Feedback control strategy for matrix rectifier

2012 ◽  
Author(s):  
Zhiping Wang ◽  
Yunxiang Xie ◽  
Yu Wang
Keyword(s):  
Feedback Control ◽  
Control Strategy ◽  
Feedback Control Strategy

scholarly journals An Optimal Feedback Control Strategy for Nonlinear, Distributed-Parameter Processes

Processes ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 758 ◽  
Author(s):  
Debaprasad Dutta ◽  
Simant Ranjan Upreti
Keyword(s):  
Feedback Control ◽  
Oil Recovery ◽  
Control Strategy ◽  
Moving Boundary ◽  
State Feedback Control ◽  
Optimal Feedback Control ◽  
Distributed Parameter ◽  
Optimal State ◽  
Non Linear ◽  
Feedback Control Strategy

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.

scholarly journals A reward–punishment feedback control strategy based on energy information for wrist rehabilitation

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142094065
Author(s):  
Jiajin Wang ◽  
Jiaji Zhang ◽  
Guokun Zuo ◽  
Changcheng Shi ◽  
Shuai Guo
Keyword(s):  
Feedback Control ◽  
Control Strategy ◽  
Control Strategies ◽  
Adaptive Controller ◽  
Feedback Mechanism ◽  
Coefficient Of Determination ◽  
Effective Control ◽  
Rehabilitation Robot ◽  
Feedback Control Strategy ◽  
Energy Information

Based on evidence from the previous research in rehabilitation robot control strategies, we found that the common feature of the effective control strategies to promote subjects’ engagement is creating a reward–punishment feedback mechanism. This article proposes a reward–punishment feedback control strategy based on energy information. Firstly, an engagement estimated approach based on energy information is developed to evaluate subjects’ performance. Secondly, the estimated result forms a reward–punishment term, which is introduced into a standard model-based adaptive controller. This modified adaptive controller is capable of giving the reward–punishment feedback to subjects according to their engagement. Finally, several experiments are implemented using a wrist rehabilitation robot to evaluate the proposed control strategy with 10 healthy subjects who have not cardiovascular and cerebrovascular diseases. The results of these experiments show that the mean coefficient of determination ( R 2) of the data obtained by the proposed approach and the classical approach is 0.7988, which illustrate the reliability of the engagement estimated approach based on energy information. And the results also demonstrate that the proposed controller has great potential to promote patients’ engagement for wrist rehabilitation.

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