A robust subspace predictive control method on noise of fully autonomous operation system for urban rail train
Based on the historical operation data of urban rail train and fully considering the impact of noise interference during the operation process,a control model containing noise is constructed by subspace identification method to ensure that it can be more consistent with the actual operating state,and the prediction accuracy is higher.Real-time data is continuously added during the control process to iterate the historical data identified by the model,and the model parameters are updated online to obtain a robust subspace predictive controller with strong anti-interference ability.In MATLAB simulation experiments,noise amplitudes of 0,2,5,10 km/h are set and compared with traditional subspace predictive control methods.The results showed that under the interference of random noise,the robust subspace predictive controller for fully autonomous operation(FAO)system of urban rail trains has a high prediction accuracy.When the noise amplitude is 10 km/h,the prediction accuracy improves by 14.21%compared with the traditional subspace prediction controller.The robust subspace predictive control method on noise of fully autonomous operation system for urban rail trains can achieve high-precision tracking of the expected curve of urban rail trains under strong interference operating conditions.
urban rail trainFAO systemnoise subspacerobustpredictive control
万方数据
维普
