With the rapid development of autonomous unmanned vehicle (AUV) technology, the requirements for sensor data integrity and reliability are increasing. Data-driven intrusion detection systems (IDSs) have been widely used as an effective module of defense to ensure AUV security. Once the IDS is destroyed, the impact on the normal driving of the AUV will be unimaginable. Unlike traditional research that seeks to improve the detection accuracy of IDS, this paper discusses the challenges posed by adversarial attacks on autonomous vehicles from the perspective of attackers. In this paper, an generative adversarial-enabled U-shaped network (GEUN) is proposed to generate adversarial sensor attacks in AUVs and reveal vulnerabilities in existing IDSs. Specifically, GEUN consists of a shared feature extraction encoder, an adversarial anomaly reconstruction decoder, a discriminator, and a detector. The feature extraction encoder is used to add multi-scale information to the input to strengthen the multi-scale feature extraction of sensor signals by the network. With the help of discriminators and detectors, the adversarial anomaly reconstruction decoder is used to generate anomalies based on the extracted multi-scale vehicle information. Through experimental comparison with other methods, it is shown that the proposed GEUN can effectively attack various common IDS, which provides guidance for future research and development in the field of security of AUVs.

The existing research on intelligent driving vehicles mainly focuses on improving the performance of safety, economy, and control accuracy, ignoring the personalized manipulation pReferences of different driving groups. The differences in driving styles and preferences of different passengers require that the driving behavior of intelligent driving systems in different traffic situations should conform to the habits of self-vehicle passengers, that is, to achieve personalized driving considering human factors. This paper provides a comprehensive and systematic review of the research status in the field of personalized driving. Firstly, it clarifies the necessity of personalized driving. Secondly, the existing decision-making and planning methods for personalized driving of single-vehicle are summarized from two aspects: machine learning-based methods and driver characteristic characterization-based methods. On this basis, the interactive decision-making and planning method of multi-vehicle games considering personalized preference in intelligent networking and mixed driving environments is summarized. Finally, the problems faced by the research of personalized intelligent driving systems and the future development trend are analyzed and prospected.

Traffic sign recognition (TSR) is one of the most important visual perception tasks for autonomous vehicles. Traffic signs situated far away occupy only a few pixels in the images captured by the cameras of the autonomous vehicle, which presents a challenge for TSR methods to give accurate and reliable results. In this paper, we propose a panoramic active visual system (PAVS) for distant traffic signs recognition. It combines the advantages of the active pan-tilt-zoom(PTZ) camera for long-distance viewing and the advantage of the panoramic camera to have a large field of view. The traffic signs will be preliminarily detected and tracked in the panoramic image and the objects with confidence lower than the threshold will be further detected by the active PTZ camera to get more reliable results. The proposed PAVS are tested with different state-of-the-art (SoTA) TSR networks in real traffic scenes and the experimental results show that, compared to the passive visual system, the performances of the proposed PAVS improve 8.5% in F1-score, 5.3% in mAP, and 60.2% in mean first detection distance (mFD) in traffic sign recognition tasks.

Autonomous vehicles (AVs) have garnered significant attention in the field of smart logistics due to their potential to greatly enhance transportation efficiency. While significant focus has been placed on the capabilities of individual AVs, less attention has been given to the complexities of coordinating multiple vehicles within dynamic traffic environments. Key factors such as combined traffic flow, varying traffic signals, and complex cooperative maneuvers play a major role in effective multi-vehicle scheduling. This paper aims to address these overlooked challenges by exploring dynamic traffic conditions and micro-level cooperation within multi-vehicle routing systems. The large-scale multi-vehicle routing problem (MVRP) is reformulated into a series of smaller-scale cooperative vehicle routing problems (VRPs). A distributed model predictive control-based (DMPC-based) planner is constructed and applied in parallel across connected autonomous vehicles (CAVs). Each instance of distributed model predictive scheduling incorporates a closed-loop vehicle dynamics to predict lane-changing and platooning maneuvers, where feedback controllers are introduced. To optimize routes toward destination, a destination-oriented search space is rebuilt with the filtered feasible routes. By accessing real-time traffic lights, a model predictive control (MPC) problem is formulated, where the vehicle-lane distribution is predicted with a sub-optimization of the shortest queue and combined with the predictive scheduling. Comparisons against existing methods reveal that the proposed planner exhibits significant improvements in time efficiency and successes to avoid traffic congestion, which is available athttps://github.com/ZNianHua/DMPC-planner-for-VRP

