scholarly journals AMT Starting Control as a Soft Starter for Belt Conveyors Using a Data-Driven Method

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1808
Author(s):  
Yunxia Li ◽  
Lei Li ◽  
Chengliang Zhang
Keyword(s):  
Adaptive Control ◽  
Tracking Error ◽  
Angular Acceleration ◽  
Data Driven ◽  
Prototype Model ◽  
Mechanical Transmission ◽  
Model Free ◽  
Soft Starter ◽  
Belt Conveyors ◽  
Starting Control

Automated mechanical transmission (AMT) is used as a soft starter in this paper. To improve the soft starting quality, a novel data-driven method is studied. By analyzing and comparing five common soft-starting acceleration curves, a segmented acceleration curve is put forward to be used as the soft-starting acceleration curve for the AMT. Based on the prototype model free adaptive control (MFAC) method, a modified MFAC method with jerk compensation is given to control the AMT output shaft’s angular acceleration and reduce driveline shock. Compared with the methods of prototype MFAC and traditional proportion integration differentiation (PID), the modified MFAC method with jerk compensation can better control the AMT output shaft’s angular acceleration and has excellent characteristics in terms of small tracking error and smaller shock. The research results provide a novel data-driven method for AMT as a soft starter.

scholarly journals AMT Starting Control as a Soft Starter Using a Novel Data-Driven Method

2021 ◽  
Author(s):  
Yunxia Li
Keyword(s):  
Tracking Error ◽  
Angular Acceleration ◽  
Data Driven ◽  
Mechanical Transmission ◽  
Modified Model ◽  
Model Free ◽  
Novel Approach ◽  
Soft Starter ◽  
Belt Conveyors

Abstract To improve the starting quality of automated mechanical transmission (AMT) as a soft starter for belt conveyors, a novel data-driven method is proposed. By analyzing the common soft-starting acceleration curves, a segmented acceleration curve is proposed as the soft-starting acceleration curve for AMT. A modified model free adaptive control (MFAC) method with jerk compensation is proposed as a data-driven method to control the AMT output shaft’s angular acceleration, which consists of a MFAC algorithm and a jerk compensation algorithm. Simulation comparisons are analyzed between the modified MFAC method with jerk compensation, the prototype MFAC method, and the proportion integration differentiation (PID) method. Results indicate better control of the modified MFAC method with jerk compensation on the AMT output shaft’s angular acceleration than the other two methods. It also has excellent properties of small tracking error and small shock, providing a novel approach for AMT as a soft starter.

scholarly journals Data-Driven Model-Free Adaptive Control of Particle Quality in Drug Development Phase of Spray Fluidized-Bed Granulation Process

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Zhengsong Wang ◽  
Dakuo He ◽  
Xu Zhu ◽  
Jiahuan Luo ◽  
Yu Liang ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Adaptive Control ◽  
Drug Development ◽  
Fluidized Bed ◽  
Tracking Error ◽  
Data Driven ◽  
Stability And Convergence ◽  
Development Phase ◽  
Granulation Process ◽  
Model Free

A novel data-driven model-free adaptive control (DDMFAC) approach is first proposed by combining the advantages of model-free adaptive control (MFAC) and data-driven optimal iterative learning control (DDOILC), and then its stability and convergence analysis is given to prove algorithm stability and asymptotical convergence of tracking error. Besides, the parameters of presented approach are adaptively adjusted with fuzzy logic to determine the occupied proportions of MFAC and DDOILC according to their different control performances in different control stages. Lastly, the proposed fuzzy DDMFAC (FDDMFAC) approach is applied to the control of particle quality in drug development phase of spray fluidized-bed granulation process (SFBGP), and its control effect is compared with MFAC and DDOILC and their fuzzy forms, in which the parameters of MFAC and DDOILC are adaptively adjusted with fuzzy logic. The effectiveness of the presented FDDMFAC approach is verified by a series of simulations.

scholarly journals Research on Segmented Belt Acceleration Curve Based on Automated Mechanical Transmission

Processes ◽  
2022 ◽  
Vol 10 (1) ◽  
pp. 106
Author(s):  
Yunxia Li ◽  
Lei Li
Keyword(s):  
Simulation Model ◽  
System Modeling ◽  
Angular Acceleration ◽  
Mechanical Transmission ◽  
Belt Conveyor ◽  
Medium Scale ◽  
Soft Starter ◽  
Simulation Results ◽  
Belt Conveyors ◽  
Starting Process

An automated mechanical transmission (AMT) is proposed as a new soft starter for medium-scale belt conveyors in this paper. The AMT is used to start the belt conveyor and shift gears step by step to make the belt conveyor accelerate softly. Based on analyzing common soft-starting acceleration curves, a segmented belt acceleration curve is proposed as a new soft-starting acceleration curve. By analyzing the AMT soft-starting system, the system modeling is built and the AMT output shaft’s angular acceleration is proposed to be controlled to control the belt acceleration. The AMT soft-starting simulation model is established in the environment of AMESim, and simulation results of the soft-starting process from the first to eighth gear positions are given. The main parameter curves of the AMT soft-starting system including the belt, driving pulley, and AMT output shaft are analyzed. The simulation model can indicate the viscoelastic property of the belt. The simulation results prove that the segmented belt acceleration is appropriate for a medium-scale belt conveyor and provide a theoretical and reasonable basis for using an AMT as a soft starter.

The model-free adaptive cross-coupled control for two-dimensional linear motor

2019 ◽  
Vol 42 (5) ◽  
pp. 1059-1069
Author(s):  
Baolin Zhang ◽  
Rongmin Cao ◽  
Zhongsheng Hou
Keyword(s):  
Adaptive Control ◽  
Real Time ◽  
Error Estimation ◽  
Tracking Error ◽  
Estimation Method ◽  
Linear Motor ◽  
Experimental Comparison ◽  
Contour Error ◽  
Two Dimensional ◽  
Model Free

In order to improve the contour error accuracy of two-dimensional linear motor, an improved cross-coupled control (CCC) scheme combining real-time contour error estimation and model-free adaptive control (MFAC) is proposed. The real-time contour error estimation method is based on CCC theory and coordinate transformation idea. It can accurately determine the contour error point of regular contour and avoid the influence of tracking error on the contour error. At the same time, for the design of two-axis error controller, only the input and output data generated by two-dimensional linear motor in reciprocating motion are used to design a multiple input multiple output-model-free adaptive control (MIMO-MFAC) algorithm, this algorithm avoids the dependence on accurate mathematical model and reduces the control difficulty. The experimental comparison showed that the proposed method improves the system tracking accuracy and contour accuracy, and verifies the proposed method correctness and effectiveness.

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