Parameters Tuning Approach for Proportion Integration Differentiation Controller of Magnetorheological Fluids Brake Based on Improved Fruit Fly Optimization Algorithm
Abstract
:1. Introduction
2. Literature Review
2.1. PID Control Methods
2.2. Fruit Fly Optimization Algorithm
2.3. Discussion
3. The Proposed Method
3.1. The Data Acquisition System of MRF Brake
3.2. The Transfer Function of MFR Brake
3.3. The Improved Fruit Fly Optimization Algorithm
- Step 1
- Initialize the master parameters of FOA. The population amount (PA), the maximum iteration number (INmax), the random flying distance range (FR), the group location range (LR), and the initial location ( of fruit fly population are determined.
- Step 2
- Calculate the random direction and distance to search for food of the fruit fly individual.
- Step 3
- Calculate the distance between the fruit fly individual and the origin, and then calculate the flavor concentration parameter which is the reciprocal of the distance.
- Step 4
- Substitute into the fitness function, calculate the value of flavor concentration function and find out the best flavor concentration in the fruit fly population. The minimum value is taken as the best flavor concentration in this paper.
- Step 5
- Obtain the best flavor concentration value and the coordinates of , the fruit fly population flies to that location through vision at this point.
- Step 6
- When the smell concentration reaches the preset precision value or the iteration number reaches the maximal IN, the circulation stops. Otherwise, Steps 2 to 4 are repeated.
Begin Initialize PA, INmax, FR, LR and For (p: = 1; p < PA; p++) { ; ; ; ; ; } [bestSmell bestindex] = min (Smell); ; ; ; For (i: = 1; i< PA; i++) ; ; For (i: = 1; i< PA; i++) ; ; ; For (i: = 1; i < PA; i++) ; while (IN < INmax) { ; Update Xi; Yi; Disti; Si; Smelli; if (bestSmell < Smellbest) { ; ; bestS = S (bestindex,:); Smellbest = bestSmell; } ; ; Update Ex; En; He; Randi; IN = IN+1; } End |
3.4. Tuning the PID Parameters Based on IFOA
4. Simulation and Experimental Results
4.1. Simulation Analysis
4.2. Experimental Results
5. Conclusions and Future Work
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Type | Radial Dimension | Axial Dimension | Average Maximum Magnetic Field Strength | Maximum Torque |
---|---|---|---|---|
MRF brake | 270 mm | 105 mm | 0.52 T | 30.6 N |
Type | Input Range | Accuracy | AD Transfer Time | Set Frequency |
---|---|---|---|---|
PCI8735 | ±5 V | 0.1% | <1.6 μs | 100 Hz |
Type | Rated Voltage | Rated Current | Transient Response Time | Resolution |
---|---|---|---|---|
DP811A | 0–40 V | 0–5 A | <50 μs | 1 mV/0.5 mA |
Controller Type | σ/% | /s | /s | Ct/s | |||
---|---|---|---|---|---|---|---|
Conventional | 16.292 | 5.546 | 0.356 | 6.5% | 0.048 | 0.138 | 10.873 |
FOA | 22.441 | 0.0813 | 0.539 | 0 | 0.052 | 0.082 | 54.144 |
IFOA | 86.243 | 0.0820 | 1.134 | 0 | 0.026 | 0.050 | 55.481 |
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Liu, X.; Shi, Y.; Xu, J. Parameters Tuning Approach for Proportion Integration Differentiation Controller of Magnetorheological Fluids Brake Based on Improved Fruit Fly Optimization Algorithm. Symmetry 2017, 9, 109. https://doi.org/10.3390/sym9070109
Liu X, Shi Y, Xu J. Parameters Tuning Approach for Proportion Integration Differentiation Controller of Magnetorheological Fluids Brake Based on Improved Fruit Fly Optimization Algorithm. Symmetry. 2017; 9(7):109. https://doi.org/10.3390/sym9070109
Chicago/Turabian StyleLiu, Xinhua, Yao Shi, and Jing Xu. 2017. "Parameters Tuning Approach for Proportion Integration Differentiation Controller of Magnetorheological Fluids Brake Based on Improved Fruit Fly Optimization Algorithm" Symmetry 9, no. 7: 109. https://doi.org/10.3390/sym9070109