Trust is a crucial aspect for autonomous vehicles (AVs) and automated driving systems (ADSs) that maintains humans’ acceptance of such functions. However, trust dynamic models describing the human-vehicle relationship rarely exist. To this end, this paper develops a quantitative trust dynamic model of the driver to an adaptive cruise control (ACC) system and applies the proposed model to a trust-based ACC. Driver’s trust level is modeled as an objective evaluation index for assessing the individual perceived trustworthiness on automation. Three contributions have been made in this work: 1) a novel quantitative dynamic model describing the driver’s trust on ACC is proposed for the first time considering the effects of the driver’s taking-over and handing-over operations as well as the steady-state driving scenarios; 2) an improved control barrier function approach with guaranteed system stability and prescribed asymptotic performance is proposed to design the trusted-based ACC, satisfying the state, input, and safety constraints; and 3) a new prescribed performance function that does not require accurate value of the initial condition is proposed to restrict the tracking errors within the predefined asymptotic boundaries, given that the initial value of the trust is hard to obtain. High-fidelity CarSim-based simulations demonstrate the rationality of the proposed trust model and the validity of the proposed control approach.

In this paper, a new passivity-based control approach is designed to improve the robustness and stability of autonomous vehicles in performing path-tracking tasks. A port-Hamiltonian model is a special geometric structure and provides a systematic and insightful framework to describe many physical systems from an energy perspective. The passivity-based control establishes a new and simple structure to achieve the path-tracking task by the port-Hamiltonian structure. Firstly, the path-tracking error system is transformed into a port-Hamiltonian system with the perturbation. The energy-shaping method is utilized to ensure the asymptotic stability, and the stability proof of the closed-loop, path-tracking error system is given. The proposed real-time, passivity-based controller is directly dependent on the passive outputs, which is robust to parameter uncertainties caused by load changes. Moreover, the control performance of steering control will be degraded when the steering angle is saturated. The yaw-moment control is introduced to enhance the driving safety. In the wheel torque distribution, an optimization-based control allocation is designed to minimize the sum of tire loads and to make them more evenly distributed to each tire. Finally, simulations are implemented in MATLAB/Simulink and CarSim co-simulation to demonstrate the effectiveness of the designed strategy under different driving conditions.

This study presents a fuel-economical driving strategy for connected and automated vehicles (CAVs) and investigates its impacts on human-driven platoon’s fuel efficiency, considering driver behavioral diversity. In mixed traffic where a limited number of CAVs and many human-driven vehicles share the road, the Eco-Driving strategy of CAVs, accomplished through vehicle connectivity and longitudinal dynamics control, can significantly reduce fuel consumption of CAVs, particularly during transient traffic conditions, by avoiding unnecessary braking and acceleration maneuvers which lead to excessive fuel consumption. This is implemented and validated by a vehicle longitudinal dynamic control design with an instantaneous power-based fuel rate estimation model. Moreover, such an Eco-Driving strategy will also help most of the following human-driven vehicles to improve fuel performance despite a highly diverse and uncertain driving characteristics spectrum. The car-following behaviors of a human-driven platoon are described by a microscopic traffic model with dedicated representations of the individual drivers’ preferences. A comprehensive statistical investigation shows the fuel-saving benefits for a human-driven platoon by adopting the proposed Eco-Driving strategy for CAVs. Some special cases are discussed, as well. The results collectively demonstrate the positive impacts of CAVs, even with a low penetration rate, could potentially have on the energy efficiency of the transportation sector within the foreseeable future.

