Bearing-free asynchronous motor RBF neural network self-adaptive inverse decoupling control and parameter identification method

Abstract

The invention discloses a bearing-free asynchronous motor RBF neural network self-adaptive inverse decoupling control and parameter identification method. An SVPWM module, a voltage inverter, a bearing-free asynchronous motor and a load of the bearing-free asynchronous motor form a whole serving as a composite controlled object. Two radial basis function neural networks are adopted to achieve inverse control and parameter identification conducted on the composite controlled object. A self-adaptive inverse controller is formed by using an RBF neural network through learning, and is serially connected in front of the composite controlled object, errors of a feedback signal and a given signal are input into an inverse controller, and accordingly closed-loop control is formed, then a self-adaptive parameter identifier is formed by using one RBF neural network through learning and identifies output quantity speed and displacement of the composite controlled object, speed-less and displacement-free sensor control is achieved, online learning of an estimation signal is aided by means of a learning algorithm, and non-linear dynamic decoupling control of the bearing-free asynchronous motor is achieved. The bearing-free asynchronous motor RBF neural network self-adaptive inverse decoupling control and parameter identification method is high in control speed and higher in identification accuracy, and a control system is excellent.

Description

technical field [0001] The invention is a multi-variable nonlinear bearingless asynchronous motor RBF neural network adaptive inverse control and parameter identification method, which is suitable for high-performance control of the bearingless asynchronous motor. Bearingless asynchronous motors inherit the advantages of magnetic bearing motors, and have the characteristics of no friction, no wear, no lubrication and sealing, sterility, no pollution, and long life. They are very suitable for injection into high-speed precision CNC machine tools, high-pressure sealed pumps, and special robots. , High-speed gyroscope, satellite flywheel, high-speed aircraft and control device, high-speed centrifuge, high-speed flywheel energy storage and other high-speed drive high-tech fields have broad application prospects and belong to the technical field of electric drive control equipment. Background technique [0002] The bearingless asynchronous motor has complex electromagnetic relati...

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Problems solved by technology

[0005] Related domestic patent applications: 1) Patent application number CN20061038711.3, titled: Control method of bearingless AC asynchronous motor neural network inverse decoupling controller, this invention patent is designed for bearingless AC asynchronous motor neural network inverse solution Coupling controller; 2) Patent application number CN200510038099.5, titled: Bearingless Switched Reluctance Motor Radial Neural Network Inverse Decoupling Controller and Construction Method, this invention designs radial neural network inverse solution for magnetic levitation switched reluctance motor Coupling controller; 3) Patent application number CN200510040065.X, based on neural network inverse five-degree-of-freedom bearingless permanent magnet synchronous motor control system and control method, this invention is a control method designed for five-degree-of-freedom bearingless permanent magnet synchronous motor; 4 ) Article number 0258-8013 (2004) 07-0117-05 RBF neural network-based ultrasonic motor parameter identification is a method for ultrasonic motor parameter identification; the idea of ​​neural network inverse controller used in the above three inventions to control the motor is somewhat related to this patent However, what the neural network used in this paper is the RBF neural network, which is different from the BP network they adopt; compared with the article 4, the present invention has essential differences in the structure, mathematical model, control method, control difficulty and requirements of the motor. Design of RBF Neural Network Adaptive Inverse Control System for Bearing Asynchronous Motors Currently, there are no related patents and literature

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