Dynamic Event-Triggered Distributed MPC for Asynchronous Vehicle Platoon Against External Disturbances and DoS Attacks

On distributed model predictive control for vehicle platooning with a recursive feasibility guarantee

This paper studies the dynamic event-triggered mechanism (DETM)-based model predictive control (MPC) problem for networked systems with false data injection (FDI) attacks and denial of service (DoS) attacks. A DETM is used to save network resources. To reflect the impact of both FDI and DoS attacks on networked systems, a new model describing hybrid cyber attacks is proposed based on indicator functions. A solvable auxiliary optimization problem is designed to get the control gain. The DETM-based MPC algorithm is presented and the recursive feasibility of this algorithm is ensured. The mean-square input-to-state stability of the closed-loop system is guaranteed. The validity of the developed DETM-based MPC algorithm is illustrated by a case study of a networked two-area smart grid.

Reference Tracking using a Non-Cooperative Distributed Model Predictive Control Algorithm

This paper introduces a distributed secondary controller with a dynamic event-triggered mechanism (DETM) designed to tackle secondary control challenges in islanded microgrids (MGs) under denial of service (DoS) attacks. DoS attacks can disrupt communication channels, causing unexpected changes in the network topology that may prevent controllers from receiving crucial information, degrading control performance. A security architecture based on Acknowledgment signal (ACK) for attack detection and topology recovery is created. Based on this architecture, a secondary control strategy of MG under DETM is proposed, which can solve the contradiction between voltage regulation and reactive power sharing in the case of DoS attack, achieve accurate reactive power sharing, reduce the deviation between voltage amplitude and standard value, and realize frequency recovery and active power sharing. The stability of the proposed control strategy is established through the application of Lyapunov's theory. Finally, the effectiveness of the proposed control strategy is verified through the utilization of an islanded MG simulation system built in MATLAB/Simulink and a Hardware-in-the-Loop (HIL) test platform.

Stability and Convergence Analysis of a Distributed MPC Scheme Based on Communication and Iterative Calculation of Control Action

In this study, an approach to design robust distributed model predictive control (MPC) is proposed for polytopic uncertain networked control systems with time delays. To reduce the computational complexity and improve the flexibility, the entire system is decomposed into multiple smaller dimensional subsystems. For each subsystem, the proposed robust distributed MPC algorithm requires solving multiple linear matrix inequality optimisation problems to minimise an upper bound on a robust performance objective. An augmented polytopic uncertainty description is invoked to handle the input delays. The conservativeness of distributed MPC algorithm is reduced by utilising a sequence of feedback control laws. An iterative on‐line algorithm for robust distributed MPC is developed to coordinate the distributed MPC controllers. Convergence and robust stability of the proposed distributed MPC are investigated. A numerical example is carried out to demonstrate the effectiveness of the proposed algorithm.

Distributed model predictive control (DMPC) is a widely used approach especially for large-scale systems like transportation, traffic control or power distribution systems where solving the MPC problem in a centralized manner is not desirable or even not possible due to the size of the problem, maintenance issues, cabling burden, etc. Distributed optimization methods, which are being employed to solve the DMPC problems, normally suffer from having an enormous number of communications to reach a consensus between the local controllers. Therefore trying to find distributed optimization schemes that can realize an agreement among the local controllers with a lower communication load deserves attention. The alternating direction method of multipliers (ADMM) is a well known method to solve consensus problems. Since the local problems in ADMM can be handled with second-order methods, the convergence will be reached with a lower number of iterations in comparison to first-order methods, e.g. the fast gradient method. In this paper, ADMM is formulated and applied to a DMPC problem first in the primal domain. The main contribution of this paper is, however, the application of ADMM to the dual of the DMPC problem, introducing an augmented connection graph, which shows considerable improvement in convergence speed and therefore requires a lower number of communications in comparison with primal ADMM.

