scholarly journals Evolutionary Algorithm-Based Friction Feedforward Compensation for a Pneumatic Rotary Actuator Servo System

2018 ◽  
Vol 8 (9) ◽  
pp. 1623 ◽  
Author(s):  
Ke Li ◽  
Yeming Zhang ◽  
Shaoliang Wei ◽  
Hongwei Yue
Keyword(s):  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Differential Evolution Algorithm ◽  
Servo System ◽  
Control Architecture ◽  
Tracking Accuracy ◽  
Pd Control ◽  
Compensation Control ◽  
Position Accuracy ◽  
Rotary Actuator

The friction interference in the pneumatic rotary actuator is the primary factor affecting the position accuracy of a pneumatic rotary actuator servo system. The paper proposes an evolutionary algorithm-based friction-forward compensation control architecture for improving position accuracy. Firstly, the basic equations of the valve-controlled actuator are derived and linearized in the middle position, and the transfer function of the system is further obtained. Then, the evolutionary algorithm-based friction feedforward compensation control architecture is structured, including that the evolutionary algorithm is used to optimize the controller coefficients and identify the friction parameters. Finally, the contrast experiments of four control strategies (the traditional PD control, the PD control with friction feedforward compensation without evolutionary algorithm tuning, the PD control with friction feedforward compensation based on the differential evolution algorithm, and the PD control with friction feedforward compensation based on the genetic algorithm) are carried out on the experimental platform. The experimental results reveal that the evolutionary algorithm-based friction feedforward compensation greatly improves the position tracking accuracy and positioning accuracy, and that the differential evolution-based case achieves better accuracy. Also, the system with the friction feedforward compensation still maintains high accuracy and strong stability in the case of load.

Circularity Error Evaluation Based on Differential Evolution Algorithm

2011 ◽  
Vol 143-144 ◽  
pp. 416-421
Author(s):  
Dong Xia Wang ◽  
Ai Guo Song ◽  
Xiu Lan Wen ◽  
Feng Lin Wang
Keyword(s):  
Global Convergence ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Differential Evolution Algorithm ◽  
Suggested Method ◽  
Error Evaluation ◽  
Circularity Error ◽  
Minimum Zone ◽  
Evolution Algorithm ◽  
Adaptive Ability

An algorithm based on the differential evolutionary (DE) computation is proposed to evaluate circularity error. It is a heuristic evolutionary algorithm based on population optimization .In the meantime, the suggested method is used to solve the minimum zone circularity error. Compared with other methods, the results show the presented method has very strong self-adaptive ability to environment and better global convergence. Examples proves that the proposed method is effective, convergence and robustness in the process of optimization. And this method makes the circularity error evaluation more accurate.

scholarly journals Set-Based Differential Evolution Algorithm Based on Guided Local Exploration for Automated Process Discovery

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Si-Yuan Jing
Keyword(s):  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Differential Evolution Algorithm ◽  
Process Models ◽  
Evolutionary Strategy ◽  
Local Optimum ◽  
Process Discovery ◽  
Event Logs ◽  
Evolution Algorithm ◽  
Automated Process

Evolutionary algorithm is an effective way to solve process discovery problem which aims to mine process models from event logs which are consistent with the real business processes. However, current evolutionary algorithms, such as GeneticMiner, ETM, and ProDiGen, converge slowly and in difficultly because all of them employ genetic crossover and mutation which have strong randomness. This paper proposes a hybrid evolutionary algorithm for automated process discovery, which consists of a set-based differential evolution algorithm and guided local exploration. There are three major innovations in this work. First of all, a hybrid evolutionary strategy is proposed, in which a differential evolution algorithm is employed to search the solution space and rapidly approximate the optimal solution firstly, and then a specific local exploration method joins to help the algorithm skip out the local optimum. Secondly, two novel set-based differential evolution operators are proposed, which can efficiently perform differential mutation and crossover on the causal matrix. Thirdly, a fine-grained evaluation technique is designed to assign score to each node in a process model, which is employed to guide the local exploration and improve the efficiency of the algorithm. Experiments were performed on 68 different event logs, including 22 artificial event logs, 44 noisy event logs, and two real event logs. Moreover, the proposed algorithm was compared with three popular algorithms of process discovery. Experimental results show that the proposed algorithm can achieve good performance and its converge speed is fast.

