采摘机械臂的自适应神经网络滑模控制

针对采摘机械臂的位置终端的精确控制问题,提出了一种将自适应神经网络与滑模控制相结合的控制策略.首先通过神经网络来估计系统模型中的不确定性,提出了一种具有神经网络补偿作用的滑模控制器,基于李雅普诺夫稳定性理论证明了闭环系统的稳定性,并改善了系统的动态性能.仿真试验表明,该控制方法能够有效地削弱抖振现象,具有很好的鲁棒性和动态特性.

Adaptive Neural Network Sliding Mode Control of Picking Manipulators

Aiming at the accurate tracking problem of picking manipulator position terminal, a control strategy is proposed based on integrating adaptive neural networks with sliding-mode control （SMC）. Firstly, neural network is used to estimate the uncertainties of the system model. Based on Lyapunov stability theorem, the structure of sliding-mode controller compensated by neural network is designed. So the stability of the system is guaranteed and the dynamic performance of the system is improved. The simulation results illustrate that the proposed method can reduce the phenomenon of chattering in effect, and have good robustness and dynamic performance.