Intelligent control method of steering engine electro-hydraulic loading system

Abstract

The invention relates to an intelligent control method of a steering engine electro-hydraulic loading system. The method comprises: a controller uses an ant colony clustering algorithm to obtain clustering information containing the clustering number and a cluster center; an RBF neural network uses the clustering number and the clustering center as the number of nerve cells of a hidden layer and a nerve cell center of the hidden layer, uses error information e and a system output signal y that are outputted by a force sensor and a displacement sensor as a pair input, and carries out on-line identification on a steering engine electro-hydraulic loading system by using a nearest neighbor clustering algorithm, thereby obtaining a neural network output; and a single nerve cell PID uses Jacobian information provided by an RBF neural network to carry out on-line self tuning on control parameters kp, ki, and kd by using a gradient descent method and then a loading force instruction signal is outputted to control movement of a steering machine. According to the invention, an on-line real-time control problem is solved; and thus the loading precision and response precision of the system are improved.

Description

technical field [0001] The invention belongs to the technical field of control system simulation, and in particular relates to an intelligent control method for an electro-hydraulic loading system of a steering gear. Background technique [0002] As a scientific experimental method, the ground simulation test of the flight control system has been widely used in the research and design, improvement and modification of flight devices. Under laboratory conditions, the electro-hydraulic loading system is usually used to simulate the changes of various force loads on the steering gear during flight, mainly used to study the influence of force load on the performance of the control system, and to complete the static and dynamic technical indicators The inspection and testing, thus transforming the classic self-destructive full object test into predictive research under laboratory conditions, has the advantages of good controllability, non-destructive, simple and convenient operati...

AI Technical Summary

Problems solved by technology

The existence of redundant force will not only seriously affect the control performance and loading accuracy of the system, but also have adverse effects on many important technical indicators such as anti-interference and response speed of the system

[0003] Considering that when the single neuron PID is used to adjust the parameters, the required identification information is not accurate enough, so the control accuracy cannot meet the system requirements

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