In this paper, an attack-tolerant fuzzy path following control method is proposed for distributed drive electric vehicles subject to aperiodic denial-of-service (DoS) attacks based on an event-triggered output feedback framework. Firstly, to construct a framework for feasible controller design under DoS attacks, a switched interval type-2 fuzzy output feedback control framework is established with consideration of vehicle dynamics nonlinearity coupled with DoS attacks. Secondly, to guarantee the stability and desired path following performance of the vehicle closed-loop control system under DoS attacks, an attack-tolerant sufficient condition is derived by constructing piecewise Lyapunov functional. Thirdly, to balance control performance and network bandwidth utilization under DoS attacks, a resilient event-triggered fuzzy output feedback control method is proposed in terms of a set of linear matrix inequalities. Finally, experimental results validate the effectiveness and superiority of the proposed method in the aspect of path following accuracy and network resource conservation following accuracy compared with existing methods.

Due to inherent vulnerabilities and openness of the communication protocol, denial-of-service attacks may occur in the vehicle path tracking system to cause unreliable communication links. Thus, this paper explores a dynamic output feedback fuzzy path tracking control method for the intelligent electric vehicle under unreliable communication links. First, to establish a foundation for both communication analysis and controller design, an interval type-2 fuzzy dynamic output feedback control model is constructed to describe the vehicle path tracking system considering dynamic nonlinearities and measurement constraints. Second, to maintain acceptable data transmission efficiency under unreliable communication links, a switched event-triggered mechanism is proposed to regulate the communication scheduling according to the detection signal of denial-of-service attacks. Third, to preserve the exponential stability and path tracking performance of the vehicle control system under unreliable communications links, a novel co-design method is developed for the fuzzy dynamic output feedback controller and switched event-triggered strategy by employing the piecewise Lyapunov-Krasovskii functional approach. Finally, the experimental results demonstrate the effectiveness and superiority of the proposed control approach compared to existing path tracking control methods under unreliable communication links.

This work proposes a robust fault-tolerant path following control strategy for Autonomous Ground Vehicles (AGVs) subjected to network delays and actuator failures. Firstly, a Takagi-Sugeno (T-S) fuzzy model is developed to characterize the nonlinear vehicle dynamics, accounting for uncertainties in vehicle speed and tire cornering stiffness. Secondly, a stability condition is derived using linear matrix inequalities (LMIs) with expanded matrices to handle network-induced delays and data loss. Thirdly, a fault-tolerant control method integrating robust H-infinity performance is proposed to ensure path following accuracy and stability. Experimental results via hardware-in-the-loop tests demonstrate the effectiveness of the proposed controller in improving tracking performance and handling actuator failures under varying conditions.

Under open-network environments with constrained bandwidth, the vehicle suspension control systems are particularly susceptible to denial-of-service attacks, which can cause intermittent communication. To address this challenge, a resilient fuzzy control method is proposed for nonlinear suspension systems of in-wheel motor drive electric vehicles under intermittent event-triggered communication. Firstly, based on a nonlinear quarter-vehicle suspension model, a switched interval type-2 fuzzy suspension model is established to describe both the suspension nonlinear dynamics and intermittent communication under denial-of-service attacks. Secondly, to maintain effective communication under denial-of-service attacks, an intermittent event-triggered strategy with dual adaptive thresholds is proposed to alleviate communication resource constraints and mitigate the adverse effects of intermittent communication. Thirdly, to guarantee the suspension performance under denial-of-service attacks, a resilient fuzzy control method is proposed for vehicle suspension systems. The piecewise Lyapunov functions and matrix inequality are employed to ensure the exponential stability and desired performance requirements. Finally, in comparison with existing vehicle suspension control methods, the proposed resilient fuzzy control method demonstrates significant performance advantages by the hardware-in-the-loop experiments.

