Abstract
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.
Highlights
Magnetic levitation system is widely used in magnetic bearing, magnetic suspension vibration isolator, magnetic suspension train, and many other fields because it is contactless, frictionless, and noiseless, etc
In order to solve the above problems, this paper proposed a deep neural network feedforward compensation controller based on an improved Adagrad algorithm
The effectiveness of the improved Adagrad optimization algorithm is verified by the benchmark function, and the effectiveness of the controller proposed is verified by simulation and experiment
Summary
Magnetic levitation system is widely used in magnetic bearing, magnetic suspension vibration isolator, magnetic suspension train, and many other fields because it is contactless, frictionless, and noiseless, etc. In order to solve the above problems, this paper proposed a deep neural network feedforward compensation controller based on an improved Adagrad algorithm. Us, the proposed controller in this paper is designed by the deep neural network identifier, feedforward compensator, and PID controller. En, an improved Adagrad optimization algorithm with better convergence accuracy and faster convergence rate is proposed to solve the problem of slow training of the neural network and network parameter delay caused by online training of the neural network controller.
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