Designing a stabilized distributed model predictive control (DMPC) with constraints is an open and important problem for a class of large-scale distributed systems, which are composed by both weakly and strongly coupled subsystems. This paper proposes a design of DMPC network to stabilize this class of large-scale systems. A coupling degree-based clustering method is first designed to classify subsystems into some middle-scale subsystems (M-subsystem) off-line according to the adjacent matrix, so that these M-subsystems are weakly coupled with each other. Then, each M-subsystem is controlled by a virtual model predictive control (MPC), which is realized by several individual controllers with running iterative cooperative DMPC algorithm, since the solution of cooperative DMPC is able to converge to a fixed point without coupling constraints. Each MPC communicates with the corresponding interacted M-subsystems’ MPCs once in a control period for exchanging future state evolution estimation. All the subsystem-based MPCs are composed of the proposed peer-to-peer DMPC network. In addition, an additional consistency and stabilization constraints are added to guarantee the recursive feasibility and stability of the overall system. The convergence of the iterative DMPC algorithm for each M-subsystem and the stabilization analysis of the overall system are provided. The simulation results show the efficiency of the proposed method. Note to Practitioners —The proposed method is designed for the systems, which are composed of both strong and weak coupling subsystems and are controlled in a distributed structure. The aim of this method is to design a distributed model predictive control (MPC) network for the operational optimization of entire system. The designed control structure and the distributed MPC algorithm with the proposed method could guarantee the recursive feasibility and asymptotic stability of the entire system. To apply this method, the first step is to obtain the models of each subsystem. Then, design the distributed MPC network structure according to the proposed cluster algorithm, which determines the set of subsystems that each virtue MPC controls. Next step is to configure the network information exchanging among these subsystems and construct the optimization problem of each subsystem. Finally, use the existing quadratically constrained quadratic program solvers to calculate the optimal control laws and apply these optimal control laws to the corresponding subsystems. This method can be used in large-scale chemical processes, air conditioner systems for multizone buildings, distributed energy generation systems, and so on.

This article examines the resilient control problem for Cyber-Physical Systems (CPSs) with multiple sensors where both sides of the control structure are vulnerable to Denial-of-Service (DoS) attacks. The study introduces two approaches; a switched observer-based Static Event-Triggered Mechanism (SETM) and a switched observer-based Dynamic Event-Triggered Mechanism (DETM) to mitigate the impact of Multi-Channel DoS (MCDoS) and Full-Scale DoS (FSDoS) while ensuring Input-to-State Stability (ISS). The comparison results are shown between the two, and the results indicate that DETM requires fewer triggering instants than SETM, which saves the control cost significantly. The control architecture of DETM is also modified so that the system can handle the maximum MCDoS changing frequency. Lastly, the optimal ETM and observer parameters are designed to enhance system resilience against the highest frequency of MCDoS attacks.

This chapter focuses on the very serious problem of denial of service (DoS) attacks against computer systems and networks. DoS attacks are attacks designed to disrupt, overwhelm, and/or damage computing resources and data. These attacks are aimed not against confidentiality of data or integrity of systems and data, but rather against availability of systems and data. Attackers launch DoS attacks for a wide variety of reasons. The probability of success is high, due in large part to the fact that the Internet itself has not been designed for security. The cost of DoS attacks is often high—several well‐known attacks have reportedly cost organizations billions of dollars. Many types of DoS attacks have surfaced so far, including resource starvation attacks, buffer overflow attacks, and packet fragmentation attacks. Distributed denial of service (DDoS) attacks, attacks in which programs planted in multiple systems cooperate, represent an even higher level of threat. Preventing DoS attacks is difficult in the first place due to a plethora of reasons; limiting the potential for damage and disruption is often the most cost‐effective strategy. Hot sites, cold sites, use of uninterruptible power supplies (UPSs) and Redundant Array of Independent Drives (RAID) solutions, and other measures can limit the damage from DoS attacks. Using properly configured and maintained firewalls is one of the best single prevention measures. Additionally, using packet filters on systems, keeping up with patches, limiting services that run on these systems, and using other measures go a long way in helping prevent susceptibility to DoS attacks.

This paper presents a distributed model predictive control (DMPC) algorithm for heterogeneous vehicle platoons with unidirectional topologies and a priori unknown desired set point. The vehicles (or nodes) in a platoon are dynamically decoupled but constrained by spatial geometry. Each node is assigned a local open-loop optimal control problem only relying on the information of neighboring nodes, in which the cost function is designed by penalizing on the errors between predicted and assumed trajectories. Together with this penalization, an equality based terminal constraint is proposed to ensure stability, which enforces the terminal states of each node in the predictive horizon equal to the average of its neighboring states. By using the sum of local cost functions as a Lyapunov candidate, it is proved that asymptotic stability of such a DMPC can be achieved through an explicit sufficient condition on the weights of the cost functions. Simulations with passenger cars demonstrate the effectiveness of proposed DMPC.