This work proposes a fractional-order compound steering control for lateral stabilization of dual motor drive electric vehicles (DDEVs) subject to multi-agent coupled game. Firstly, given that the compound steering involves the interactions between the active steering and differential torque, a closed-loop control framework-based multi-agent coupled game theory is proposed to coordinate the dynamic interaction information. Secondly, accounting for the complexity of nonlinear systems, a piecewise affine method is described to segmentally linearize the system and reduce the computational burden. Furthermore, the coupled game optimization problem for DDEVs with fuzzy nonlinearities is solved by integrating the dynamic programming strategy. Thirdly, considering that integer-order differential equations have limitations in describing complex characteristics of the vehicle dynamics, a fractional-order differential equation-based control strategy is developed to guarantee the stability of the control system by addressing the coupled game optimization problem of the vehicle dynamics. Finally, experimental results are performed to examine the effectiveness and merits of the proposed dynamic programming-based fractional-order compound steering control method in enhancing the lateral stabilization of DDEVs.

Under extreme conditions, the yaw stabilization of the four-wheel-independent-drive electric vehicle (FWID-EV) is crucial for vehicle safety. This work proposes a flexible prescribed performance control (FPPC)-based Lorentzian-relaxation filtered adaptive dynamic programming (LRF-ADP) method to solve the cooperative differential game (CDG) between the active front steering (AFS) and the torque vectoring control (TVC). First, to guarantee the transient and steady-state performances of the vehicle, a flexible prescribed performance function is developed to deal with the control singularity. Second, to enhance the computational efficiency of the controller, a filtered Hamilton-Jacobi-Bellman equation is established with the dynamic-sample-size method and hysteresis switching strategy-based experience replay (ER) algorithm. Third, to ensure the convergence for the policy iteration (PI) of the controller, a Lorentzian-relaxation strategy is proposed to regulate the degrees of the relaxation. Moreover, to examine the effectiveness and practicability of the proposed method, the software-in-the-loop and hardware-in-the-loop tests are conducted under emergency maneuvers, respectively.

In this paper, an event-triggered fuzzy security path following control problem is investigated for autonomous ground vehicles subject to aperiodic denial of service attacks. Firstly, a switched interval type-2 fuzzy model is established to depict the vehicle path following system, in which both the vehicle dynamic nonlinearities and the aperiodic denial of service attacks are well addressed. Secondly, to guarantee that the latest packets are sent out immediately at the end of the denial of service attacks, a novel attack-dependent event-triggered scheme is developed to improve the signal transmission efficiency and reduce the performance loss caused by denial of service attacks. Then, by constructing a piecewise Lyapunov function based on the average dwell time of the denial of service attacks, a security control method is proposed to guarantee the exponential stability and the path following performance of the switched fuzzy path following system. Finally, the superiority of the proposed control strategy is verified by experimental tests as compared with the current path following control methods.

This article focuses on the path tracking control problem for autonomous ground vehicles under switched triggered and sensor attacks. Firstly, an interval type-2 Takagi-Sugeno fuzzy model is established to effectively approximate the tire dynamic nonlinearities and varying velocity in the path tracking control system, in which the random deception attack encountered in the sensor is considered. Secondly, a novel switched triggered communication mechanism is presented to decrease the frequency of signal transmission and save the network resources. The switched triggered mechanism includes both the time-triggered mode and event-triggered mode, which obey a Bernoulli distribution. Then, based on a positive Lyapunov-Krasovskii functional and matrix inequalities, a set of conditions are developed for the path tracking controller design to achieve the asymptotic stability and performance requirements. Finally, experimental results are presented to evaluate and validate the performance of the proposed path tracking control method.

This article proposes an observer-based discrete-time cascaded control (ODCC) strategy for lateral stabilization of Steer-by-Wire (SbW) vehicles with consideration of uncertainties and disturbances. First, for the observation of the sideslip angle and yaw rate, an information fusion-based unscented Kalman filter (IFUKF) is designed to reduce the negative effect from the variation of the parameters; Second, aiming to eliminate the errors of control variables for lateral stabilization of SbW vehicles, a discrete-time sliding mode predictive control (DSMPC) is presented to deal with matched and mismatched uncertainties and input constraint; Third, to reduce the tracking error between the actual front wheel steering angle and the desired one generated by the DSMPC, a combination of discrete-time fast terminal sliding mode and active disturbance rejection control is proposed to tackle the problems of parameter uncertainties and disturbances in the SbW system. Performance evaluations are conducted via both software-in-the-loop and hardware-in-the-loop to examine the availability and practicability of the ODCC strategy.