Various collision-avoidance assistance (CAA) systems, such as automatic emergency braking (AEB) and lane-keeping assistance (LKA), have been developed in the last decades to enhance the active safety of ground vehicles. Meanwhile, more electronic computing units (ECUs) have been embedded inside a vehicle to support the diversified CAA systems, which complicate the automotive electrical/electronic architecture and increase the cost. Instead of addingextraECUs, we propose to allocate theexistingimplementation resources, i.e., the available processor time and memory space to the CAA systems, per individual driver’s maneuver capabilities. As an illustrative example, we first show that two drivers can exhibit distinct maneuvers in a pre-crash situation on highway, according to which they can be classified as either steering-oriented or braking-oriented. Then, we design two CAA systems: an AEB and an LKA, based on the ultra-local model predictive control method. Furthermore, we show that by adjusting the prediction horizons of the two controllers, the implementation resources can be allocated to the two CAA systems in different fashions, which yields three control modes: standard mode, steering-enhanced mode, and braking-enhanced mode. Finally, by comparing the control performance of each driver-type/control-mode pair through both CarSim-Simulink joint simulations and driver-in-the-loop simulator experiments, we demonstrate that by allocating more resources to compensate for the weakness of a driver’s maneuver, the CAA systems can provide enhanced driving safety by consuming the same overall amount of the implementation resources.

Car-following models describe how a driver follows the leading vehicle in the same lane. They serve as the cornerstone of microscopic traffic-flow simulations and play an essential role in analyzing human factors in traffic casualty, congestion, efficiency, and emissions. An extensive and continuously growing number of car-following models in the literature raises the requirement to evaluate and compare different models objectively. Generally, a car-following model is evaluated after model parameter calibration: the optimal residual between the calibrated model output and the measured counterpart is used as a metric to assess a car-following model’s performance. However, model parameter calibration, usually formed as a numerical optimization problem, suffers from several issues, such as local optimality and heavy computational burden. More importantly, different formulations of the cost function can lead to distinct calibration outcomes and contradictory conclusions of the model evaluation results. This paper proposes instead a purely algebraic framework for evaluating a class of car-following models whose parameters can be linearly identified. Car-following models with nonlinear relationships among parameters, e.g., the behavioral car-following models, are out of the scope of analysis in this paper. Algebraic manipulations performed on a model finally produce a system error index, which is a uniform metric for evaluating and comparing different car-following models. During the whole process, no cost function needs to be designed a priori, and no computationally expensive numerical optimization is involved. Three car-following models are evaluated and compared under the proposed algebraic framework.

Due to the simultaneous existence of model uncertainties and external disturbances, designing automated ground vehicle path-following controllers is recognized as a challenging task. The$H_{\infty }$robust control methodology, as one of the accomplished strategies for controller robustification, has been commonly adopted by researchers to address the vehicle path-tracking problems. Nevertheless, despite its advantages, the$H_{\infty }$controller is only capable of limiting the total “energy” of the tracking errors. On the other hand, from a safety standpoint, constraining the “peak” of the tracking errors may carry an equal or more importance. To establish a guaranteed upper bound on the path-tracking errors, this paper proposes a novel methodology to synthesis the ground vehicle path-following controller in light of the energy-to-peak robust control theory. Additionally, to address the time-varying uncertainties presented in the tire dynamics, robust stabilization constraints based upon the small-gain theorem are also formulated into the overall controller design problem. Comparative study regarding the disturbance rejection performance between the proposed controller and the conventional$H_{\infty }$approach is conducted via CarSim-Simulink joint simulations. Furthermore, the robustness and disturbance attenuation ability of the energy-to-peak path-tracking controller is experimentally verified on a scaled car.

The ability to detect lane markings under varying lighting conditions is essential for autonomous mobile robots and automatic vehicle driving assistance systems. Because the object color information is subject to illumination variation, this article presents a novel and computationally efficient algorithm based on the extremum-seeking method to achieve illumination-resilient lane detection and path-following tasks for autonomous driving. Lane detection is performed in the hue-saturation-value color space by distinguishing the colored lane marks from the background. The system’s inputs are the upper and lower thresholds in each of the hue, saturation, and value channels. We define a cost function as the combination of detection accuracy and lane coverage to evaluate the algorithm performance. Two extremum-seeking schemes, one with fixed dither amplitudes and another with adaptive dither amplitudes, are designed to adjust the system inputs to minimize the cost function. The two proposed methods are validated and compared through video-based simulation studies and scaled-car field experimental tests.

