Learning control using neural networks

The basic features of the learning-type neural network (NN) controller are clarified. Analytical and experimental results show its stability, convergence and generalization ability compared with the adaptive-type NN and conventional learning control. As an application of the learning-type NN, a nonlinear optimum regulator is presented whose learning ability can obtain optimum conditions without solving a difficult Riccati equation. Moreover, it can be applied to a nonlinear control system because of its nonlinear mapping ability, although the conventional optimum regulator can only be applied to a linear system. Finally task planning is proposed in terms of skill acquisition using the learning-type NN, which implies the possibility of making an interface with an upper symbolic-level control