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This paper introduces an adaptive control scheme for quadrotor suspended load systems, to track desired trajectory with variable payload and wind disturbances. The dynamic model of the quadrotor suspended load system is developed, taking into account the impact of the wind field described by the Dryden model. To attenuate the effects of payload variation and wind disturbances on the system, an adaptive control method based on disturbance observers is devised. Additionally, the uniform boundedness of all error signals is demonstrated. The effectiveness of the designed control method is verified through simulations, which serves to strengthen its applicability in practical applications. [ABSTRACT FROM AUTHOR]

Summary The primary objective of this paper is to enhance the efficient utilization of communication resources. To achieve this, the paper delves into the disturbance observer based adaptive heading control strategy for Unmanned Marine Vehicles with event‐triggered and input quantization. Furthermore, in order to mitigate the impact of slow time‐varying external disturbances within the control system, the disturbance observer is employed for estimation. Within the context of a networked control, control input is subjected to quantization via an input quantizer, and the process of input quantization is described by using a linear analytical model. Importantly, no foreknowledge of quantization parameters is necessary for the quantized feedback controllers. Subsequently, the sliding mode control method is combined with event‐triggered mechanismss to design quantization feedback control system. The stability of the closed‐loop system is established in line with the fundamental tenets of Lyapunov stability theory, validating the bounded nature of both observation and heading tracking control errors. The effectiveness of the proposed heading control scheme is further underscored through a series of simulation experiments. [ABSTRACT FROM AUTHOR]

In this article, by creating a novel distributed full‐form dynamic linearization based model‐free adaptive containment control (FFDL‐MFACC) approach, the containment control problem of networked heterogeneous autonomous surface vehicles (ASVs), suffering from complex external disturbances and unavailable kinetic model‐information, is resolved. In light of the data‐driven strategy, a full‐form dynamic linearization‐based data model is efficiently established. Then, by further deploying the commonly used rotation matrix, a distributed FFDL‐MFACC scheme is thereafter developed such that accurate tracking of a predefined convex hull spanned for all follower vehicles can be achieved. Afterwards, a disturbance observer is further designed to accurately estimate the lumped disturbances and the estimation is served as a compensation within the distributed full‐form dynamic linearization. Rigorously theoretical analysis indicates the devised distributed FFDL‐MFACC method can ensure the asymptotic containment tracking of networked heterogeneous ASVs. Finally, simulation results are illustrated to verify the advantages of the devised approach. [ABSTRACT FROM AUTHOR]

Summary: False data injection attacks can disrupt the steering control actions and make a real threat to both the security and safety of autonomous vehicles. In this paper, a secure event‐triggered lateral control approach of autonomous vehicles subject to actuator attacks is investigated. Firstly, an arbitrary unknown actuator attacks is considered in the secure lateral steering control of autonomous vehicles. Thus, to save communication resources for the limited bandwidth CAN bus, the periodic event‐triggered transmission scheme is utilized, transforming the established lateral steering control into a time‐delay system through the consideration of periodic event‐triggered sampling. Then, an adaptive control compensation scheme is developed to mitigate the malicious effects caused by actuator attacks. The proposed secure control approach is skilled in compensating the unknown attacked steering control actions in an adaptive way. The stabilization criteria under the adaptive secure control law is well derived by Lyapunov–Krasovskii method and some linear inequality matrices operations. At last, the effectiveness of the proposed secure control scheme is verified by some numerical experiments borrowed from a practical vehicle. [ABSTRACT FROM AUTHOR]

Summary: This article studies robust adaptive control for a class of autonomous vehicle platoons. In particular, two innovative adaptive control laws are proposed to address both position and velocity tracking for a vehicle platoon. In addition, it is shown that robust asymptotic string stability can be delivered by the underlying adaptive control laws for the vehicle platoon, in the sense that these adaptive control laws are capable of mitigating parameter uncertainties involved in the nonlinear vehicular dynamics and achieving truly cooperative adaptive cruise control while ensuring the required safety spacing between each neighboring vehicle pair asymptotically. It is also shown that the control performance of the vehicle platoon can be further improved if the operating equilibrium points of all vehicles can be adaptively estimated, leading to two linear time‐invariant control laws for individual vehicles under both position and velocity controls and for the vehicle platoon. Simulation studies illustrate the effectiveness of the proposed control method, validating the results obtained for the class of vehicle platoons. [ABSTRACT FROM AUTHOR]