Large language models (LLMs), AI systems trained to process and generate human language, are increasingly being integrated into autonomous vehicles, leading to the emergence of LLM-assisted driving systems (LADSs). Rigorous evaluation of LADSs is essential for driving technological progress and building user trust. However, there is currently a lack of specific evaluation metrics for LADSs, and existing LLM evaluation methods are not readily applicable to LADSs. To evaluate the performance of LADSs, this study proposes four assessment indices that collectively consider driving robustness, safety, and ethical decision-making. First, cosine similarity is employed to guide the injection of disturbances, establishing a basis for quantitative input-output analysis. Second, robustness and safety indices are proposed to characterize vehicle performance, while an LLM-based evaluator is used to assess ethical behavior. To enhance alignment with human judgment, a language-numerical optimization algorithm is developed for prompt tuning. By integrating the knowledge base, Cohen's Kappa (κ) between the experienced driver and the LLM-based evaluator reaches 0.81, indicating strong agreement. Additionally, this study first identifies and analyzes a novel phenomenon termed "extreme thinking". Building on these results, a multi-dimensional safety assessment index is proposed to evaluate LADSs. The proposed indices and methods are validated using over 1000 data segments collected from both simulations and experiments.

Autonomous vehicle platoons are particularly suitable for repetitive tasks due to their capability for efficient coordination, enhanced safety, and reduced driver fatigue. The offline-designed control policy faces difficulties in adapting to changing conditions without driver intervention or vehicle self-learning, which can lead to inadequate coordination among vehicles and an increased risk of collisions or disruptions. This paper presents an iterative learning distributed model predictive control (ILDMPC) strategy designed for 2-dimensional (2-D) autonomous vehicle platoons, allowing vehicles to learn from their previous iterations to minimize control errors and improve overall performance. First, the combined lateral and longitudinal dynamics incorporating load transfer of heterogeneous autonomous vehicle platoons are modeled together. Then, traffic regulations and mechanical constraints are defined and integrated into an optimal control problem with multiple objectives using the ILDMPC framework. This approach differentiates the platoon leader (PL) from the platoon followers (PFs) by employing distinct References. Additionally, the iterative learning is integrated into the optimal control problem via a convex terminal cost within a finite time horizon, completing the ILDMPC strategy. This strategy allows autonomous vehicle platoons to iteratively perform repetitive tasks, achieving optimal performance through iterative online learning. Simulations are carried out to demonstrate the effectiveness of the proposed controller, which is validated to evolve existing control laws and result in a 40% improvement as quantified by the error-based indicator. The associated codes are accessible athttps://github.com/ZNianHua/ILDMPC

In this study, we propose an innovative multi-stage control framework for human-lead-platooning of autonomous vehicles in complex, mixed traffic environments. The framework begins with collecting aggressive driving data from expert human drivers under various weather conditions and visibility levels, which inform a Refined Intelligent Driver Model for predicting the driving states of human-driven vehicles. A novel trust mechanism is then introduced to guide the trajectory selection for each autonomous follower, leveraging a reference set provided by the human-driven leader. In parallel, user-centric preferences (e.g., motion sickness, emotional fear, situational urgency) are captured and converted into precise acceleration and control constraints through a Fuzzy Logic System. Finally, a distributed switching model predictive control algorithm coordinates lane changes and vehicle-following tasks in real time for each follower. The proposed approach is validated through hardware-in-the-loop testing, demonstrating both effectiveness and adaptability in diverse traffic scenarios.

