scholarly journals Comparison of Feedback Control Strategies for Operation of Granulation Loops

2021 ◽  
Vol 50 (4) ◽  
pp. 736-751
Author(s):  
Ludmila Vesjolaja ◽  
Bjørn Glemmestad ◽  
Bernt Lie
Keyword(s):  
Feedback Control ◽  
Control Strategy ◽  
Fine Particles ◽  
Control Strategies ◽  
Pi Controller ◽  
Oscillatory Behavior ◽  
Double Loop ◽  
Oscillatory Behaviour ◽  
Loop Control ◽  
Median Diameter

Granulation is a particle enlargement process during which fine particles or atomizable liquids are converted into granules via a series of complex granulation mechanisms. In this paper, two feedback control strategies are implemented to make granulation loop processes more steady to operate, i.e., to suppress oscillatory behavior in the produced granule sizes. In the first control strategy, a classical proportional-integral (PI) controller is used, while in the second, a double-loop control strategy is used to control the median diameter of the granules leaving the granulator. The simulation results showed that using the proposed control design for the granulation loop can eliminate the oscillatory behaviour in the produced granule median diameter and make granulation loop processes more steady to operate. A comparison between the two proposed control strategies showed that it is preferable to use the double-loop control strategy.

Dynamic Simulation for Grid-Connected Inverters of Distributed Generation Based on DIgSILENT Software

2013 ◽  
Vol 291-294 ◽  
pp. 2042-2046
Author(s):  
Zhang Le Zhao ◽  
You Bing Zhang ◽  
Jun Qi
Keyword(s):  
Distributed Generation ◽  
Control Strategy ◽  
Control Method ◽  
Control Strategies ◽  
Simulation Models ◽  
Double Loop ◽  
Rotating Frame ◽  
Control Methods ◽  
Outer Loop ◽  
Loop Control

This paper introduces some typical control methods for the grid-connected inverters in the distributed generation (DG) systems, the double-loop control strategy is focused on and analyzed in detail. The proposed outer-loop control strategies are summarized. Meanwhile, the inner-loop control method established on dq rotating frame is introduced. The simulation models of the inverters for DG in the DIgSILENT software are introduced, and the simulations for the proposed control strategies are realized.

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

2010 ◽  
Vol 132 (2) ◽  
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.

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.

scholarly journals Research on multidimensional evaluation of tracking control strategies for self-driving vehicles

2020 ◽  
Vol 12 (3) ◽  
pp. 168781402091296 ◽  
Author(s):  
Yuan-yuan Ren ◽  
Jie Wang ◽  
Xue-lian Zheng ◽  
Qi-chao Zhao ◽  
Jia-lei Ma ◽  
...  
Keyword(s):  
Performance Evaluation ◽  
Feedback Control ◽  
Control Strategy ◽  
Tracking Control ◽  
Evaluation System ◽  
Feedforward Control ◽  
Control Strategies ◽  
Dynamic Feedback ◽  
Multidimensional Evaluation ◽  
Control Dynamic

Performance evaluation is a necessary stage in development of tracking control strategy of autonomous vehicle system, which determines the scope of application and promotes further improvement. At present, most of the tracking control strategies include performance evaluation. However, performance evaluation criteria differ from work to work, lacking comprehensive evaluation system. This article proposes a multidimensional integrated tracking control evaluation system based on subjective and objective weighting, taking into account the tracking accuracy, driving stability, and ride comfort. Through the co-simulation of CarSim and Simulink, qualitative analysis and quantitative analysis based on multidimensional evaluation system of five coupled longitudinal and lateral control strategies (lateral: pure pursuit feedforward control, dynamic-model-based optimal curvature control (dynamic feedforward control), Stanley feedback control, kinematics feedback control, and dynamic feedback control; longitudinal: the incremental proportion–integration–differentiation control) under typical operating conditions are carried out to analyze the operating range and robustness of each tracking control strategy. The results show that the Stanley tracking control strategy and the dynamic feedback tracking control strategy have a wide range of applications and robustness. The consistency of qualitative analysis results and the quantitative analysis results verify the validity and feasibility of the evaluation system.

CLOSED-LOOP CONTROL OF THE THALAMOCORTICAL RELAY NEURON'S PARKINSONIAN STATE BASED ON SLOW VARIABLE

2013 ◽  
Vol 23 (04) ◽  
pp. 1350017 ◽  
Author(s):  
CHEN LIU ◽  
JIANG WANG ◽  
YING-YUAN CHEN ◽  
BIN DENG ◽  
XI-LE WEI ◽  
...  
Keyword(s):  
Feedback Control ◽  
Control Strategy ◽  
Closed Loop ◽  
Slow Variable ◽  
Closed Loop Control ◽  
Fast Variable ◽  
Qualitative And Quantitative Analysis ◽  
Feedback Controllers ◽  
Loop Control ◽  
Qualitative And Quantitative

A novel closed-loop control strategy is proposed to control Parkinsonian state based on a computational model. By modeling thalamocortical relay neurons under external electric field, a slow variable feedback control is applied to restore its relay functionality. Qualitative and quantitative analysis demonstrates the performance of feedback controller based on slow variable is more efficient compared with traditional feedback control based on fast variable. These findings point to the potential value of model-based design of feedback controllers for Parkinson's disease.

Double‐loop control strategy for SRGs

2017 ◽  
Vol 11 (1) ◽  
pp. 29-40 ◽  
Author(s):  
Qing Wang ◽  
Hao Chen ◽  
Renming Zhao
Keyword(s):  
Control Strategy ◽  
Double Loop ◽  
Loop Control

A Method for Teaching Feedback Control Using the Enhanced Mass-Spring-Damper System

2011 ◽  
Author(s):  
Dean H. Kim
Keyword(s):  
Feedback Control ◽  
Control Strategies ◽  
Open Loop ◽  
Open Loop Control ◽  
Improve Performance ◽  
Loop Control ◽  
System Input ◽  
Advanced Controls ◽  
Mass Spring ◽  
Force Generator

This paper presents a method that the author has developed to teach students about the need for feedback control and to facilitate the understanding of controller implementation. The initial discussion focuses on the limitations of open-loop control to improve performance of the traditional mass-spring-damper system. The key contribution is the introduction of an enhanced mass-spring-damper system with a position sensor and force generator, resulting in voltages as system input and output. This enhanced system provides a foundation for discussion of basic feedback control strategies such as PID-Control in addition to advanced controls concepts. The analysis is provided in time-domain to facilitate the understanding of these important controls concepts.

Sign in / Sign up

Export Citation Format

Share Document