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.

This paper proposes a novel energy-efficient control method for vehicle suspensions that addresses key application-oriented issues, including inevitable disturbance, inherent nonlinearity, state-coupling effects, and energy consumption. Unlike conventional schemes, the proposed method explicitly quantifies the influence of disturbance and state-coupling by designing dedicated effect indicators and exploiting the beneficial aspects of nonlinear dynamics. Positive effects are harnessed, while negative effects are transformed into advantageous contributions, thereby delivering superior robustness, lower energy consumption, and improved response rapidity. Specifically, a fuzzy disturbance observer is developed to accurately estimate the disturbance factors, including both parametric/unmolded uncertainty and external disturbance. Effect indicators are introduced to characterize the positive and negative impacts of disturbance and coupling on the active suspension system, and these insights are incorporated into the design of a novel adaptive controller. In addition, biologically inspired nonlinear reference model is deliberately introduced to make use of favorable nonlinear stiffness and damping effects. Furthermore, Lyapunov’s theory is employed to ensure the asymptotic stability of the overall suspension system. Experimental validation demonstrated that the proposed approach achieves excellent transient performance and significant energy savings, with reductions of up to 80% or more.

This article proposes an adjustable-error neural network (NN) approximator and incorporates it into the adaptive neural tracking controller design of uncertain nonlinear systems. Noted that the error between the unknown nonlinear function and the NN approximator cannot be adjusted under the traditional NN control framework, as it is solely determined by the selection of neurons, basis functions, and the estimation of the ideal weight vector. This inherent constraint compromises the precision of the NN approximation and the convergence accuracy of the tracking error. To improve the approximation accuracy of unknown nonlinear functions in adaptive neural control systems, an adjustable-error NN approximator is designed, in which the error between the approximator and the unknown nonlinear function can be adjusted by designed parameters. Based on the proposed NN approximator, an adaptive neural tracking controller is designed for a class of uncertain nonlinear systems, which achieves higher accuracy of the tracking error compared with traditional methods. The stability of the resulting closed-loop system is proved in the Lyapunov sense, and the convergence of the tracking error is also analyzed. The effectiveness of the proposed scheme is verified by simulation and experiment.

This work investigates the fault-prediction optimal output regulation problem for the structural reliability feedback (SRF) system, and it aims to design a reliability feedback controller that minimizes the probability of fault (PoF) of the SRF system. Distinguished from the existing feedback control, the tracking of the upper bound of the PoF is considered to ensure the fault-prediction in the feedback control. The proposed design converts the PoF tracking problem into the satisfaction of the generalized damage energy (GDE). Furthermore, the impact of inaccurate measurement is eliminated by tolerating the epistemic uncertainty via a novel probabilistic policy iteration (PI). Moreover, the uniformly ultimately bounded (UUB) condition of the SRF system is guaranteed by employing the subset method. Finally, comparative investigations are conducted to examine the superiority of the proposed approach.

Reality digital twin (DT) model needs to be updated periodically, then the physical entity can be maintained efficiently. Since some physical entities may not be able to upload the entire status information actively, and also considering the universality of practical applications, we propose to use unmanned ground vehicle (UGV) as an information collector to assist in updating the reality DTs, where the UGV iterates over each target point (TP) to gather information by on-board sensors. Furthermore, considering the large amount of updating data and the limited computing resources on the UGV, we leverage mobile edge computing (MEC) technology to collaboratively process the data. In addition, to quantify the freshness of DTs, we propose the concept Age of DTs (AoDTs) as a metric to quantify the freshness of DT model. Thus, an AoDT minimization problem is established, which jointly optimizes offloading decisions, UGV waypoints selections, and TPs’ visiting orders, while also taking the obstacle avoidance into account. Considering the difficulty of the problem, we propose a novel low-complexity iterative algorithm to solve it. During which, a modified traveling salesman problem (TSP) solution is also proposed by taking into consideration the additional distance required to bypass the obstacles on each interwaypoints path. Finally, extensive simulation results show that the proposed algorithm can effectively reduce the AoDT, comparing to the benchmark algorithms.