Adaptive cruise control (ACC) is the core building block of future full autonomous driving. Numerous recent research demonstrated that Autonomous Vehicles (AVs) adopting shorter headways generally increase road capacity and may relieve congestion at bottlenecks for moderate demand scenarios. However, with high demand scenarios, bottlenecks can still be activated causing capacity breakdown. Therefore, extra control measures as dynamic traffic control near bottlenecks is necessary. The challenge is harder on urban freeways with consecutive bottlenecks which affect each other. This paper aims to improve the performance of ACC systems in a high demand scenario. A multi‐bottleneck dynamic headway control strategy based on deep reinforcement learning (DRL) that adapts headways to optimize traffic flow and minimize delay is proposed. The controller dynamically assigns an optimal headway for each controlled section, based on state measurement representing the current traffic conditions. The case study is a freeway stretch with three consecutive bottlenecks which is then extended to include eight bottlenecks. Three different RL agent configurations are presented and compared. It is quantitatively demonstrated that the proposed control strategy improves traffic and enhances the system delay by up to 22.30%, and 18.87% compared to shortest headway setting for the three‐bottleneck and the eight‐bottleneck networks, respectively. [ABSTRACT FROM AUTHOR]

High‐level autonomous vehicles (AVs) have more possibilities for improving traffic efficiency. The improvement of traffic efficiency for mixed flow at near‐saturated short‐distance tandem signalized intersections (STSI) needs attention. Most of the existing studies design a generalized control rule for AVs, ignoring the heterogeneity among different AVs. Herein, a multivehicle trajectory planning framework based on a multiagent reinforcement learning (MRL) algorithm is designed to heuristically explore the optimal traffic efficiency of mixed flow at STSI. The core algorithm of this framework is improved from the classical MRL algorithm multi‐agent proximal policy optimization based on the idea of the virtual group instead of designing control rules. The trajectories planned by the framework show outstanding performance in improving throughputs and reducing emissions at the global system level, comparing natural driving, classic adaptive cruise control model and cooperative adaptive cruise control model. The framework can be used to explore optimal traffic efficiency for mixed flow and better heterogeneous rules for high‐level AVs. [ABSTRACT FROM AUTHOR]

Summary: In this article, the problem of fixed‐time distributed optimization is researched for third‐order fully heterogeneous nonlinear connected and autonomous vehicles. To address this problem, a fixed‐time distributed optimization algorithm is proposed via a two‐step strategy. First, an optimization algorithm is proposed to generate a virtual reference signal that can converge to the optimal solution. Then, we further construct an adaptive tracking controller to track the virtual signal based on the first step. In the design process of the controller, the virtual control variables and the actual control input are obtained by using the backstepping technique. Furthermore, a fast fixed‐time filter is introduced to avoid the issue of discontinuous gradient functions. Fuzzy logic systems are employed to address the unknown part of the nonlinear function. By using tools from the fixed‐time stability criterion, convex optimization theory, and Lyapunov stability theory, it can be demonstrated that the provided strategy drives all optimization variables to the optimal solution within a fixed time and guarantees the closed‐loop system signals are bounded. Finally, the effectiveness of control strategy is verified via a numerical simulation. [ABSTRACT FROM AUTHOR]

