This paper addresses the challenge of predicting chaotic behavior in automatic control systems and robotics, especially in unstable environments. Chaotic elements in observational data diminish the effectiveness of traditional statistical methods, necessitating novel predictive approaches. We propose a predictive framework based on a multi-expert data analysis model for control applications. Preliminary predictions are generated by software experts as weak classifiers, while a supervising expert consolidates these into a final decision. This approach resembles stacking algorithms used in ensemble decision-making. Our methodology enhances predictive accuracy in chaotic environments, leveraging the structural redundancy of multi-expert systems for improved robustness. Empirical results indicate that it strengthens decision-making in unpredictable scenarios, paving the way for future research on managing chaotic dynamics in automatic control and robotics.