A Design of Neural-Net Based Self-Tuning PID Controllers

Recently, neural network techniques have widely used in designing adaptive and learning controllers for nonlinear systems. However, it costs a lot of time for learning of the neural network included in the control system. Furthermore, the physical meaning of neural networks constructed as a result, is not obvious. In this paper, a design scheme of self-tuning PID controllers is proposed, which has a structure of fusing self-tuning and neural network techniques. The newly proposed scheme enables us to adjust PID gains quickly.

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