This paper proposes a trust-evaluation-based distributed model predictive control (DMPC) strategy for safe and scalable consensus of heterogeneous multi-agent systems (MASs) in a zero-trust environment. Dempster-Shafer theory (DST) is employed to quantify inter-agent trustworthiness and derive a global trust metric, thereby mitigating the impact of network threats and false testimonies during the trust evaluation process. Based on the computed trust, artificial reference trajectories are constructed to define the safe and scalable consensus state that each agent tracks, enabling adaptive regulation of reliance on neighbor information through real-time trust weights. The consensus problem is then reformulated as a trust-aware DMPC tracking problem that depends solely on locally received information, supporting distributed decision-making under zero-trust communication conditions. Sufficient conditions are derived to ensure recursive feasibility of the optimization problem, exponential stability of the closed-loop system, and the achievement of safe and scalable consensus. The effectiveness of the proposed strategy is validated via numerical simulations and vehicle platoon experiments.

The stability analysis of networked switched systems becomes highly challenging when multiple factors-such as frequent switching, denial-of-service (DoS) attacks, transmission delays, and actuator faults-coexist under an event-triggered mechanism (ETM). These intertwined factors cause complex timing mismatches that invalidate most synchronization-based control frameworks. To address this challenge, this article proposes a resilient event-triggered fault-tolerant control strategy that captures multisource asynchrony by classifying multiple key instants and modeling their interactions through a Lyapunov-based scheme. Unlike most existing studies that rely on synchronized switching assumptions or oversimplify the timing structure by ignoring delays and DoS attacks, this work explicitly incorporates these asynchronous phenomena into a unified analytical framework. First, to ensure timely packet transmission, a hybrid ETM is designed by combining time-triggering and event-triggering conditions. A switched Lyapunov function is then constructed by classifying different intervals, thereby unifying the analysis of asynchronous behaviors and DoS-induced disruptions. Furthermore, a resilient codesign strategy is developed, where the event-triggered parameters and fault-tolerant control gains are jointly designed under an explicit trade-off among the average dwell time parameter of switching signals, DoS attack parameters, and the sampling period. Under the proposed framework, global exponential stability with $H_{\infty } $ performance is guaranteed despite the presence of transmission delays, actuator faults, and DoS attacks. Finally, the effectiveness of the proposed method is demonstrated using a quarter-vehicle suspension system.

The control of magnetorheological fluid dual clutch (MRFDC) has been challenging due to their modeling challenges, high complexity, strong nonlinearity, and rate-dependent hysteresis, especially in the transient states in which they are supposed to perform gear shifting and traction tracking. Motivated by this, this article presents a data-driven discrete-time sliding mode control (DSMC) approach for the transmission torque control of the magneto-rheological fluid dual clutch (MRFDC). This control method eliminates the model dependence and simplifies the control strategy synthesis by employing a compact form dynamic linearization data model, which is constructed from real-time output torque and input current measurements of the MRFDC. Furthermore, based on the proposed data model, the DSMC is employed based on a minimum operator sliding mode reaching law to deal with the rate-dependent hysteresis and nonlinearity of the MRFDC. Experimental studies validate that the presented control method provides satisfactory torque tracking performance in both transient and steady states.

Cooperative driving of connected and automated vehicles (CAVs) is envisioned as a promising approach to improving fuel efficiency, safety, and traffic flow. However, achieving robust and efficient control in heterogeneous CAV platoons remains challenging, especially under uncertain dynamics and external disturbances. Most existing reinforcement learning (RL)-based platoon controllers often ignore model uncertainties or rely on centralized training, limiting their scalability and robustness in real-world applicability. To address these limitations, this study proposes a fully distributed, model-free RL framework integrated with a robust compensator for optimal platoon control of heterogeneous vehicles with unknown dynamics. The RL agent simultaneously learns the optimal control policy and estimates control-relevant dynamic parameters using only local input-output data, without requiring explicit vehicle models. These estimates are then used in real time to construct a disturbance-rejection input that ensures robust trajectory tracking. A distributed observer based on consensus theory is embedded within the hybrid controller to estimate leader-relative reference trajectories using only local neighbor information, eliminating the need for global communication. Theoretical analysis guarantees policy convergence and bounded tracking errors under dynamic uncertainties. The proposed method is experimentally validated using the high-fidelity Mixed Traffic Simulation (MiTaS) platform, combining the SUMO microscopic traffic simulator with MATLAB, demonstrating improved tracking, damping of traffic oscillations, and up to 15.8% fuel savings compared to recent RL-based methods.

