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

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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Application of PAT-Based Feedback Control Approaches in Pharmaceutical Crystallization

Crystals ◽

10.3390/cryst11030221 ◽

2021 ◽

Vol 11 (3) ◽

pp. 221

Author(s):

Ye Gao ◽

Teng Zhang ◽

Yiming Ma ◽

Fumin Xue ◽

Zhenguo Gao ◽

...

Keyword(s):

Feedback Control ◽

Size Distribution ◽

Control Strategy ◽

Production Efficiency ◽

Crystallization Process ◽

Control Strategies ◽

Process Analysis ◽

Separation And Purification ◽

Model Free ◽

Feedback Control Strategy

Crystallization is one of the important unit operations for the separation and purification of solid products in the chemical, pharmaceutical, and pesticide industries, especially for realizing high-end, high-value solid products. The precise control of the solution crystallization process determines the polymorph, crystal shape, size, and size distribution of the crystal product, which is of great significance to improve product quality and production efficiency. In order to develop the crystallization process in a scientific method that is based on process parameters and data, process analysis technology (PAT) has become an important enabling platform. In this paper, we review the development of PAT in the field of crystallization in recent years. Based on the current research status of drug crystallization process control, the monitoring methods and control strategies of feedback control in the crystallization process were systematically summarized. The focus is on the application of model-free feedback control strategies based on the solution and solid information collected by various online monitoring equipment in product engineering, including improving particle size distribution, achieving polymorphic control, and improving purity. In this paper, the challenges of feedback control strategy in the crystallization process are also discussed, and the development trend of the feedback control strategy has been prospected.

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Application of Combined Feedforward and Feedback Controller With Shaped Input to Benchmark Problem

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4000651 ◽

2010 ◽

Vol 132 (2) ◽

Cited By ~ 4

Author(s):

Young Joo Shin ◽

Peter H. Meckl

Keyword(s):

Feedback Control ◽

Control Strategy ◽

Control Strategies ◽

Control Design ◽

Single Frequency ◽

Benchmark Problems ◽

Control Force ◽

Benchmark Problem ◽

Advantages And Disadvantages ◽

Robust Control Design

Benchmark problems have been used to evaluate the performance of a variety of robust control design methodologies by many control engineers over the past 2 decades. A benchmark is a simple but meaningful problem to highlight the advantages and disadvantages of different control strategies. This paper verifies the performance of a new control strategy, which is called combined feedforward and feedback control with shaped input (CFFS), through a benchmark problem applied to a two-mass-spring system. CFFS, which consists of feedback and feedforward controllers and shaped input, can achieve high performance with a simple controller design. This control strategy has several unique characteristics. First, the shaped input is designed to extract energy from the flexible modes, which means that a simpler feedback control design based on a rigid-body model can be used. In addition, only a single frequency must be attenuated to reduce residual vibration of both masses. Second, only the dynamics between control force and the first mass need to be considered in designing both feedback and feedforward controllers. The proposed control strategy is applied to a benchmark problem and its performance is compared with that obtained using two alternative control strategies.

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

Processes ◽

10.3390/pr7100758 ◽

2019 ◽

Vol 7 (10) ◽

pp. 758 ◽

Cited By ~ 2

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.

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