采用逆神经网络模型的移动机器人速度控制回路研究

针对移动机器人运动轨迹容易受到不确定外界因素干扰的问题,采用逆神经网络模型设计移动机器人控制系统。分别采用逆神经网络控制器和传统PI控制器模型对两轮差动移动机器人运动速度和角速度进行跟踪控制。传统PI控制器模型使用了近似于线性的等效负载驱动器,而逆神经网络控制器使用前馈多层感知神经网络模型,该模型结合了其运动学和动力学的数学模型,在特定工作区域内,对逆神经网络模型进行离散训练。在平面内,对移动机器人的速度跟踪控制进行仿真。结果表明:采用PI控制器模型,移动机器人车轮运动速度和角速度与理论值存在较大误差,而采用逆神经网络模型时误差较小。采用逆神经网络模型设计移动机器人速度控制回路,可以提高移动机器人运动性能,更好地适应外界环境的变化。

Research on Speed Control Loop of Mobile Robot Based on Inverse Neural Network Model

In order to solve the problem that the trajectory of mobile robot is easily disturbed by uncertain external factors, the control system of mobile robot was designed by using the inverse neural network model. The inverse neural network controller and the traditional PI controller model were introduced to track the velocity and angular velocity of two wheeled differential mobile robot. In the traditional PI controller model, approximately linear equivalent load driver was used, while in the inverse neural network control,the feedforward multilayer perceptual neural network model was used. The inverse neural network control model combined the mathematical model of kinematics and dynamics, and it was trained discretely in a specific working area. In the plane, the speed tracking control of the mobile robot was simulated. The results show that with the PI controller, the wheel speed and angular speed of the mobile robot have a large error with the theoretical value, while the error is small when the inverse neural network model is adopted. By using inverse neural network model to design the speed control loop of mobile robot, the performance of the mobile robot can be improved and it can better adapt to the changes of the external environment.