基于改进神经网络的机器人跟踪控制算法设计

The algorithm design of robot tracking control based on improved neural network

针对传统6R机器人路径跟踪过程中存在的控制精度差、效率低等问题,提出一种基于改进神经网络的机器人路径跟踪控制算法。对机器人位置和速度等信息进行状态检测,利用神经网络对机器人的运动轨迹进行预测和修正,并根据实际误差对机器人控制指令进行优化。基于岭回归算法对经典神经网络进行改进和优化,提升算法的迭代效率和参数优化能力,更加准确地预测和修正机器人的运动轨迹。仿真结果表明:与传统的PID控制算法相比,基于改进神经网络的6R机器人路径跟踪控制算法有更好的控制精度和稳定性,且所耗用的时间被控制在10 s之内。

Aiming at the problems of poor control accuracy and low efficiency in the path tracking of traditional 6R industrial robots,a robot path tracking control algorithm based on improved neural network is proposed.The position and speed of the robot are detected,the trajectory of the robot is predicted and corrected by neural network,and the control instruction of the robot is optimized according to the actual error.Based on ridge regression algorithm,the classical neural network is improved and optimized to improve the iterative efficiency and parameter optimization ability of the algorithm,so as to predict and correct the motion trajectory of the robot more accurately.The simulation results show that compared with the traditional PID control algorithm,the 6R robot path tracking control algorithm based on improved neural network has better control accuracy and stability,and consumes less time.It is contained within 10 seconds.