Sensor and actuator deception attacks often manipulate states and control commands, which lead to system performance deterioration or even instability. This article focuses on the platoon control problem for a group of connected and automated vehicles (CAVs) subject to both deception attacks and external disturbances. First, modeling both attacks and external disturbances as lumped disturbances, and a novel adaptive fixed-time disturbance observer (AFxTDO) without requiring prior knowledge of the disturbance boundary is further constructed to estimate the lumped disturbances within given time with zero estimation errors. Then, a new global fixed-time stability result with faster convergence rate is developed, together with the given AFxTDO, a variable exponent fixed-time sliding-mode control (VFxTSMC) scheme is established, such that the platoon tracking errors can converge to a predetermined region within the given time while avoiding the singularity phenomenon, improving the convergence speed and reducing the number of controller parameters. Meanwhile, individual vehicle stability and string stability also can be guaranteed within fixed-time by the suggested control scheme. Finally, the simulation results reveal the effectiveness and superiority of the proposed algorithm.

Sensor and actuator attacks can alter the truth values of vehicle states and control input through false data injection, leading to performance degradation. In this paper, secure control of vehicular platoon control systems (VPCS) subject to joint sensor-actuator attacks, and the unknown disturbances is investigated. First, a novel adaptive sensor fusion algorithm is designed to estimate the actual position with smaller fusion errors. Subsequently, a predefined-time extended state observer (PTESO) is further constructed based on the estimated position to recover the continuous-time states (i.e., velocity and acceleration) and disturbances accurately within a user-defined settling time. Then, a variable exponent predefined-time sliding mode controller (VPTSMC) with few design parameters is developed to guarantee the predefined-time stability of the platoon system, namely string stability and individual vehicle stability. Meanwhile, the singularity phenomenon is also avoided. Finally, a serious of simulations are carried out to show the effectiveness of the suggested control scheme.

The fixed-time control is investigated for an uncertain two-dimensional (2-D) planar vehicular platoon system with global prescribed performance. First, a novel fixed-time prescribed performance control (FxTPPC) method is designed to guarantee the tracking errors converge to the prescribed region with small overshoot inside a predefined time. Moreover, the restriction of PPC method that relies on the initial condition of tracking errors is also released, which means global PPC can be achieved. Then, a new control scheme, by virtue of the composite fixed-time sliding mode surface, is constructed for the third-order 2-D plane platooning system, such that the tracking errors, including distance errors and heading errors, approach to a predetermined region in a given time, while avoiding the singularity problem and improving the convergence rate of the system. In addition, fixed-time string stability and reachability of fixed-time prescribed performance are also achieved. Finally, by constructing numerical simulations and experiment studies, the validity of the proposed scheme is demonstrated in the end.

This study investigates the online optimization control problem for a vehicular platoon in the presence of unmodeled vehicle dynamics, unknown external disturbances, and uncertain inter-vehicle communication topology. A novel practical performance-prescribed reinforcement learning-based distributed sliding mode (PRLDSM) control framework is constructed for the platoon to strengthen the robustness and possess the online self-learning capacity for optimizing and control. Specifically, a composite controller is proposed for the platoon which consists of an online optimal controller and a sliding mode controller. By the strong learning capacity provided by the PRLDSM controller, the optimal policy and cost function can be recursively approximated by online simultaneous tuning of both actor and critic neural networks. Moreover, it is proved that all the signals in the closed-loop platoon control system are uniformly ultimately bounded and approach zero by selecting appropriate parameters. Theoretical analysis eventually guarantees that the tracking errors can be stabilized to satisfy both the individual vehicle stability and disturbance string stability with the prescribed transient response and steady-state accuracy. The obvious feature of the proposed PRLDSM controller is that the requirement of the exact vehicle dynamics and topological matrix of the inter-vehicle communication topology can be avoided. Both numerical examples and experimental studies illustrate the effectiveness of the proposed control method.

This paper proposes a privacy-preserving distributed collaboration (PPDC) scheme for connected autonomous vehicles (CAVs) to cross signal-free intersections based on the cloud, while securing the private data of the vehicles. Firstly, this paper converts the cooperation problem into a multi-objective problem that aims to improve the efficiency of traffic and fuel economy. Secondly, to prevent the privacy of the transmitted data of vehicles from being inferred by untrusted cloud servers or external attackers, an affine masking-based privacy strategy is designed. Specifically, the vehicle first uploads the encrypted state data to the cloud with the affine masking method. Then the cloud returns the control input by solving the newly constructed optimization problem, which is different but equivalent to the original problem. Then the vehicle calculates the real control input by the inverse affine masking mechanism. Simulation examples show that the proposed PPDC scheme can guarantee collision avoidance and the privacy protection of transmitted data of CAVs, improve traffic efficiency as well as fuel economy, and avoid extensive computation burden.