This paper presents a novel detection and rerouting mechanism for distributed adaptive platoon control of non‐linear autonomous connected vehicles under denial of service (DoS) attacks. DoS attacks can cause delays or losses of data packets due to blocked communication channels, leading to reducing platoon performance or even collisions among vehicles. To tackle this issue, the proposed mechanism detects and reroutes communication topology depending on the real‐time topology and the number of link failures. Real‐time detection divides the scenario of DoS attacks into three parts. According to the different scenarios, rerouting mechanisms will be utilized. A controller adapted to real‐time variable communication topology is also designed in this scheme. The adjacency matrix of the real‐time communication topology generated by the rerouting mechanism is used to update the controller so that the platoon can remain in a stable state without being affected by DoS attacks. In addition, the sliding mode controller and the observer are designed by solving linear matrix inequalities, and the platoon stability and internal stability are proven. Numerical simulation studies demonstrate that the proposed mechanism and control design can reduce the vehicle state estimate error and platoon‐tracking error to ideal states under DoS attacks. The proposed method solves the problem that the existing methods have not considered the number of link failures and the inability to restore communication when the communication topology is paralyzed. [ABSTRACT FROM AUTHOR]

This article researches the problem of trajectory tracking for unmanned marine vehicles (UMVs) under disturbances and denial‐of‐services (DoS) attacks in the wireless channel. An equivalent data‐driven model of UMVs with ocean disturbances is established by using partial form dynamic linearization algorithm. The disturbances and the input of UMVs have different pseudo partitioned Jacobean matrix, and the disturbances are estimated by using extended state observer, which improves the immunity of UMVs to disturbances in the environment. It is the first time that the DoS attacks are considered under the data‐driven model for UMVs, and a novel data‐driven adaptive trajectory tracking control framework is constructed. The article proposes an attack predictive compensation mechanism to mitigate their effects of DoS attacks, which follows the Bernoulli distribution. Based on it, the data‐driven adaptive trajectory tracking controller is designed such that the error of trajectory tracking is convergent under DoS attacks and external disturbances. Finally, the effectiveness of the proposed data‐driven control scheme and the predictive compensation mechanism is validated through the simulations. [ABSTRACT FROM AUTHOR]

To find the most‐likely‐failure scenarios given a certain operation domain, a critical‐scenario‐based test is supposed as an effective method. However, for the state of the art, critical‐scenario‐generation approaches commonly based on random‐search and take amounts of computing resource, some of them are also inapplicable in real time. Moreover, the approaches sometimes fail to obtain critical results, which are strongly relevant to the choice of initial condition. In order to address the above challenges, the authors proposed a Real‐time Critical‐scenario‐generation framework in this paper. The authors proposed an aggressive‐driving algorithm based on the model predictive control method to lead the agent vehicle. The agent vehicle will be controlled to directly create critical scenarios for a black‐box target under test, and the real‐time critical‐scenario test can be brought into reality. A specially designed cost function is presented that guides scenarios to evolve towards the interested conditions, and a self‐adaptive coefficient iteration is designed that enables the approach to be applied within a wider range of initial conditions. The authors carried out both simulation and Vehicle‐in‐the‐Loop (VIL) test; in the VIL test, the authors' approach improves 15.45% criticality of scenarios with around 9.7 times of efficiency, or improves 38.67% criticality with still around 1.7 times of efficiency with further iterations. [ABSTRACT FROM AUTHOR]

Recent research has demonstrated that cooperative and automated driving functions can improve traffic efficiency and safety. Traffic efficiency can be increased and the overall vehicle energy consumption can be reduced through cooperative driving functions not only on the highway, but also in urban areas. To this end, this paper presents a linear model predictive control (MPC) algorithm for the optimization of velocity trajectories of vehicles in a platoon in an urban traffic environment. A novel centralized approach is implemented to optimize the total energy consumption of all vehicles while improving traffic efficiency. To solve the optimal control problem, for the first time, an additional data‐driven velocity prediction model of the vehicle in front of the platoon is used to consider the influence of realistic prediction errors when evaluating the developed control strategy in a vehicle simulation environment. The simulated total energy consumption is compared with a rule‐based cooperative adaptive cruise control (CACC) algorithm. Furthermore, the traffic density and the platoon string stability, the robustness and computer time of the centralized approach are also included in the evaluation. Simulation results indicate that, considering all evaluation criteria, the centralized cooperative control strategy can improve energy efficiency by up to 15% in the investigated urban scenario. [ABSTRACT FROM AUTHOR]

