Tracking Chaotic Macroeconomic Behavior: A Fuzzy-CTM Controller with Retrospective Policy Comparison

Chaotic economic systems present significant challenges to financial stability due to their inherent sensitivity to initial conditions and complex nonlinear dynamics. This paper introduces a hybrid control strategy that integrates Lyapunov stability theory with fuzzy logic to track and stabilize a chaotic macroeconomic model. The model incorporates three key variables—unemployment rate, inflation rate, and gross domestic product (GDP) per capita—which are regulated through adjustments to the interest rate. The proposed controller architecture combines a PID control structure, inverse system dynamics, and a fuzzy logic subsystem. Its performance is evaluated through a comparative analysis between the interest rates generated by the controller and the actual historical interest rates from the same period. The results demonstrate the controller's effectiveness in dampening chaotic fluctuations and guiding the economic system toward a desired equilibrium, showing improved stability and reduced sensitivity to disturbances compared to conventional methods. This approach offers policymakers a practical, data-informed framework for enhanced macroeconomic control and scenario planning.