多关节机械臂轨迹规划和轨迹跟踪控制研究

发布时间：2018-04-04 18:40

本文选题：机械臂　切入点：轨迹规划　出处：《哈尔滨工业大学》2017年硕士论文

【摘要】：随着国家《中国制造2025》的提出,将工业机器人等智能装备作为未来发展的重点。对于工业级机械臂而言,轨迹规划和轨迹跟踪问题的研究为其能够精准安全的工作提供了重要的保障。根据给定目标的具体任务可以准确的规划出机械臂关节变量的运动轨迹,再对机械臂生成的轨迹进行轨迹跟踪控制,使得控制输入驱动力矩能够达到机械臂精确跟踪给定目标轨迹的需求。因此轨迹规划和轨迹跟踪是机械臂能够完成给定任务的基础,具有重要的研究意义。首先本文根据Denavit-Hartenberg法建立了BO-6-3六自由度机械臂的坐标模型并根据各个连杆的D-H参数求得了各个关节之间的位姿关系,进而可以通过齐次变换矩阵得到机械臂末端执行器相对于机械臂本体的位姿,并推导出机械臂正运动学方程,分析了利用几何法求取运动学逆解的简要过程。采用拉格朗日公式法对平面内两自由度机械臂进行了机械臂动力学建模,同时分析了机械臂动力学正解和动力学逆解之间的关系。然后本文提出了基于差分进化(Differential Evolution)优化BP神经网络求解机械臂运动学逆解的方法,并与BP神经网络进行了比较,仿真结果表明DEBP神经网络求得的逆解精度高同时也分析了传统求解运动学逆解方法的不足之处。在关节空间和笛卡尔空间内分别进行机械臂的轨迹规划,在关节空间内通过运动学的逆解求得关节角度值序列,并采用五次多项式插值法进行运算,求得了关节空间内关节角的位置、速度和加速度的变化曲线。在笛卡尔空间内采用直线插补法完成了从初始位置到终止位置的轨迹规划,完成了目标指定任务。最后本文采用了双幂次趋近律与改进的终端滑模面相结合的滑模变结构控制策略,对平面两自由度机械臂进行轨迹跟踪控制研究。针对传统幂次趋近律收敛速度慢,抖振现象明显等缺点,采用了双幂次趋近律的滑模控制方法,保证了系统能够在有限时间内快速的到达滑动模面。与此同时传统的终端滑模面在对机械臂关节角的位置误差和速度误差跟踪时精度较低,也不能很好的控制当系统进入滑动模面瞬间的状态情况,易于产生较强的抖振现象,因此本文又采用了改进的终端滑模面。将双幂次趋近律和改进的终端滑模面结合后,针对机械臂动力学方程推导出机械臂系统的控制律。将在选定同一滑模面的情况下针对不同的趋近律进行对比分析,同理在选定同一趋近律的情况下针对不同的滑模面进行对比分析。仿真结果表明双幂次趋近律和改进的终端滑模面的结合在具有外界干扰的情况下还能够使机械臂系统保持较强的鲁棒稳定性和鲁棒性,有效的提升了机械臂的跟踪精度,并在一定程度上减弱了机械臂的抖振现象。
[Abstract]:With the proposal of "made in China 2025", intelligent equipment, such as industrial robots, will be regarded as the focus of future development.For industrial manipulator, the research on trajectory planning and trajectory tracking provides an important guarantee for its precise and safe work.According to the specific task of the given target, the trajectory of the manipulator joint variables can be accurately planned, and then the trajectory generated by the manipulator can be tracked and controlled.The control input drive torque can meet the requirements of the manipulator to track the given target trajectory accurately.Therefore, trajectory planning and trajectory tracking are the basis for the robot arm to complete a given task, which is of great significance.In this paper, the coordinate model of BO-6-3 six-degree-of-freedom manipulator is established according to the Denavit-Hartenberg method, and the position and pose relationship between joints is obtained according to D-H parameters of each connecting rod.Furthermore, the position and orientation of the end actuator relative to the body of the manipulator can be obtained by the homogeneous transformation matrix, and the forward kinematics equation of the manipulator is derived, and the process of obtaining the inverse kinematics solution by using the geometric method is analyzed.The dynamic modeling of the manipulator with two degrees of freedom in a plane is carried out by using the Lagrange formula method, and the relationship between the dynamic forward solution and the inverse solution of the manipulator is analyzed.Then, a BP neural network optimization method based on differential evolution evolution is proposed to solve the inverse kinematics of manipulator, and compared with BP neural network.The simulation results show that the inverse solution obtained by DEBP neural network has high accuracy and the shortcomings of the traditional inverse kinematics solution method are also analyzed.In the joint space and the Cartesian space, the trajectory planning of the manipulator is carried out, and the sequence of joint angle values is obtained by the inverse kinematics solution in the joint space, and the fifth order polynomial interpolation method is used to perform the operation.The position, velocity and acceleration curves of the joint angle in the joint space are obtained.In the Cartesian space, the trajectory planning from the initial position to the termination position is completed by using the linear interpolation method, and the target assignment task is completed.Finally, a sliding mode variable structure control strategy combining the double power approach law and the improved terminal sliding mode surface is used to study the trajectory tracking control of the planar two-degree-of-freedom manipulator.Aiming at the shortcomings of the traditional power law such as slow convergence rate and obvious chattering phenomenon, the sliding mode control method of the double power approach law is adopted to ensure the system can reach the sliding mode surface quickly in the limited time.At the same time, the precision of the traditional terminal sliding mode surface in tracking the position error and velocity error of the joint angle of the manipulator is low, and the system can not be controlled well when the system enters the instant state of the sliding mode surface, so it is easy to produce strong chattering phenomenon.Therefore, an improved terminal sliding mode surface is adopted in this paper.The control law of the manipulator system is derived based on the dynamic equation of the manipulator by combining the double power approach law with the improved terminal sliding mode surface.In the case of selecting the same sliding mode surface, the contrast analysis is carried out for different convergence laws, and the same law is chosen for different sliding mode surfaces.The simulation results show that the combination of the double power approach law and the improved terminal sliding mode surface can make the manipulator system keep robust stability and robustness, and improve the tracking accuracy of the manipulator effectively.The chattering phenomenon of the manipulator is reduced to some extent.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP241

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