TY - CHAP

T1 - From Chaos to Control: Leveraging Multi-Expert Strategies for Predictive Accuracy in Autonomous Systems

AU - Григорьев, Дмитрий Алексеевич

AU - Мусаев, Андрей Александрович

AU - Sokolov, Boris

PY - 2025

Y1 - 2025

N2 - 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.

AB - 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.

KW - Predicting

KW - automatic control systems

KW - chaotic processes

KW - instability

KW - multi-expert data analysis

KW - robotics

UR - https://www.mendeley.com/catalogue/da8412b8-2d74-37dd-8c48-a87b92e967a1/

U2 - 10.3233/ATDE241279

DO - 10.3233/ATDE241279

M3 - Chapter

SN - 9781643685649

VL - 64

T3 - Advances in Transdisciplinary Engineering

SP - 479

EP - 486

BT - Mechatronics and Automation Technology

PB - IOS Press

ER -