The stability analysis is studied for$H_{\infty } $controlled networked active quarter-vehicle suspension systems with a resilient event-triggered scheme (RETS) under periodic denial-of-service (DoS) jamming attacks in this article. For the networked suspension system, the system-state signals are measured by sensors and transmitted to the cloud controller through a wireless network and then the control signal is transferred to the actuator to control it. An event-triggered scheme (ETS) is designed to reduce the workload of data transmission, which is effective to select some most useful information to transmit and discard some redundant data. DoS attacks can block the data transmission when it is active, so a resilient event-triggered$H_{\infty } $control method is built based on the Lyapunov stability theory. The exponential stability of the controlled suspension system, as well as the$H_{\infty } $performance, is analyzed in this article. Some simulation results show that the proposed control method is effective to improve driving comfort and driving safety and reduce the workload of data transmission under periodic DoS attacks.

Road intersections will soon be congested with countless connected autonomous vehicles (CAVs) to meet a variety of transportation needs. In such an environment, CAVs will need to work together and coordinate with one another. Smart infrastructure should also be in place to enable CAVs to efficiently utilize shared intersection resources and carry out their mobility duties. The interaction among CAVs and between CAVs and infrastructure for efficient intersection management is facilitated by a key technology of intelligent transportation system (ITS), known as cooperative vehicle-intersection system (CVIS). Towards developing a better insight into this fast-developing technology, this study presents a thorough review of the state of the art in CVIS research in the context of three hierarchal layers of shared perception, intersection control, and vehicle control. To describe the workflow of cooperative perception systems, a systematic architecture of infrastructure-based perception supported by edge computing and multi-node multi-sensor fusion techniques is explored. Different shared perception methodologies, aiming at the perceptual fusion of CAVs and infrastructure to achieve comprehensive environmental perception are critically analyzed to identify the landscape of cooperative detection and tracking. With an emphasis on essential interaction between CAVs and infrastructure, various cooperative control strategies are then investigated for the co-design of crossing scheduling and motion control of multiple CAVs at intersections. The applications of CVIS in cooperative driving automation in terms of efficiency, safety, and sustainability are qualitatively analyzed. This paper concludes by identifying gaps and challenges in vehicle-intersection cooperation along with recommendations on future research directions.

Boresight angle misalignment is a major error source in a mobile LiDAR system (MLS), which directly affects the overall accuracy and quality of MLS scanned point clouds data. However, the current calibration of the boresight angle misalignment mainly relies on artificial target features or a manual adjustment, and the intensive labors dramatically limit the calibration flexibility. To solve these problems, this paper develops a novel on-site calibration method for boresight angle misalignment based on multiple natural features constraints, which can automatically incorporate multiple natural features extracted from surrounding environments to generate more accurate calibration results for MLS boresight angle without used any artificial targets or specific facilities. First of all, an improved 4-points congruent sets (I-4PCS) algorithm is proposed for registering the MLS point clouds in forward and backward scanned overlapping areas and realizing smooth global registration for point clouds data. Secondly, a weight principal component analysis (WPCA) approach is presented to automatically extract the appropriate multiple natural features from the well registered point clouds and establish the appropriate features representation. Thirdly, according to the extracted multiple features, the certain geometric constrains equations for spherical, linear/cylindrical, planar features are established based on a model adjustment strategy. Lastly, the boresight angle misalignment calibration can be achieved through fitting the corresponding geometric constrains equations and minimizing the weighted through a least-squares adjustment process. The experimental results demonstrate that the proposed method can effectively on-site calibrate the boresight angle misalignment error, and the overall performance of MLS is significantly improved after the calibration based on multiple natural features constraints.