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.

The steering dynamics of marine robots play a pivotal role in achieving precise maneuvering. However, complex and unpredictable ocean disturbances pose challenges for rapid online learning of accurate dynamics. This paper presents the Online Memory Koopman Learning (OM-Koop) framework, a hybrid model that combines physical priors, online data and stability preserving mechanisms to solve the nonlinear dynamic capture challenge and dynamically adapt to the marine environment. Firstly, we construct the Koopman operator-based uncertainty model online using the state error of the steering model and sliding window methods. The model can effectively capture the nonlinear features that are not represented in the predefined steering model. To ensure stability, the eigenvalues of the Koopman operator are constrained during online learning, guaranteeing Lyapunov stability. Secondly, in order to improve the efficiency of online learning, the Long Short-Term Memory (LSTM) neural network is involved in the construction process of the Koopman operator, which enhances the model’s memory capability. Finally, through field experiments using Autonomous Surface Vehicles (ASVs) and Autonomous Underwater Vehicles (AUVs) in field environments, comparative analyses with other learning strategies show that OM-Koop has excellent adaptability and robustness while guaranteeing Lyapunov stability. Note to Practitioners—The motivation of this article is that the steering dynamic behavior of marine robots is highly affected by unpredictable ocean environments, which poses great challenges in achieving precise manipulation in practical applications. In this paper, we propose the OM-Koop framework to address these challenges by integrating physical priors, online data learning, and stability preserving mechanisms. Theoretical analyses show that the proposed framework ensures Lyapunov stability while dynamically adapting to nonlinear disturbing forces imposed by the environment. Field experiments on AUV and ASV validate the robustness and adaptability of the framework, demonstrating its potential for practical deployment in dynamic marine environments. And the proposed framework has prospects for practical applications in other robotic systems.

Decoupling control of the air supply system is crucial for enhancing the performance and prolonging the service life of proton exchange membrane (PEM) fuel cells. However, the strong coupling and nonlinearity inherent in the system pose significant challenges. Current decoupling techniques typically rely on model knowledge and commonly overlook the avoidance of compressor surge, which motivates our work with a twofold contribution. We first design a data-driven feedforward (DDF) and propose a feasible domain constraint (FDC) to avoid surge. Subsequently, an adaptive generalized supertwisting algorithm (AGSTA) is presented that eliminates the residual tracking errors of the DDF. Furthermore, its gradient descent principle and stability are demonstrated. The proposed method has been validated on an air supply system test bench and a hardware-in-the-loop (HiL) platform carrying a fuel cell electric vehicle (FCEV) model. The results indicate that our approach is more advantageous in terms of tracking accuracy, response speed, overshoot suppression and computational cost.

This article proposes the row-stochastic event-based quantized (RSEQ) algorithm to address the distributed optimization problem with multiple communication constraints, including limited communication costs and bandwidth. In RSEQ, a novel event-based dynamic quantizer is designed to resist the negative effects of communication constraints on the algorithm. The quantizer encompasses the event generator and the dynamic encoder/decoder, which collectively adapt the frequency and size of information sharing based on real-time state. The RSEQ only requires the construction of a row-stochastic weight matrix, which leads to lower conservatism compared to algorithms based on column-stochastic matrices. Additionally, the introduction of an acceleration term enables RSEQ to linearly converge to the globally optimal solution without the deployment of the average gradient estimator. Instead, a Perron vector estimator needs to be employed to counteract the unbalancedness of the directed network. With the effect of the event generator, the Perron vector estimator can also be left inactive after a certain number of iterations, which means that the transmission of only state information between agents can linearly converge to the global optimal solution under directed networks. Finally, the effectiveness of the algorithm is demonstrated through an economic dispatch problem in smart grids.

