Concerning the problem of train-ground safety communication in the Chinese Train Control System (CTCS)-3 train control system, a new model for information interaction between Radio Block Center (RBC) and train based on Petri net theory was designed by using the hierarchical modelling idea, and the simulation tool of Colored Petri Net (CPN) tools was used to dynamically simulate the whole process of generating, encrypting and transmitting the transmission information between train and RBC. The designed model was mainly divided into three parts. Firstly, the Movement Authority (MA) was requested by the train. Then, the MA under full supervision mode was generated by the RBC. Finally, the MA was received by train through wireless network and the safety control of train was performed according to the MA. The dynamic simulation and state space analysis tools were used to simulate and analyze the proposed model. The simulation results show that, the designed model can meet the design requirements of train-ground information transmission with boundedness, activity, regression and fairness. The designed model can be used for safe transmission of train-ground information, reducing software design flaws.

To reduce the negative impact of stochastic noise in iterative learning control system, an iterative learning controller design method based on the Infinite Impulse Response (ⅡR) digital filter was proposed. For the first batch, the output errors from two repeated experiments were filtered by wavelet transform. Then the input/output data during the wavelet filtering process were used to obtain an equivalent ⅡR filter, which would be used to reconstruct the error objective function and optimize the iterative learning controller. Finally, the obtained ⅡR filter was applied to filter out the stochastic noise from subsequent batches until the convergence condition was met. Through simulation, compared with wavelet filtering, it could be demonstrated that by applying the proposed method, the 2-norm of output error could be reduced by nearly 15% and the ringing caused by setting the wavelet filter threshold too small was also avoided. The cumulative noise between the batches could be reduced by about 9%. The simulation results show that the proposed algorithm not only significantly reduces the negative effect of stochastic noise, but also effectively improves the accuracy of the tracking system.

In order to solve the turning problem of PID controller parameters, an automatic tuning method based on Internal Model Control (IMC) algorithm and system identification was proposed. In this approach, an identification method based on the open-loop unit step response was employed. The input/output data during the transient process were used to obtain a First Order Plus Dead Time (FOPDT) or Second Order Plus Dead Time (SOPDT) model. Then the parameters of PID controller were determined by IMC algorithm. As to the determination of the IMC filter parameter λ, two parameters, γ and σ were introduced in this method. Then the parameter λ was determined by the relationship between the square of output error and the two parameters above. In the simulation experiments, compared with the traditional IMC based PID controller, the Integral Absolute Error (IAE) index can be improved by about 20% without the input disturbance, while the index can be improved by about 10% with disturbance. The simulation results show that in the premise of ensuring the system robustness, the proposed algorithm not only improves the speed of the transient response, but also effectively restrains the overshoot of the system output.