The malicious physical attacks from both sensor and actuator sides make real threats to the security and safety of autonomous ground vehicles (AGVs). This paper focuses on the problem of neural‐network‐based event‐triggered adaptive security control (ET‐ASC) scheme for path following of AGVs subject to arbitrary abnormal actuator signal. First, we assume that an arbitrary abnormal signal is caused by arbitrary malicious attacks or disturbances from actuators. Then, radial basis function neural network (RBF‐NN) is used to reconstruct such abnormal actuator signal. Second, modelling issues on security path following control of AGVs with Sigmoid‐like ETC scheme are shown when the AGV is suffering from abnormal actuator signal. In what follows, an ET‐ASC scheme is developed to mitigate the adverse effects of abnormal actuator signal with the reconstructed abnormal signal based on a novel Sigmoid‐like event‐triggered communication scheme. By using the proposed RBF‐NN‐based ET‐ASC scheme, H∞$$ {H}_{\infty } $$ control performance can be guaranteed under arbitrary malicious actuator signal rather than such attacks following a specific probability distribution. Final, some simulation experiments are provided to verify the effectiveness of proposed ET‐ASC scheme. [ABSTRACT FROM AUTHOR]

Vehicles in the platoon can sufficiently incorporate the information via V2X communication to plan ecological speed trajectories and pass the intersection smoothly. Most existing eco‐driving studies mainly focus on the optimal control of a single vehicle at an individual signalized intersection, while rarely involving the cooperative optimization of a group of vehicles at successive signalized intersections. In this study, a hierarchical cooperative eco‐driving control for a connected autonomous vehicle (CAV) platoon is proposed to enhance traffic mobility and energy efficiency, wherein the velocity trajectory of the leading vehicle at each isolated signalized intersection is planned using the pseudo‐spectral method, and then the cooperative optimization of following vehicles in the platoon is conducted via rolling optimization, with the aim of improving driving comfort, safety and energy economy for the platoon. The simulation results highlight that the proposed hierarchical cooperative eco‐driving strategy can lead to preferable vehicle‐following behaviours and platoon driving performance, and the overall energy consumption and trip time of vehicle platoon are respectively reduced by 26.10% and 2.83%, compared with that under manual driving. Furthermore, the overall energy economy is promoted by 4.95% and 4.60%, compared with cooperative adaptive cruise control and intelligent driver model‐based platoon control strategies. [ABSTRACT FROM AUTHOR]

This study proposes a reconfigurable fault‐tolerant control method using an adaptive fault identification technique with application to an unmanned surface vehicle with consideration of environmental disturbances and system constraints, including both second‐order input operations (actuator magnitude and rate faults) and multi‐classification fault modeling features (lock‐in‐place or hard‐over, and loss of effectiveness). To begin with, unknown parameter compensator‐based observers with adaptive projection laws are designed for the respective double‐parameter and single‐parameter adaptive fault identification cases, and it is proved that the actual input estimation and fault parameter estimation errors are bounded. Then, double‐parameter and single‐parameter adaptive reconfigurable fault‐tolerant controllers are synthesized by combining the finite‐time baseline tracking control and terminal sliding‐mode mechanism to guarantee the second‐order dynamical tracking errors converge to the origin for the proper unmanned surface vehicle operation regardless of actuator magnitude and rate faults. Finally, simulation results and comparisons demonstrate the effectiveness and superiority of the proposed reconfigurable fault‐tolerant control algorithm. [ABSTRACT FROM AUTHOR]