The air suspension is an advanced suspension system for vibration suppression of vehicles. However, the real-time controllability of the air suspension is weak due to the time-delay characteristics of the air spring. This study designs a novel magnetorheological semiactive air suspension (MSAS) system and examines the fault characteristics of the MSAS system to improve the performance of vibration suppression. First, the generalized fuzzy hysteresis model is novelly proposed to approximate the hysteresis nonlinearity of the magnetorheological fluid damper. Then, the MSAS model with various fault modes is constructed to study the dynamic performance of the MSAS system under different fault modes. Furthermore, the intermediate estimator is adopted to detect the generation of the sensor and actuator faults. Based on the fault estimation, a reinforcement learning-based fault-tolerant (RLF) controller is proposed to improve the dynamic performance of the MSAS system. Moreover, a double wishbone suspension is built to examine the effectiveness of the proposed RLF controller. Experimental results show that the dynamic performance of the MSAS system with the proposed RLF controller is improved in comparison with the MSAS system with the model-based controllers and the passive suspension.

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.

With the development of intelligent connected vehicle technology, human-machine shared control has gained popularity in vehicle following due to its effectiveness in driver assistance. However, traditional vehicle following systems struggle to maintain stability when driver reaction time fluctuates, as these variations require different levels of system intervention. To address this issue, the proposed human-machine shared vehicle following assistance system (HM-VFAS) integrates driver outputs under various states with the assistance system. The system employs an intelligent driver model that accounts for reaction time delays, simulating time-varying driver outputs. Acontrol authority allocation strategy is designed to dynamically adjust the level of intervention based on real-time driver state assessment. To handle instability from driver authority switching, the proposed solution includes a two-layer adaptive finite time sliding mode controller (A-FTSMC). The first layer is an integral sliding mode adaptive controller that ensures robustness by compensating for uncertainties in the driver output. The second layer is a fast non-singular terminal sliding mode controller designed to accelerate convergence for rapid stabilization. Based on the driver-in-the-loop experimental results using the intelligent cockpit system, the performance of the HM-VFAS was evaluated. Results show that the proposed control strategy maintains a safe distance under time-varying driver states, with the actual acceleration error relative to the target acceleration maintained within$\pm 0.6\!\ \text {m/s}^{2}$and the maximum acceleration error reduced by$1.3\!\ \text {m/s}^{2}$. Compared to traditional controllers, the A-FTSMC controller offers faster convergence and less vibration, reducing the stabilization time by 26.8%.

The control problem for connected vehicular platoons requires balancing control optimality, saving computational and communication resources, and ensuring security. In this paper, a fuzzy prescribed performance adaptive optimal control strategy is developed for platoon system, and a distributed event-triggered (ET) mechanism is introduced. The main contributions include: 1) A prescribed performance adaptive dynamic programming (ADP) control architecture under distributed event-triggering is developed. The designed control method not only ensures the safety distance requirements of the platoon, but also significantly reduces the computing and communication costs, while guaranteeing the control optimality under the above objectives; 2) The stability proof of the platoon system considering the above complex control objectives is completed. Stable convergence of fuzzy logic system (FLS) is ensured by designing an experience replay-based critic update rule; 3) Considering the practicability in real working conditions, the cost function of the ADP controller robust to actuator saturation and disturbance is designed. Compared with existing methods, our approach achieves optimal control, robustness, and enhanced communication efficiency. Finally, the effectiveness and applicability of the controller are verified by simulations.

To enhance the trajectory tracking capability of steer-by-wire (SBW) vehicles while reducing driver’s workload, a human-machine shared control (HMSC) strategy using improved reinforcement learning-based model predictive control (RL-MPC) is proposed. In this paper, two main contributions have been made: 1) for driver loop, a variable steering ratio (VSR) strategy applied to SBW system is designed based on an improved fuzzy controller, whose parameters are optimized through simulated annealing (SA) algorithm; 2) for intelligent control system loop, an improved RL-MPC method is proposed to realize the high precision steering tracking control for autonomous vehicles (AVs), in which MPC and deep deterministic policy gradient (DDPG) are deeply integrated to combine their short-term optimization ability and long-term value estimation capability. Moreover, to shorten the time of overall training and ensure that the optimal control strategy can be explored, the DDPG agent is pretrained before the parallel training of RL-MPC. CarSim-MATLAB/Simulink co-simulation results show that in the whole tracking process, the lateral position error and yaw angle error of the vehicle are significantly reduced, indicating that the tracking accuracy is greatly improved. Meanwhile, the steering wheel angle and speed are also reduced, which means that the driver will spend less energy during the steering process.

