Analysis of Active Suspension Control Based on Improved Fuzzy Neural Network PID
Abstract
:1. Introduction
2. Materials and Methods
2.1. Active Suspension Simulation Model
- The elastic center of the vehicle body coincides with the center of mass;
- The vehicle body is rigid, and the occupants move in the same way as the vehicle body;
- There is no sliding between the tires and the road, and the wheels are always in contact with the ground;
- The vertical vibration characteristics of the wheel are reduced by a spring that does not take into account the damping effect.
2.2. Road Excitation Model
2.2.1. White Noise Road Excitation
2.2.2. Step Noise Road Excitation
2.3. Controller Design Principle
2.3.1. FNN-PID Controller
2.3.2. PID Control
2.3.3. FNN Control
2.3.4. FNN Optimization Algorithm
- Gradient Descent;
- 2.
- Particle swarm algorithm
2.3.5. Hybrid Algorithm Optimization Process
- The fuzzy neural network parameters, , are initialized;
- Particle swarm initialization. Parameters such as those of population size, particle dimensions, and initial inertia weight, as well as learning factor, are set first, after which a set of particle positions is generated at random and the particle’s maximum and minimum velocities are determined; between the extremes of highest and minimum velocity, each particle’s velocity is determined randomly;
- After updating the velocity and position of the particle, the fitness value of the particle at each iteration step is calculated, and the individual optimal extremum, , and the population optimal extremum, , are updated;
- If the termination condition is satisfied, the corresponding network parameters are passed to the FNN;
- The FNN acquires the initial values of the parameters and then calculates them and updates the network parameters online by back-propagation through the gradient descent method. The final optimal solutions are output.
3. Results
4. Discussion
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Road Grade | |
---|---|
A | 16 |
B | 64 |
C | 256 |
D | 1024 |
Variable | Value |
---|---|
Sprung mass M/kg | 240 |
Unsprung mass m/kg | 30 |
Tire stiffness K1/(N/m) | 160,000 |
Spring rate K2/(N/m) | 16,000 |
Suspension damping c/(N•s)/m | 980 |
Index | Passive | PID Controller | FNN-PID Controller |
---|---|---|---|
SMA (m/s2) | |||
DDS (m) | |||
DTD (m) |
Class | Index | Passive | PID Controller | FNN-PID Controller |
---|---|---|---|---|
A | SMA (m/s2) | 0.0490 | 0.0391 | 0.0334 |
DDS (m) | ||||
DTD (m) | ||||
B | SMA (m/s2) | 0.0979 | 0.0782 | 0.0681 |
DDS (m) | ||||
DTD (m) | ||||
C | SMA (m/s2) | 0.1820 | 0.1440 | 0.1257 |
DDS (m) | ||||
DTD (m) | ||||
D | SMA (m/s2) | 0.3476 | 0.2673 | 0.2390 |
DDS (m) | ||||
DTD (m) |
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Li, M.; Li, J.; Li, G.; Xu, J. Analysis of Active Suspension Control Based on Improved Fuzzy Neural Network PID. World Electr. Veh. J. 2022, 13, 226. https://doi.org/10.3390/wevj13120226
Li M, Li J, Li G, Xu J. Analysis of Active Suspension Control Based on Improved Fuzzy Neural Network PID. World Electric Vehicle Journal. 2022; 13(12):226. https://doi.org/10.3390/wevj13120226
Chicago/Turabian StyleLi, Mei, Jiapeng Li, Guisheng Li, and Jie Xu. 2022. "Analysis of Active Suspension Control Based on Improved Fuzzy Neural Network PID" World Electric Vehicle Journal 13, no. 12: 226. https://doi.org/10.3390/wevj13120226