A review of multi-motor coordinated control technologies: advanced strategies, intelligent algorithms, and future trends

Multi-motor control systems are central to industrial automation in smart manufacturing, utilizing their capabilities for high-precision coordination, dynamic load distribution, and stability in robotics, Computer Numerical Control machining, and related domains. However, practical deployment faces challenges such as multi-physical field disturbances, time-varying parameter uncertainties, network delays, and nonlinear dynamics, which degrade control accuracy and operational reliability. Researchers address these issues through advanced strategies combining centralized and distributed architectures with classical methods like adaptive control and cross-coupling algorithms. Recent advancements integrate deep learning and reinforcement learning to enhance system modeling and uncertainty compensation, offering improved performance in complex scenarios. Hybrid control frameworks demonstrate progress but encounter unresolved limitations: incomplete theoretical foundations for multi-motor synchronization, insufficient synergy between AI techniques and traditional control approaches, and unmet real-time demands for edge computing-enabled architectures in heterogeneous motor clusters. Empirical studies confirm hybrid strategies outperform conventional methods in managing disturbances, while algorithmic fusion enhances adaptability and fault tolerance. Future research must prioritize refining synchronization control theory, developing deeper integrations of AI with classical frameworks, and optimizing distributed architectures with edge computing to meet industrial real-time requirements. Cross-domain technological integration, such as combining Internet of Things and advanced control algorithms, will further accelerate automation advancements. This review systematically evaluates state-of-the-art multi-motor control technologies, highlighting their technical strengths and limitations to guide both theoretical innovation and practical implementations. By addressing these challenges, the field can advance toward more robust, adaptive, and scalable automation systems, ultimately supporting the evolving demands of smart manufacturing and high-end industrial equipment. • Summarize latest multi-motor control progress (strategies, algorithms, trends), trace architecture evolution (centralized→distributed→hybrid intelligent), and clarify features/applications in industrial automation, robotics, renewable energy. • Compare sliding mode control (SMC), active disturbance rejection control (ADRC) and deep learning-enhanced algorithms, analyze their advantages, disadvantages and performance in dealing with multi-physical field interference and time-varying uncertainties, and experimentally verify that hybrid strategies are superior in interference management, and algorithm fusion can improve the adaptability and fault-tolerance capabilities of the system. • Point out the key challenges in the field, specify future directions such as heterogeneous motor integration, energy efficiency-accuracy collaborative optimization, and digital twin real-time enhancement by combining technologies with industry needs, so as to provide references for theories and applications.