This paper proposes a novel sub-polytope integrated approach (sPIA) that features an intrinsic bump-free transition, aiming to reduce conservatism in the design of path tracking control for autonomous vehicles with large range time-varying longitudinal velocity. The approach encapsulates the interdependent time-varying parameters associated with longitudinal velocity as a set of finite-vertex sub-polytopes interconnected via junction points, thereby reducing the conservatism induced by modeling overbounding. The integration of junction points and the formulation of sub-region activation rules provide a theoretical foundation for avoiding abrupt changes in feedback gains, ensuring a bump-free transition between sub-regions. A gain-scheduling state feedback controller is designed, employing parameter-dependent Lyapunov functions to further attenuate design conservatism. The effectiveness of the proposed method in reducing design conservatism is demonstrated by a comparative analysis of the optimal H∞performance indices across various sub-polytope integration schemes. Furthermore, the superiority of the method is exemplified via simulations within real-world driving scenarios, utilizing the high-fidelity CarSim-Simulink platform. The results indicate that the proposed sPIA outperforms traditional polytopic methods in path tracking performance. This improvement, together with the effective avoidance of bumps during sub-regional transitions, confirms the efficacy of the proposed approach. Moreover, the real-time performance of the method is verified by hardware-in-the-loop experiments.

In low-light environments, insufficient illumination greatly reduces the vehicle’s vision system’s ability to detect and recognize vehicles and pedestrians. To overcome this problem, this research proposes an enhanced algorithm, YOLOv8-LLE, specifically designed for object detection in low-light environments. The algorithm firstly introduces Retinexformer for low light image enhancement, then replaces the Spatial Pyramid Pooling-Fast (SPPF) with a focal modulation network FocalNets to suppress the noise more efficiently, and finally introduces the neck of the Efficient Multi-Scale Attention to retain more information of features while effectively reducing the number of network parameters. Due to the lack of low-light scene datasets for autonomous driving, this paper also proposes a low-light image dataset construction method. Experimental results show that the constructed low-light image dataset can better restore the image features in real low-light environment. Compared with the original model, the proposed YOLOv8-LLE improves 2.6%, 6%, 3.1%, and 3.7% in Precision, recall, [email protected], and [email protected]:0.95, respectively. Real-world vehicle experiments further validate the superior performance of YOLOv8-LLE in detecting vehicles and pedestrians in actual low-light environments.

In the vehicular ad-hoc network (VANET) environment, the constant changes in traffic systems due to the real-time mobility of vehicles pose a challenge in developing communication strategies that can swiftly adapt to dynamic topological variations. Traditional protocols such as CSMA/CD offer dynamic access to shared media, but their efficiency is compromised by CSMA/CA's collision avoidance strategy, which reduces channel use due to its backoff algorithm. This paper introduces a novel Graph Convolutional Network-based Multi-Agent Reinforcement Learning MAC protocol (GM-MAC) for vehicular networks. Unlike the Neuro-DCF protocol, GMMAC incorporates local vehicular data directly, enabling more context-aware learning. Compared to other GNN-based MARL approaches like GraphComm, GMMAC better handles real-time changes and interactions among vehicles. Experimental results show that GMMAC significantly improves successful transmission numbers and reduces collisions, outperforming traditional CSMA/CA methods and other MARL approaches without GCN integration.

Fatigue driving has been regarded as one of the most important factors that cause traffic accidents. This paper proposes a robust human-machine shared control strategy to improve the vehicle performance for different driver fatigue states. Firstly, the time-varying driver steering model is proposed to address the model mismatch caused by fatigue driving. And the driver fatigue evaluation system is established based on facial features to quantify driver fatigue levels. Based on the quantified fatigue levels, a novel strategy for allocating authorities of the driver and controller is developed for building the driver-vehicle interaction system. Then, to weaken the influence of parameter perturbations caused by the time-varying driver states, we design a fatigue-based shared controller through state feedback. The actuator saturation and system constraints are considered in the controller design through the robust set-invariance property to improve vehicle safety and driving comfort. The driver-in-the-loop platform is conducted to validate the effectiveness of the proposed shared steering controller. The experimental results show that the proposed strategy can adaptively optimize the human-machine authorities according to fatigue states and comprehensively improve vehicle performance.

