多关节机械手的模糊神经网络自适应控制

Fuzzy Neural Network Adaptive Control of Multi-joint Manipulator

针对多关节机械手因关节抖震产生误差导致多关节机械手控制不稳定问题,提出了一种基于粒子群优化模糊神经网络的多关节机械手运动控制算法。该算法利用神经网络能够很好地预测和逼近目标值的特性和模糊函数对神经网络的互补性,并使用粒子群算法对神经网络的关节输出角度进行优化,极大地减小了多关节机械手因抖震产生的误差,尽可能避免了机械手的速度跟踪误差和位置跟踪误差,利用Lyapunov稳定性理论证明了该方法的稳定性,并通过对双关节机械手系统的仿真验证了该算法的可行性和合理性。

Aiming at the problem of instability of multi-joint manipulator control due to joint chattering errors,this paper proposes a multi-joint manipulator motion control algorithm based on particle swarm optimization and fuzzy neural network.The algorithm uses the neural network's ability to predict and approximate the target value and the complementarity of the fuzzy function to the neural network,and uses the particle swarm algorithm to optimize the joint output angle of the neural network,which greatly reduces the factor of the multi-joint manipulator.The error caused by chattering avoids the speed tracking error and position tracking error of the manipulator as much as possible.The stability of the method is proved by the Lyapunov stability theory,and the feasibility and feasibility of the algorithm are verified by the simulation of the dual-joint manipulator system rationality.