Neural computation for robust approximate pole assignment
This paper provides an approach for output feedback robust approximate pole assignment. It is formulated as an unconstrained optimization problem and solved via the gradient flow approach which is ideally suited for neural computing implementation. A schematic circuit architecture of the neural network is suggested. Simulation results are used to demonstrate the effectiveness of the proposed method.
robustness approximate pole assignment output feedback gradient flow neural networks