This paper studies a hierarchical model predictive control (MPC) strategy to optimize the powertrain and aftertreatment systems for connected diesel-powered vehicles. With the development of vehicle connectivity and autonomy, it is convenient to acquire vehicle speed prediction and future geographic information for improving driving safety and fuel economy. Inspired by such achievements, preview speed and geographic information is utilized to enhance the control performance of diesel engines and urea-based selective catalytic reduction (SCR) systems simultaneously in this work. With the short-term prediction of vehicle speed and road grade, the upper-level controller for the diesel engine could respond in advance and hence reduce fuel consumption by avoiding sudden braking and acceleration. Similarly, according to the engine-out NO$_{x}$emissions predicted through upper-level control actions, the lower-level dosing controller of SCR system could remove the NO$_{x}$emissions more efficiently as well. Finally, to explore the effectiveness of the designed predictive control strategy, several simulations are implemented based on the real experimental data. The comparison results demonstrate the remarkable improvements of our proposed approach.

Vehicle carbon emission models are essential for monitoring and managing transportation emissions. Considering the numerous vehicle types and diverse driving conditions, modeling each vehicle individually is not possible. A generalized carbon emission module (GCEM) is proposed to realize fast model transfer across both light-duty gasoline and heavy-duty diesel vehicles. It has five main components: normalized torque function, category-specified fuel consumption rate function, denormalized function, emission-standard correction, and fuel-specified conversion. GCEM estimates carbon emissions based on the engine bench results of a reference vehicle, avoiding time-consuming and costly experiments. By normalizing engine torque, the significant power disparities within the same vehicle category can be effectively addressed. Integrating GCEM within the parallel transportation system is a feasible solution for monitoring complex traffic emissions. Through comprehensive comparisons, GCEM demonstrates a superior generalized ability in this data-limited scenario than three baseline models, both for various test vehicles and diverse driving conditions. As real-world traffic is a typical data-limited scenario, GCEM is a promising and practical transfer method for estimating vehicle carbon emissions.

To cater to fault diagnosis of rotating machinery under complex working conditions, unsupervised domain adaptation technology has been widely explored and applied. Existing methods mainly reduce domain bias in two ways, including metric learning and discriminator-based adversarial learning. Different from these technologies, in this work, we only resort to entropy optimization strategies and develop a novel entropy-oriented domain adaptation (EODA) model for intelligent diagnosis of rotating machinery. Specifically, a convolutional network with a cosine-distance classifier is introduced to construct the model framework, which can reduce intraclass variation and make the output more confident. In addition, negentropy-guided prediction diversity optimization and minimax entropy game-guided prototype-feature alignment are co-designed to realize domain adaptation. Extensive experiments based on two different mechanical systems are used to validate our method. Comprehensive results and discussions demonstrate that our EODA can achieve compelling performance.

Vehicle platoon control based on vehicle-to-vehicle (V2V) communication is one of the promising technologies to improve the performance of transportation systems. This paper presents a distributed controller to optimize a vehicle platoon’s fuel consumption by combining the switching feedback control and economic model predictive control (EMPC) methods. The closed-loop dynamics involving switching feedback gains with the constant time headway (CTH) policy are established firstly, and the multiple-predecessor following (MPF) communication topology is considered. In order to obtain the economy optimal feedback gain, we design a local optimal control problem for each vehicle, based on which the average dwell time is defined and the distributed EMPC algorithm is designed. Based on linear matrix inequalities (LMIs) and the Lyapunov theorem, the feedback gain selection method and the lower bound for average dwell time that guarantees asymptotic stability are analyzed rigorously. Then a modified algorithm that can ensure string stability is designed. Numerical simulations show a maximum of 6.84% fuel benefit compared with pure tracking-oriented methods.

