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In this paper, an optimization of a direct inverse control (DIC) algorithm is discussed. The DIC algorithm is constructed by using a neural network that was trained to find the mathematical inverse model of the plant. In the case of a DIC to control a small helicopter as a UAV in our case, it is required to collect the flight experiment data, such as the PWM signal into servo motors, and the flight output such as the pitch, roll, yaw and the altitude. By using a real small helicopter TREX 450, the neural network based DIC model was performed with an acceptable of error, however, in order to have a better performance, an optimized neural network DIC model is proposed by retraining the neural networks DIC model using a new data generated from a determined optimal reference pathway. The experiment results show that the optimized neural networks DIC model have a better performance with lower total error rate compare with that of the un-optimized neural networks DIC model

Optimized direct inverse control to control altitude of a small helicopter