Optimal feedback control strategies for state-space systems with stochastic parameters

1998 ◽  
Vol 43 (10) ◽  
pp. 1469-1475 ◽  
Author(s):  
J.H. Lee ◽  
B.L. Cooley
Keyword(s):  
Feedback Control ◽  
State Space ◽  
Control Strategies ◽  
Optimal Feedback Control ◽  
Space Systems ◽  
Optimal Feedback ◽  
Stochastic Parameters ◽  
State Space Systems

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.

An Optimal Feedback Control Framework for Grasping Objects with Position Uncertainty

2011 ◽  
Vol 23 (10) ◽  
pp. 2511-2536 ◽  
Author(s):  
Vassilios N. Christopoulos ◽  
Paul R. Schrater
Keyword(s):  
Feedback Control ◽  
Control Strategies ◽  
Sensory Information ◽  
Optimal Feedback Control ◽  
Position Uncertainty ◽  
Optimal Feedback ◽  
Location Uncertainty ◽  
Differential Control ◽  
Key Aspects ◽  
Natural Tasks

As we move, the relative location between our hands and objects changes in uncertain ways due to noisy motor commands and imprecise and ambiguous sensory information. The impressive capabilities humans display for interacting and manipulating objects with position uncertainty suggest that our brain maintains representations of location uncertainty and builds compensation for uncertainty into its motor control strategies. Our previous work demonstrated that specific control strategies are used to compensate for location uncertainty. However, it is an open question whether compensation for position uncertainty in grasping is consistent with the stochastic optimal feedback control, mainly due to the difficulty of modeling natural tasks within this framework. In this study, we develop a stochastic optimal feedback control model to evaluate the optimality of human grasping strategies. We investigate the properties of the model through a series of simulation experiments and show that it explains key aspects of previously observed compensation strategies. It also provides a basis for individual differences in terms of differential control costs—the controller compensates only to the extent that performance benefits in terms of making stable grasps outweigh the additional control costs of compensation. These results suggest that stochastic optimal feedback control can be used to understand uncertainty compensation in complex natural tasks like grasping.

On the Open-Loop and Feedback Processes That Underlie the Formation of Trajectories During Visual and Nonvisual Locomotion in Humans

2009 ◽  
Vol 102 (5) ◽  
pp. 2800-2815 ◽  
Author(s):  
Quang-Cuong Pham ◽  
Halim Hicheur
Keyword(s):  
Feedback Control ◽  
Control Strategies ◽  
Open Loop ◽  
Optimal Feedback Control ◽  
Control Mechanisms ◽  
Experimental Conditions ◽  
Optimal Feedback ◽  
On Line ◽  
Kinematic Properties

We investigated the nature of the control mechanisms at work during goal-oriented locomotion. In particular, we tested the effects of vision, locomotor speed, and the presence of via points on the geometric and kinematic properties of locomotor trajectories. We first observed that the average trajectories recorded in visual and nonvisual locomotion were highly comparable, suggesting the existence of vision-independent processes underlying the formation of locomotor trajectories. Then by analyzing and comparing the variability around the average trajectories across different experimental conditions, we were able to demonstrate the existence of on-line feedback control in both visual and nonvisual locomotion and to clarify the relations between visual and nonvisual control strategies. Based on these insights, we designed a model in which maximum-smoothness and optimal feedback control principles account, respectively, for the open-loop and feedback processes. Taken together, the experimental and modeling findings provide a novel understanding of the nature of the motor, sensory, and “navigational” processes underlying goal-oriented locomotion.

Sign in / Sign up

Export Citation Format

Share Document