A Novel Chaotic Particle Swarm-Optimized Backpropagation Neural Network PID Controller for Indoor Carbon Dioxide Control

Abstract

In the continuously evolving landscape of novel smart control strategies, optimization techniques play a crucial role in achieving precise control of indoor air quality. This study aims to enhance indoor air quality by precisely regulating carbon dioxide (CO2) levels through an optimized control system. Prioritizing fast response, short settling time, and minimal overshoot is essential to ensure accurate control. To achieve this goal, chaos optimization is applied. By using the global search capability of the chaos particle swarm optimization (CPSO) algorithm, the initial weights connecting the input layer to the hidden layer and the hidden layer to the output layer of the backpropagation neural network (BPNN) are continuously optimized. The optimized weights are then applied to the BPNN, which employs its self-learning capability to calculate the output error of each neuronal layer, progressing from the output layer backward. Based on these errors, the weights are adjusted accordingly, ultimately tuning the proportional–integral–derivative (PID) controller to its optimal parameters. When comparing simulation results, it is evident that, compared to the baseline method, the enhanced Chaos Particle Swarm Optimization Backpropagation Neural Network PID (CPSO-BPNN-PID) controller proposed in this study exhibits the shortest settling time, approximately 0.125 s, with a peak value of 1, a peak time of 0.2 s, and zero overshoot, demonstrating exceptional control performance. The novelty of this control algorithm lies in the integration of four distinct technologies—chaos optimization, particle swarm optimization (PSO), BPNN, and PID controller—into a novel controller for precise regulation of indoor CO2 concentration.