RBFNN-Based Adaptive Event-Triggered Control for Heterogeneous Vehicle Platoon Consensus

Unknown input observer based neuro-adaptive fault-tolerant control for vehicle platoons with sensor fault and output quantization

This study proposes the time-/event-triggered adaptive neural control strategies for the asymptotic tracking problem of a class of uncertain nonlinear systems with full-state constraints. First, we design a time-triggered strategy. The effect caused by the residuals of the estimation via radial basis function (RBF) neural networks (NNs), and the reasonable upper bounds on the first derivative of the reference signal and the derivative of each virtual control, can be eliminated by designing appropriate adaptive laws and utilizing the basic properties of RBF NNs. Moreover, the construction of the barrier Lyapunov functions (BLFs) in this work ensures the compliance of the full-state constraints and also holds the asymptotic output tracking performance. Then, based on the time-triggered strategy, we further design a relative threshold event-triggered strategy. The proposed event-triggered adaptive neural controller can solve the main control objective of this work, that is: 1) the full-state constraint requirements of the system are not violated and 2) the output signal asymptotically tracks the reference signal. Compared with the traditional method, the event-triggered strategy can improve the utilization of communication channels and resources and has greater practical significance. Finally, an example of single-link robot under the proposed two strategies illustrates the validity of the constructed controllers.

This paper investigates the consensus problem of linear multi-agent systems subject to external disturbances via distributed event-triggered adaptive control. First, a distributed event-triggered adaptive output feedback control strategy is proposed for each agent. It is shown that under this control strategy, the consensus problem can be solved for any connected undirected communication graph in a fully distributed manner without using any global information. Then a distributed self-triggered adaptive output feedback control strategy is designed with which continuous monitoring of the measurement error is no longer needed. It is further shown that for the proposed event-triggered and self-triggered control strategies, no agent will exhibit Zeno behavior. Finally, the effectiveness of the proposed two control strategies is illustrated on a group of two-mass-spring systems.

This paper focuses on the problem of the event-triggered adaptive containment control for a class of nonlinear multiagent systems (MASs) with prescribed performance and immeasurable states. First, the radial basis function neural networks (RBFNNs) are adopted to approximate the uncertain smooth nonlinear function, and the neural network-based state observer is designed to estimate the unmeasurable state. Besides, to reduce the control resource consumption and get a better balance between the system performance and network constraints, the switching threshold-based event-triggered control strategy is introduced. Based on this, the novel distributed containment controller is designed by utilizing the adaptive backstepping technique and the dynamic surface control (DSC) technique to guarantee that the output of each follower converges to the convex hull formed by multileader. Moreover, the containment errors can be converged to the prescribed boundary and all signals in closed-loop system are semi-global uniformly ultimately bounded (SGUUB) as well. Finally, the simulation examples are carried out to illustrate the efficiency of the proposed controller.

Connectivity and autonomy are considered two of the most promising technologies to improve mobility, fuel consumption, travel time, and traffic safety in the automated transportation industry. These benefits can be realized through vehicle platooning. A vehicle platoon is composed of a group of connected automated vehicles (CAVs) traveling together at consensual speed, following the leading vehicle (leader) while maintaining a prespecified inter-vehicle distance. This paper reviews the different existing control techniques associated with the transitional platoon maneuvers such as merge/split and lane change. Different longitudinal and lateral vehicle dynamics that are mainly used in the transitional platoon maneuvers are discussed. The most used control algorithms for both longitudinal and lateral control used for transitional platoon maneuvers are reviewed and the advantages and limitations of each control strategy are discussed. The most recent articles on platoon control maneuvers have been analyzed based on the proposed control algorithm, homogeneously or heterogeneously of platoon members, type of platoon maneuver, the aim of control problem, type of implementation, and used simulation tools. This paper also discusses different trajectory planning techniques used in lateral motion control and studies the most recent research related to trajectory planning for automated vehicles and summarizes them based on the used trajectory planning technique, platoon or/and lane change, the type of traffic, and the cost functions. Finally, this paper explores the open issues and directions for future research.

This article investigates adaptive control problems for unknown second-order nonlinear multiagent systems (MASs) via an event-triggered approach. An adaptive event-triggered consensus controller is given to second-order MAS with unknown nonlinear dynamics. We prove that the proposed consensus controller is free from Zeno behavior. Next, an adaptive event-triggered tracking controller is developed for leader-follower MAS with the leader having bounded nonzero control input. Both consensus and tracking controllers are fully distributed, which means that event-triggered controllers only use local cooperative information. Finally, an unknown second-order nonlinear MAS is used to verify the given event-triggered controllers.

This article studies the fuzzy adaptive event-triggered (ET) consensus control issue of nonlinear multiagent systems (NMASs) under jointly connected switching networks. Since the leader and its high-order derivatives are unknown under jointly connected switching networks, a novel distributed ET reference generator equipped with an ET mechanism is constructed to estimate them. Meanwhile, the continuous information transmission among agents is avoided and the network channel utilization is optimized. Subsequently, fuzzy logic systems (FLSs) are employed to approximate unknown dynamics, and a fuzzy adaptive ET consensus control algorithm only using intermittent communication is designed by backstepping control methodology. It is demonstrated that all the closed-loop signals are semi-globally uniformly ultimately bounded (SGUUB), with the tracking errors converging to a small neighborhood around zero. Finally, we apply the developed fuzzy adaptive ET consensus control algorithm to unmanned surface vehicles (USVs), and the simulation results verify the effectiveness of the proposed ET consensus control algorithm.

