Abstract: This study aimed to address the poor control system stability of power inspection robots affected by high temperature, high pressure, strong magnetic fields, and multiple obstacles. An intelligent FFOA (Firefly Optimization Algorithm) and TEP (Transient Evaluation Performance) algorithm were proposed. First, the FFOA was improved by dynamically adjusting the random offset range to balance global search and local optima. Second, a TEP characteristic function was established to simplify system performance evaluation metrics. Finally, the intelligent FFOA and TEP were integrated into traditional closed-loop control to achieve rapid quantification and optimization of control parameters under varying operating conditions. Simulation results showed that the proposed algorithm effectively handled signal mutations and dynamic load changes in complex environments, significantly improving the system’s dynamic response and anti-interference capabilities. The algorithm streamlined the development process of intelligent control for power inspection robots, enhancing system stability and robustness.