This article proposes a novel improved adaptive event-triggered (AET) control algorithm for networked Takagi-Sugeno (T-S) fuzzy systems with asynchronous constraints. First, taking the limited bandwidth of the network into consideration, an improved AET mechanism is proposed to save the communication resource. Superior to the existing event-triggered mechanism, the improved AET scheme introduces two adjusting parameters, which further contribute to the economization of the communication resource. Second, with consideration of asynchronous premise variables, a reconstructed approach is applied to synchronize the time scales of membership functions of the fuzzy system and the fuzzy controller. Third, to derive a less conservative sufficient condition for the controller design, a new augmented Lyapunov-Krasovskii functional with event-triggered information and triple integral terms is constructed. Meanwhile, by applying a Bessel-Legendre inequality and extended reciprocally convex matrix inequality together, a new control algorithm is derived with less conservatism. Finally, simulations on a cart-damper-spring system are implemented to evaluate and verify the performance and advantages of the proposed algorithm.

This article investigates the event-triggered fuzzy filtering issue for vehicle sideslip angle estimation with consideration of data quantization and dropouts. First, an uncertain Takagi–Sugeno fuzzy model is developed to describe vehicle nonlinear dynamics resulted from nonlinear tire dynamics, varying velocity, uncertain mass, and yaw moment inertia. Then, an adaptive event-triggered scheme is introduced between the sensor and the filter for the decision of releasing sampled data to economize limited network resource. Moreover, the network-induced constraints, such as delay, data quantization, and dropouts, are taken into account to improve the robustness of the filtering method. Based on the Lyapunov stability theory, a new event-triggered asynchronous fuzzy filtering method is proposed by establishing an augmented Lyapunov–Krasovskii functional candidate and applying integral inequalities in the derivation. Finally, simulation results are presented to verify the advantages of the proposed method in comparison with the existing results.

This paper is concerned with the human-machine shared control issue for vehicle lane keeping systems via a new fuzzy observer-based event-triggered method. In order to capture system nonlinear and uncertain characteristics such as nonlinear tire dynamics, varying velocity and driver behavioral uncertainties, Takagi-Sugeno fuzzy approach is employed to model the global driver-vehicle-road system. After system modeling, the fuzzy observer-based control structure is considered because a full states information is not available in practical driving environment. Then, most existing human-machine shared control methods are based on the periodic sampling communication mechanism. However, since the network bandwidth is limited, the above mechanism may cause oversampling and communication congestion. Thus, an adaptive event-triggered mechanism is introduced between the observer and the controller to mitigate the communication burden and improve the bandwidth utilization. Based on Lyapunov functional theory, a set of sufficient conditions are given to calculate desired human-machine shared controllers. Finally, simulation tests are implemented on Matlab/Simulink-CarSim platform and simulation results illustrate that the proposed method can achieve a favorable improvement in the lane keeping capability, the driver handling comfort and the network bandwidth utilization.

This paper concerns the robust gain-scheduling control issue for autonomous path following systems with stochastic network-induced delay. Firstly, to effectively approximate the highly nonlinear tire dynamics, the linear fractional transformation formulations are employed to describe the tire cornering stiffness with a norm-bounded uncertainty. Secondly, by taking the data dropout and delay encountered in signal computation and transmission into account, a more generalized lumped delay form is proposed to unify the time-varying data dropout and network-induced delay. Moreover, a Markovian process is presented to describe the lumped delay as a stochastic distribution. Thirdly, to address the issue of varying vehicle velocity, a linear parameter varying model is established to capture vehicle lateral behaviors. Based on the stochastic stability theory, a new robust gain-scheduling path following control method is proposed for the autonomous vehicles. Finally, the experimental study is presented to bridge the gap between the theoretical and practical investigations on path following control of autonomous vehicles, and results validate the superior performance of the proposed method compared with existing works.