Abstract
In this paper, deep PID neural network (PIDNN) controller is used in a nonlinear thermal regulatory control problem. We have a hot runner system and a nozzle with round tip as controlled plants. The control goal is to regulate their temperature responses for tracking a constant set point stably and precisely without exactly knowing mathematic models of plants during entire process by using deep PIDNN controller. The parameters (or weights) of controller are updated on-line based on gradient descent rule with Adam optimizer. Comparing to the results with PID control, deep PIDNN controller reduces overshoot, saves much power and enhance the control performance that temperatures almost fluctuate within ±0.2°C tolerance of set point in steady state.
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