抄録
We have developed small computer controlled airplanes using many PID control loops; however, the tuning of their control parameters is difficult. In this paper, an artificial neural network is used as the controller. The control algorithm is organized by the back propagation algorithm to learn the inverse dynamics of the flying robots. A problem of back propagation-type learning is apt to appear in local optimum solutions. Therefore, many sets of initial values of input gains at the synapses of neurons are prepared similar to DNA codes in immune bodies. When a local optimum solution of the neural network is found, the DNA code of learned gains is memorized at the T cell. Cells near these having the memorized DNA are eliminated, and new cells far from them are added to the process. It is verified by computer simulation that a better control algorithm is automatically organized for small flying robots having complicated dynamics.
