In recent years, with the deepening research on exoskeletons for children with cerebral palsy, increasing evidence has highlighted their unique characteristics. Unlike adult exoskeletons, pediatric exoskeletons cannot be simply realized by scaling down adult designs; instead, special attention must be given to their unique training requirements. Although current studies have incorporated specific design adaptations and summarized the distinct features of these devices, a comprehensive review of control strategies remains lacking. This study adopts a structured narrative review approach, referencing the PRISMA framework to enhance transparency in the literature selection. Relevant publications were identified based on clearly defined inclusion and exclusion criteria, but no formal systematic review or meta-analysis was conducted. The exoskeleton control strategies from the 106 selected articles are classified using a hierarchical framework, dividing them into the supervision layer, action layer, and execution layer, with a further categorization into 12 specific control methods. Findings indicate that the supervision level primarily employs finite state machines and linear phase estimation, while the action level predominantly utilizes position trajectory control, torque trajectory control, and impedance control. At the execution level, closed-loop torque control and position control are commonly adopted. Overall, existing studies still face challenges in personalized adaptation, real-time control, and application scenarios. With advancements in controller hardware and the introduction of novel actuators, emerging technologies such as machine learning, virtual constraints, and sliding mode control may offer promising directions for future pediatric exoskeleton control design.