This study investigates the high-order internal model (HOIM) based data-driven iterative learning control of HSRs subject to faded channels. Firstly, the nonlinear train dynamics are converted into an input/output data-based model by using a linearization approach. Then, the HOIM of the desired speed trajectories is introduced and the fading channel is used to model the unreliable transmission network. Next, the model free adaptive iterative learning control (MFAILC) strategy is implemented based on the train input and faded output information, and the theoretical convergence analysis of the speed error is carried out. Eventually, the validity of the MFAILC scheme is checked in simulation by applying the CRH-380 HSRs on a StarSim hardware-in-loop semi-physical platform.

The coordination of multiple high-speed trains (MHSTs) is considered an effective means to enhance train tracking accuracy and operational efficiency. In this study, the model free adaptive control (MFAC) of MHSTs under false data injection attacks (FDIAs) and input limits is investigated within the framework of data-driven control. The advantage of this control scheme lies in its ability to mitigate performance degradation caused by model inaccuracies. Firstly, a dynamic model for MHSTs is established and then transformed into an equivalent linearized form that solely relies on input and output (I/O) data. Secondly, considering power limitations in the traction network and external FDIAs during direct train-to-train (T2T) communication, a data-driven coordinated MFAC scheme for MHSTs is developed. Finally, the effectiveness of the controller, coordination performance among MHSTs, as well as the impact of external constraints on MHSTs are verified through numerical simulations.

Cooperative control of multiple high-speed trains (MHSTs) is considered as a pivotal technology for enhancing the safety and efficiency of train groups. This study investigates the problem of distributed iterative learning control for MHSTs under the input saturation, employing a data-driven control strategy. Moreover, a novel independent weights-based communication protocol is developed to improve the coordination efficiency of MHSTs. Firstly, a novel linearization method is utilized to transform the dynamic model of MHSTs into an equivalent linearized model associated with saturated inputs. Secondly, based on this model, we develop both a train-to-train (T2T) communication protocol and a distributed model free adaptive iterative learning control (DMFAILC) scheme that are independent of any specific models or structural information about MHSTs. Subsequently, the overall stability is analyzed using the compression mapping method, and the complete tracking of HSTs and the coordination performance of MHSTs are confirmed through Simulink simulations and real-time StarSim hardware-in-the-loop (HIL) semi-physical platforms. Note to Practitioners—The practical challenges that drive this research on cooperative control of MHSTs are the intricate nature of train models and the inherent instability in train operating conditions. Moreover, potential applications include automatic operation of HSTs and coordination of MHSTs. Specifically, the contributions of this work include: (1) eliminating the need for precise models of HSTs; (2) establishing stability conditions for HSTs under saturation conditions; and (3) enhancing coordination efficiency among MHSTs through an independent weight protocol. However, a limitation of this study is that the theoretical findings have not been validated on real HSTs. To address the current lack of practical implementation of the train cooperative control strategy, further investigations will be conducted to incorporate more realistic constraints, such as those related to traction network limitations and optimal scheduling considerations.

A novel iterative learning control (ILC) strategy is developed for displacement and velocity tracking control of high-speed trains (HSTs) across all operational phases. In practical operations, HSTs encounter complex nonlinear uncertainties, such as variations in coupler forces and aerodynamic resistance, which are more appropriately characterized by norm-bounded models rather than traditional Lipschitz continuous disturbances. To capture these effects accurately, a multi-particle dynamic model of HSTs with norm-bounded uncertainties is formulated, considering the coupler dynamics, mechanical resistance, and aerodynamic resistance acting upon different carriages. Based on this model, a robust ILC scheme, together with an associated parameter updating law, is designed to ensure precise tracking control despite the presence of nonlinear uncertainties. Furthermore, the classical resetting condition in conventional ILC frameworks is replaced by a practical alignment condition that better reflects the continuous operation characteristics of HSTs. A composite energy function (CEF) is constructed to rigorously prove the convergence of control errors. Real-time hardware-in-the-loop (HIL) simulations are conducted to validate the effectiveness of the proposed method. The proposed strategy achieves effective tracking control and stability across traction, cruising, coasting, and full braking stages.

