Multi-train Adaptive Cooperation Control Algorithm Based On Sampling Feedback

Urban rail transit plays a significant role in mitigating traffic congestion because of its characteristics of large capacity,high efficiency,punctuality and energy saving.Advanced control algorithms are important techniques to ensure that trains operate safely and efficiently.Due to the particularity and complexity of the urban rail transit operating environment,the performance of trains is affected by many factors such as line condition,air resistance and unknown disturbance.The nonlinearity of train dynamic model and the uncertainty of external environment can directly influence the tracking performance of the control algorithm.In addition,in order to improve the operation efficiency and capacity of rail transit system,information exchange and cooperative control among multiple trains have become hot research areas,and put forward more functional requirements for the core control method of train operation control system.Based on the sampling feedback control,the automatic tracking control algorithm and multi-train cooperative control algorithm are studied as follows:Firstly,an adaptive fuzzy backstepping control algorithm based on sampling feedback is investigated.As the fact that the parameters of Davis’s equation are dependent on empirical values and cannot be adjusted in real time,a nonlinear dynamic model of train based on fuzzy logic system is constructed.The sampling feedback characteristics of the speed and position data are analyzed,and the sampled-data observer is designed to estimate unknown states.Based on the design idea of backstepping,an adaptive fuzzy tracking control algorithm is proposed to realize accurate tracking of train target curves.The effectiveness of sampled-data observer and control algorithm is verified by simulation results.Secondly,an adaptive dynamic surface control algorithm based on nonlinear gain feedback is presented under uncertain resistances.To overcome the problem that the basic resistance is uncertain and the additional resistance cannot be accurately modeled,the adaptive law with parameter self-learning ability of basic resistance is designed.The RBF neural network is used to approximate the additional resistance and compensate the resistance deviation.In order to achieve accurate tracking of the target position curve and target speed curve,the adaptive dynamic surface control algorithm based on nonlinear gain feedback is proposed.The nonlinear gain feedback is used to improve the robustness and dynamic performance of the controller.The dynamic surface control technology is introduced to eliminate the differential iteration of virtual control laws,which simplifies the controller structure and reduces the computational complexity.Simulation results show that the presented algorithm has good tracking characteristics,and tracking errors are limited to allowable range.Finally,an adaptive tracking control algorithm based on multi-train cooperation is proposed.Benefit from train to train communication technology,the speed and position information can be directly exchanged among trains.The nonlinear coupling relationship between multiple trains is analyzed.The multi-train adaptive cooperation control algorithm based on sampling feedback and nonlinear gain feedback is proposed.The cooperative distribution control laws of multiple trains are designed,and all signals in closed-loop system are semi-globally uniformly ultimately bounded.Simulation results show that the algorithm realizes the dynamic tracking control for multiple trains,and achieves the control target of safety,stability and high efficiency.