Realization of Robot Flexible Motion Control Algorithm Using Model-Based Design Method
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摘要: 运动控制算法是机器人系统设计的关键技术，通常面临通用性差、嵌入算法复杂、设计周期长等问题。本文结合嵌入复杂的柔性S形加减速运动控制算法，提出了一种基于模型的软硬件协同高效设计方法，可以大大缩短机器人运动控制系统的设计周期，提高开发效率。通过对柔性运动控制算法建模,建立了一组易于解算的接口参数列表,算法将根据输入列表参数自适应变化运动速度规划,提高应用灵活性。在Simulink中完成了柔性运动控制算法的模型设计与仿真测试；然后通过MathWorks工具箱为模型自动生成嵌入式C代码和可编程逻辑IP核；最后在以Zynq-7000为核心的运动控制器中实现算法功能。实验结果验证了基于模型的软硬件协同设计方法的可行性和有效性。Abstract: As the core technology of the robot controller, the motion control algorithm has important influence on the robot motion performance including stability, reliability, and rapidity. However, the application of motion control algorithms usually faces the problems of poor generality, complex embedded algorithm, and long design cycles. Aiming at these problems, this study combines the complex flexible S-shaped acceleration/deceleration (ACC/DEC) motion control algorithm and proposes a model-based hardware-software collaboration design method, which can greatly shorten the design cycle of the robot motion control system and improve the development efficiency. By modeling the flexible motion control algorithm and establishing a set of interface parameter list easily calculated, the proposed algorithm can adaptively change the motion speed planning according to the parameters in the list such that the flexibility of application can be improved. The model design and simulation test for flexible motion control algorithms are completed in Simulink platform. The Math Works toolbox is utilized to automatically generate the embedded C code and programmable logic IP cores for the software and hardware models. Finally, the proposed algorithm function is realized in the motion controller based on zynq-7000. It is verified via the simulation results that the designed model can achieve the effect of the S-shaped ACC/DEC algorithm and the speed profile has great flexible characteristics. Moreover, the ACC/DEC algorithm deployed on the Zynq-7000 is in agreement with the simulation results. Hence, the model-based software and hardware co-design method has important application value in the field of personalized robots.
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图 3 不同条件下S形加减速算法的仿真结果
Figure 3. Simulation results of S-shaped ACC/DEC algorithm under different conditions
图 15 仿真、实验、测量结果比较
Figure 15. Comparison of simulation, experiment and measurement results
表 1 不同条件下的柔性S形加减速算法
Table 1. S-shaped ACC/DEC algorithms under different conditions
Classification Name Condition Feature Group 1 7-Segment S-shaped speed curve $L > {L_{\text{α}} }$ and ${t_{\rm{m}}} \in \left( {0,{t_{\rm{r}}}/2} \right)$ / 6-Segment S-shaped speed curve $L \in \left( {{L_{\text{β}} },{L_{\text{α}} }} \right]$ and ${t_{\rm{m}}} \in \left( {0,{t_{\rm{r}}}/2} \right)$ Without ${T_4}$ 5-Segment S-shaped speed curve $L > {L_{\text{α}} }$ and ${t_{\rm{m}}} = {t_{\rm{r}}}/2$ Without ${T_2},{T_6}$ 4-Segment S-shaped speed curve $L \in \left( {{L_{\text{β}} },{L_{\text{α}} }} \right]$ and ${t_{\rm{m}}} = {t_{\rm{r}}}/2$ Without ${T_2},{T_4},{T_6}$ Group 2 Trapezoidal speed curve (3-segment) $L > {L_{\text{α}} }$ and ${t_{\rm{m}}} = 0$ Only ${T_2},{T_4},{T_6}$ Triangle speed curve (2-segment) $L \in \left( {{L_{\text{β}} },{L_{\text{α}}}} \right]$ and ${t_{\rm{m}}} = 0$ Only ${T_2},{T_6}$ Group 3 Speed curve without acceleration (1-segment) $L \in \left( {0,{L_{\text{β}}}} \right]$ and ${v_0} > 0$ Only ${T_4}$ 
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表 2 可编程逻辑硬件资源使用情况
Table 2. Programmable logic hardware resource usage
Module Consumed resource (Utilization) Slice LUT Slick register LUT as memory DSP48E1 Pulse generator 152 (0.29%) 298 (0.28%) 0 0 Iterative velocity control 2330 (4.38%) 824 (0.77%) 0 4 (1.82%) System reset module 16 (0.03%) 33 (0.03%) 1 (0.01%) 0 AXI 539 (1.01%) 677 (0.64%) 61 (0.35%) 0 Total 3037 (5.71%) 1832 (1.72%) 62 (0.36%) 4 (1.82%)
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