This work investigates the robust lateral tracking control problem of autonomous vehicles subject to unmodeled system uncertainties, external disturbances as well as input constraints that commonly exist in the vehicle dynamics. The tracking controller design under such uncertain environments is challenging due to the under‐actuated characteristics of the vehicle dynamics. Targeting at this issue, a nonlinear vehicle model is firstly established and transformed into a novel parametric model to facilitate the controller design. A robust adaptive learning control (RALC) approach is then proposed based on the parametric vehicle model, where an input‐dependent auxiliary system is employed to compensate the influence of the input constraints. The convergence of the tracking errors is rigorously analyzed based on the framework of composite energy function. The proposed RALC scheme is proven to be able to achieve an impressive path tracking performance under perturbed and constrained scenarios. Moreover, the proposed technique in tackling the under‐actuated dynamics is novel that can be applied to deal with other generic non‐square systems. The proposed controller is validated with case studies under various conditions. [ABSTRACT FROM AUTHOR]

The recovery of unmanned underwater vehicle (UUV) by unmanned surface vehicle (USV) has the characteristics of autonomy, safety, and efficiency. Taking the recovery of UUV by USV as the engineering background, this paper studies the guidance and anti‐interference motion control of USV in the recovery process. Aiming at the problem of dynamic guidance when recovering UUV, the USV guidance strategy for UUV recovery is studied. Fuzzy guidance is introduced as the dynamic terminal guidance method, and a layered guidance strategy combining classical guidance and fuzzy guidance is proposed. On the basis of the theory of compact form dynamic linearization‐based model‐free adaptive control (CFDL‐MFAC), the motion control of USV in the process of recovering UUV under the influence of model perturbation, external interference, and other uncertainties is studied. Theoretical analysis and experimental results show that there is a contradiction in the matching of dynamic change speed between the USV heading control subsystem and CFDL‐MFAC. By introducing the difference item into the standard control criterion to weaken the integral effect in the heading control subsystem of USV, a difference‐type compact format model‐free adaptive control method (DCFDL‐MFAC) is proposed, and the stability of DCFDL‐MFAC method is proved theoretically. The effectiveness and practicability of the proposed method are verified by simulation tests and field tests of "Dolphin IB" small USV. [ABSTRACT FROM AUTHOR]

To reduce the degradation of multi‐vehicle cooperative control performance for connected and automated vehicles caused by time‐varying communication delays when the edge‐cloud is used for centralized control through the vehicle to infrastructure communication, and further improve the traffic efficiency based on driving safety at unsignalized multi‐intersection. A networked predictive control method based on an improved model‐free adaptive predictive control method and multi‐intersection distributed cooperative control scheme is proposed to realize the multi‐vehicle cooperative control under the conditions of time‐varying communication delays at an unsignalized multi‐intersection system, including multi‐intersection edge‐cloud networked predictive control layer and multi‐vehicle car‐following control layer. The control objective can be calculated by the moving horizon prediction control scheme based on compact form dynamic linearization technology through the edge computing controller, and then assigns the anticipated speed to each target vehicle entering the intersection subsystem based on decoupling the unsignalized multi‐intersection system into the multiple networked control intersection subsystems. The extensive numerical simulation results confirmed the benefits of the proposed scheme compared to the benchmark methods. [ABSTRACT FROM AUTHOR]

In practical autonomous vehicle systems, model uncertainty and measurement noise are two challenging factors that deteriorate path tracking accuracy and system stability. This article proposes an adaptive robust controller to deal with these two factors and enhance path tracking accuracy. First, the path tracking model considering time‐varying uncertainty is built and represented as nominal and uncertain portions. Second, the control law is designed separately for both portions. A linear quadratic regulator controller is utilized to stabilize the nominal system. An additional robust control law is proposed to suppress the matched uncertainty and measurement noise, with an adaptive scheme aimed at estimating the bound outside uncertainty. The path tracking system with the developed control law is proved to possess uniform boundedness, uniform ultimate boundedness properties, and robustness against mismatched uncertainty. Eventually, the effectiveness of the developed controller is validated using both MATLAB/Simulink‐TruckSim co‐simulation and an autonomous vehicle platform. The results demonstrate that the designed adaptive robust control can achieve accurate path tracking in the presence of time‐varying uncertainty and measurement noise. [ABSTRACT FROM AUTHOR]

