Remarks on an adaptive type self-tuning controller using neural networks

The authors propose an adaptive type of self-tuning controller which offers the possibility of enhanced robustness. This controller does not use a desired feedback gain predetermined by conventional control theories as a teaching signal. Simulated results using a second-order plant show the nonlinear neural network effect for the nonlinear plant. They also show that a controller using a linear neural network is better than one using a nonlinear neural network in the region of small nonlinear and parasite terms. Experimental results using a force confirmed that the controller is useful for an actual system