Search

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]

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]

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: 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]

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]

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]

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]

Model reference adaptive control (MRAC) offers the potential to adapt in real‐time to changes in the performance of small unmanned air vehicles. There are significant challenges with their use, however, primarily in the implementation and assurance of long‐term system stability. This paper presents flight test results for a combined model reference adaptive control (CMRAC) law applied to the height control loop of a multirotor. Key features include the implementation of CMRAC with a baseline controller allowing for in‐flight switching between the two; the use of an augmented state to improve the tracking performance and a CMRAC implementation that provides a shorter transient phase, faster parameter convergence, and closer tracking of the desired reference model response when compared with standard MRAC. With the current exponential growth of interest in unmanned air vehicles, the potential benefits of using CMRAC for control system development are significant, particularly for new vehicles with short development and testing phases and in cases where there are significant configuration changes in flight or prior to rapid deployment. [ABSTRACT FROM AUTHOR]

This paper investigates the formation control of connected autonomous vehicle (CAV) platoons moving in multi lanes using distance‐based formation control techniques based on rigid graphs and V2V communication. A hierarchical architecture is proposed to decompose the cooperative control into velocity planning and vehicle dynamic control. A new velocity planning method is first developed via a distributed distance‐based formation controller so that each vehicle can keep platoon and change lane. Then, for the vehicle dynamics with nonlinearities and bounded disturbances, an adaptive controller is designed for regulating driving/braking torque to achieve the longitudinal velocity output of the velocity planner. The steering controller is designed to adjust the yaw angle of each vehicle to track and change lanes. Furthermore, stability analysis is conducted based on the Lyapunov theory. Finally, the applications of the proposed control designs to various of automated highway system (AHS) scenarios including lane‐change, curve lane and platoon overtaking, are simulated and numerically analysed to validate the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

In this article, the lateral path following problem of unmanned ground vehicle with bounded disturbances is studied. A dual‐mode model predictive control (MPC) algorithm based on adaptive event triggered mechanism is proposed. Compared with the existing MPC algorithm, adaptive event triggered model predictive control (AEMPC) can ensure the tracking accuracy and reduce the optimization computation. Feasibility and stability of AEMPC algorithm are guaranteed by constraint region parameter design. Finally, simulation results of the vehicle dynamics model are given, and the comparisons are made. [ABSTRACT FROM AUTHOR]