Simulation of a multi-parameter indoor air purification control system using fuzzy logic–PID hybrid control tuned by genetic algorithms

Abstract Indoor Environmental Quality (IEQ) critically impacts human health, comfort, and productivity, yet existing air purification systems often rely on single-sensor data, manual monitoring, or cloud-based processing, resulting in limited intelligence, increased latency, and reduced suitability for resource-constrained environments such as Uganda. To address these challenges, this paper presents the design, mathematical modelling, and simulation of a novel multi-parameter indoor air purification control system. The proposed framework integrates six environmental sensors (dust, smoke, gas, volatile organic compounds (VOC), temperature, and humidity) with a hybrid Fuzzy Logic–Proportional–Integral–Derivative (PID) controller whose parameters are optimally tuned using a Genetic Algorithm (GA). A first-principles mathematical model of the DC motor-driven purifier actuator is developed to describe the relationship between the input voltage and volumetric airflow. To enhance the physical realism of the simulations, the system model incorporates practical operating factors, including fan inertia, room-air mixing, pollutant transport, filter dynamics, and environmental sensor noise. The complete system is implemented in the MATLAB/Simulink environment using a Mamdani-type fuzzy inference system with 49 rules and a GA-based multi-objective fitness function to minimise overshoot, settling time, and steady-state error. Simulation results demonstrate that the proposed GA-tuned Fuzzy-PID controller achieves a stable and well-damped response with a rise time of 53.93 s, a settling time of 119.99 s, and a maximum overshoot of 2.14%, reflecting the expected dynamics of practical indoor air purification systems. The proposed framework provides a scalable, low-latency, and network-independent intelligent control solution that establishes a realistic foundation for future prototype development and experimental validation in low-resource environments.