多叶光栅是医用直线加速器的核心部件, 采用两相圆筒形永磁同步直线电机(permanent magnet synchronous tubular linear motor, PMSTLM)作为驱动器时, 电机能耗对部件运行性能有重要影响。该文提出一种基于速度规划的多叶光栅直线电机能耗优化方法。首先, 针对运动学约束不能充分利用电机推力这一问题, 引入动摩擦系数得到电机推力的力矩边界, 基于该力矩边界提出改进指数型加减速速度规划(IESP); 然后, 引入加速距离、减速特性系数作为自变量, 通过分析全行程能耗、电机行程中间位移段切换时间和加速距离、减速特性系数之间的映射关系, 建立能耗—切换时间速度规划优化模型, 使用二代非劣排序遗传算法(NSGA-Ⅱ)进行能耗、切换时间的多目标优化, 得到切换时间不变、全行程能耗相对较小的加减速规划结果。实验结果表明, 本文提出的能耗优化方法相比梯形加减速规划(TSP), 在同样切换时间内能耗降低了21.5%。该方法能够降低PMSTLM运行时的能耗, 满足多叶光栅的正常使用要求。

Objective: Medical linear accelerator is a high-end piece of equipment used in cancer radiotherapy. A key component of this system is the multi-leaf collimator (MLC), which considerably influences the accuracy and efficiency of radiotherapy. This study examines the fast MLC, which employs a two-phase permanent magnet synchronous tubular linear motor (PMSTLM) to directly drive the leaves and enhance the precision of radiation dose distribution. However, factors such as inductance, magnetic chain, and resistance in the PMSTLM are influenced by temperature changes, resulting in parameter changes that affect the current loop's steady-state error. Moreover, drastic fluctuations in current result in increased energy consumption. The heightened energy usage negatively impacts the blade motion accuracy, thus compromising the overall quality of radiotherapy. The objective of this study is to address these challenges by reducing the energy consumption of linear motors used in MLC. Methods: This study proposes a method for optimizing the energy consumption of linear motors in MLC by improving velocity planning at the instruction level of the control system. To address the issue of kinematic constraints not fully utilizing the motor thrust, a dynamic friction coefficient is introduced to determine the moment boundary of the motor thrust. Based on this boundary, an improved exponential acceleration and deceleration speed planning method is developed. Furthermore, acceleration distance and deceleration characteristic coefficients are introduced as independent variables. The mapping relationship between full-stroke energy consumption, the transition time of the displacement section in the middle of the motor travel, and these coefficients is established. Using this relationship, an optimization model for energy consumption, transition time, and speed planning is formulated. The second-generation non-inferiority sorting genetic algorithm (NSGA-Ⅱ) is employed to perform multi-objective optimization of energy consumption and transition time. The result is utilized as commands for the controller and is validated through experimental testing. Results: Through the proposed method, this study achieved acceleration and deceleration planning results with constant transition time and relatively low energy consumption for the full stroke. Experimental data indicate that the method reduces energy consumption by 21.5%, compared to trapezoidal acceleration-deceleration planning (TSP) under identical transition time conditions. The proposed method effectively reduces the energy consumption of PMSTLM operation while maintaining the normal functional requirements of the MLC. Conclusions: The energy consumption optimization method proposed in this paper combines exponential acceleration and deceleration planning, an energy consumption-transition time speed planning model, and the NSGA-Ⅱ algorithm to enhance the performance of MLC. Based on theoretical research and experimental validation, the following conclusions are drawn. The proposed method can optimize the intermediate displacement section corresponding to the transition time by effectively utilizing the velocity peak. This is achieved by choosing appropriate acceleration distances and deceleration characteristic coefficients. Transition time and energy consumption are conflicting optimization objectives. By employing the energy consumption-transition time optimization model, choosing appropriate optimization parameters can considerably reduce energy consumption while ensuring that the transition time meets the performance requirements of the MLC. The experimental results verify the effectiveness of the proposed method. Energy consumption is reduced by 21.5% compared to that of the TSP method.