Inverted pendulum control method based on neural network and reinforced learning

Abstract

The invention, which belongs to the technical field of artificial intelligence and control, relates to a neural network and enhanced learning algorithm, particularly to an inverted pendulum control method based on a neural network and reinforced learning, thereby carrying out self studying to complete control on an inverted pendulum. The method is characterized in that: step one, obtaining inverted pendulum system model information; step two, obtaining state information of an inverted pendulum and initializing a neural network; step three, carrying out and completing ELM training by using a straining sample SAM; step four, controlling the inverted pendulum by using an enhanced learning controller; step five, updating the training sample and a BP neural network; and step six, checking whether a control result meets a learning termination condition; if not, returning to the step two to carry out circulation continuously; and if so, finishing the algorithm. According to the invention, a problem of easy occurrence of a curse of dimensionality in continuous state space as well as a control problem of a non-linear system having a continuous state can be solved effectively; and the updating speed becomes fast.

Description

technical field [0001] The invention relates to an inverted pendulum control method based on a neural network and reinforcement learning, relates to a neural network and reinforcement learning algorithm, can perform self-learning, and completes a control device for an inverted pendulum, and belongs to the field of artificial intelligence and control technology. In particular, it involves combining the reinforcement learning algorithm with ELM-BP, utilizing the generalization performance of the neural network, and adopting the actor-critic architecture to design a new method that can effectively control the inverted pendulum system with a continuous state space. Background technique [0002] The inverted pendulum control system is an unstable, complex and nonlinear system, and it is an ideal model for testing control theories and methods, and an ideal experimental platform for teaching control theory and carrying out various control experiments. The research on the inverted p...

AI Technical Summary

Problems solved by technology

[0005] In order to solve the above problems, the present invention provides an inverted pendulum control method based on neural network and reinforcement learning, which can not only realize fast stability control of the inverted pendulum system, but also use the reinforcement learning algorithm in the field of artificial intelligence, which can be used without marking, without Building and updating a neural network to maintain the balance of an inverted pendulum in the case of a mentor

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