时延力反馈遥操作系统的跟踪性能研究

发布时间：2018-12-25 20:28

【摘要】：在遥操作系统中,由于远距离或实施有限的数据传输,会导致大量的时延,并且,通常时延的反馈信号会产生主机器人和从机器人之间的位置和力跟踪误差,从而影响系统的整体稳定性。同时,遥操作系统存在系统模型不确定项,如:时延、系统动力学参数不确定项、未知的外部干扰、以及未知的主、从机器人各关节的内部摩擦等,这会也会大大的降低遥操作系统的透明性,甚至引起系统不稳定。针对上述问题。本文在有固定时延条件下,基于自适应控制和神经网络其他智能控制方法,以有效地改善系统动态的跟踪性能、稳定性和透明性为目标,本文采用位置误差(Position Error Based,PEB)遥操作控制结构,设计了多个时延力反馈遥操作系统双边控制器。本文的主要研究内容可以总结为:(1)针对遥操作系统具有动力学参数不确定性和时延的问题,本文采用了一种基于PEB控制结构的自适应控制方法,与传统的自适应双边控制器算法,该方法对动力学参数不确定值的估计更加接近真实值,且具有更好的系统跟踪性能。(2)考虑遥操作系统存在未知的外部干扰、主-从机器人机械内部摩擦,以及时延,基于Lypunov直接法设计了一种基于模型逼近的神经网络自适应控制方法,并融入到PEB遥操作控制结构中。仿真实验结果表明该方法能够有效地应用到具有未知外部干扰和机器人各个关节内部摩擦的复杂的遥操作系统模型上,并具有良好的跟踪性能。(3)为了使遥操作系统更具有实际意义,有必要对遥操作系统跟踪误差收敛时间进行有效控制,在PEB遥操作控制结构中,基于终端滑模控制与自适应控制,设计了一种连续有限时间收敛的自适应终端滑模双边控制方法,解决了这一问题。仿真实验结果表明该方法确实在收敛时间上得到了提高,但是与前一个方法相比,跟踪误差精度略有降低。(4)成功将本文所设计的研究方法应用于主、从机器人都为两自由度两连杆机械臂,存在固定通信通道时延的遥操作系统仿真平台上,实验结果说明本文所设计的方法的具有良好的实际控制性能。
[Abstract]:In teleoperation systems, long distance or limited data transmission can lead to a large number of delays, and the feedback signals of delay usually produce position and force tracking errors between the master robot and slave robot. Thus, the overall stability of the system is affected. At the same time, there are uncertainties in the system model, such as time delay, uncertain dynamic parameters of the system, unknown external interference, and internal friction of the joints of the unknown master and slave robot, etc. This will also greatly reduce the transparency of the remote operating system, and even lead to system instability. In response to the above problems. In this paper, based on adaptive control and other intelligent control methods based on neural network, the dynamic tracking performance, stability and transparency of the system are effectively improved under the condition of fixed delay. The position error (Position Error Based, is adopted in this paper. PEB) telecontrol control structure and design several bilateral controllers of delay force feedback teleoperation system. The main research contents of this paper can be summarized as follows: (1) aiming at the problem of dynamic parameter uncertainty and delay in remote operating system, an adaptive control method based on PEB control structure is adopted in this paper. Compared with the traditional adaptive two-sided controller algorithm, the estimation of the uncertain dynamic parameters is closer to the real value, and it has better tracking performance. (2) considering the unknown external interference in the remote operating system, Based on the Lypunov direct method, a neural network adaptive control method based on model approximation is designed for the internal friction and delay of the master-slave robot, and it is integrated into the PEB teleoperation control structure. The simulation results show that the proposed method can be effectively applied to the complex remote operating system model with unknown external interference and internal friction of each joint of the robot. And has good tracking performance. (3) in order to make the remote operating system more practical significance, it is necessary to effectively control the tracking error convergence time of the remote operating system, in the PEB teleoperation control structure, Based on terminal sliding mode control and adaptive control, a continuous finite time convergent adaptive terminal sliding mode bilateral control method is designed to solve this problem. The simulation results show that the convergence time of the proposed method is improved, but the tracking error accuracy is slightly lower than the previous one. (4) the proposed method is successfully applied to the main method. The robot is a two-link manipulator with two degrees of freedom and there is a fixed communication channel delay on the remote operating system simulation platform. The experimental results show that the method designed in this paper has good practical control performance.
【学位授予单位】：电子科技大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP242

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