This paper proposes a constrain spatial adaptive iterative learning controller (CSAILC) for the displacement-speed trajectory tracking of automatic train control system with unknown parametric/nonparametric uncertainties and speed constraints. First, the nonlinear dynamic model of train operation is transformed from temporal domain into spatial domain utilizing a spatial state differentiator. Besides, the displacement-related parametric/nonparametric uncertainties are updated in the iteration axis. Furthermore, a barrier function is involved to satisfy the speed constraint, and the corresponding convergence analysis of the proposed CSAILC for automatic train control (ATC) is derived based on the spatial composite energy function. In addition, numerical simulations of train tracking control are carried out, and simulation results indicate that the proposed CSAILC achieves good effectiveness in a high-speed train (HST) control system.

Data-driven intelligent transportation systems (D²ITSs) have drawn significant attention lately. This work investigates a novel multi-agent-based data-driven distributed adaptive cooperative control (MA-DD-DACC) method for multi-direction queuing strength balance with changeable cycle in urban traffic signal timing. Compared with the conventional signal control strategies, the proposed MA-DD-DACC method combined with an online parameter learning law can be applied for traffic signal control in a distributed manner by merely utilizing the collected I/O traffic queueing length data and network topology of multi-direction signal controllers at a single intersection. A Lyapunov-based stability analysis shows that the proposed approach guarantees uniform ultimate boundedness of the distributed consensus coordinated errors of queuing strength. The numerical and experimental comparison simulations are performed on a VISSIM-VB-MATLAB joint simulation platform to verify the effectiveness of the proposed approach.

In this study, a data-driven adaptive iterative learning Kalman consensus filtering (DD-AILKCF) method is designed for high-speed trains to address the parameter identification and speed consistent optimal estimation problem. The nonlinear train dynamics model is transformed into a linear-like state-space model by using the Full Form Dynamic Linearization (FFDL) technique. Meanwhile, four types of sensors are used to obtain different kinds of datasets to implement the multi-sensor system. The method proposed in this paper consists of two steps. First, an adaptive iterative learning Kalman filtering (AILKF) algorithm is proposed to estimate the fast-time varying train parameter in the iteration domain. Then, based on the identified parameter, a distributed multi-source heterogeneous network consensus filtering (MHN-CF) algorithm is proposed for the speed estimation of high-speed trains. The convergence of the proposed algorithm is derived based on the Lyapunov Function. The proposed method is compared with existing methods by numerical simulations, and the results indicate that the proposed method achieves good effectiveness in improving the accuracy of high-speed train speed estimation.

This article proposes a data-driven distributed filtering method based on the consensus protocol and information-weighted strategy for discrete-time sensor networks with switching topologies. By introducing a data-driven method, a linear-like state equation is designed by utilizing only the input and output (I/O) data without a controlled object model. In the identification step, data-driven adaptive optimization recursive identification (DD-AORI) is exploited to identify the recurrence of time-varying parameters. It is proved that for discrete-time switching networks, estimation errors of all nodes are ultimately bounded when data-driven distributed information-weighted consensus filtering (DD-DICF) is executed. The algorithm combines with the received neighbors and direct or indirect observations for the target node to produce modified gains, resulting in a novel state estimator containing an information interaction mechanism. Subsequently, convergence analysis is performed on the basis of the Lyapunov equation to guarantee the boundedness of DD-DICF estimate error. Simulations verify the performance of the DD-DICF against the theoretical results as well as in comparison with some existing filtering algorithms.

Fast-changing demand in real traffic systems always leads to asymmetrical traffic flow and queues, which aggravates congestion and energy waste. In this paper, the traffic signal control problem of multi-phase intersections was studied with fast-changing traffic flows. First, a novel model-free adaptive control-based symmetrical queuing balancing method was designed by using the full-format dynamic linearization (FFDL) technique. Second, in order to deal with the fast-changing traffic flow, a radial basis function neural network (RBFNN) was added to adjust parameters in a two-layer structure. Moreover, a variable cycle tuning algorithm was introduced to further reduce the time loss. Using the simulation, the proposed algorithm was compared with three other control strategies under low and high traffic demand, respectively, and the results showed the capability of the proposed algorithm.

This paper studies the containment control problem of heterogeneous multi-agent systems (MASs) with multiple leaders. The follower agent dynamics are assumed to be unknown and nonlinear. First, each follower is transformed into an incremental data description based on the dynamic linearization technique. Then, a distributed model-free adaptive containment control law is proposed such that all followers will be driven into the convex hull of the leaders. Furthermore, the algorithm is extended to the time-switching and dynamic leaders case. As a data-driven approach, the proposed controller design uses only the received input and output (I/O) data of these agents rather than agent mathematical models. Finally, to test the potential in real applications, three representative examples considering various environment factors, including external disturbances, are simulated to show the effectiveness and resilience of this method.