Safety and comfort are critical in the formation control of high-speed trains (HSTs). In this paper, a novel distributed composite adaptive control strategy with safety constraints is proposed to enhance the robustness and tracking precision of HSTs formation systems under unknown external disturbances. First, a universal nonlinear transformation technique is employed to handle position and velocity constraints, thereby removing the feasibility condition requirement in virtual controllers. Then, distributed control laws are designed using the command filter backstepping method, a disturbance observer, and an adaptive estimation technique. Furthermore, a rigorous analysis framework based on Lyapunov’s theorem is established, addressing state stability, prescribed tracking error performance, and Zeno behavior. Finally, simulation experiments are conducted to validate the effectiveness and superiority of the proposed control scheme.

In this article, the levitation control problem of electromagnetic levitation systems subject to track irregularities, air gap and vertical velocity and voltage fluctuation constraints are taken into account. An equivalent tracking model is established, which incorporates the flux density as a state variable to simplified the derivation of the robust constraints. With these robust constraints in place, a novel robust hierarchical control strategy is developed, which combines feedback linearization techniques with event-triggered robust model predictive control (ERMPC). Moreover, the theoretical properties are investigated, including recursive feasibility and closed-loop stability. Finally, to verify the superiority of the proposed control, simulations are conducted and compared with the conventional ERMPC, RMPC algorithms, and the proposed strategy under different parameter environments. The results demonstrate that the proposed algorithm can effectively reduce the frequency of controller updates and improve levitation performance while addressing various constraints, thereby enhancing system stability and safety.

This paper proposes an innovative composite anti-disturbance asymptotic tracking control strategy for a class of nonlinear non-strict feedback systems facing unknown nonlinearities and external disturbances. Based on the separation principle, a new adaptive learning disturbance observer (ALDO) is first introduced, designed to accurately estimate the unknown nonlinear dynamics and time-varying disturbances within the system. This ALDO possesses online learning capabilities, enabling it to estimate the unknown upper bound of the disturbance derivative in real time, thus significantly reducing reliance on prior knowledge of the disturbance derivative and ensuring finite-time stability of the disturbance estimation error system. Furthermore, utilizing adaptive backstepping technology, a virtual controller is designed to compensate for mismatching disturbances within the closed-loop system. Additionally, a composite controller is developed to effectively compensate for disturbances and guarantee asymptotic convergence of the tracking control error to zero while maintaining signal boundedness within the system. Finally, the effectiveness and practicality of this control strategy are verified through a practical application case involving an electromechanical system. Note to Practitioners—This article investigates a class of nonlinear non-strict feedback systems affected by multi-disturbances, where the unknown nonlinear dynamics are functions of the full-state variable, rendering traditional adaptive methods inapplicable due to the potential for the algebraic loop problem. In complex engineering environments, where various external disturbances adversely impact system stability and performance, and prior knowledge of such disturbances is elusive, the disturbance observer-based composite control strategy explored in this paper stands out as crucial. The proposed strategy adeptly mitigates or eliminates disturbances via feedforward and feedback channels, enabling asymptotic tracking of the system while safeguarding its stable performance and reliability, holding promising applications in engineering domains like power systems, robot control, and traffic signal systems.

Recent works have combined monocular event camera and inertial measurement unit to estimate the SE(3) trajectory. However, the asynchronicity of event cameras brings a great challenge to conventional fusion algorithms. In this paper, we present an asynchronous event-inertial odometry under a unified Gaussian Process (GP) regression framework to naturally fuse asynchronous data associations and inertial measurements. A GP latent variable model is leveraged to build data-driven motion prior and acquire the analytical integration capacity. Then, asynchronous event-based feature associations and integral pseudo measurements are tightly coupled using the same GP framework. Subsequently, this fusion estimation problem is solved by underlying factor graph in a sliding-window manner. With consideration of sparsity, those historical states are marginalized orderly. A twin system is also designed for comparison, where the traditional inertial preintegration scheme is embedded in the GP-based framework to replace the GP latent variable model. Evaluations on public event-inertial datasets demonstrate the validity of both systems. Comparison experiments show competitive precision compared to the state-of-the-art synchronous scheme.