Early detection of driver mental fatigue is one of the active areas of research in smart and intelligent vehicles. There are various methods, based on measuring the physiological characteristics of the driver utilising sensors and computer vision, proposed in the literature. In general, driver behaviour is unpredictable that can suddenly change the nature of driving and dynamics under mental fatigue. This results in sudden variations in driver body posture and head movement, with consequent inattentive behaviour that can end in fatal accidents and crashes. In the process of delineating the different driving patterns of driver states while active or influenced by mental fatigue, this paper contributes to advancing direct measurement approaches. In the novel approach proposed in this paper, driver mental fatigue and drowsiness are measured by monitoring driver’s head posture motions using XSENS motion capture system. The experiments were conducted on 15 healthy subjects on a MATHWORKS driver-in-loop (DIL) simulator, interfaced with Unreal Engine 4 studio. A new modified bidirectional long short-term memory deep neural network, based on a rectified linear unit layer, was designed, trained and tested on 3D time-series head angular acceleration data for sequence-to-sequence classification. The results showed that the proposed classifier outperformed state-of-art approaches and conventional machine learning tools, and successfully recognised driver’s active, fatigue and transition states, with overall training accuracy of 99.2%, sensitivity of 97.54%, precision and F1 scores of 97.38% and 97.46%, respectively. The limitations of the current work and directions for future work are also explored.

This paper considers the multi-objective platooning control problem of a group of connected and automated vehicles (CAVs) with bidirectional topologies. A new distributed economic model predictive control (DEMPC) algorithm is presented to reconcile the conflict of the control objectives of tracking, safety, stability and fuel economy of the heterogeneous vehicle platoon with guaranteed string stability. Using transient engine energy efficiency indices and cooperative performance of tracking and string stability, two distributed receding horizon optimal control problems are orderly formulated by a Lyapunov-based coupling constraint. Moreover, a new concept of$\gamma $-string stability is defined for the platoon with bidirectional topologies. Some distributed terminal conditions are then derived to guarantee the recursive feasibility and asymptotic stability of the DEMPC as well as$\gamma $-string stability of the platoon in the presence of constraints. Compared to traditional platooning control, the new DEMPC has a 4.2% energy-saving of vehicles while achieving the cooperative tasks of the platoon in several simulation scenarios.

The platoon of connected autonomous vehicles plays an essential role in future intelligent transportation. It can improve traffic efficiency and release traffic congestion. However, there are lots of existing challenging problems of the control of connected autonomous vehicles, such as the negative impact caused by wireless communication and disturbance. To solve these challenges, a multi-objective asymmetric sliding mode control strategy is proposed in this paper. Firstly, the asymmetric degree is introduced in the topological matrix. Then, a sliding mode controller is designed targeting platoon’s tracking performance. Moreover, Lyapunov analysis are used via Riccati inequality to find the controller’s gains and guarantee internal stability and Input-to-output string stability. Finally, a non-dominated sorting genetic algorithm is utilized to find the Pareto optimal asymmetric degree regarding the overall performance of the platoon, including tracking index, fuel consumption, and acceleration standard deviation. Four different information flow topologies, including a random topology are studied. The results indicate that the proposed asymmetric sliding mode controller can ensure platoon’s stability while improving its performance. The tracking ability is improved by 54.61% and 75.17%, fuel economy is improved by 0.78% and 6.34% under the Urban Road and Highway Case Study, respectively.

A timely understanding of target vehicles (TVs) lateral behavior is essential for the decision-making and control of host vehicle. Existing physical model-based methods such as motion-based method and multiple centerline-based method are generally constructed based on TV pose and longitudinal velocity, and tend to ignore TV preview driving characteristic and other useful information such as lateral velocity and yaw rate. To address these issues, a driver preview and multiple centerline model-based probabilistic behavior recognition architecture is proposed for timely and accurate TV lateral behavior prediction. Firstly, a driver preview model is used to describe vehicle preview driving characteristic, and TV preview lateral offset and preview lateral velocity are calculated with TV states and road reference information. Then, the preview lateral offset and preview lateral velocity are combined with multiple centerline model for TV lateral behavior reasoning based on the interacting multiple model-based probabilistic behavior recognition algorithm. With this method, TV preview driving characteristic and lateral motion states are combined for precise TV lateral behavior description. Furthermore, to predict short-term lateral behavior, a preview lateral velocity-dependent transition probability matrix model constructed with Gaussian cumulative distribution function is proposed. Simulation and experimental results show that the proposed method considering vehicle preview driving characteristic predicts TV lateral behavior earlier than the conventional method.