The paper studies the faded communication-based coordinated model-free adaptive iterative learning control (MFAILC) of multiple high-speed trains (MHSTs) against periodic denial-of-service (PDoS) attacks. First, considering the nonlinearity and uncertainty of the train operation, the dynamic model of MHSTs is constructed, and then followed by the newly established linear data-relationship model. Next, the random faded channel is expressed by Rice fading model, and the PDoS attacks are introduced with the help of the random coefficients. After giving the theoretical analysis, the compensation scheme is conducted, and the research is further extended to the switching topologies. Finally, a set of numerical tests is conducted to confirm the practicability of the MFAILC approaches.Note to Practitioners—HSTs have the characteristics of high speed, high safety, etc. The practical problems that motivate this work are the complexity of train model, the instability of the networks and the urgent requirement to further improve the operation efficiency. Meanwhile, the possible application areas include the automatic operation of HSTs and the cooperative operation of train groups. Specifically, the potential of this work includes: 1) eliminating the requirement of detailed modeling of train dynamics; 2) providing a theoretical basis for reliable train operation in an unstable network environment, and 3) improving the efficiency of train group operation through cooperation. Nevertheless, the limitation of this paper is that the results have not been verified on the actual trains and railways. To extend it to be more practical, we will further investigate more practical constraints in the train operation environment, such as the constraints of the traction network, the constraints of the track adhesion condition, and also continue to optimize the controller parameters iteratively.

The coordination of multiple high-speed trains (MHSTs) can improve the transportation efficiency and the safety performance. However, the complicated dynamic characteristics of MHSTs and the unreliable train-to-train (T2T) (wireless) communication modes are challenging the conventional control approaches. Focusing on the adverse impact from denial-of-service (DoS) attacks and faded channels caused by the T2T networks, the study designs a distributed adaptive iterative learning controller (DAILC) with recovery and compensation mechanisms, which is a model-free approach. Relying on the novel equivalent linearization strategy, a DAILC is established by using the distributed tracking errors, and the theoretical analysis has verified the complete tracking performance of MHSTs. The results in simulation test demonstrate the feasibility of the proposed DAILC and the effectiveness of the recovery and compensation mechanisms.

This paper addresses the event-triggered model free adaptive control (MFAC) problem for unknown nonlinear systems under denial-of-service (DoS) attacks, where the design and analysis are discussed under the data-driven framework. Firstly, by using the novel pseudo partial derivative, the nonlinear systems are converted into an equivalent data-relationship model. Then, the DoS attacks are described as limited by their frequency and duration, and without any more specific assumptions about the attack structure or strategy. Next, a novel event-triggered MFAC scheme is proposed. By employing the Lyapunov stability theory, the stability performance is analyzed. Furthermore, a compensation algorithm is designed to against the adverse impact brought by the DoS attacks. Finally, simulations including a numerical example and a load frequency control (LFC) example for multi-area power systems are given to demonstrate the validity and applicability of the proposed schemes.

This paper studies the distributed data-driven event-triggered model free adaptive iterative learning control (ETMFAILC) of multiple high-speed trains (MHSTs) under iteration-varying topologies, which breaks away from the dependence on the train dynamics. Firstly, the nonlinear MHSTs with unknown dynamics are converted into a linear model. Then, combining the proposed event-based triggering condition and the linear model, the ETMFAILC scheme under the fixed topology is designed. Next, theoretical analysis proves the bounded input bounded output (BIBO) stability of MHSTs. Finally, the study is extended to the switching topologies and the validity of the ETMFAILC is verified by a numerical example.