In this paper, we investigate the PID controller design problem of path following for an autonomous ground vehicle (AGV). Firstly, a bicycle model is adopted and a vehicle offset model from the target path is integrated to the bicycle model. The PID controller considers the vehicle longitudinal speed variation for adaption to the road curvature and the stochastic delay induced by the network communication. This is realized by transforming the tuning problem of proportional-integral-derivative (PID) gains for path following into a design problem of a static-output-feedback (SOF) controller for a time-delayed linear parameter varying (LPV) model form. A sufficient condition is adopted to guarantee the stability of the closed-loop system. In order to achieve better tracking performance, we propose a strategy in which the PID gains are piecewise constant and are dependent on the road-curvature ranges. The stability of the switched system is guaranteed via the common Lyapunov function method. Grey wolf optimizer (GWO) is employed to solve the optimization problem with maximum absolute tracking error as the optimization objective and stability condition and actuator dynamics as constraints. Both simulation results based on the CarSim-Simulink joint platform and hardware-in-loop experiment results are used to verify the effectiveness of the proposed control strategy.

In unmanned vehicles, LiDARs and GPS/INSs are the most popular sensors to achieve perception and positioning. Precise calibration of the extrinsic parameters between the LiDAR and the GPS/INS is necessary for a successful implementation of sensor fusion. The extrinsic transformation between the LiDAR and GPS/INS is 6D ($x$,$y$,$z$,$yaw$,$pitch$,$roll$), but the motion of a vehicle is mainly 3D ($x$,$y$,$yaw$). The problem is to calculate the 6D extrinsic parameters with the limitation of 3D motion (plane constraint). The solution to this problem has been breaking the plane constraint by designing specific vehicle motions. This paper proposes a new method, a trajectory-based hand-eye calibration method, which makes full use of the large range of unmanned vehicles. The trajectories with large and small ranges are used to solve the rotation and translation, respectively. It is proved that the extrinsic parameters can be solved when the trajectory range of the unmanned vehicle is sufficiently large. The method proposed is tested with simulation, custom and KITTI datasets, and compared with the state-of-the-art methods. The results demonstrate that the accuracy and efficiency of the method proposed are comparable to the state of the art methods.

This paper is concerned with a new control method for path tracking of autonomous ground vehicles. We exploit the fuzzy model-based control framework to deal with the time-varying feature of the vehicle speed and the highly uncertain behaviors of the tire-road forces involved in the nonlinear vehicle dynamics. To avoid using costly vehicle sensors for feedback control while favoring the simplest control structure for real-time implementation, a new fuzzy static output feedback (SOF) scheme is proposed. In particular, though the robust set-invariance property and Lyapunov-based arguments, the physical constraints on the steering input saturation and the vehicle state can be taken into account in the control design to improve the driving safety and comfort. The theoretical development relies on the use of fuzzy Lyapunov functions and the non-parallel distributed compensation control concept to reduce the design conservatism. Exploiting some specific convexification techniques, the control design is reformulated as an optimization problem under linear matrix inequalities with a single line search, which are efficiently solved via semidefinite programming techniques. The proposed fuzzy path tracking controller is evaluated through various dynamic driving tests conducted with high-fidelity CarSim/Matlab co-simulations. Moreover, to emphasize the advantages of the new fuzzy SOF controller, a performance comparison with the CarSim driver model is also performed.

This paper presents a new control method for path tracking of autonomous vehicles. Takagi-Sugeno fuzzy control is used to handle the time-varying vehicle speed and the uncertain tire-road forces involved in the nonlinear vehicle dynamics. To avoid using costly vehicle sensors while keeping a simple control structure, a new fuzzy static output feedback (SOF) scheme is proposed. Moreover, robust set-invariance is exploited to take into account the physical limitations on the steering input and the vehicle state in the control design for safety and comfort improvement. Based on Lyapunov stability arguments, a non-parallel distributed compensation SOF controller is designed for autonomous driving with reduced conservatism. The control design is reformulated as an optimization problem under linear matrix inequalities, easily solved with available numerical solvers. The path tracking performance of the proposed fuzzy controller is evaluated via dynamic driving tests conducted with high-fidelity CarSim/Simulink co-simulations.

