Name
Abstract | ||
|---|---|---|
In real-time movement controlling systems, it is essential to deploy a suitable sensor to measure tilt angle on-line. When the tilt angle is bigger than the preset threshold, the remedial operation should be made timely. This paper proposes an on-line dynamic tilt angle measurement method by compensating gyroscope drift error. First, gyroscope is selected as a measuring unit because it can overcome the interference of external forces to measure dynamic angular speed. Second, an improved least squares support vector machine (LSSVM) is proposed for gyroscope drift-error compensation. The vector base learning method is used to reduce those less important support vectors, and the sparsity of LSSVM can be changed by adjusting the value of
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. Third, the quantum-behaved particle swarm optimization algorithm is introduced as the optimization method to optimize regularization parameter C and kernel parameter
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of LSSVM model because it has good optimization result and fast convergence speed. Fourth, the experiment platform and implementation process are illustrated in detail. Finally, the corresponding experimental results demonstrate that the proposed methodology could measure the dynamic tilt angle accurately. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tim.2018.2878073 | IEEE Transactions on Instrumentation and Measurement |
Keywords | Field | DocType |
Gyroscopes,Support vector machines,Optimization,Training,Measurement uncertainty,Dynamics,Kernel | Particle swarm optimization,Gyroscope,Angular velocity,Least squares support vector machine,Control theory,Support vector machine,Measurement uncertainty,Electronic engineering,Regularization (mathematics),Interference (wave propagation),Mathematics | Journal |
Volume | Issue | ISSN |
68 | 9 | 0018-9456 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Luqiang Shi | 1 | 0 | 2.03 |
| Yigang He | 2 | 55 | 19.50 |
| Bing Li | 3 | 24 | 8.15 |
| Yuting Wu | 4 | 0 | 0.68 |
| Yuan Huang | 5 | 12 | 7.67 |
| Tongtong Cheng | 6 | 0 | 1.35 |