基于DSP的智能小型机械臂移动控制系统设计
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摘要
小型机械臂移动控制受到局部扰动误差的影响,导致控制精度不高,提出基于Kalman滤波反馈调节的智能小型机械臂移动控制模型,并采用DSP作为控制器的核心处理器进行控制系统设计。首先进行小型机械臂移动控制算法设计,以机械臂的定姿参量作为控制约束参量,进行机械臂的定姿控制和姿态参量输出的Kalman滤波融合,根据融合定姿输出进行控制律优化。然后在DSP集成信息处理环境下进行控制系统的硬件开发,主要对控制执行器、中央控制单元以及控制指令的输出单元进行详细设计描述。系统测试结果表明,采用该方法进行智能小型机械臂移动控制的输出性能较好,机械臂的定姿误差较低,控制收敛性较好。
The small manipulator movement control is affected by the local disturbance error,which leads to the low control precision. An intelligent small manipulator movement control model based on Kalman filter feedback regulation is proposed. The control system is designed with DSP as the core processor of the controller. Firstly,the control algorithm of the small manipulator is designed,and the attitude determination parameter of the manipulator is used as the control constraint parameter. The attitude control of the manipulator and the Kalman filtering fusion of the attitude parameter output are carried out,and the control law is optimized according to the fused attitude determination output.Then,the hardware of the control system is developed under the DSP integrated information processing environment,and the control actuator is mainly used. The central control unit and the output unit of the control instruction are designed and described in detail. The system test results show that the proposed method has better output performance and lower attitude determination error. The control convergence is good.
The small manipulator movement control is affected by the local disturbance error,which leads to the low control precision. An intelligent small manipulator movement control model based on Kalman filter feedback regulation is proposed. The control system is designed with DSP as the core processor of the controller. Firstly,the control algorithm of the small manipulator is designed,and the attitude determination parameter of the manipulator is used as the control constraint parameter. The attitude control of the manipulator and the Kalman filtering fusion of the attitude parameter output are carried out,and the control law is optimized according to the fused attitude determination output.Then,the hardware of the control system is developed under the DSP integrated information processing environment,and the control actuator is mainly used. The central control unit and the output unit of the control instruction are designed and described in detail. The system test results show that the proposed method has better output performance and lower attitude determination error. The control convergence is good.
引文
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[3]彭程,白越,乔冠宇.共轴八旋翼无人飞行器的偏航静态抗饱和补偿控制[J].机器人,2018,40(2):240-248.PENG Cheng,BAI Yue,QIAO Guanyu.Static Anti-windup Compensation Control of Yaw Movement for a Coaxial Eight-Rotor UAV.ROBOT,2018,40(2):240-248.
[4]史瑞东,张秀丽,姚燕安.基于CPG的沙漠蜘蛛机器人多模式运动控制方法[J].机器人,2018,40(2):146-157.
[5]田耀斌.多模式移动连杆机构理论研究[D].北京:北京交通大学,2015.Tian Y B.Research on theories of mobile linkage with multiple locomotion modes[D].Beijing:Beijing Jiaotong University,2015.
[6]王俊刚,汤磊,谷国迎,等.超冗余度机械臂跟随末端轨迹运动算法及其性能分析[J].机械工程学报,2018,54(3):18-25.
[7]Tang L,Wang J,Zheng Y,et al.Design of a cable-driven hyper-redundant robot with experimental validation[J].The International Journal of Advanced Robotic Systems,2017,14(5):1-12.
[8]柳长安,鄢小虎,刘春阳,等.基于改进蚁群算法的移动机器人动态路径规划方法[J].电子学报,2011,39(5):1220-1224.
[9]栾小丽,王志强,刘飞.多变量非方系统集中式PI控制器设计[J].控制与决策,2016,31(05):811-816.
[10]Liu J C,Chen N,Yu X.Modified two-degrees-of-freedom internal model control for non-square systems with multiple time delays[J].J of Harbin Institute of Technology,2014,21(2):122-128.
[11]Jing Q B,Hao F,Wang Q.A multivariable IMC-PID method for non-square large time delay systems using NPSO algorithm[J].J of Process Control,2014,23(5):649-663.
[12]Chiranjeeevi T,Vijetha I V V,Chakravarthi B,et al.Tuning and control of multi-variable systems[J].Int J of Electronics and Electrical Engineering,2014,2(4):309-320.
[13]张秀丽,谷小旭,赵洪福,等.一种基于串联弹性驱动器的柔顺机械臂设计[J].机器人,2016,38(4):385-394.
[14]游有鹏,张宇,李成刚.面向直接示教的机器人零力控制[J].机械工程学报,2014,50(3):10-17.
[15]Uijlings J R R,van de Sande K E A,Gevers T,et al.Selective search for object recognition[J].International Journal of Computer Vision,2013,104(2):154-171.