This article studies neural-network (NN) adaptive control for strict-feedback nonlinear systems with matched uncertainties and event-triggered communication. Radial basis function NNs (RBFNNs) are used in the backstepping design approach to compensate for nonlinear uncertain functions. The concept of valid compact sets for RBFNN adaptive controllers is proposed, where a local RBFNN approximator is defined and the closed-loop state can remain. To guarantee the existence of such valid compact sets, a new property on RBFNNs is presented, which shows that, in some properly designed RBFNNs, the norm of their ideal weight vectors can always become arbitrarily small. By utilizing this property, the selections on valid compact sets are investigated, resulting in rigorous proof on RBFNN adaptive controllers to solve a local tracking problem with a given smooth enough reference signal. Subsequently, to save limited communication resources, a Zeno-free event-triggering mechanism in controller-to-actuator channels is proposed. Under this event-triggered adaptive controller, the corresponding tradeoff among the tracking performance, computational burden, and communication consumption is analyzed. Furthermore, two extensions are made to the general local function approximator, which is in the form of a weight vector multiplying a group of basis functions, and to the communication in sensor-to-controller channels. Finally, several simulation results are provided to illustrate the efficiency and feasibility of the obtained results.

This paper jointly designs the communication and control schemes for vehicle platoons by taking into consideration of the limited communication and computing resources of vehicles. Aiming at achieving the stable tracking of the member vehicles at a desired inter-vehicle distance and the same velocity, while improving the communication and computation efficiency, we propose a bandwidth-aware transmission scheduling (BATS) mechanism and an event-triggered distributed model predictive control (ET-DMPC) scheme for the longitudinal control of a vehicle platoon. Firstly, by introducing bandwidth-aware parameters, the triggering threshold for the event-triggered BATS mechanism is dynamically adjusted according to vehicle and bandwidth states. Then, an output-based ET-DMPC scheme is designed to ensure that the platoon tracks a reference velocity and maintains the desired inter-vehicle distance. Lastly, extensive simulations are carried out and the results demonstrate the effectiveness of the proposed BATS mechanism and ET-DMPC scheme in terms of adaptive communication scheduling and low computational burden.

In this article, the dynamic surface control (DSC) based on the event-triggered control (ETC) framework is discussed for the states constrained systems in nonaffine form with disturbances. The uncertain smooth nonlinear terms produced at recursive each step are approximated by the radial basis function neural networks (RBFNNs). An invertible symmetric mapping is adopted to deal with the full state constraints. An auxiliary signal is designed to handle the input unmodeled dynamics. The event-triggered input is constructed in the DSC framework. The stability analysis illustrates all the signals are bounded in the controlled system. Each state is strictly limited within the artificially preset boundary restrictions. A simulation is employed to verify the theoretical findings.KeywordsEvent-triggered controlDynamic surface controlFull state constraintsInvertible symmetric mapping

Event-triggered adaptive fixed-time fuzzy control for uncertain nonlinear systems with unknown actuator faults

Adaptive event-triggered tracking control for strict-feedback nonlinear ODE cascaded n+1 coupled hyperbolic PDE systems.

This article discusses the consensus problem of the discrete-time multiagent system (MAS) by event-triggered secure control in infinite time domain against complex cooperative attacks, which consists of false data injection attacks (FDIAs) and replay attacks (RAs). Due to the openness of the network, attackers attempt to destroy the consensus by damaging the integrity of the measured signal in the information transmission channel. A novel complex cooperative attacks model is first built. FDIA and RA, which appear randomly on the data communication channel from sensor to controller, are modeled in a unified framework through Bernoulli process. The state-dependent event-triggered control scheme (ETCS), which reduce the demand for network bandwidth, is adopted to update control signals. This article is to design an appropriate ETCS to make MAS reach consensus under FDIA and RA. The sufficient conditions are derived to ensure the event-triggered consensus and consensus with guaranteed position performance by using the stochastic analysis method. Finally, the simulation results show the feasibility of the proposed ETCS.

Review on big data applications in safety research of intelligent transportation systems and connected/automated vehicles

In this paper, an event-triggered adaptive control scheme is proposed by backstepping for a class of strict feedback systems with the saturation actuator. The unknown parameters and modelling errors are also considered. By constructing the auxiliary signal system and introducing auxiliary signals into the controller design, the uncertainties caused by unknown saturation effects are compensated for. Based on this auxiliary signal, an event-triggered adaptive control scheme is constructed. The main contributions of this proposed controller can be summarized as follows: (1) A new event-triggered mechanism is proposed by setting a maximum triggering time interval limit. It integrates time triggering in the event triggering mechanism, and avoiding the phenomenon of system performance not being optimized due to control signals not being updated for a long time. It was also proven that this proposed triggering mechanism can prevent the occurrence of Zeno-behavior; (2) The unknown constant parameters in the upper bound function of the unknown nonlinear terms are considered. By estimating these parameters the compensation accuracy for such unknown uncertainties is improved. Finally, simulation results validate the effectiveness of this proposed controller.