Adaptive control theory has ushered in a fruitful era for the research and development of intelligent ground vehicle transportation systems. Notably, owing to its intelligence of handling parametric uncertainties via online learning and adaptation, the adaptive control methodology has attracted a great deal of attention in tackling autonomous/automated vehicle control problems. In this paper, we aim to improve the existing adaptive-control-based path-following controllers from two aspects. First, a non-quadratic-Lyapunov-function-based model reference adaptive controller is synthesized to achieve enhanced$\mathcal {L}^{\mathbf {1+\alpha }}$tracking performance. Second, a$\mathcal{C}^{ \boldsymbol {\infty }}$-differentiable smooth parameter projection scheme is employed for preventing the disturbance-induced control parameter drift. The stability of the redesigned path-tracking adaptive controller is analyzed. Furthermore, validations and comparative studies are conducted via hardware-in-the-loop experiments.

This study addresses the path-tracking control issue of autonomous vehicles (AVs) when the GPS measurement is temporarily unavailable. In such a case, the vehicle states, location or the curvature of the reference path might be unobtainable, while the camera can be potentially used to detect the path-tracking states. To this end, this paper proposes a fuzzy-observer-based composite nonlinear feedback (CNF) controller with a Takagi-Sugeno (T-S) vehicle lateral dynamic model to guarantee the normal path-tracking maneuver and improve the transient performance. A parallel distributed compensation (PDC)-based CNF control method is developed to realize the control objective with the T-S vehicle model considering the transient performance and actuator saturation. The closed-loop stability and H∞index performance integrating the tracking and estimation errors have been proved with a Lyapunov approach. The observer-based controller design has been implemented based on the formulation of the linear matrix inequalities (LMIs). High-fidelity simulations using CarSim-Matlab/Simulink have demonstrated the validity of the proposed approach in terms of enhancing the tracking performance under the input saturation and disturbances in GPS-denied environments.

Trajectory following of autonomous vehicle is a challenging task because of the multiple constraints imposed on the plant. Therefore, Model Predictive Control (MPC) is becoming prevail in vehicle motion control as it can explicitly handle system constraints. However, MPC, grounded in real-time iterative optimization, entails a considerable computational burden for current electronic control units. To mitigate the MPC execution load, a popular strategy is to linearize the original (nonlinear) system around the current working point and then design a Linear Time-Varying MPC (LTVMPC). Nevertheless, the successive linearization introduces extra modeling errors, which may impair the control performance. Indeed, if the plant model satisfies the `differential flatness' condition, it can be exactly linearized to the Brunovsky's canonical form. In contrast to the LTV model, this newly appeared linear form reserves all the nonlinear features of the native plant model. Based on this equivalent linear system, a Flatness Model Predictive Controller (FMPC) can be formulated. FMPC on the one hand, improves the control performance over an LTVMPC because it avoids extra modeling errors from the local linearization. On the other hand, it entails a much lighter computational load versus a nonlinear MPC thanks to its linear nature. Real-time simulations conducted on a hardware-in-the-loop system indicate the advantages of the proposed FMPC in autonomous vehicle trajectory following.

Handover scenarios referred as the transitions between a human driver and an automated driving system are challenging because some human drivers are not adapted to the vehicle steering characteristics in a handover process and thus may exhibit deteriorative driving performances. This paper proposes a robust controller to assist human drivers in the handover scenarios. A driver-vehicle model including the driver steering input is developed to enhance the cooperation between a human driver and an automated driving controller. The driver parametric uncertainties are explicitly modeled to consider different human driving performances. Then a robust controller is designed to assist the driver considering his/her steering input and parametric uncertainties. The effectiveness of the designed controller is validated through several driver-in-the-loop tests on a driving simulator. The handover test results show that the driver steering loads and the vehicle lateral deviations can be reduced by the designed controller, thereby improving the driving safety in the handover processes.