Driver mental fatigue is a major influential factor that results in inattentive driver condition ultimately to fatal crashes. In this paper, the driver mental fatigue patterns based on the driver’s body postural behaviour are identified using a novel adaptive pattern recognition technique. The experiments were conducted on 20 healthy subjects in a MATHWORKS simulated driving environment. The posture of the driver was measured using the XSENS motion capture system. To monitor the actions performed under the influence of mental fatigue, variations in the acceleration of the head, neck, and sternum were extracted and deployed in an unsupervised manner. A fully adaptive version of the symbolic aggregate approximation algorithm based on unsupervised clustering was developed that identifies the time-series patterns of driver fatigue posture. The time-variant fatigue patterns were dynamically segmented and symbolized according to the discrepancy in the postural behaviour. The experimental results indicate that the proposed algorithm successfully detects the time-variant fatigue patterns of multivariate dataset compared to the original symbolic pattern recognition tool. The limitations of the current approach and future work in improving driver safety are explored.

Soft sensors are widely used to predict quality variables which are usually hard to measure. It is necessary to construct an adaptive model to cope with process non-stationaries. In this study, a novel quality-related locally weighted soft sensing method is designed for non-stationary processes based on a Bayesian network with latent variables. Specifically, a supervised Bayesian network is proposed where quality-oriented latent variables are extracted and further applied to a double-layer similarity measurement algorithm. The proposed soft sensing method tries to find a general approach for non-stationary processes via quality-related information where the concepts of local similarities and window confidence are explained in detail. The performance of the developed method is demonstrated by application to a numerical example and a debutanizer column. It is shown that the proposed method outperforms competitive methods in terms of the accuracy of predicting key quality variables.

Accurate positioning is an essential requirement of autonomous vehicular navigation system (AVNS) for safe driving. Although the vehicle position can be obtained in global position system friendly environments, in GPS denied environments (such as suburb, tunnel, forest, or underground scenarios) the positioning accuracy of AVNS is easily reduced by the trajectory error of the vehicle. In order to solve this problem, the plane, sphere, cylinder and cone are often selected as the ground control targets to eliminate the trajectory error for AVNS. However, these targets usually suffer from the limitations of incidence angle, measuring range, scanning resolution, and point cloud density, etc. To bridge this research gap, an adaptive continuum shape constraint analysis (ACSCA) method is presented in this article to design a new target with optimized identifiable specific shape to eliminate the trajectory error for AVNS. First of all, according to the proposed ACSCA method, we conduct extensive numerical simulations to explore the optimal ranges of the vertexes and the faces for target shape design, and based on these trials, the optimal target shape is found as icosahedron, which composes of ten vertexes, 20 faces and combines the properties of plane and volume target. Moreover, the algorithm of automatic detection and coordinate calculation is developed to recognize the icosahedron target and calculate its coordinates information for AVNS. Finally, a series of experimental investigation were performed to evaluate the effectiveness of the designed icosahedron target in GPS denied environments. The experimental results demonstrate that compared with the plane, sphere, cylinder and cone targets, the developed icosahedron target can produce better performances than the above targets in terms of the clustered minimum registration error, ambiguity and range of field-of-view; also can significantly improve the positioning accuracy of AVNS in GPS denied environments.