In this paper, we investigate the Least Squares-Support Vector Machine (LS-SVM) control for path following problem of autonomous ground vehicles (AGV). Firstly, the steering angle input and the lateral offset output of the path following system model which is the two degree of freedom (DOF) vehicle model with integrated vision model are collected to form the training data of the LS-SVM model. Then, the LS- SVM model for the path following of vehicle is trained from the collected data and the controller is solved from the LS-SVM model. The effectiveness of the proposed control approach is validated by the performance of the closed-loop path following system in Matlab/Simulink.

In this article, the position sensor deception attack detection and estimation problem is investigated for a local vehicle in a vehicle platoon. In a platoon system, the position measurement is critical as the distances between neighboring vehicles are relatively small. However, the position measurement of vehicles is usually vulnerable to deception attacks as it relies on external information, such as GPS and environment information from cameras. Therefore, position sensor deception attack detection and estimation should be addressed for local vehicles in a platoon. To deal with this problem, a linearized model is presented to describe the longitudinal dynamics of a local vehicle. Moreover, modeling uncertainties, measurement noises, and piecewise constant deception attacks injected in position measurement are specified along with this model. Based on this model, a scheme based on a modified unbiased finite impulse response (UFIR) estimator is proposed to generate an intermediate estimated value related only to the attack. Then, the deception attack is recovered based on this value through a function fitting strategy. Based on analysis results, simulations are conducted to verify the effectiveness of the proposed attack detection and estimation scheme.

This paper provides a new solution for path following control of autonomous ground vehicles. H2control problem is considered to attenuate the effect of the road curvature disturbance. To this end, we formulate a standard model from the road-vehicle dynamics, the a priori knowledge on the road curvature, and the path following specifications. This standard model is then represented in a Takagi-Sugeno fuzzy form to deal with the time-varying nature of the vehicle speed. Based on a static output feedback scheme, the proposed method allows avoiding expensive vehicle sensors while keeping the simplest control structure for real-time implementation. The concept of V-stability is exploited using Lyapunov stability arguments to improve the transient behaviors of the closed-loop vehicle system. In particular, the physical upper and lower bounds of the vehicle acceleration are explicitly considered in the design procedure via a parameter-dependent Lyapunov function to reduce drastically the design conservatism. The proposed H2design conditions are expressed in terms of linear matrix inequalities (LMIs) with a single line search parameter. The effectiveness of the new path following control method is clearly demonstrated with both theoretical illustrations and hardware experiments under realworld driving situations.

As the requirements on powertrain efficiency of electric vehicles (EVs) are increasing, integrated motor-transmission (IMT) powertrain systems for EVs are becoming a promising solution. For the integration of IMT powertrain systems, the system state information and the actuator status are usually required for the closed-loop controller design or the on-board fault diagnosis. Embracing the demands, an observer for simultaneous estimation of input and system state of an IMT powertrain system is studied in this paper. It is well-known that controller area network (CAN) has been dominant in the vehicle network, which is used to communicate among controllers, sensors, and actuators. However, the CAN bus always induces time-varying delays when there are a number of communication nodes on the bus. The CAN-bus induced delay would result in vibrations in the vehicle powertrain or even deterioration of the entire closed-loop system. To deal with the CAN-bus induced delay in the estimation work for IMT powertrain systems, the potential random delays are considered in a three-state nonlinear model which represents the behavior of an IMT system. To estimate the input and state simultaneously, an adaptive unscented Kalman filter (AUKF) is adopted. As we know, the adopted AUKF has the benefits of dealing with system nonlinearities and calculating the noise covariance matrix automatically. Simulations and comparisons are carried out. We can see from the results that the proposed observer estimates the input and system state well. Moreover, the resulting estimation error is smaller comparing with the estimation error of the observer based on extended Kalman filter algorithm.

