scholarly journals Combined Integrating Control Based on Dynamic Optimization Estimation

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Angang Chen ◽  
Zhengyun Ren ◽  
Zhiping Fan ◽  
Xue Feng
Keyword(s):  
Time Delay ◽  
Dynamic Optimization ◽  
Control Method ◽  
Current Output ◽  
Process Systems ◽  
Delay Term ◽  
Average Value ◽  
New Type ◽  
Memory Characteristics

In this paper, a new type of controller based on the combined integrating systems is proposed, which is called a combined integrating controller. The controller’s current output is determined only by the current input and the average value of the controller’s previous output. The innovation is to introduce a time-delay positive feedback in the controller. The memory characteristics of the time-delay term keep accumulating error information and repeatedly learning so that the system can stably track and restrain any input signal without error. Simultaneously, the combined integrating controller is applied to conventional process systems based on dynamic optimization estimation in the case study to show absolute superiority over the nonpredictive control method (such as the classical PID control method).

scholarly journals Distance Constrained Based Adaptive Flocking Control for Multiagent Networks with Time Delay

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Qing Zhang ◽  
Ping Li ◽  
Zhengquan Yang ◽  
Zengqiang Chen
Keyword(s):  
Time Delay ◽  
Lyapunov Stability ◽  
Decentralized Control ◽  
Multiagent System ◽  
Control Method ◽  
Lyapunov Stability Theory ◽  
Adaptive Controllers ◽  
Multiagent Networks ◽  
New Type ◽  
System With Time Delay

The flocking control of multiagent system is a new type of decentralized control method, which has aroused great attention. The paper includes a detailed research in terms of distance constrained based adaptive flocking control for multiagent system with time delay. Firstly, the program on the adaptive flocking with time delay of multiagent is proposed. Secondly, a kind of adaptive controllers and updating laws are presented. According to the Lyapunov stability theory, it is proved that the distance between agents can be larger than a constant during the motion evolution. What is more, velocities of each agent come to the same asymptotically. Finally, the analytical results can be verified by a numerical example.

scholarly journals Modified Smith Predictor Based on H 2 and Predictive PI Control Strategy

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Shiqing Sang ◽  
Pengcheng Nie
Keyword(s):  
Time Delay ◽  
Control Strategy ◽  
Control Algorithm ◽  
Control Method ◽  
Order Approximation ◽  
Current System ◽  
Smith Predictor ◽  
Pi Control ◽  
Delay Term ◽  
Modified Smith Predictor

In this paper, a new type of modified Smith predictor based on the H 2 and predictive PI control strategy is proposed. The modified Smith predictor not only has H 2 robust performance but also has a similar predictive PI control structure. By introducing a time delay term, the modified Smith predictor controller overcomes the shortcoming that the conventional control algorithm can only use the low-order approximation of time delay term to design the control algorithm. The modified Smith predictor controller’s output is related to the current system error and related to the output in a period before the controller. Simultaneously, the modified Smith predictor controller is applied to conventional process systems based on dynamic optimization estimation in the case study to show absolute superiority over the nonpredictive control method (such as the classical PID control method).

Deep learning and complex network theory based analysis on socialized manufacturing resources utilisations and an application case study

2021 ◽  
pp. 1063293X2110031
Author(s):  
Maolin Yang ◽  
Auwal H Abubakar ◽  
Pingyu Jiang
Keyword(s):  
Deep Learning ◽  
Complex Network ◽  
Network Models ◽  
Neural Network Models ◽  
Complex Network Theory ◽  
New Type ◽  
Manufacturing Resource ◽  
Manufacturing Resources ◽  
Resource Characteristics

Social manufacturing is characterized by its capability of utilizing socialized manufacturing resources to achieve value adding. Recently, a new type of social manufacturing pattern emerges and shows potential for core factories to improve their limited manufacturing capabilities by utilizing the resources from outside socialized manufacturing resource communities. However, the core factories need to analyze the resource characteristics of the socialized resource communities before making operation plans, and this is challenging due to the unaffiliated and self-driven characteristics of the resource providers in socialized resource communities. In this paper, a deep learning and complex network based approach is established to address this challenge by using socialized designer community for demonstration. Firstly, convolutional neural network models are trained to identify the design resource characteristics of each socialized designer in designer community according to the interaction texts posted by the socialized designer on internet platforms. During the process, an iterative dataset labelling method is established to reduce the time cost for training set labelling. Secondly, complex networks are used to model the design resource characteristics of the community according to the resource characteristics of all the socialized designers in the community. Two real communities from RepRap 3D printer project are used as case study.