Driver's foot behaviour is unpredictable and can suddenly change the nature of driving and dynamics under the influence of different factors that stimulates the driving style. Such effects result in sudden variations in foot dynamics and trajectory between accelerator and brake pedals inducing vagueness in smart active control system. This paper is an extension to the intrusive approach where driver's foot trajectory and shifting between pedals are monitored using XSENS motion capture system. The main objective is to predict the foot patterns associated with acceleration and braking. The experiments were conducted on 10 young subjects on MATHWORKS driver-in-loop (DIL) simulator, interfaced with Unreal Engine 4 studio. A new modified bidirectional long short-term memory (Bi-LSTM) deep neural network based on a rectified linear unit layer was designed, trained, tested and compared with traditional machine learning algorithms on 3D time-series foot orientation data for the sequence-to-sequence classification. The results show that the proposed classifier performs well and successfully recognizes the driver's foot behaviour with overall accuracy of 99.8%. Such identified patterns will help in determining the foot posture and the degree of intention in pressing the particular pedal. Moreover, the patterns will be useful for early intervention by smart systems to cope with the longitudinal mistakes made during driving. The limitations of the current work and directions for future work are explored.

In this paper, a four-wheel electric braking system configuration is proposed for electric vehicles and its braking performance is compared with other conventional braking system configurations at different initial vehicle speeds and different road conditions in the case of emergency braking. In order to make the vehicle wheel slip ratio track the optimal slip ratio, a control method that combines sliding mode control and extended state observer is designed. Neural-network sliding mode control is designed for the driver's braking command tracking in the normal braking condition. In order to improve braking energy recovery, a new braking torque distribution strategy is developed for the proposed four-wheel electric braking system based on the motor characteristics and vehicle dynamics. The designed braking torque distribution strategy is able to improve the energy recovery by adjusting the braking torque distribution ratio between the front and rear wheel braking torque while tracking the driver's braking command. Numerical simulations have been conducted and the simulation results show that although the braking performance of the four-wheel electric braking system is worse than the conventional braking system at high initial braking speed, it still is able to meet the vehicle braking international standards and simplifies the braking system structure and saves cost. The proposed braking torque distribution strategy can improve energy recovery efficiency compared with the average allocation strategy and deceleration based allocation strategy. The simulation results show that the four-wheel electric braking system configuration with the proposed braking torque distribution strategy is suitable for low to medium speed light electric vehicles.

In a Steer-by-Wire (SbW) system, the mechanical linkages which connect the steering wheel to front wheels are replaced by a digitally controlled steering system. In spite of improved vehicle safety due to better steering capability, the SbW system actuator failure may lead to unwanted steering performance or even instability. A fault tolerant model predictive control (MPC) with fault compensation for SbW systems based on delta operator for actuator faults is proposed. It deploys an observer to estimate both the fault information and the faulty SbW system states. At each sampling time, the MPC immediately compensates for the fault. The gains of the observer and the fault tolerant MPC controller are obtained by solving a linear matrix inequality derived from the Lyapunov theory. The simulation results illustrate that the proposed fault tolerant control strategy can counter the various types of actuator faults and maintain superior steering performance to shift operator-based MPC controller.

The Steer-by-Wire (SbW) system is an electronically controlled steering system that is able to improve steering capability without mechanical links between the steering wheel and the front wheels. However, failure of the SbW system actuator may lead to steering performance degradation and result in instability. In this paper, a fault tolerant sliding mode predictive control (SMPC) strategy for an SbW system is proposed. The sliding mode control is applied to improve the robustness of the model predictive control (MPC) in the presence of modeling uncertainties and disturbances, while the MPC is applied to enhance the fault tolerant capability of the steering control processes. The chaos particle swarm optimization (CPSO) algorithm is introduced to optimize the MPC and a two-stage Kalman filter is introduced to simultaneously provide fault information and state estimation. The performance of the proposed approach is validated through computer simulation. The results demonstrate that the proposed SMPC-CPSO controller is more robust and provides better tracking performance in the presence of model uncertainties, disturbance, and actuator faults than SCMP-PSOs (heterogeneous comprehensive learning particle swarm optimization, evolutionary particle swarm optimizer, etc), SMPC-differential evolution, MPC, SMPC, and MPC-PSO.