Eliminating the influence of cogging torque and model uncertainty on the tracking control of a dc motor when its speed varies nonperiodically is a challenge. In this paper, an adaptive robust triple-step control method is proposed for compensating cogging torque and model uncertainty. First, a new presentation of the cogging torque and a simplified model of the friction torque are presented to facilitate the online estimation of the unknown model parameters. The load torque, motor disturbance, and model errors are considered as model uncertainty. Based on these considerations, a control-oriented model that contains unknown parameters and model uncertainty is obtained. Second, benefitting from the new presentation, an adaptive algorithm is employed to identify the unknown parameters online. The model uncertainty is estimated by an extended state observer. Third, the model-based triple-step nonlinear method is extended to a system with both parameter uncertainty and model uncertainty, and an adaptive robust triple-step nonlinear controller is derived. The robust stability of the closed-loop system is proven in the framework of Lyapunov theory. Finally, the effectiveness and the satisfactory control performance of this controller are evaluated through comparative experiments on a J60LYS05 motor.

As a typical example of cyber-physical systems, intelligent vehicles are receiving increasing attention, and the obstacle avoidance problem for such vehicles has become a hot topic of discussion. This paper presents a simultaneous trajectory planning and tracking controller for use under cruise conditions based on a model predictive control (MPC) approach to address obstacle avoidance for an intelligent vehicle. The reference trajectory is parameterized as a cubic function in time and is determined by the lateral position and velocity of the intelligent vehicle and the velocity and yaw angle of the obstacle vehicle at the start point of the lane change maneuver. Then, the control sequence for the vehicle is incorporated into the expression for the reference trajectory that is used in the MPC optimization problem by treating the lateral velocity of the intelligent vehicle at the end point of the lane change as an intermediate variable. In this way, trajectory planning and tracking are both captured in a single MPC optimization problem. To evaluate the effectiveness of the proposed simultaneous trajectory planning and tracking approach, joint veDYNA-Simulink simulations were conducted in the unconstrained and constrained cases under leftward and rightward lane change conditions. The results illustrate that the proposed MPC-based simultaneous trajectory planning and tracking approach achieves acceptable obstacle avoidance performance for an intelligent vehicle.

In this paper, we investigate the H∞ filtering problem of discrete-time Takagi'Sugeno (T-S) fuzzy systems in a network environment. Different from the well used assumption that the normalized fuzzy weighting function for each subsystem is available at the filter node, we consider a practical case in which not only the measurement but also the premise variables are transmitted via the network medium to the filter node. For the network characteristics, we consider the multiple packet dropouts which are described by using a Markov chain. It is assumed that the filter uses the most recent packet. If there are packet dropouts occurring, the filter adopts the information for the last received packet. Suppose that the mode of the Markov chain is ordered according to the number of consecutive packet dropouts from zero to a preknown maximal value. For each mode of the Markov chain, it only has at most two jumping actions: 1) jump to the first mode and the current packet is transmitted successfully and 2) jump to the next mode and the number of consecutive packet dropouts increases by one. We aim to design mode-dependent and fuzzy-basis-dependent T-S fuzzy filter by using the transmitted packet subject to the described network issue. With the augmentation technique, we obtain a stochastic filtering error system in which the filter parameters and the Markovian jumping variable are all involved. A sufficient condition which guarantees the stochastic stability and the H∞ performance is derived with the Lyapunov method. Based on the sufficient condition, we propose the filter design method and the filter parameters can be determined by solving a set of linear matrix inequalities (LMIs). A tunnel-diode circuit in a network environment is presented to show the effectiveness and the advantage of the proposed design approach.

