Abstract
This paper presents several control methods and realizes the stable tracking for the inverted pendulum system. Based on the advantages of RBF and traditional PID, a novel PID controller based on the RBF neural network supervision control method (PID-RBF) is proposed. This method realizes the adaptive adjustment of the stable tracking signal of the system. Furthermore, an improved PID controller based on RBF neural network supervision control strategy (IPID-RBF) is presented. This control strategy adopts the supervision control method of feed-forward and feedback. The response speed of the system is further improved, and the overshoot of the tracking signal is further reduced. The tracking control simulation of the inverted pendulum system under three different signals is given to illustrate the effectiveness of the proposed method.
Highlights
Inverted pendulum system has been widely investigated in the past few decades based on two important characteristics of high order and strong coupling, which are important problems in control field
Inverted pendulum control methods have a wide range of applications in military, aerospace, robotics, and general industrial processes, such as balancing problems during robot walking, verticality issues during rocket launch, and attitude control issues during satellite flight. e RBF neural network learning control algorithm has been a hot topic in current academic research
In [1], it is presented that RBF network to estimate complex and precise dynamics mainly solves the problem of uncertainty and external interference in the context of complex space. is method is used to solve the problem of model uncertainty and input error
Summary
Inverted pendulum system has been widely investigated in the past few decades based on two important characteristics of high order and strong coupling, which are important problems in control field. In [25], the RBF neural network implements self-feedback control, accurate prediction, and real-time control of reasonable data It has improved tracking accuracy and estimated unmodeled dynamics and external interference issues in [26]. As more and more academic researchers understand the approximation characteristics of RBF, they add RBF neural networks to various fields to study the dynamic characteristics of different systems It mainly solves the problems of nonlinearity, uncertainty, and external interference and uses the Lyapunov function to ensure the effectiveness of the algorithm, so that it reduces system errors and reaches a stable state [27,28,29,30,31,32,33,34]. In order to study the signal tracking problem, we consider the inverted pendulum model based on PID and RBF neural network control. Where q (M + m)(I + ml2) − (ml2) is a constant
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