This work focuses on the yaw stabilization of the four-wheel-independent-drive electric vehicle (FWID-EV) with the constrained active front steering (AFS) and direct yaw-moment control (DYC). First, a modified tire model is employed in the design of the unscented Kalman filter to realize the estimation of the tire-road friction coefficient (TRFC), and a backpropagation neural network is developed to online estimate the tire cornering stiffness; Second, a yaw stabilization supervisor is designed to solve the conflicts between the AFS and DYC systems, and the mode-boundary maps of the tire operating regions are utilized to generate the triggered signals so as to activate the systems; Third, a hierarchical adaptive model predictive control (MPC), including the estimation, activation, compensation, and distribution layers is proposed for yaw stabilization of the FWID-EV under the extreme conditions. Emergency maneuvers under big path curvature, low TRFC, and high vehicle speed are designed. Both software-in-the-loop and hardware-in-the-loop tests are performed to examine the effectiveness and practicability of the proposed methods, respectively.

This article investigates the issue of privacy preservation in distributed optimization, where each node possesses a local private objective function and collaborates to minimize the sum of those functions. A novel dynamic average consensus-based distributed Newton algorithm is introduced to achieve consensus, optimality, and differential privacy. Each node utilizes its local gradient and Hessian as time-varying reference signals, facilitating information exchange with neighbors for tracking the average. To safeguard privacy, persistent Laplace noise is introduced into the exchanged data, affecting the estimated optimal solution, gradient, and Hessian averages. To counteract the noise’s impact, the internode coupling strength is adaptively reduced over time through decay factors, allowing for noise attenuation as the algorithm progresses. The algorithm’s convergence to the optimal solution, assuming global function smoothness and strong convexity, is theoretically proven. The algorithm’s accurate convergence to the optimal solution, assuming global function smoothness and strong convexity, is theoretically proven. Furthermore, the efficiency and reliability of the algorithm are empirically validated through simulations of an IEEE 14-bus test system.

To enhance path tracking performance in the presence of steering motor faults, this paper introduces an active fault-tolerant control strategy for electric vehicles with four inwheel motors. Firstly, the single-track vehicle dynamics model and steering faults model are established. The control framework includes an upper-level linear parameter-varying model predictive controller for active front steering, an upper-level event-triggered predictive controller for direct yaw control, and a lower-level torque allocation controller. The bi-directional long short-term memory (Bi-LSTM) network is used for low-latency rapid detection of steering system faults. If the fault is detected, the upper-level controller for direct yaw control is triggered to mitigate the negative impact of the steering actuator faults. Based on the high-fidelity CarSim model, the simulation test is conducted under a double-lane change scenario with severe stuck faults in the steering system. The simulation results indicate that the proposed scheme can reduce the cumulative tracking error by 37.86% under the set stuck faults compared with the baseline method.

Resorting to the principle of rigid body kinematics, a novel framework for a multirobot network is proposed to form and maintain an invariant rigid geometric shape. Unlike consensus-based formation, this approach can perform both translational and rotational movements of the formation geometry, ensuring that the entire formation motion remains consistent with the leader. To achieve the target formation shape and motion, a distributed control protocol for multiple Euler-Lagrange robotic vehicles subject to nonholonomic constraints is developed. The proposed protocol includes a novel prescribed performance control (PPC) algorithm that addresses the second-order dynamics of the robotic vehicles by employing a combination of nonsingular sliding manifold and adaptive law. Finally, the effectiveness of the proposed formation framework and control protocol is demonstrated through the numerical simulations and practical experiments with a team of four robotic vehicles.