The rapid growth of autonomous driving in recent years has posed some new research challenges to the traditional vehicle control system. For example, in order to flexibly change the yawing rate and moving speed of a vehicle based on the detected road conditions, autonomous driving control often needs to dynamically tune its control parameters for better trajectory tracking and vehicle stability. Consequently, the execution time of driving control can increase significantly, resulting in missing the end-to-end (E2E) deadline from detection to computation and actuation, and thus possible accidents.In this paper, we propose AutoE2E, a two-tier real-time middleware system that helps the automotive OS meet the E2E deadlines of all the tasks despite execution time variations, while achieving the maximum possible computation precision (and thus minimum tracking errors) for driving control. The inner loop of AutoE2E dynamically controls the CPU utilizations of all the on-board processors to stay below their respective schedulable utilization bounds, by adjusting the invocation rates of the vehicle tasks running on those processors. The outer loop is designed to adapt the computation time and precision of driving control, when the inner loop loses its control capability due to rate saturation caused by vehicle speed changes. Our evaluations, both on a hardware testbed with scaled cars and in larger-scale simulation, show that AutoE2E can effectively reduce the deadline miss ratio by 35.4% on average, compared to well-designed baselines, while having smaller precision loss and tracking errors.

Personalized driving assistance system for vehicle collision avoidance has recently received a considerable amount of attention. Consensus has been reached that both the overall driver-vehicle control performance and the driver acceptance can be increased by embedding individual driver preferences and characteristics into the assistance system design. However, the majority of the existing personalized controllers has not yet taken the available computational resource into account. Indeed, as stricter requirements on emissions, safety, and vehicle connectivity drastically complicate the automotive electronics, it becomes common to aggregate several functions inside one single computing unit. Function consolidation simplifies electronic architecture and saves costs. However, it aggravates the competition for computational resources among different applications. Therefore, this paper proposes a novel perspective for personalized driving assistance system design through computational resource re-allocation. For a driver inherently adept at longitudinal (or lateral) control and less capable of lateral (or longitudinal) control, a stronger support from the collision avoidance system and the underlying computational resource can be allocated towards steering (or braking) assistance by this design. Carsim-Simulink conjoint simulations demonstrate that the overall driver-vehicle control performance can be substantially improved with the same computational resource consumption.

In a Vehicular Cyber Physical System (VCPS), ensuring the real-time delivery of safety messages is an important research problem for Vehicle to Vehicle (V2V) communication. Unfortunately, existing work relies only on one or two pre-selected control channels for safety message communication, which can result in poor packet delivery and potential accident when the vehicle density is high. If all the available channels can be dynamically utilized when the control channel is having severe contention, then safety messages can have a much better chance to meet their real-time deadlines. In this article, we propose MC-Safe, a multi-channel V2V communication framework that monitors all the available channels and dynamically selects the best one for safety message transmission. During normal driving, MC-Safe monitors periodic beacons sent by other vehicles and estimates the communication delay on all the channels. Upon the detection of a potential accident, MC-Safe leverages a novel channel negotiation scheme that allows all the involved vehicles to work collaboratively, in a distributed manner, for identifying a communication channel that meets the delay requirement. MC-safe also features a novel coordinator selection algorithm that minimizes the delay of channel negotiation. Once a channel is selected, all the involved vehicles switch to the same selected channel for real-time communication with the least amount of interference. Our evaluation results both in simulation and on a hardware testbed with scaled cars show that MC-Safe outperforms existing single-channel solutions and other well-designed multi-channel baselines by having a 23.4% lower packet delay on average compared with other well-designed channel selection baselines.

Autonomous vehicles often need human driver to take over in some complicated conditions. Such a sudden takeover could jeopardize the vehicle's safety and stability if not han-dled properly. Hence, if the driver's takeover intention can be recognized as early as possible, the vehicle can have sufficient time to make important takeover preparation. The existing in-car monitoring systems are mostly based on camera, which have several key limitations, such as brightness condition and motion obscurity. On the other hand, WiFi-based wireless sensing has recently shown a great promise in human activity recognition, but mainly for large-scale movements performed in the room environment. In this paper, we propose WiDrive, a real-time in-car driver activity recognition system based on Channel State Information (CSI) changes of WiFi signals. WiDrive consists of three major components: A novel algorithm to extract small-scale in-car human activity features, a real-time recognition system based on Hidden Markov Model (HMM), and an online adaptation algo-rithm to adapt for different drivers and vehicles. We implement WiDrive with commercial WiFi devices and evaluate it in real cars. Our results show that WiDrive has an average recognition accuracy of 91.3% and improves the takeover safety.

