Development of Self-Tuning Intelligent PID Controller Based on BPNN for Indoor Air Quality Control

For those who spend most of their time working indoors, the indoor air quality (IAQ) could affect their working efficiency and health. This paper presents an intelligent proportional-integral-derivative (PID) controller for IAQ control. Different from the traditional PID controller, this novel controller combined with Back-Propagation Neural Networks (BPNN) technology will regulate the PID parameters kp, ki, kd automatically. In the present study, the algorithm of the BPNN-based PID controller is first discussed in details, and the control performance is then tested by simulation using MATLAB. The difficulty in IAQ control is the existence of control disturbance, time delay and measurement errors. The results show that the combined control algorithm has better performance on the systemic stability, disturbance resistance, fast response rate and small overshoot compared with traditional PID controller. Keywords—Back-propagation, neural network, PID control, IAQ control, stability analysis.

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