In this paper, we investigate the filtering problem of discrete-time Takagi-Sugeno (T-S) fuzzy uncertain systems subject to time-varying delays. A reduced-order filter is designed. With the augmentation technique, a filtering error system with delayed states is obtained. In order to deal with time delays in system states, the filtering error system is first transformed into two interconnected subsystems. By using a two-term approximation for the time-varying delay, sufficient delay-dependent conditions of finite-time boundedness and H∞performance of the filtering error system are derived with the Lyapunov function. Based on these conditions, the filter design methods are proposed and the filter gain matrices can be obtained by calculating a set of linear matrix inequalities. A numerical example is used to illustrate the effectiveness of the proposed approaches.

This paper focuses on the filter design for nonuniformly sampled nonlinear systems which can be approximated by Takagi-Sugeno (T-S) fuzzy systems. The sampling periods of the measurements are time varying, and the nonuniform observations of the outputs are modeled by a homogenous Markov chain. A mode-dependent estimator with a fast sampling frequency is proposed such that the estimation can track the signal to be estimated with the nonuniformly sampled outputs. The nonlinear systems are discretized with the fast sampling period. By using an augmentation technique, the corresponding stochastic estimation error system is obtained. By studying the stochastic stability and the energy-to-peak performance of the estimation error system, we derive the linear-matrix-inequality-based sufficient conditions. The parameters of the mode-dependent estimator can be calculated by using the proposed iterative algorithm. Two examples are used to demonstrate the design procedure and the efficacy of the proposed design method.

This paper investigates the step tracking control problem for discrete-time nonlinear systems in a networked environment with a limited capacity. The nonlinear system is represented by a Takagi-Sugeno (T-S) fuzzy system, and a network-induced delay is incorporated in the modeling of the connection link. In order to compensate for the network link effects and eliminate the tracking error, we employ some techniques mainly used in the predictive control and the integral control. Moreover, a quadratic cost function which includes terms related to the performance of the system and the actuating capacity is used. We assume that the lumped network-induced delay lies within a known set, and that the occurrence probability for each element in the set is known a priori. Then, the delay information will be incorporated into the delay-dependent tracking controllers. The parameters for the tracking controller are derived by solving an optimization problem. A networked inverted pendulum is used to illustrate the efficacy of the proposed design method.

In this study, the authors deal with the mixed ℋ2/ℋ∞ filtering problem for continuous-time systems. The system model is subject to parameter variation and the parameters of the model vary slowly in the polytope. The parameters of the designed filter are dependent on variation, which is measured online. A new design approach is proposed by increasing the flexible dimensions in the solution space. An illustrative example shows the new features of the proposed design approach.

The problem of equalisation for communication channels with inter symbol interference (ISI) is investigated in this study. One practical yet challenging constraint for a channel with high transmission rate is incorporated into the modelling of the equalisation system: the communication channel is subject to uncertainties, which are assumed to be within a polytope with finite vertices. By using the augmentation method, the filtering error system of the equalisation problem is also characterised as a system with polytopic uncertainties. Sufficient conditions on the stability and the ℋ∞ performance for the filtering error system are obtained. A design method for the equaliser is proposed such that the filtering error system can achieve minimal ℋ∞ performance index even with the channel uncertainties. Two illustrative design examples demonstrate the design procedure and the effectiveness of the proposed method.

In this paper, we present a new design method for the${\cal H}_{\infty }$filtering of discrete-time Takagi–Sugeno (TS) fuzzy systems. The parameters of the filter are assumed to be linearly dependent on the normalized fuzzy weighting functions. By using an augmentation technique, the design parameters are incorporated into a filtering error system. In order to derive less-conservative results and reduce the filtering error, a new condition is established to ensure the${\cal H}_{\infty }$performance of the filtering error system. By introducing more slack matrices, the solution set of the filter parameters is extended. By using a partitioning technique, a design method for the${\cal H}_{\infty }$filter is proposed in terms of linear matrix inequalities (LMIs). An example demonstrates the improvement of the proposed design method over an existing approach.