scholarly journals A Novel Combined Evolutionary Algorithm for Optimal Planning of Distributed Generators in Radial Distribution Systems

2019 ◽  
Vol 9 (16) ◽  
pp. 3394 ◽  
Author(s):  
Rabea Jamil Mahfoud ◽  
Yonghui Sun ◽  
Nizar Faisal Alkayem ◽  
Hassan Haes Alhelou ◽  
Pierluigi Siano ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Radial Distribution ◽  
Distribution Systems ◽  
Differential Evolution Algorithm ◽  
Initial Population ◽  
Optimal Planning ◽  
Lévy Flights ◽  
Distributed Generators ◽  
Levy Flights

In this paper, a novel, combined evolutionary algorithm for solving the optimal planning of distributed generators (OPDG) problem in radial distribution systems (RDSs) is proposed. This algorithm is developed by uniquely combining the original differential evolution algorithm (DE) with the search mechanism of Lévy flights (LF). Furthermore, the quasi-opposition based learning concept (QOBL) is applied to generate the initial population of the combined DELF. As a result, the new algorithm called the quasi-oppositional differential evolution Lévy flights algorithm (QODELFA) is presented. The proposed technique is utilized to solve the OPDG problem in RDSs by taking three objective functions (OFs) under consideration. Those OFs are the active power loss minimization, the voltage profile improvement, and the voltage stability enhancement. Different combinations of those three OFs are considered while satisfying several operational constraints. The robustness of the proposed QODELFA is tested and verified on the IEEE 33-bus, 69-bus, and 118-bus systems and the results are compared to other existing methods in the literature. The conducted comparisons show that the proposed algorithm outperforms many previous available methods and it is highly recommended as a robust and efficient technique for solving the OPDG problem.

Adaptive differential evolution algorithm identification of photoelectric tracking servo system

2016 ◽  
Vol 45 (7) ◽  
pp. 0731002 ◽  
Author(s):  
程 龙 Cheng Long ◽  
陈 娟 Chen Juan ◽  
陈茂胜 Chen Maosheng ◽  
徐 婧 Xu Jing ◽  
王卫兵 Wang Weibing ◽  
...  
Keyword(s):  
Differential Evolution ◽  
Differential Evolution Algorithm ◽  
Servo System ◽  
Adaptive Differential Evolution ◽  
Evolution Algorithm

The Multi-Objective Differential Evolutionary Algorithms and its Application in Optimal Allocation of Water Resources

2012 ◽  
Vol 518-523 ◽  
pp. 4093-4096
Author(s):  
Yan Hong Long ◽  
Li Yang Yu
Keyword(s):  
Water Resources ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Evolutionary Algorithm ◽  
Swarm Intelligence ◽  
Optimal Allocation ◽  
Differential Evolution Algorithm ◽  
Multi Objective ◽  
Evolution Algorithm ◽  
Optimization Search

Abstract: Differential evolution algorithm (differential evolution DE) is a multi-objective evolutionary algorithm based on groups， which instructs optimization search by swarm intelligence produced by co-operation and competition among individuals within groups. This paper presents it to the research of optimal allocation of water resources. Accord to the application of the example, the results shows that reasonable and effective.

Simulation on Observe Compensation Control for Electric Disturbances in CNC Servo System

2010 ◽  
Vol 44-47 ◽  
pp. 675-679
Author(s):  
Guo Yong Zhao ◽  
Yu Gang Zhao
Keyword(s):  
Tracking Error ◽  
Servo System ◽  
Tracking Accuracy ◽  
Cnc System ◽  
Machining Precision ◽  
Compensation Control ◽  
Position Controller ◽  
High System ◽  
The Ideal

The various disturbances destroy CNC system tracking accuracy greatly. The ideal servo system should obtain high system precision and machining precision even if under disturbances action. The influence of electric disturbances to CNC servo system is researched in detail. Moreover, the electric disturbances on the electric interface of servo system driver elements are observed, and the observe compensation quantity is added to the position controller output. The simulations aimed at saw-tooth wave electric disturbance signal show that the developed approach can reduce tracking error and enhance the restrain disturbance characteristic.

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