The consideration of reliability in controller design is able to avoid the potential actuator faults from inappropriate strategies. This work presents an optimal reliability-critical controller to avoid potential actuator faults by developing a probabilistic adaptive dynamic programming (ADP) algorithm with the estimation of fault interruption. The proposed algorithm distinguishes from existing ADPs in that the structural reliability is considered in policy iteration, endowing the resultant controller with the capacity to avoid potential actuator faults. The algorithm relaxes the generalized damage energy-Hamilton–Jacobi–Bellman equation to a reliability-critical problem, which is solved by proposing a probabilistic policy iteration method. Instead of studying the stability regardless of physical damage, the effect of physical damage is considered in the system stability in the form of structural reliability, and the probabilistic policy iteration guarantees the optimal relation between the stability and structural reliability. Finally, the effectiveness of the proposed algorithm is verified by conducting experimental tests.

Process industry indicator describes the production status and is crucial to the stable process operation. Its low sampling frequency makes it difficult to meet the indicator perception needs for real-time process control. Indicator estimation is a promising alternative to improve its obtaining frequency. However, the low sampling frequency of indicators leads to observation scarcity, discouraging shortening the estimation period. Moreover, fluctuations in working conditions (WCs) result in difficulty in reliable estimation. Therefore, a variable-period estimation method is proposed to change the estimation period reliably in the absence of observations. First, the WCs are identified by extracting semantic information from logs. Second, the network group is proposed, which achieves variable-period estimation by adjusting the number of subnetworks. Moreover, two mechanism constraints and a continuous accumulation mapping are proposed to ensure the estimation credibility. A case study of the zinc electrowinning process is provided to validate the method.

Wind power is of strategic importance for reducing carbon dioxide emissions, minimizing environmental pollution, and enhancing the sustainability of energy supply. Health monitoring of wind turbines is a crucial technology to ensure the quality of grid-connected power. Insufficient labeled data and class imbalance problems are two critical issues for intelligent fault diagnosis of wind turbines. In this article, an intelligent fault diagnosis method based on stacked capsule autoencoders is proposed to address the issues of inadequate labeled data and class imbalance. A prior knowledge-based convolution layer is applied to optimize the initialization of capsules, making it more conducive to learning spectral information. The pose representations of parts and objects can be improved, and a method for embedding spectral templates is proposed. The stacked capsule autoencoder in this study can learn partial templates unsupervised through likelihood estimation and establish the mapping between capsules and fault types. The experimental results, obtained from the CWRU dataset and a private dataset from a wind turbine drive-train simulation platform, demonstrate that the proposed method is robust to imbalanced and small-sized datasets. It can perform stable and effective unsupervised training by utilizing a sufficient amount of normal class data to expedite learning convergence.

Due to the inherently coupled dynamics of vehicle and powertrain levels, this paper proposes an energy management strategy that co-optimizes the power split and operating mode selection for hybrid electric vehicles. A mixed integer nonlinear model predictive control (MPC) problem is formulated to guarantee the sub-optimality and robustness of the strategy. The optimization objectives are to achieve minimal fuel consumption, battery degradation inhibition and state-of-charge maintenance within the system physical constraints. However, the existence of logic events and continuous variables poses a significant challenge to solve the optimal control problem. A Pontryagin’s Minimum Principle (PMP)-Dynamic Programming (DP) based solution method is presented, avoiding segmented linear approximation to the powertrain model and relaxation approach to the problem. The PMP calculates the optimal control sequence and cost for each mode, then determines the optimal operating mode based on DP. Moreover, the Gaussian process regression (GPR) model is developed for vehicle speed prediction to deal with the stochastic uncertainties of driving conditions. Experimental results are demonstrated that the proposed algorithm offers about 2% to 10% cost reduction compared to the conventional MPC, while still keeping relatively close to the result of DP.

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.