As the anti-skid control and speed tracking control of high-speed trains (HSTs) are interrelated and mutually constrained, it is very challenging to achieve simultaneous tracking of speed and slip ratio. In this paper, it is proposed for the first time that when the traction braking force has been limited to a reasonable range by the adhesion anti-skid condition, the high-precision tracking of the position and velocity curves of HSTs can be achieved, and the tracking error of the slip ratio is acceptable. Firstly, a novel modelling of the adhesion dynamics of HSTs under realistic gusts of wind disturbance is developed, and a model based on strict feedback is obtained by linearising the feedback for high-order non-linear systems. Then, this paper first presents the design scheme of fast terminal sliding mode controller based on disturbance observer for HSTs, which can ensure the anti-disturbance performance and accurate tracking of HSTs. Finally, an example is used to verify the validity of the results. Note to Practitioners—The actual traction/braking force of HSTs is provided by adhesion, and the existing literature on slip ratio tracking essentially sacrifices the tracking performance of the traction/braking system to meet the anti-skid requirements, while considering velocity and position tracking alone may leave the train in an undesired condition such as “idling” or “skidding”. This paper seeks to achieve high accuracy tracking of velocity and position profiles of HSTs under restricting the slip ratio to a reasonable range, which is expected to be an effective method for train tracking controller design. To avoid discomfort to passengers, nonlinearities dynamics are considered and an anti-disturbance controller is designed to compensate the impact of natural wind disturbances. To enhance the practicality, feedback linearization is introduced to reduce the complexity of the controller implementation. The designed fast terminal sliding mode controller also avoids jitter and singularity problems and obtains finite time stability. The stability analysis and numerical simulation have confirmed the effectiveness of the proposed scheme, which still needs to be verified by experiments in the future.

In this paper, a novel fixed-time composite anti-disturbance control framework is proposed for$n$-degrees of freedom ($n$-DOF) flexible-link manipulator systems with modelling uncertainties and external disturbances. The aim is to ensure that the considered system achieves suppression of elastic vibrations while tracking time-varying trajectories. First, based on the singular perturbation theory, the nonlinear coupled system is decomposed into a slow subsystem and a fast subsystem. Second, a disturbance observer based on the super-twisting algorithm is designed to estimate multiple disturbances within a fixed time, and then the fixed-time tracking control scheme for the slow subsystem is developed by means of the designed observer. For fast dynamics, a barrier Lyapunov function is introduced to implement the fixed-time vibration suppression control scheme. The fixed-time stability of the tracking error system is demonstrated via the Lyapunov function method. Finally, simulation results of two-link flexible manipulator systems verify that the proposed control algorithm can improve the tracking speed and precision.

A novel hierarchical control framework combining computed-torque-like control (CTLC) with disturbance-observer-based event-triggered robust model predictive control (DO-ET-RMPC) is proposed for the trajectory tracking control of robotic manipulators with bounded disturbances and state and control input constraints. The CTLC approach is first used to cancel the exact nonlinear dynamics of the original tracking error system to obtain a set of decoupling linear tracking error subsystems, thus reducing the optimization complexity of model predictive control (MPC). The composite DO-ET-RMPC scheme is then developed based on the so-called dual-mode MPC approach to robustly stabilize the tracking error subsystems, which could improve the robustness of MPC and save its computational resources simultaneously. The continuous-time theoretical properties of the DO-ET-RMPC scheme, considering disturbances and state and control input constraints simultaneously, are provided for the first time, including the avoidance of Zeno behavior, robust constraint satisfaction, recursive feasibility, and stability. In the end, the superiorities of the proposed control scheme are verified by the comparative simulations.

This article addresses the composite hierarchical antidisturbance control issue for the front steering vehicle system with uncertain parameters. First, the front steering vehicle system is modeled as an interval type-2 Takagi–Sugeno (T–S) system, which contains the unknown disturbance matching the input channel and the norm bounded disturbance. Considering that the embedded membership functions of the interval type-2 fuzzy system are unknown, the definite part and the uncertain part of the interval type-2 T–S fuzzy system are separated to facilitate the subsequent analysis. Next, a novel disturbance observer design structure under the fuzzy framework is introduced. Through the novel design method, the expression of the unknown disturbance can be transformed into a new form containing only the uncertain parameter, and the estimation is realized by estimating the uncertain parameter. Then, a fuzzy integral sliding mode controller is adopted in combination with the disturbance estimation to ensure the stability of the composite system and the reachability of the specified fuzzy integral switching surface. Finally, simulation results of the front steering vehicle model that demonstrate the effectiveness of the proposed control scheme are provided.

