相较于传统的交流异步电机，永磁同步电机（Permanent Magnet Synchronous Motor，PMSM）具有噪声小、功率密度高以及运行速域宽等显著优势，成为交流电机应用场合的研究热点之一。PMSM应用范围的不断扩大对系统控制精度、稳定性和动态响应速度等控制性能都提出了越来越高的要求。在双端PMSM驱动的带式输送机中，采用直驱方式可以省去齿轮箱等机械环节，能够使得系统的安装空间减小、功率密度和可靠性提升；且能够满足长距离运输、需要智能调速的现代化物流系统需求，近年来已成为带式输送机领域的研究热点。双端驱动的带式输送机需要双电机同步协调运行以确保系统的运行指标，进而满足运输效率、安全以及企业效益需求，因此提高双电机协同控制的性能具有很重要的工程价值。

模型预测电流控制（Model Predictive Current Control，MPCC）作为一种先进控制算法，具有动态性能好、鲁棒性强、适用于多变量系统的优点，能够很好的适用于非线性、强耦合的时变系统。针对多矢量MPCC计算量大和三矢量MPCC不能实现全局最优的问题，本文提出了基于最优矢量快速选择法的PMSM模型预测控制算法。该算法首先使用一次比较确定预测电压矢量所处象限，后再根据预测电压矢量所处象限，选择需要比较基本电压矢量并计算对应的代价函数值，在第二次比较后就得到了预测电压矢量所处的具体扇区位置；当预测电压矢量位于扇区边界时，可以直接确定矢量组合方式而不需要后续比较。这种对扇区进行重新划分的方式代替了传统MPCC使用的遍历法，大幅减轻了系统的计算负担。同时计算出三电压矢量组合和双电压矢量组合的代价函数值进行比较，选取具有更小代价函数值的组合作为输出电压矢量组合，以获得更好的控制效果。最后通过仿真和实验证明了所提改进算法具有更小的转矩脉动和转速脉动。

通过对现有带式输送机多电机同步控制结构的分析，发现交叉耦合结构中耦合系数的选取对双电机的同步状态起着至关重要的作用。针对交叉耦合结构中耦合系数固定且难以整定的问题，本文提出了一种变耦合系数模型预测控制算法，将两台电机的转速差、位置差作为变量引入变耦合系数模型预测控制器。同时在每台PMSM的模型预测电流控制器中对代价函数进行改进，加入一致的约束项，用以确保两台电机在预测时域内逐步收敛，避免独立优化导致的策略冲突。为了确保优化问题的解在实际应用中具有可行性和合理性，还设置了相应的约束条件。最后通过仿真和实验证明了所提策略能够使两台电机拥有更稳定的转速、更小的转矩脉动和更好的同步性。

Compared to traditional alternating-current induction motors, the Permanent Magnet Synchronous Motor (PMSM) demonstrates significant advantages including lower noise, higher power density, and wider operating speed range, making it a prominent research focus in alternating-current motor applications. In belt conveyors driven by dual-ended PMSMs (Permanent Magnet Synchronous Motors), the adoption of direct drive eliminates mechanical components such as gearboxes. This reduction in mechanical complexity enables a more compact system installation footprint while enhancing power density and reliability. Furthermore, this configuration meets the demands of modern logistics systems requiring long-distance transportation and intelligent speed regulation. Consequently, it has become a prominent research focus in the belt conveyor field in recent years. These dual-motor driven systems necessitate synchronized coordination between two PMSMs to ensure operational efficiency, safety, and economic benefits, thereby underscoring the critical engineering significance of enhancing cooperative control performance.

Model Predictive Current Control (MPCC), as an advanced control algorithm, exhibits superior dynamic performance, strong robustness, and multi-variable system compatibility, making it particularly suitable for nonlinear, strongly coupled time-varying systems.To resolve the inherent limitations of conventional MPCC - excessive computational load in multi-vector implementations and suboptimal global performance in three-vector approaches - this paper proposes an improved PMSM predictive control algorithm based on an optimal vector rapid selection method. The algorithm first determines the predictive voltage vector quadrant through initial comparison, then selects candidate basic voltage vectors and computes corresponding cost functions based on quadrant positioning. The specific sector location is identified through secondary comparison. When predictive vectors reside at sector boundaries, vector combinations can be directly determined without further comparisons. This sector reconfiguration strategy replaces traditional exhaustive search methods, substantially reducing computational burden. Simultaneous evaluation of three-vector and two-vector combinations through cost function comparison enables selection of optimal vector combinations with minimized cost values, thereby achieving enhanced control performance. Simulation and experimental results verify that the proposed algorithm significantly reduces torque and speed ripples.

Through analysis of existing multi-motor synchronization structures in belt conveyors, this study reveals the critical influence of coupling coefficient selection in cross-coupled configurations on dual-motor synchronization. To address the limitations of fixed coupling coefficients in conventional cross-coupled structures, a variable coupling coefficient model predictive control algorithm is developed. This approach integrates speed and position differences between dual motors into the predictive controller as dynamic variables. Furthermore, consistent constraint terms are incorporated into the cost function of each PMSM's MPCC controller to ensure gradual convergence within the prediction horizon and prevent strategy conflicts from independent optimization. Practical feasibility is guaranteed through constraint condition implementation. Experimental validation demonstrates that the proposed strategy achieves more stable speed synchronization, reduced torque pulsation, and enhanced synchronization performance.

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