Maglev Train Hybrid Braking Control With Time-Lag Compensation
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Solution Overview
Problem
Current control methods for middle-low speed maglev trains lack effective handling of time lag in electro-hydraulic hybrid braking, leading to inefficiencies in operation control, particularly due to the unique traction mode and time lag inconsistencies in these systems.
Innovation Solution
A cooperative control method utilizing a controlled autoregressive integrated moving average model with time lag compensation, combined with wavelet denoising and a generalized predictive control model, to accurately process operation data and improve braking control by compensating for time lag in the electro-hydraulic hybrid braking process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional control methods are used for electro-hydraulic hybrid braking, then the control system is simple, but time lag cannot be compensated leading to reduced control accuracy
Solution Approach 1:
The Smith predictor performs preliminary prediction of the system's future state based on the current state and model parameters, compensating for time lag before the actual control action is applied. This allows the control system to anticipate and counteract delays in the electro-hydraulic braking response.
Solution Approach 2:
The controlled autoregressive integrated moving average model continuously processes feedback information from the system, using past outputs and inputs to predict future behavior and adjust control actions, thereby compensating for time lag through iterative refinement of predictions.
2Stability of the object's composition
If operation data with noise is used directly, then the processing is simple, but control stability deteriorates
Solution Approach 1:
Wavelet denoising extracts the useful signal from noisy operation data by separating it into different frequency components, removing noise while preserving the essential braking characteristics. This extraction process isolates the meaningful information needed for stable control.
Solution Approach 2:
The controlled autoregressive integrated moving average model transforms the noisy operation data into smoothed predicted values by changing the temporal parameters and using statistical processing, thereby converting unstable raw data into stable control inputs.
Data Source
AI summary
A cooperative control method and system for electro-hydraulic hybrid braking of a middle-low speed maglev train is provided, which relates to the field of vehicle braking control. The method includes: denoising operation data of a middle-low speed maglev train; using a controlled autoregressive integrated moving average model as an electro-hydraulic hybrid braking process model for the middle-low speed maglev train, and processing denoised operation data by using a least square method to determine parameters in the controlled autoregressive integrated moving average model; establishing a generalized predictive control model with time lag compensation according to the controlled autoregressive integrated moving average model and a Smith predictor; and performing cooperative control on an electro-hydraulic hybrid braking process of the middle-low speed maglev train by using the generalized predictive control model with time lag compensation. A time lag in the electro-hydraulic hybrid braking process of the middle-low speed maglev train is reduced; control accuracy of the electro-hydraulic hybrid braking process of the middle-low speed maglev train is improved to a certain extent; and a speed tracking effect is improved.