Abstract
A discussion is devoted to designing the super maneuver flight control law using backstepping method with immune particle swarm optimization. For backstepping control law design, nonlinear airplane and thrust vector engine model are integrated and reconfigurated into a new form. The cerebellar model articulation controller (CMAC) neural network is used to approximate the model uncertainty and disturbance. In order to improve the control performance, the immune particle swarm optimization algorithm is used to select automatically the control parameters. This method is verified with the super maneuver flight simulation; the control performance and efficiency are much improved.
| Original language | English |
|---|---|
| Pages (from-to) | 500-505 |
| Number of pages | 6 |
| Journal | Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University |
| Volume | 33 |
| Issue number | 3 |
| State | Published - 1 Jun 2015 |
Keywords
- Angle of attack
- Angular velocity
- Backstepping
- Backstepping control
- Cerebellar model articulation controller (CMAC)
- Computer simulation
- Control
- Controllers
- Degrees of freedom
- Design
- Efficiency
- Immune particle swarm optimization
- Iterative methods, low pass filters
- Lyapunov functions
- Mathematical models
- Matrix method
- Mechanics
- Neutral networks
- Optimization
- Particle swarm optimization (PSO)
- Probability
- Super maneuver
- Vectors