For the sake of safety, the vehicle path tracking control should not only ensure the stability of the path tracking error containing the lateral offset and the orientation error but also guarantee that both the transient and steady states of the lateral offset are within a specified safe boundary. However, the time‐varying uncertainties of a vehicle system make the control design a tough task. This paper develops an adaptive robust control (ARC) which guarantees both the tracking stability and the bounded error property for autonomous vehicles. First, to handle the bounded error requirement, a barrier function based state transformation which converts the constrained lateral offset into an unconstrained state is proposed. Then, the path tracking control task is cast into an equality constraint of the system state. On this basis, a novel adaptive robust constraint‐following controller is developed to make the transformed system follow the proposed equality constraint. Through Lyapunov minimax analysis, it is proved that the resulting control guarantees the approximate constraint‐following performance and the bounded error property despite the presence of system uncertainties. Finally, the main theoretical results are verified through CarSim‐Simulink co‐simulation results. [ABSTRACT FROM AUTHOR]

In the last few years, the importance of trucks on inland cargo transportation has not stopped increasing. Meanwhile, truck platooning is emerging, along with automated driving, to reduce costs using new technologies. In this context, this research aims to provide a first study on the effects of truck platoons on freeways' road safety, focusing on the reduction of visibility caused by truck platoons with shorter gaps on horizontal curves. This safety issue will also affect motorways and multilane roads. A geometric model has been developed and computed, which provides the available sight distances and the stopping sight distance (SSD) for a vehicle overpassing a platoon in a circular curve without transition curves. There are many variables, such as radius, lane width, vehicle and truck platoon parameters, and relative position. The overpassing vehicle has been included in the model for both human‐driven and automated, considering the adaptive cruise control radar cone of visibility. The main result of this study is the minimum curve radius in order to allow a safe SSD, considering different design criteria. Moreover, depending on the level of automation of the vehicle, this minimum radius will be different, being higher for automated vehicles. Results prove the importance of the studied phenomenon and the necessity to implement further countermeasures. Additionally, a case study where the effects of truck platooning on the visibility of a real motorway stretch are evaluated. [ABSTRACT FROM AUTHOR]

Summary: Steer‐by‐wire (SBW) systems are considered as one of the most significant innovations among the technologies developed for advanced driver‐assistance systems and autonomous vehicles. The main control challenge in a SBW system is to follow the steering commands in the face of parametric uncertainties and external disturbances; crucially, perturbations in inertial parameters and damping forces give rise to state‐dependent uncertainties, which cannot be bounded a priori by a constant. However, the state‐of‐the‐art control methods of SBW system rely on a priori bounded uncertainties, and thus, become inapplicable when state‐dependent dynamics become unknown. This work, to the best of the authors' knowledge for the first time, proposes an adaptive control framework that can tackle the state‐dependent uncertainties and external disturbances in a typical SBW system without any a priori knowledge of their structures and of their bounds. The stability of the closed‐loop system is studied analytically via uniformly ultimately bounded notion and the effectiveness of the proposed solution is verified via simulations against the state‐of‐the‐art solution. [ABSTRACT FROM AUTHOR]

In this paper, we design a coordinated steering and braking control scheme to ensure the tractor‐semitrailer vehicle system is practically stable. The tractor's steering input is designed to realize lane tracking and the semitrailer's differential braking torque is designed to improve the stability of the semitrailer. This control methodology is developed in two steps. Firstly, the expected steering input and the expected differential braking force are derived by introducing a set of given tracking constraints and considering the possible initial condition derivation from the constraints based on the Udwadia‐Kalaba approach. Secondly, we develop an adaptive robust control law to tackle the parameter uncertainty, which may be (possibly) time‐varying, and design the required braking torques, which are the actual inputs, to generate the desired braking forces. Furthermore, this control methodology can deal with nonlinear mechanical systems with both holonomic constraints and nonholonomic constraints. Numerical simulations demonstrate that the control algorithm could guarantee the vehicle dynamics are practically stable and achieve lane following maneuvering. [ABSTRACT FROM AUTHOR]