scholarly journals A New Norm-Observed Calibration Method Based on Improved Differential Evolution Algorithm for SINS

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yang Liu ◽  
Gongliu Yang ◽  
Qingzhong Cai ◽  
Lifen Wang
Keyword(s):  
Angular Velocity ◽  
Parameter Identification ◽  
Differential Evolution Algorithm ◽  
Calibration Method ◽  
Calibration Error ◽  
Differential Evolutionary Algorithm ◽  
Optimization Function ◽  
Position Calibration ◽  
Mutation Strategies ◽  
Improved Differential Evolution Algorithm

It is vital for a strapdown inertial navigation system (SINS) to be calibrated before normal use. In this paper, a new kind of norm-observed calibration method is proposed. Considering that the norm of the output of accelerometers and gyroscopes can be exactly the norm of local acceleration of gravity and Earth rotation angular velocity, respectively, optimization function about all-parameter calibration and the corresponding 24-position calibration path is established. Differential evolutionary algorithm (DE) is supposed to be the best option in parameter identification due to its strong search and fast convergence abilities. However, the high-dimensional individual vector from calibration error equations restrains the algorithm’s optimum speed and accuracy. To overcome this drawback, improved DE (IDE) optimization is specially designed: First, current “DE/rand/1” and “DE/current-to-best/1” mutation strategies are combined as one with complementary advantages and overall balance during the whole optimization process. Next, with the increase of the evolutionary generation, the mutation factor can adjust itself according to the convergence situation. Multiple identification tests prove that our IDE optimization has rapid convergence and high repeatability. Besides, certain motivation of external angular velocity is added to the gyroscope calibration, and a series of dynamic observation paths is formed, further improving the optimization accuracy. The final static navigation experiment shows that SINS with calibration parameters solved by IDE has better performance over other identification methods, which further explains that our novel method is more accurate and reliable in parameter identification.

Research on Three-Position Calibration Method for the Installation Error of Inclinometer with Two-Axis Gravity Accelerometer

2014 ◽  
Vol 1030-1032 ◽  
pp. 1237-1241
Author(s):  
Jiang Hong Deng ◽  
Xin Yuan Chen ◽  
Liang Cai Zeng
Keyword(s):  
Output Voltage ◽  
Calibration Method ◽  
Calibration Error ◽  
Error Angle ◽  
Iterative Calculation ◽  
Attitude Angle ◽  
Drilling Tools ◽  
Error Calibration ◽  
Position Calibration

At present,The installation error calibration of gravity accelerometer for the automatic vertical drilling tools is complex and difficult . For this,the paper presents three-position calibration method for the installation error of inclinometer with two-axis gravity accelerometer. The method is based on the equation about actual attitude angle of the inclinometer, installation error angular of two-axis gravity accelerometer and output voltage. The voltage value of the three position is measured, and iterative calculation with MATLAB is used to obtain the installation error angle. The results of simulation prove that this method can meet the requirements of installation error angle calibration of drilling tools , the calibration error is less than 1.5%.

The Economic Dispatch of Wind Integrated Power System based on an Improved Differential Evolution Algorithm

2020 ◽  
Vol 13 (3) ◽  
pp. 384-395
Author(s):  
Haiqing Liu ◽  
Jinmeng Qu ◽  
Yuancheng Li
Keyword(s):  
Differential Evolution ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Economic Dispatch ◽  
Differential Evolution Algorithm ◽  
Bee Colony ◽  
Evolution Algorithm ◽  
Improved Differential Evolution Algorithm ◽  
Dynamic Economic Dispatch ◽  
Integrated Power System

Background: As more and more renewable energy such as wind energy is connected to the power grid, the static economic dispatch in the past cannot meet its needs, so the dynamic economic dispatch of the power grid is imperative. Methods: Hence, in this paper, we proposed an Improved Differential Evolution algorithm (IDE) based on Differential Evolution algorithm (DE) and Artificial Bee Colony algorithm (ABC). Firstly, establish the dynamic economic dispatch model of wind integrated power system, in which we consider the power balance constraints as well as the generation limits of thermal units and wind farm. The minimum power generation costs are taken as the objectives of the model and the wind speed is considered to obey the Weibull distribution. After sampling from the probability distribution, the wind speed sample is converted into wind power. Secondly, we proposed the IDE algorithm which adds the local search and global search thoughts of ABC algorithm. The algorithm provides more local search opportunities for individuals with better evolution performance according to the thought of artificial bee colony algorithm to reduce the population size and improve the search performance. Results: Finally, simulations are performed by the IEEE-30 bus example containing 6 generations. By comparing the IDE with the other optimization model like ABC, DE, Particle Swarm Optimization (PSO), the experimental results show that obtained optimal objective function value and power loss are smaller than the other algorithms while the time-consuming difference is minor. The validity of the proposed method and model is also demonstrated. Conclusion: The validity of the proposed method and the proposed dispatch model is also demonstrated. The paper also provides a reference for economic dispatch integrated with wind power at the same time.

scholarly journals Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

2019 ◽  
Vol 24 (3) ◽  
pp. 80 ◽  
Author(s):  
Prasert Sriboonchandr ◽  
Nuchsara Kriengkorakot ◽  
Preecha Kriengkorakot
Keyword(s):  
Differential Evolution ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Differential Evolution Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Relative Errors ◽  
Improved Differential Evolution Algorithm

This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP.

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