Intelligent Optimization and Control Framework for Complex Dynamic Systems: Integrating Two-Tier Fuzzy Evaluation and Differential Nonlinear Optimization

Abstract

The optimal state regulation of highly complex and uncertain dynamic systems under multi-objective and multi-constraint conditions has emerged as a critical research area in the field of intelligent optimization. Addressing the limitations of existing models in uncertainty representation, nonlinear dynamic modeling, and global optimization capabilities, this paper proposes an intelligent optimal control framework that integrates a two-tier fuzzy comprehensive evaluation model with a differential equation-based nonlinear optimal control algorithm. This framework employs a hierarchical fuzzy decision-making mechanism to precisely quantify multidimensional uncertainties and conflicting interests within complex systems. Furthermore, it achieves high-precision dynamic control of the system state by combining a first-order differential dynamic model with advanced nonlinear optimization strategies. The results demonstrate that the proposed algorithm significantly outperforms mainstream methods in terms of control accuracy, energy efficiency, and robustness, showcasing its exceptional adaptability and practical engineering value. Future work will focus on incorporating deep learning and reinforcement learning models to further enhance the algorithm's level of intelligence and generalization capabilities.