As the concept of variable stiffness (VS) and variable damping (VD) has increasingly drawn attention because of its superiority on reducing unwanted vibrations, dampers with property of varying stiffness and damping have been an attractive method to further improve vehicle performance and driver comfort. This paper presents the design, prototyping, modeling, and experimental evaluation of a VS and VD magnetorheological (MR) vehicle suspension system. It was first characterized by an INSTRON machine. Then, a phenomenological model was proposed to capture the characteristics of the damper and TS fuzzy approach was used to model the quarter car system where the proposed damper was installed. Different controllers, including skyhook, short-time Fourier transform and state observer based controller were designed to control the damper. Experimental results demonstrate that the quarter car system with the VS and VD suspension performs best in terms of reducing the sprung mass accelerations comparing with other suspensions.

Detection of motor intention from brain signals combined with robot assistive technologies has potential to be used as an effective rehabilitation process for post-stroke patients. The work conducted on the deployment of AMADEO hand rehabilitation robotic device and Electroencephalogram based Brain Computer Interference (EEG-BCI) system to explore the technical feasibility of the approach in hand motor recovery of post-stroke patients is presented. Two different protocols consisting of simple visual cues and a 2D interactive game are presented to healthy subjects when performing hand movement. The motor intent signals produced during each protocol are detected using Support Vector Machine (SVM) algorithm. Moreover, the signals produced by different single electrodes are analyzed to identify the electrode making the highest contribution to the intent signal and the performance of SVM with respect to each protocol. Overall, an average True Positive Rate (TPR) of 71.72% and True Negative Rate (TNR) of 63.33% for visual cue protocol and an average TPR of 88.56% and TNR of 70.81% for game protocol are obtained.

This paper investigates the problem of adaptive integral sliding mode control for general Takagi-Sugeno fuzzy systems with matched uncertainties and its applications. Different control input matrices are allowed in fuzzy systems. The matched uncertainty is modeled in a unified form, which can be handled by the adaptive methodology. A fuzzy integral-type sliding surface is utilized and the parameter matrices can be determined according to user's requirement. Based on the designed sliding surface, a new sliding mode controller is proposed, and the structure of the controller depends on the difference between the disturbance input matrices and the control input matrices. It is shown that under the proposed sliding mode controller, the resulting closed-loop system can achieve the uniformly ultimate boundedness. Furthermore, simulation examples are presented to show the merit and applicability of the proposed fuzzy sliding mode control method.

This paper investigates the problem of fault detection for nonlinear discrete-time networked systems under an event-triggered scheme. A polynomial fuzzy fault detection filter is designed to generate a residual signal and detect faults in the system. A novel polynomial event-triggered scheme is proposed to determine the transmission of the signal. A fault detection filter is designed to guarantee that the residual system is asymptotically stable and satisfies the desired performance. Polynomial approximated membership functions obtained by Taylor series are employed for filtering analysis. Furthermore, sufficient conditions are represented in terms of sum of squares (SOSs) and can be solved by SOS tools in MATLAB environment. A numerical example is provided to demonstrate the effectiveness of the proposed results.

In this paper, a novel neural network (NN)-based four-channel wave-based time domain passivity approach (TDPA) is proposed for a teleoperation system with time-varying delays. The designed wave-based TDPA aims to robustly guarantee the channels passivity and provide higher transparency than the previous power-based TDPA. The applied NN is used to estimate and eliminate the system's dynamic uncertainties. The system stability with linearity assumption on human and environment has been analyzed using Lyapunov method. The proposed algorithm is validated through experimental work based on a 3-DOF bilateral teleoperation platform in the presence of different time delays.

This paper investigates the problem of adaptive fuzzy tracking control for nonlinear strict-feedback systems with input delay and output constraint. Input delay is handled based on the information of Pade approximation and output constraint problem is solved by barrier Lypaunov function. Some adaptive parameters of the controller need to be updated online through considering the norm of membership function vector instead of all sub-vectors. A novel adaptive fuzzy tracking control scheme is developed to guarantee all variables of the closed-loop systems are semiglobally uniformly ultimately bounded, and the tracking error can be adjusted around the origin with a small neighborhood. The stability of the closed-loop systems is proved and simulation results are given to demonstrate the effectiveness of the proposed control approach.