The spatiotemporal distribution of drivers' attention to preview was inferred from their steering movements while tracking a winding roadway in a laboratory setting. For most subjects, the average driving attentional distribution over six daily sessions was relatively stable and generalized across different control devices. However, there was considerable day-to-day variability in the attentional distributions. This variability was modeled as a strong interaction between two dynamic processes, the attentional emphasis of selected regions and inhibition of surrounding regions. The model combines a novel application of a reaction-diffusion model of biological pattern formation with an optimal control model of attention to preview. The combined model treats attentional dynamics as an example of the biological spacing of a limited cognitive resource, which is also shaped by the demands of action.

This paper proposes a fault-tolerant control method for distributed-drive electric vehicles (DD-EV) considering individual driver steering characteristics that may vary among human drivers. Both vehicle and driver models are explicitly utilized for the fault-tolerant control design with considerations on modeling inaccuracies. A sliding-mode control scheme is generated to tolerate the DD-EV actuator fault and control the vehicle motions in a tailored cooperation with the specific driver. Such a personalized and fault-tolerant control method can specifically assist the human driver in post-fault vehicle motion control and reduce both physical and mental workloads of the driver. Co-simulation results of the controller using Matlab and CarSlm®indicate that the control law can provide appropriate control assistance to different drivers, achieving effective human-vehicle control cooperation in post-fault vehicle maneuvers.

Ensuring the real-time delivery of safety messages is an important research problem for Vehicle to Vehicle (V2V) communication. Unfortunately, existing work relies only on one or two pre-selected control channels for safety message communication, which can result in poor packet delivery and potential accident when the vehicle density is high. If all the available channels can be dynamically utilized when the control channel is having severe contention, safety messages can have a much better chance to meet their real-time deadlines. In this paper, we propose MC-Safe, a multi-channel V2V communication framework that monitors all the available channels and dynamically selects the best one for safety message transmission. MC-Safe features a novel channel negotiation scheme that allows all the vehicles involved in a potential accident to work collaboratively, in a distributed manner, for identifying a communication channel that meets the delay requirement. Our evaluation results both in simulation and on a hardware testbed with scaled cars show that MC-Safe outperforms existing single-channel solutions and other well-designed multi-channel baselines by having a 12.31% lower deadline miss ratio and an 8.21% higher packet delivery ratio on average.

This paper presents a robust fuzzy H∞control strategy for improving vehicle lateral stability and handling performance through integration of direct yaw moment control system (DYC) and active front steering. Since vehicle lateral dynamics possesses inherent nonlinearities, the main objective is dedicated to deal with the nonlinear challenge in vehicle lateral dynamics by applying Takagi-Sugeno (T-S) fuzzy modeling approach. First, the nonlinear Brush tire dynamics and the nonlinear functions of longitudinal velocity are represented via a T-S fuzzy modeling technique, and vehicle parametric uncertainties are handled by the norm-bounded uncertainties. An uncertain nonlinear vehicle lateral dynamic T-S fuzzy model is then obtained with multi-fuzzy-rules. The resulting robust fuzzy H∞state-feedback controller is designed with the parallel distributed compensation strategy and premise variables, and solved via a set of linear matrix inequalities derived from Lyapunov asymptotic stability and quadratic H∞performance. Simulations for two different maneuvers are implemented with a high-fidelity, CarSim®, full-vehicle model to verify the effectiveness of the developed approach. It is confirmed from the results that the proposed controller can effectively preserve vehicle lateral stability and enhance yaw handling performance.

In recent years, significant emphases and efforts have been placed on developing and implementing advanced driver assistance systems (ADAS). These systems need to work with human drivers to increase vehicle occupant safety, control, and performance in both ordinary and emergency driving situations. To aid such cooperation between human drivers and ADAS, driver models are necessary to replicate and predict human driving behaviors and distinguish among different drivers. This paper presents a combined driver model that is able to not only identify different individual driver behaviors, but also predict a driver's behavior in rare vehicle maneuvers such as collision avoidance (CA) based on his/her daily driving data. The driver model consists of a compensatory transfer function and an anticipatory component and is integrated with the design of the individual driver's desired path. It has been shown that the proposed driver model can replicate each driver's steering wheel angle signal for a variety of highway and in-city maneuvers. The utility of the proposed driver model is its ability to predict a driver's steering wheel angle signal for a CA maneuver from only daily nonemergency driving data. The driver model is then validated by comparing two different drivers' model parameter sets to the group average to show that each driver has a unique set of parameters. Finally, the driver model is validated by showing that its daily driving parameters differ from its predicted CA parameters.

