State Key Laboratory of High Performance Complex Manufacturing, Central South University, Changsha 410083, China.
SANY Automobile Manufacturing Co.,Ltd, Changsha 410100, China.
Comput Intell Neurosci. 2022 Jul 22;2022:4461546. doi: 10.1155/2022/4461546. eCollection 2022.
We offer a neural network-based control method to control the vibration of the engineering mechanical arm and the trajectory in order to solve the problem of large errors in tracking the path when the engineering mechanical arm is unstable and under the influence of the outside world. A mechanical arm network is used to perform tasks related to learning the unknown dynamic properties of a engineering mechanical arms keyboard without the need for prior learning. Given the dynamic equations of the engineering mechanical arm, the dynamic properties of the mechanical arm were studied using a positive feedback network. The adaptive neural network management system was developed, and the stability and integrity of the closed-loop system were proved by Lyapunov's function. Engineering mechanical arm motion trajectory control errors were modeled and validated in the Matlab/Simulink environment. The simulation results show that the management of the adaptive neural network is able to better control the desired path of the engineering mechanical arm in the presence of external interference, and the fluctuation range of input torque is small. The PID control has a large error in the expected trajectory tracking of the engineering mechanical arm, the fluctuation range of the input torque is as high as 20, and the jitter phenomenon is more serious. The use of detailed comparisons and adaptive neural network monitoring can perform well in manipulating the trajectory of the engineering mechanical arm. The engineering mechanical arm uses an adaptive neural network control method, in which the control precision of engineering mechanical arm motion trajectory can be improved and the out-of-control phenomenon of mechanical arm motion can be reduced.
我们提供了一种基于神经网络的控制方法来控制工程机械臂的振动和轨迹，以解决工程机械臂不稳定且受到外界影响时路径跟踪误差较大的问题。机械臂网络用于执行与学习工程机械臂键盘未知动态特性相关的任务，而无需事先学习。给定工程机械臂的动力学方程，使用正反馈网络研究了机械臂的动力学特性。开发了自适应神经网络管理系统，并通过 Lyapunov 函数证明了闭环系统的稳定性和完整性。在 Matlab/Simulink 环境中对工程机械臂运动轨迹控制误差进行了建模和验证。仿真结果表明，自适应神经网络的管理能够在存在外部干扰的情况下更好地控制工程机械臂的期望路径，并且输入扭矩的波动范围较小。PID 控制在工程机械臂的期望轨迹跟踪中存在较大误差，输入扭矩的波动范围高达 20，抖动现象更为严重。使用详细的比较和自适应神经网络监测可以很好地控制工程机械臂的轨迹。工程机械臂采用自适应神经网络控制方法，可以提高工程机械臂运动轨迹的控制精度，减少机械臂运动失控现象。