scholarly journals Deep Learning Based Instance Segmentation of Titanium Dioxide Particles in the Form of Agglomerates in Scanning Electron Microscopy

Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 968
Author(s):  
Paul Monchot ◽  
Loïc Coquelin ◽  
Khaled Guerroudj ◽  
Nicolas Feltin ◽  
Alexandra Delvallée ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Scanning Electron Microscopy ◽  
Titanium Dioxide ◽  
Learning Community ◽  
Spherical Particles ◽  
Average Value ◽  
Size Characterization ◽  
Scanning Electron

The size characterization of particles present in the form of agglomerates in images measured by scanning electron microscopy (SEM) requires a powerful image segmentation tool in order to properly define the boundaries of each particle. In this work, we propose to use an algorithm from the deep statistical learning community, the Mask-RCNN, coupled with transfer learning to overcome the problem of generalization of the commonly used image processing methods such as watershed or active contour. Indeed, the adjustment of the parameters of these algorithms is almost systematically necessary and slows down the automation of the processing chain. The Mask-RCNN is adapted here to the case study and we present results obtained on titanium dioxide samples (non-spherical particles) with a level of performance evaluated by different metrics such as the DICE coefficient, which reaches an average value of 0.95 on the test images.

Long-lead seasonal rainfall forecasting using time-delay recurrent neural networks: a case study

2007 ◽  
Vol 22 (2) ◽  
pp. 229-241 ◽  
Author(s):  
Mohammad Karamouz ◽  
Saman Razavi ◽  
Shahab Araghinejad
Keyword(s):  
Neural Networks ◽  
Time Delay ◽  
Recurrent Neural Networks ◽  
Seasonal Rainfall ◽  
Rainfall Forecasting

Research on a New Type of Lithium Battery String Equalization and Management System

2011 ◽  
Vol 383-390 ◽  
pp. 1470-1476
Author(s):  
Hao Wang ◽  
Ding Guo Shao ◽  
Lu Xu
Keyword(s):  
Lithium Batteries ◽  
Management System ◽  
Lithium Battery ◽  
Large Scale ◽  
Control Method ◽  
Balance Control ◽  
Industrial Applications ◽  
Control Technique ◽  
New Type ◽  
Management Functions

Lithium battery has been employed widely in many industrial applications. Parameter mismatches between lithium batteries along a series string is the critical limits of the large-scale applications in high power situation. Maintaining equalization between batteries is the key technique in lithium batteries application. This paper summarizes normal equalization techniques and proposed a new type of lithium Battery Equalization and Management System (BEMS) employing the isolated DC-DC converter structure. The system is integrated both equalization functions and management functions by using distributed 3-level controlled structure and digital control technique. With this control method the flexibility of the balance control strategy and the compatibility for different battery strings are both improved dramatically. The experimental results show optimizing equalization, efficiency and the battery string life span has been extended.

scholarly journals Robust control method for bionic gait of machine legs based on time delay feedback

2018 ◽  
Vol 173 ◽  
pp. 02009
Author(s):  
Lu Xing-Hua ◽  
Huang Peng-Fen ◽  
Huang Wei-Peng
Keyword(s):  
Time Delay ◽  
Robust Control ◽  
Control Method ◽  
Fuzzy Controller ◽  
Delayed Feedback Control ◽  
Accurate Estimation ◽  
Gait Control ◽  
Robot Leg ◽  
Correlation Parameters ◽  
Robotic Leg

The bionic machine leg is disturbed by the joint during the walking process, which is easy to produce time delay, which causes the robustness of the control of the machine leg is not good. In order to improve the robustness of the bionic gait control of the machine leg, a robust control method for the bionic gait of the machine leg based on time - delay feedback is proposed. The gait correlation parameters of robot leg are collected by sensor array, and the dynamic model of bionic gait is constructed. The fuzzy controller of bionic gait of robot leg is constructed by using time-delay coupling control method. The delayed feedback control error compensation method of machine leg correction is taken to improve the steady control performance of the robotic leg, reduce the steady-state error, improve the robustness of the control machine leg. The simulation results show that this method is robust to the bionic gait control of the machine leg. The output error of the gait parameter can quickly converge to zero, and the accurate estimation of the attitude parameter is stronger.

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