Abstract— This paper introduces the inverse control design using neural network based self tuning regulator (STR). The control algorithm performs equally very well to both minimum phase and non-minimum phase of linear plants. The controller is the radial basis function neural network (RBFNN) and acts as inverse of the plant. The plant parameters are… (More)

- Artificial neural network
- Radial basis function
- Basis function
- Neural Network Simulation
- Self-tuning
- Minimum phase
- Parameter
- Linear system
- Controllers
- Coefficient
- Control system
- Strychnine
- System identification
- Adaptive algorithm
- modeling
- Software development process
- Input/output
- Inner loop
- Identifier
- Nonlinear system
- ARX
- Diagram
- Biological Neural Networks
- Trajectory
- Simulation