The safety and high-precision attitude control of launch vehicles are threatened by degradation fault and multiple disturbances (such as model uncertainty, uncertain inertia, and external disturbance) during the reentry stage. To address these challenges, an adaptive sliding mode observer (ASMO)-based composite control scheme is proposed in this article for launch vehicles to achieve simultaneous compensation and suppression of the degradation fault and multiple disturbances. Since the mismatched model uncertainty that coupled with the system state exhibits strong uncertainty, an ASMO is designed to estimate it by adaptively learning the upper bound of the derivative of the mismatched model uncertainty. In order to attenuate the effect of the degradation fault, uncertain inertia, and external disturbance, three adaptive laws are accordingly designed to identify them online. By combining the ASMO and the designed adaptive laws, a composite controller is constructed, and the degradation fault and multiple disturbances are simultaneously compensated and suppressed. The coordinated optimization performance and refinement of antidisturbance control and fault-tolerant control are effectively enhanced. Moreover, by introducing a prescribed performance function, the attitude tracking error response is constrained within a predefined range. Simulation and experiments validate the effectiveness of the proposed scheme.

This paper investigates the position and velocity tracking control of the high-speed train (HST) formation with communication delay and proposes a distributed anti-disturbance control criterion based on the graph theory and the Lyapunov-Razumikhin theory. Firstly, the tracking error systems of the$n$-HSTs convoy is established with the consideration of the non-linearity of aerodynamic drag, the gust force disturbance and the time-varying heterogeneous delay caused by the transport process. Secondly, a distributed control strategy for the HST formation under communication delays based on disturbance observers is proposed, which can ensure the asymptotic stability of the tracking error system for the HST formation under disturbances and effectively improves the system robustness. Finally, a simulation example is given to verify the validity of the results.

This article proposes a disturbance-observer-based self-triggered distributed model predictive control (DSDMPC) algorithm with an adaptive prediction horizon mechanism for discrete-time nonlinear multiagent systems (MASs) with disturbances and system constraints. First, decentralized discrete-time nonlinear disturbance observers are designed. They are combined with a space decomposition technique to concurrently estimate and eliminate the matched disturbances of MASs. Robust tightened state and control input constraints are generated based on the disturbance estimation information, Lipschitz continuity, and discrete Gronwall–Bellman inequality. Second, an self-triggered DMPC (SDMPC) algorithm with an adaptive prediction horizon mechanism is developed to restrain residual disturbances and robustly stabilize the disturbance-compensated MASs with aperiodic scheduling, asynchronous communication, and computational reduction. The recursive feasibility of the optimal control problem and closed-loop stability are discussed. Simulation results confirm the effectiveness of the proposed control algorithm.

Multiple disturbances are the main factors to degrade high-accuracy velocity tracking of gimbal servo system in control moment gyro. In this article, a noncascade structured velocity-tracking control scheme in accordance with an adaptive refined disturbance observer (ARDO) is figured out and applied to suppress multiple disturbances and promote tracking accuracy. However, because disturbances affect the current directly through the same channel under the noncascade structure, there are two issues: 1) overcurrent protection and 2) mismatched disturbance repression. For this purpose, a finite-time current-constrained controller is constructed to ensure the current constraint and handle the mismatched problem simultaneously. An ARDO consists of a disturbance observer (DO) to estimate rotor imbalance disturbance that is modeled by an exogenous system, and an adaptive sliding mode DO to handle residual disturbances with unknown change rate. Ultimately, experimental verifications are conducted to confirm the validity of proposed composite control strategy.

This article investigates the fault tolerant control problem with anti-disturbance performance of high speed trains (HSTs) with actuator constraints in case of multiple random faults that may occur. By taking multiple disturbances into account, a new multiple point-mass stochastic jump system model of the HST is firstly established to describe the possible multiple faults. Based on the adaptive disturbance observer, a novel composite adaptive anti-disturbance fault tolerant control strategy is proposed to ensure that the velocity and position tracking error system of HSTs is stochastically stable. Moreover, except for the case where the transition probabilities (TPs) of the failure process are completely known, this article also discusses and analyzes the cases where the TPs are partially unknown and completely unknown, respectively. Finally, some examples are given to evaluate the effectiveness of the results.