To achieve rapid and high-precision servo control of an electronic throttle, an adaptive control scheme is proposed based on the extremum seeking (ES), which consists of a variable-gain adaptive proportional-integral (ES-API) controller and an adaptive compensator (ES-ACP). The two gains (${K_{p}}$,${K_{i}}$) of the ES-API controller are designed as maps with respect to the tracking error, and the parameters of these maps are learned by ES. Additionally, the ES-ACP is applied to compensate for the strong nonlinearity inherent in an electronic throttle control (ETC) system, whose parameters are also learned by ES. During parameter learning, an objective function is utilized to quantify the tracking error of the opening angle of the electronic throttle plate, and then the parameters are learned using a step reference signal and a ramp reference signal. ES optimizes the above parameters by reducing the objective function to achieve a more favorable tracking response. Five reference signals are used to evaluate the learned controller after the parameter learning process is completed. Experiments were performed on a test bench equipped with an electronic throttle, and the experimental results show that the control scheme is capable of tracking multiple reference trajectories quickly and accurately.

In this article, a generalized fuzzy subset (GFS) method is proposed to assess the time-varying multistate reliability of the perturbation failure coupling measurement system. First, a perturbation-failure coupling mechanism is designed to define the propagation chain of perturbations so as to integrate all the possible perturbations as the inputs of the GFS method. Second, to assess the time-varying multistate reliability, a GFS reliability model is constructed based on the composite limit state. Furthermore, the concept of the uncertain subset boundary is presented to conduct the reliability assessment via a group of embedded interval type-2 fuzzy sets. To address the deficiency of the GFS reliability model, a data-driven strategy is designed to establish the implicit relation between the limited expert knowledge and the membership function. Finally, the experimental tests are carried out to examine the superiority of the GFS method, and parametric studies are also conducted to study the reliability of the PFCM system.

Without any knowledge of the vehicle model and its parameters, a novel complete model-free path-following control strategy is developed for autonomous vehicles based on the time-delay estimation (TDE) technique. Different from the existing time-delay control (TDC) approaches, an adaptive nonsingular terminal sliding mode (ANTSM) control law is designed to stabilize the path-following errors without any information of the suitable control gain, which is significant in the conventional TDC scheme, and the boundary of the TDE error, which is necessary for the sliding-mode-based control scheme. The proposed model-free control structure can dynamically update the gain of the designed controller and the boundaries of the TDE error, and the practical finite-time convergence of the preview error can be achieved. In the HIL tests, the comparative results demonstrate that the proposed ANTSM model-free control strategy can provide superior comprehensive tracking performance over both the model-based sliding mode controller and the conventional TDC controller, while the autonomous vehicle follows desired paths.

With consideration of tire force saturation in vehicle motions, a novel path-following controller is developed for autonomous vehicles with unknown-bound disturbances and unknown actuator parameters. An adaptive sliding-mode fault-tolerant control (ASM-FTC) strategy is designed to stabilize the path-following errors without any information of disturbance boundaries, actuator fault boundaries and steering ratio from the steering wheel to the front wheels. By selecting the distance from the center of gravity to the center of percussion as the preview length, the effects of the lateral rear-tire force are decoupled and cancelled out, and then the preview error, which represents the path-following performance, can be only commanded by the front-tire force. To further address the issue of unknown tire-road friction limits, a modified ASM-FTC strategy is presented to improve the path-following performance as the lateral tire force is saturated. Simulation results show that the modified ASM-FTC controller demonstrates superior tracking performance over the normal ASM-FTC while the autonomous vehicle follows desired paths.

With the consideration of actuator faults, including the unknown steering mechanism misalignments and motor traction losses, this article presents a fixed-time control protocol to follow reference paths and velocities for autonomous ground vehicles (AGVs) with preset performance constraints. To provide sufficient large boundaries for the initial states, the hyperbolic tangent function is employed to predefine the constraints with respect to the path-following and velocity control performance. Based on the homeomorphic mapping and barrier Lyapunov theorem, the fixed-time prescribed performance control (PPC) objective-integrated fault-tolerant scheme can be achieved for the controlled AGV. In comparison to three different fixed-time controllers without the fault-tolerant or PPC scheme, the hardware-in-the-loop (HIL) test results demonstrate that the proposed control protocol can always provide superior control performance for the AGV under various maneuvering conditions.