This article considers the speed tracking control problem for high-speed train systems (HSTs) under the condition of passing through neutral sections in the presence of parametric uncertainties. Noticing the prominent feature of HSTs operation, i.e., the spatial repetitiveness, a novel spatial iterative learning control (ILC) scheme is proposed. First, the motion dynamic model of HSTs is constructed with the aid of temporal-spatial conversion. Meanwhile, input saturation constraint is introduced to address the limitation of system power supply capability and the loss of traction/braking force in neutral section. Then, the ILC law and the associated parametric updating law are devised to address the system uncertainties and realize the adaptive tracking control simultaneously. The stability of the closed-loop system and the convergence of the tracking errors are confirmed based on a space-weighted Lyapunov–Krasovskii-like composite energy function (CEF). Finally, numerical simulations are performed to illustrate the effectiveness of the proposed control scheme.

This article investigates the problem of event-triggered model-free adaptive iterative learning control (MFAILC) for a class of nonlinear systems over fading channels. The fading phenomenon existing in output channels is modeled as an independent Gaussian distribution with mathematical expectation and variance. An event-triggered condition along both iteration domain and time domain is constructed in order to save the communication resources in the iteration. The considered nonlinear system is converted into an equivalent linearization model and then the event-triggered MFAILC independent of the system model is constructed with the faded outputs. Rigorous analysis and convergence proof are developed to verify the ultimately boundedness of the tracking error by using the Lyapunov function. Finally, the effectiveness of the presented algorithm is demonstrated with a numerical example and a velocity tracking control example of wheeled mobile robots (WMRs).

This article presents an event-triggered data-driven load frequency control (LFC) method for multiarea interconnected power systems via model-free adaptive control, where the dynamic model of the power system is assumed to be unknown completely. By introducing the dynamic linearization technique for the unknown power system, an equivalent data relationship model between the area-control-error (ACE) data and the input signal is established. Then, a data-driven LFC scheme is developed only relying on the input and output data of the power system. Meanwhile, an event-triggered strategy is also proposed in the design of data-driven LFC such that the communication and computation burden of the system can be reduced. Whether the current instant is the transmission instant is determined by judging the proposed triggering condition at each sampling instant. It is showed that the presented event-triggered data-driven LFC method is independent to any model information of the power system and does not need to measure any state signals. Simulation tests are carried out to verify the effectiveness of the presented control method.

This paper studies the dissipative control problem of the multibody high-speed train (HST) systems with nonuniform sampling mechanisms and logarithmic quantizers, in which the transmitted signals are subject to random fading phenomenon. The tracking error dynamic model of HST is firstly established and the logarithmic quantizers for both the input and output (I/O) signals are designed. The faded I/O signals are described by the Rice fading model, in which the mathematical expectation and variance are given in advance. Then, based on the Lyapunov-Krasovskii functional approach with the consideration of time-varying delay, sufficient conditions are derived to ensure the convergence of tracking error and that HST is strictly dissipative. Further, the design method of the gain matrix is obtained by employing the linear matrix inequalities (LMI) techniques and a compensation algorithm is designed to offset the adverse effect brought by the fading measurements. Finally, the effectiveness of the proposed controller is verified by a numerical example from Japan Shinkansen HST.

This article studies the resilient control problem for a class of unknown nonlinear systems with fading measurements under malicious denial-of-service (DoS) attacks. The system output is assumed to be transmitted through a fading channel, where the fading phenomenon is described by a Rice fading model. The strategy of the attacker is to periodically interfere with the networked channels to reduce the success rate of data transmissions. First, a dynamic linearization method along the iteration domain is introduced to convert the nonlinear system into an equivalent data-related model. Then, a model-free adaptive iterative learning control (MFAILC) scheme is presented, which is independent of model information. The convergence of the MFAILC scheme is deduced theoretically and the influence of DoS attacks and stochastic fading phenomenon on system stability are also analyzed. Finally, the effectiveness of the design is verified by a numerical simulation and a trajectory tracking example of wheeled mobile robots (WMRs).