This paper studies the composite refined anti-disturbance control problem of a 2-degree-of-freedom robot arm system modeled by a semi-Markov jump system with multiple disturbances, which includes harmonic disturbances with unknown frequency and amplitude as well as energy bounded disturbances. Firstly, the semi-Markov jump system model is proposed to construct a novel linear model of the 2-degree-of-freedom robot arm subject to two types of disturbances. Next, in order to estimate the uncertain harmonic disturbance, a novel higher order disturbance observer is introduced to convert the uncertain harmonic disturbance into some parameter uncertainty and then estimate the parameter uncertainty. In addition, a corresponding composite anti-disturbance control scheme is formulated to reject and attenuate the above two types of disturbances, respectively. Furthermore, sufficient conditions that can guarantee that the system is stochastically stable are given. Finally, a simulation study of the obtained model is carried out to illustrate the validity of the composite control design method proposed in this paper.

This paper addresses the fault tolerant control problem for high-speed trains in case of multiple possible failures. A new multiple point-mass model with system faults is built based on a stochastic jump system model approach. A novel active fault tolerant composite hierarchical anti-disturbance control strategy based on the disturbance observer is proposed such that the resulting composite system is stochastically stable with position and velocity tracking performance. According to whether the transition probabilities (TPs) of the failure and fault detection and isolation process can be accessed completely, three different cases (TPs are completely known, partially known, and completely unknown) are analyzed. For each case, based on the Lyapunov functional approach, a composite hierarchical controller is synthesized via a convex optimization problem. Finally, the simulations are given to illustrate the performance of the proposed methodologies.

In this paper, we develop robust adaptive nonsingular terminal sliding mode (NTSM) control methodologies to solve the position and the velocity tracking control problem of the automatic train operation (ATO) system subject to unknown parameters, model uncertainty, and external disturbances. A novel nonlinear nonsingular terminal sliding manifold is proposed by considering that its parameter is unknown, which need to be estimated via a proposed non-negative adaptive law. And a corresponding novel robust adaptive NTSM control strategy, which enables the position tracking error and the velocity tracking error of the ATO system to converge to zero, and eliminates the singularity caused by terminal sliding mode controller, is proposed. Furthermore, unknown parameters of the sliding manifold and the ATO system can be estimated online by the proposed methodology. Simulation results show the effectiveness of the proposed methodologies in this paper.

Modeling and analyzing the behaviors and characteristics of crowds in emergency is a challenging task with significant practical meanings. In this paper, a fuzzy logic approach is proposed to describe crowd evacuation behaviors, taking into account the effect of assailants. First, the microscopic pedestrian model and the assailant model are developed according to their different intentions in evacuation scenarios. Pedestrians are further divided into three categories depending upon whether they are affected by assailants. The individual's behaviors are determined by the integration of recommendations of local obstacle-avoiding behavior, regional path-searching behavior, and global goal-seeking behavior with adjustable weighting factors, which are automatically adjusted based on the perceptual information obtained from the complex interaction with surrounding environments. Then, the proposed pedestrian model is validated by comparing the simulated fundamental diagram with a large variety of empirical and experimental data. Finally, simulations in a hall with a single exit are implemented. It is shown that the model can truly reappear typical collective phenomena such as “arching and clogging” and “faster-is-slower effect.” The variations of the model and scenario parameters, such as pedestrian's desired speed, exit width, assailant's desired speed, and duration of attack, greatly influence the evacuation efficiency. In addition, a novel “circuity phenomenon,” i.e., pedestrians will give up the direction of goal when they encounter assailants or they see assailants and, at the same time, perceive a very crowded exit, is observed in crowd evacuation simulations.

The problem of designing robust ℋ∞filters for uncertain Markovian stochastic systems with time-varying delays is addressed in this paper. It is assumed that the Markovian systems are perturbed by Itô-type stochastic disturbances and subjected to parametric uncertainties and mode transition rate uncertainties. The main specification of robust ℋ∞filters under design is to ensure that the filtering error system is robustly stochastically stable and a prescribed ℋ∞disturbance attenuation level is met in the face of all admissible parameter uncertainties and time-delays. It is shown that the desired robust ℋ∞filters can be readily designed by solving some linear matrix inequalities which are derived by using a stochastic Lyapunov-Krasovskii functional and the free-weighting matrix technique.