In ice and snow-rutted conditions, it is challenging to design a vehicle stability controller to simultaneously resolve the conflict between the accuracy of the system model and the easy implementation of the controller. To this end, the application of a data-driven control method for vehicle stability control represents a novel, feasible opportunity. This article introduces Givens rotation and forgetting factors to efficiently update the subspace prediction equation with online data. An online data-driven predictive control (ODPC) method is proposed on this basis. To address the problem that persistently excited (PE) condition will cause fluctuations in the steady-state response of ODPC, a data-driven switched predictive control strategy (DSPCS) employing attenuated excitation (AE) signals and hysteresis comparisons based on posterior prediction errors is proposed. In addition, an implementation method involving the Laguerre function (LF) parameterization of the control input is proposed to improve the computational efficiency further. Numerical simulation results show that both the ODPC method and the DSPCS can effectively track given yaw rate and sideslip angle reference under the influence of ruts. Furthermore, the DSPCS can effectively reduce steady-state response fluctuations. In addition, the LF parameterization is superior regarding computational time.

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.

As tire forces approach road friction limits, vehicles may always exhibit performance degradation and even instability. The actual capacity of the available road friction imposes coupled limits on a vehicle’s longitudinal and lateral accelerations. In this paper, a varying speed method is proposed to design feasible speeds and accelerations, which ensure that the autonomous vehicle will not reach the tire-road friction limits during traversing a clothoid-based path. With the consideration of uncertain traction losses and vehicle parameters, a second-order super-twisting (ST) based speed control strategy is proposed to track above feasible speeds based on varying speed method, and to stabilize the sliding-mode variable of the speed tracking error with relative degree 1. Meanwhile, a second-order quasi-continuous (QC) based path-following control strategy is proposed to follow a desired transition path, and to stabilize the sliding-mode variable of the composite path-following errors with relative degree 2. On this basis, the proposed controllers have been verified to lead good robustness for tracking the ideal speeds and following the desired paths. As compared with the boundary of the autonomous vehicle running at a constant speed, the feasible speed boundary using varying speed method without exceeding the tire-road friction limits can be enlarged up to about 1.59 times, which is decided by the direction change between the entry and exit points of the desired path.

This paper studies the distributed consensus protocol for the connected vehicle platoon with heterogeneous time-varying delays and switching topologies. A third-order dynamics model with powertrain inertial lag is proposed to characterize the node longitudinal dynamics of vehicles in platoon. A novel distributed adaptive consensus protocol considering the time-varying delays and the random switched inter-vehicular communication topologies is designed to stabilize the heterogeneous vehicle platoon in the presence of external disturbance. The delay-range-dependent approach is used to deal with the system heterogeneous time-varying delays by considering the characteristics of the heterogeneous platoon. Directed graphs are adopted to describe the accessible information flow among vehicles. The necessary and sufficient conditions for the unified closed-loop vehicle platoon system are derived by using matrix analysis and Lyapunov-Krasovskii approach. Numerical simulations demonstrate the proposed method is effective.

Road traffic accidents, especially those accidents with multiple-vehicle collision usually cause injuries and mortalities. Currently, studies on collision avoidance mainly focus on the control strategies for adjacent two vehicles or multiple vehicles in a single platoon direction. This paper proposes a bi-directional collision avoidance system for multiple vehicles to minimize the collision risk under the model predictive control (MPC) framework through switching the vehicle-following mode based on the inter-vehicular states. A hierarchical structure with an upper layer and a lower layer is designed. A dual-operational mode switching strategy and the vehicle-following model are formulated in the upper layer, together with the development of the acceleration redistribution cooperative strategy for vehicle platoon. While the lower layer is designed to track the desired acceleration received from the upper layer by considering the practical situation of the control commands. To tackle complex transitional operation, a dynamic weighted tuning strategy is proposed and integrated it with the MPC. The numerical results show that the proposed system outperforms the conventional collision avoidance system and is effective to avoid a collision or minimize the total impact of the vehicle platoon when the collision is unavoidable.

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.