This study investigates the control problem of connected vehicles in urban roads where autonomous intersection manager (AIM) platforms are involved to manage the network traffic flow. A hierarchical framework for integrated vehicular platoon-road control is established to guarantee plant stability and string stable platoons and coordinate traffic flow. At the first layer, the intersection manager plays the role of regulating the velocity of the leader of approaching vehicular platoons based on a road traffic density model, which aims to fairly balance the traffic flow among the connected intersections. At the second layer, an integral sliding mode (ISM) controller is introduced to maintain platoon velocity consistency with its leader and the desired inter-vehicle distance. The presented ISM controller is capable of rejecting the inter-vehicle communication delay and the leader’s acceleration disturbance, which is illustrated by numerical simulations.

In this paper, the distributed security control problem of connected vehicle systems (CVSs) is investigated under denial of service (DoS) attacks and uncertain dynamics. DoS attacks usually block communication channels, resulting in the vehicle inability to receive data from the neighbors. In severe cases, it will affect the control performance of CVSs and even cause vehicle collision and life threats. In order to keep the vehicle platoon stable when the DoS attacks happen, we introduce a random characteristic to describe the impact of the packet loss behavior caused by them. Dependent on the length of the lost packets, we propose a security platoon control protocol to deal with it. Furthermore, the security platoon control problem of CVSs is transformed into a stable problem of Markov jump systems (MJSs) with uncertain parameters. Next, the Lyapunov function method and linear matrix inequations (LMI) are used to analyze the internal stability and design controller. Finally, several simulation results are presented to illustrate the effectiveness of the proposed method.

This paper studies the problem of designing the load frequency control (LFC) scheme for the inter-connected multi-area power systems (MAPSs) with asynchronous sampled-data coomunication and missing control inputs. Due to the effect of asynchronous sampled-data communication and missing control inputs, the MAPS is modeled as sampled-data switched systems. By using the modeling transformation technique, MAPS is further represented as a time-delay switched system with multiple time delays. Combined with average dwell time technique and time-delay systems analysis approach, a series of conditions are obtained for MAPS to maintain grid frequencies and tie line power exchanges with neighborhood areas at scheduled values. Moreover, the relation between the stability and the control inputs missing rate is established and the upper bound of the allowable control inputs missing rate is found. Based on those obtained condition, a useful algorithm is proposed to solve the controller gain for the area control error based sampled-data LFC. The designed LFC scheme not only can guarantee the stability of the MAPS but also can reject the effect of load disturbance. The applicability of the proposed design method is validated by a numerical example with inter-connected three-area power systems.

This study investigates the problem of distributed trajectory optimization and platooning of a group of heterogenous vehicles. A distributed hierarchical framework is proposed for trajectory optimization and tracking control. The role of the upper layer is to provide an optimal trajectory for the vehicle, which is realized by minimizing the inter-vehicle spacing with regard to the desired values using convex optimization. The second layer contains an adaptive sliding mode controller for the vehicle to track the optimal trajectory obtained. To compensate for uncertain vehicle dynamics, a parameter adaptation law is involved in the controller. In the context of sliding mode control based on the tracking error dynamics, the controller parameters are determined so that both internal and string stability are guaranteed. Simulation examples with comparative results are presented to illustrate the effectiveness of the results.

This paper investigates a sampled-data control problem for connected vehicles subject to switching topologies, communication delays, and external disturbances. A tracking error-based sampled-data platoon control method is proposed, where the neighboring vehicles state information is transmitted via the VANET with communication delay. By representing the switching communication topology by a Markovian chain, the platoon control system is modeled as a Markovian switching time-delay system with disturbance. In the context of Markovian jumping system theory, a platoon control methodology is obtained to guarantee that the tracking errors can be stabilized mean-square exponentially with a given disturbance attenuation level. The platoon controllers with both fixed and variable gains are suggested. The results are extended to cover partially unknown transition rates of the Markov chains. The numerical examples with comparative results are given to illustrate the effectiveness of the results.

This paper investigates cooperative adaptive cruise control (CACC) of vehicles, focusing on the effect of medium access control (MAC) protocol and the inaccurate acceleration measurement. A Markov chain is used to describe the randomness in vehicular network access under a MAC protocol; a reduced-order observer is proposed to estimate the relative acceleration of neighboring vehicles. Based on stochastic system techniques, a series of sufficient conditions are given in the form of backward recursive Riccati Difference Equations (RDE) to guarantee the stable tracking error. Numerical simulations are given to verify the effectiveness of the proposed approach.

This paper is concerned with the problem of vehicular platoon control in vehicular ad hoc networks subject to capacity limitation and random packet dropouts. By introducing binary sequences as the basis of network access scheduling and modeling random packet dropouts as independent Bernoulli processes, we derive a closed-form methodology for vehicular platoon control. In particular, an interesting framework for network access scheduling and platoon control codesign is established based on a set of priority rules for network access control. The resulting platoon control and scheduling algorithm can resolve network access conflicts in vehicular ad hoc networks and guarantee string stability and zero steady-state spacing errors. The effectiveness of the method is demonstrated by numerical simulations and experiments with laboratory-scale Arduino cars.