Structural vibration control method based on reinforcement learning, medium and equipment

Abstract

The invention discloses a structural vibration control method based on reinforcement learning, a medium and equipment. The method comprises the following steps: establishing a kinetic equation and a reward function of a controlled system; establishing and initializing a strategy network, a target strategy network, a value network and a target value network; establishing a playback pool; data interaction is achieved. Meanwhile, the control signal, the feedback signal and the reward signal are stored in a playback pool, the control signal, the feedback signal and the reward signal are provided for a reinforcement learning algorithm in a random sampling mode to update parameters of a strategy network and a value network, and a soft update mechanism is adopted to update parameters of a target strategy network and a target value network; obtaining a final strategy neural network as a controller; and deploying a controller, taking the feedback signal acquired by the sensor as the input of the neural network, and outputting a control signal after the forward calculation of the neural network to complete the control operation of the structural vibration. The invention provides a more intelligent control method for vibration control of a complex structure, and has excellent control performance and engineering practicability.

Description

technical field [0001] The invention belongs to the technical field of vibration control, and in particular relates to a structural vibration control method, medium and equipment based on reinforcement learning. Background technique [0002] There are three main methods of vibration control: passive control, active control and semi-active control. Passive control does not require external energy, but only some passive elastic or damping elements. It has the advantages of simple structure and high reliability, but the suppression effect on low-frequency vibration is poor. With the continuous improvement of the structure's requirements on the vibration environment, coupled with the continuous development of control theory, motion sensing technology and computer science, vibration active / semi-active control technology has achieved many successes in the fields of aerospace, vehicles and civil engineering. application. Compared with passive control, active / semi-active control h...

AI Technical Summary

Problems solved by technology

For example, fuzzy control relies heavily on the experience and knowledge of control experts or operators, but without such control experience, it is difficult to design a high-level fuzzy controller; due to the inherent paradigm of supervised learning, neural network control needs to provide a large amount of labeled data. The neural network is trained, and the generation of label data requires the support of human knowledge, so the neural network control is actually a "fitter" of human knowledge; the main idea of ​​adaptive control is to gather Lyapunov (Lyapunov) function, the process Still requires a lot of expert knowledge

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