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首页> 外文期刊>Mathematical Problems in Engineering: Theory, Methods and Applications >Position Control of Magnetic Levitation Ball Based on an Improved Adagrad Algorithm and Deep Neural Network Feedforward Compensation Control
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Position Control of Magnetic Levitation Ball Based on an Improved Adagrad Algorithm and Deep Neural Network Feedforward Compensation Control

机译：基于改进Adagrad算法和深度神经网络前馈补偿控制的磁悬浮球位置控制

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摘要

To control the position of the magnetic levitation ball more accurately, this paper proposes a deep neural network feedforward compensation controller based on an improved Adagrad optimization algorithm. The control structure of the controller consists of a deep neural network identifier, a deep neural network feedforward compensator, and a PID controller. First, the dynamic inverse model of the magnetic levitation ball is established by the deep neural network identifier which is trained online based on the improved Adagrad algorithm, and the trained network parameters are dynamically copied to the deep neural network feedforward compensator. Then, the position control of the magnetic levitation ball system is realized by the output of the feedforward compensator and the PID controller. Simulations and experiments illustrate that the accuracy of the deep network feedforward compensation control based on an improved Adagrad algorithm is higher, and its control system shows good dynamic and static performance and robustness to some extent.
机译：为了更准确地控制磁悬浮球的位置，该文提出一种基于改进的Adagrad优化算法的深度神经网络前馈补偿控制器。控制器的控制结构由深度神经网络标识符、深度神经网络前馈补偿器和PID控制器组成。首先，基于改进的Adagrad算法，利用在线训练的深度神经网络标识符建立磁悬浮球动态逆模型，并将训练好的网络参数动态复制到深度神经网络前馈补偿器中;然后，通过前馈补偿器和PID控制器的输出实现磁悬浮球系统的位置控制。仿真和实验表明，基于改进的Adagrad算法的深度网络前馈补偿控制精度更高，其控制系统在一定程度上表现出良好的动静态性能和鲁棒性。

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