Collaborative adaptive sliding mode constraint control method and system based on data driving

Abstract

The invention relates to a collaborative adaptive sliding mode constraint control method based on data driving, and the method comprises the steps: building a mathematical model of a linear traction system considering an end effect under a d-q axis coordinate system, adding a correction end effect item into the mathematical model, and obtaining the corrected mathematical model of the linear traction system, converting the corrected mathematical model into a speed dynamic model of the linear traction system, converting the speed dynamic model into a generalized nonlinear model, and converting the generalized nonlinear model into a linear model; constructing a sliding mode surface by using a linear traction system error, and designing an anti-saturation compensator in the system error to eliminate actuator saturation; and designing a parameter estimation algorithm based on data driving to carry out parameter adaptive estimation on the sliding mode surface. According to the invention, the robustness of the controlled system can be greatly improved.

Description

technical field [0001] The present invention relates to the technical field of traction system drive, in particular to a data-driven collaborative adaptive sliding mode constraint control method and system. Background technique [0002] In various modes of urban rail transit, the multi-linear induction traction system composed of multiple linear induction motors is widely used because it directly generates linear motion without any converter and eliminates mechanical loss. Compared with rotary induction motors whose dynamics are similar to linear induction motors, linear induction motors have more obvious advantages, such as simple mechanical structure, low cost, low noise, low friction, good dynamic performance and high starting thrust, etc. Nevertheless, there are disadvantages like end effects, slip frequency, and air gap dynamics due to the time-varying parameters used in the operation of LIMs, such as the velocity of the moving object and the ambient temperature. In ad...

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Problems solved by technology

[0003] At present, many control strategies have been developed and applied to linear induction motor drives. For example, the projection-based adaptive command filtering backstepping control method can overcome the uncertainty of linear motor position caused by time-varying disturbances in linear motors. Discrete time neural network The inverse optimal control strategy can control the position of the linear motor by minimizing the cost function and is applied to the real-time linear motor. The loss minimization control method based on the loss model can reduce the loss of the linear motor, but due to the complexity of modeling, most Both methods require plant-specific model information, which can be complex and difficult to obtain

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