Direct adaptive control using dyadic networks

A new type of wavelet based linearly parameterized network, dyadic network, is proposed in this paper, with application to an inverse dynamic based adaptive control. The function approximation capability of dyadic networks is supported by dyadic wavelet theory. Dyadic networks are easy to construct and the time cost is limited. Simulation results of a flight control system are presented to illustrate the performance of dyadic networks.

Authors

• Jingyu Lin
• Zengqi Sun

Keywords

• Adaptive control
• Neural networks
• Neurons
• Radial basis function networks
• Multidimensional systems
• Computer science
• Function approximation
• Costs
• Aerospace simulation
• Discrete wavelet transforms
• Direct Adaptive Control
• Simulation Results
• Time Cost
• Network Performance
• Flight Control
• Neural Network
• Learning Rate
• Smooth Function
• Input Vector
• Feed-forward Network
• Functional Scale
• PI Controller
• Finite Support
• Input Elements
• Number Of Basis Functions
• Radial Basis Function Network
• Pitch Rate