Prescribed-Time Projective Synchronization for Different Dimensional Complex Networks via Fuzzy Reinforcement Learning

This paper investigates the prescribed-time projective synchronization (PTPS) for complex networks (CNs) with different dimension. To solve this problem, a projective synchronization error is constructed and a novel performance value function integrated with the prescribed time and desired accuracy is proposed. Subsequently, a fuzzy controller is introduced to address the prescribed-time projective synchronization issue. The controller is realized through a fuzzy adaptive dynamic programming (ADP)-based framework. Additionally, the convergence analysis of the proposed methodology is provided, demonstrating that the projective synchronization error can converge to a predefined residual set within the prescribed time, which means the synchronization of CNs is solved. Finally, a numerical example is presented to verify the obtained results.