Fuzzy PI Controller for Air Conditioning Room Temperature Control

Control Method of Fuzzy PI Controller for Air Conditioning Room Temperature Objects

In air conditioning room temperature control systems, traditional PI controllers may struggle to adapt well to nonlinear, time-varying temperature characteristics. The fuzzy PI controller combines the adaptive capability of fuzzy logic with the stability of conventional PI control, enabling more flexible parameter adjustment to improve system dynamic response and steady-state accuracy.

Room Temperature Model Construction The air conditioning room temperature object can typically be simplified as a transfer function model with first-order inertia plus pure delay. This model describes the dynamic characteristics of indoor temperature changes relative to the air conditioning output power. Model parameters include thermal capacitance, thermal resistance, and delay time, which can be determined through experimental data or empirical formulas.

Fuzzy PI Control Algorithm Design The fuzzy PI controller primarily consists of two parts: fuzzy inference and PI control. The fuzzy inference section dynamically adjusts PI parameters (proportional coefficient Kp and integral time Ti) based on temperature error and error change rate. During implementation, it's necessary to define fuzzy rules for input variables, establish a fuzzy rule base, and determine defuzzification methods for output variables.

Implementation Approach Establish membership functions for temperature error and error change rate Design fuzzy rule tables, such as "if error is large and changing rapidly, then increase Kp" Apply defuzzification methods like centroid calculation Pass adjusted parameters to the PI controller Validate control performance through simulation

Application Advantages Compared to fixed-parameter PI control, the fuzzy PI controller better handles factors like thermal inertia and external disturbances in air conditioning systems, achieving smoother temperature regulation while avoiding overshoot and oscillation. In practical engineering, MATLAB/Simulink can be used for modeling and simulation testing.

Note: During actual implementation, model parameters and fuzzy rules should be adjusted according to specific air conditioning system characteristics. Model validation using experimental data is recommended when necessary.