In WSN, DoS (denial of service) attack makes shortcoming system. The packets travel over and over in the sensor network. By that, all the assets like data transmission, memory, and vitality are squandered by this attack. However, the attacker ought to optimize its attacker plan for request to boost the impact on the system performance because of the deficiency of vitality at the aggressor side. Denial of service (DoS) attack on the Internet has become a squeezing issue. By staying away from these sorts of attacks, network performance can be improved. Therefore, security is a fundamental requirement for these networks. Effective routing is necessary in order to overcome the issued faced by the crosslayer in the DOS attack of the WSN network for the purpose of good transmission. This research work mainly focuses on performance evaluation using optimization methods. To establish the efficient path in the crosslayer against DoS attack, this paper has proposed enhanced lion optimization with an efficient path routing equalization technique (LOEPRE). If any failure node occurs in the network, then the node is recognized and the transfer of the data packet is done to the other node. Retransmission of data causes overload in the network. The proposed model focuses on these issues and overcomes these issues by improving the path efficiently with robust security. It consists of three phases: the initial phase includes the route discovery in the network. In the second phase, the transfer of data is done in the high router path for security purposes. Finally, the efficient path routing equalization technique is used for minimizing the overload in the network; it provides the equalized path length in the network and is highly efficient. Hence, the proposed LOEPRE technique is used to achieve energy efficiency in wireless network for prolonged network lifetime and minimum packet latency and minimize consumption of energy. Moreover, the simulation outcome of the proposed LOEPRE method is highly robust while comparing to the existing methods EFCRS, SSPRA ELOER, EFLOR, and TSTP. It achieves better performance than existing algorithms in comparing metric connectivity ratio, end-to-end delay, overhead, throughput, and packet delivery ratio.

This article mainly focuses on the problem of resilient <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> filtering for cyber–physical systems (CPSs) subject to denial-of-service (DoS) attacks and sensor saturation. A new event-triggered mechanism (ETM) considering both DoS attacks and limited network bandwidth is put forward to guarantee the secure performance of the filter. Under this mechanism, inherent periodical transmission attempts are generated during DoS active periods, by which the latest measurement output of the system can be successfully transmitted to the filter after the end of the DoS attack; while during DoS sleep periods, the ETM degenerates into a traditional one. Furthermore, a valid DoS attack model is proposed to further characterize the following two scenarios occurring between adjacent sampling instants: 1) the end of the DoS attack and 2) both the start of the DoS attack and the end of the DoS attack. Sufficient conditions for designing the secure filters of CPSs against DoS attacks are achieved by using the piecewise Lyapunov–Krasovskii functional approach. Finally, the validity of our designed approach is manifested by an illustration of quarter-vehicle suspension systems (SSs).

The consensus problem of nonlinear multi-agent systems is studied in this paper, considering the semi-Markov switching topologies under DoS (denial-of-service) attacks. In this regard, an event-triggered mechanism is introduced for multi-agent systems under DoS attacks. Moreover, combining the semi-Markov topology with the event-triggered mechanism, a new consensus controller under DoS attacks is proposed. Furthermore, by choosing the Lyapunov–Krasovskii function and applying the matrix inequality technique, a sufficient and necessary condition is derived for the system stability as well as the state consensus. Subsequently, H∞ norm is proposed to enhance the performance of the multi-agent systems. Finally, a numerical example is provided to show the effectiveness and applicability of the proposed approach.

To measure the degree of basic service of wireless sensor network (WSN) with denial of service (DoS) attacks, it is necessary to evaluate survivability of WSN. This paper presents new method based on services and Markov Chain. It distinguishes services, analyzes influence of services under DoS attacks, gets the formula of survivability. Probability of states is obtained by markov chain, then the quantitve survivability is getted in different states. Simulations prove that DoS attacks obviously decrease the survivability of WSN, but adding node energy and improving node density in certain range can reduce the influence of DoS attacks.