimal Feedback Control ◽  
Target Region ◽  
Optimal Feedback ◽  
Control Scheme

How to perform targeting of chaotic systems in a global sense is an important question. In this paper, we address this problem by introducing a mixed strategy global sub-optimal feedback control scheme. The idea is to partition the state space into 2 parts, namely, the target region and its complement. The proposed controller will take different forms depending on which partition of the state space the system is in. Simulations are also provided to illustrate the proposed scheme.

Tips on Stochastic Optimal Feedback Control and Bayesian Spatiotemporal Models: Applications to Robotics

2014 ◽  
Vol 137 (3) ◽  
Author(s):  
Jongeun Choi ◽  
Dejan Milutinović
Keyword(s):  
Sensor Networks ◽  
Feedback Control ◽  
Control Theory ◽  
Mobile Sensor Networks ◽  
Optimal Feedback Control ◽  
Mobile Sensor ◽  
Optimal Feedback ◽  
Spatiotemporal Models ◽  
Feedback Control Theory

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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