ADAPTIVE CONTROL AND OPTIMIZATION METHODS IN COMPLEX ROBOTIC SYSTEMS USING ARTIFICIAL INTELLIGENCE ALGORITHMS

This research addresses the critical problem of controlling complex robotic systems characterized by parametric uncertainty and external disturbances. Within the scope of this work, a novel adaptive control model was developed based on the integration of artificial intelligence elements and modern control theory. The proposed approach combines classical nonlinear control theory, Lyapunov stability analysis, and neural function approximation mechanisms into a single, unified system. The primary advantage of this model is that it does not require prior exact knowledge of system parameters and ensures high precision and energy efficiency by minimizing the control functional in real-time. Unknown system dynamics are identified via neural networks, and dynamic adjustments are applied to control signals. The study provides a rigorous mathematical proof of the system’s global asymptotic stability and derives laws for adapting control coefficients. The effectiveness of the developed methodology has been verified through simulation modeling and practical experiments. The results demonstrate that the proposed control algorithm outperforms traditional PID controllers and classical adaptive methods in terms of accuracy, robustness against errors, and speed. This work establishes a new scientific and practical foundation for advancing robotics, automated manufacturing, and intelligent control systems. Consequently, it significantly enhances the autonomy of complex robotic systems and enables their effective deployment in diverse dynamic environments.