| 摘要: |
| 目的 针对具有外界干扰不确定性的柔性关节机械手实际轨迹跟踪稳定性问题,提出一种自适应动态面控
制与神经网络相结合的方法。 方法 对于非线性系统中的函数以及未知参数,根据径向基函数(RBF)神经网
点对其进行逼近,并对来自外界对系统的干扰项,通过设计阻尼项将其补偿,再根据动态面的相
络的特
关知识对该非线性
系统中的控制器进行设计且实现关节轨迹跟踪控制。 结果 仿真结果表明:在非线性系统中,该方法能够克服干扰
不确定性项,实现机械手连杆转角 q 较好的跟踪效果,误差缩在 5%以内,具有较强的跟踪稳定性,且随着时间的进
行,跟踪误差愈发减小且趋向于 0,对于参数的估计以及逼近都达到了理想的阈值。 结论 该方法保证了闭环非线
性系统半全局稳定,又可利用参数调节的方式达到跟踪误差任意小,且设计的控制器不但保证了机械手的位置跟
踪稳定性,而且很好地解决了跟踪抖动问题。 |
| 关键词: 机械手 自适应神经网络控制 动态面控制 轨迹跟踪 |
| DOI： |
| 分类号: |
| 基金项目: |
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| Trajectory Stabilization Tracking Control for a Class of RBF Network Manipulators |
|
HUANG Yong
|
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School of Mechanical Engineering University of Shanghai for Science and Technology Shanghai 200093 China
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| Abstract: |
| Objective Aiming at the stability of the actual trajectory tracking of the flexible joint manipulator with the
uncertainty of external disturbances a method combining adaptive dynamic surface control and a neural network was
proposed. Methods According to the characteristics of the radial basis function RBF neural network the functions and
unknown parameters in the nonlinear system were approximated and the interference item from the outside to the system
was compensated by designing the damping item. According to the knowledge of the dynamic surface the controller in the
nonlinear system was designed and the joint trajectory tracking control was realized. Results The simulation results show
that this method can overcome the disturbance uncertainty item in the nonlinear system and achieve a better tracking effect
of the connecting rod rotation angle q of the manipulator and the error is reduced within 5%. This method has strong
tracking stability. As time goes on the tracking error becomes smaller and tends to 0 and the estimation and
approximation of the parameters have reached ideal thresholds. Conclusion This method ensures the semi-global stability
of the closed-loop nonlinear system the parameter adjustment can be utilized to achieve arbitrarily small tracking errors
and the designed controller not only ensures the position tracking stability of the manipulator but also solves the problem
of tracking jitter well. |
| Key words: manipulator adaptive neural network control dynamic surface control trajectory tracking |