Human driver's pedal behavior is difficult to model because it is the output of a very complicated virtual stochastic system. This paper provides a new way to describe driver's pedal behavior after decomposing it into a sequence of actions. By considering the vehicle and road information as the inputs and the pedal action as the output, an input-output hidden Markov model (IOHMM) is used to describe the pedal behavior. The state transition and output distribution functions are designed, and the relation between the input and the key variables of the output distributions is analyzed and modeled using statistical methods. The model parameters can be identified for individual drivers using the generalized estimation-maximization method. One-step probability prediction test shows that the proposed model can capture and distinguish each individual driver's driving style. The prediction capability of the proposed model is evaluated by comparing it with the human driver data collected on a driving simulator along with three other models. The results show that the proposed IOHMM-based driver pedal behavior model performs well in prediction horizons from 1 to 60 s.

In this paper, a gain-scheduling, robust, and shared controller is proposed to assist drivers in tracking vehicle reference trajectory. In the controller, the driver steering parameters such as delay time, preview time, and steering gain are assumed to be varying with respect to the different characteristics of drivers, vehicle states, and driving scenarios. Meanwhile, the modeling errors and uncertainties in the tire cornering stiffness are also considered in the driver-vehicle system model and the controller design. A global objective function, considering the tracking error, the driver's physical and mental workloads, and the control effort, is designed to optimize the overall performance of the driver-vehicle system. Constraint on eigenvalue placement is added to the controller design to improve the performance of the closed-loop driver-vehicle system. Simulation results under different maneuvers show that the controller can significantly improve the system performance and reduce the driver's workloads. The controller can reduce the delay time of the driver-vehicle system in emergency maneuvers, particularly for inexperienced drivers.

This paper presents an energy-efficient and terrain-information-and-preceding-vehicle-information-incorporated energy management strategy for fully electric vehicles (FEVs) equipped with in-wheel motors. Saving driving energy with terrain preview and preceding vehicle movement prediction are crucial to prolong the driving distance for an FEV. Unlike conducting energy optimization under the assumption that the preceding vehicle movements are already known in most studies, the front vehicle movements are predicted during each control cycle based on the vehicle-to-vehicle communication, and the FEV vehicle velocity and motor torque distribution are optimized by a nonlinear model predictive controller to reduce energy consumption. The energy-saving objective is achieved by including, in the cost function, the motor energy consumption in each control cycle, while the safety objective is accomplished by keeping a suitable relative distance from the preceding vehicle. Since the nonlinear vehicle longitudinal model is applied, the gridding initial torque plane is utilized in each time step to search for the global minimum. Simulation results show that this method has a better energy-saving performance than the control method without using the preceding vehicle movement information, and the algorithm proposed here has a wide applicability under various driving conditions.

With the increase in driver assistance systems, driver models are becoming more important to vehicle control, driving safety, and performance. To make these driver assistance systems better cooperate with human drivers, the driver models need to be able to predict human driving behaviors and distinguish among different drivers. In this paper, a combined driver model consisting of a compensatory transfer function and an anticipatory component based on road geometry is integrated with the design of the individual driver's desired path. The proposed driver model parameters are obtained from human subject test data collected in a driving simulator. It has been shown that the proposed combined driver model is able to replicate each driver's steering wheel angle signals for a variety of maneuvers at different vehicle speeds. The driver model is then validated by first using a polynomial to interpolate the driver model and desired path parameters for an intermediate speed. It is also validated by comparing two different drivers' model parameter sets to show that each driver has a unique set of parameters. The final validation is to show that the proposed individual driver's desired path offers more accurate steering wheel fits than the previous geometric centerline.

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.

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.