The studies on the autonomous electric vehicle are quite attractive due to fewer human-induced errors and improved safety in recent years. Extensive research has been done on the autonomous steering control of the mobile robot, but study on the on-road autonomous electric vehicle is still limited. This paper proposes a potential field method to achieve the trajectory control of the autonomous electric vehicle with in-wheel motors. Instead of strictly following a desired path, this method can form a steering corridor with a desired tracking error tolerance and the vehicle can be steered smoothly with less control effort. In this paper, the innovative potential filed function is presented first to determine the desired vehicle yaw angle. Then, according to this desired yaw angle, a two-level trajectory controller is proposed to achieve the trajectory control. Simulation results are shown to prove that this suggested trajectory controller can successfully control the vehicle to move within the desired road boundary and improve the handling and stability performance of the vehicle.

This paper presents an adaptive fuzzy control approach for a category of uncertain nonstrict-feedback systems with input saturation and output constraint. A variable separation approach is introduced to overcome the difficulty arising from the nonstrict-feedback structure. The problem of input saturation is solved by introducing an auxiliary design system, and output constraint is handled by utilizing a barrier Lyapunov function. Combing fuzzy logic system with the adaptive backstepping technique, the semi-global boundedness of all variables in the closed-loop systems is guaranteed, and the tracking error is driven to the origin with a small neighborhood. The stability of the closed-loop systems is proved, and the simulation results reveal the effectiveness of the proposed approach.

The modeling and measurement of the biodynamic response of the seated human body has recently been an active research topic, with major applications to ergonomics and automotive suspension control system technologies. This paper presents a holistic literature survey of topics including the latest research in the area of vibration signal processing and modeling of the biodynamic response of the seated human to vibrations. This paper reviews recent sensing systems that are reported to measure the motion of the seated body. The data processing techniques that are currently accepted are surveyed and these include impedance, transmissibility measures, frequency response function estimation, and model development. A review of applications of biodynamic response analysis and modeling to seating vibration isolation technologies and vibration monitoring systems is presented within this paper. This survey paper provides a discussion on the direction that the future research in this field will aim toward based on the trends in the recent research and the introduction and application of new technologies.

This paper is concerned with the adaptive sliding mode control problem of uncertain nonlinear systems. Interval type-2 Takagi-Sugeno (T-S) fuzzy model is employed to represent uncertain nonlinear systems. The input matrices of the nonlinear systems are allowed to be different for the sliding mode controller design. The uncertain parameters are described by the lower and upper membership functions. An integral sliding mode surface is designed for analysis of sliding motion. Based on the sliding mode surface, a novel sliding mode controller is designed to guarantee that the closed-loop system is uniformly ultimately bounded. Some simulation results are given to illustrate the effectiveness of the presented control scheme.

Accurate vehicle longitudinal velocity estimation is important for wheel slip ratio control in antilock braking systems. To overcome the problem of nonlinear tyre-road friction characteristic when designing an observer for velocity estimation, this paper presents a novel approach by using the model-based fuzzy technique. The nonlinear vehicle braking system is modelled by a Takagi-Sugeno fuzzy model first. A fuzzy observer is then constructed by using the available measurements of wheel angular velocity and braking torque with the estimated premise variables. All the possible disturbances and uncertainties are considered so that the designed observer is robust under an Hoo performance index from the disturbances to the estimation error. The design of the observer is achieved by solving a set of linear matrix inequalities. Numerical simulations on a quarter-vehicle braking model are used to validate the effectiveness of the proposed approach.

A new active Hydraulically Interconnected Suspension (HIS) has been developed to compensate the limitations of conventional active suspensions such as expensive cost and high energy consumption. In this paper, the mechanism of proposed active HIS system has been briefly introduced. Fuzzy Logic Control, Fuzzy proportional-integral-derivative (PID) control and optimal linear quadratic regulator (LQR) theory have been adopted to control vehicle body's roll motion. A combination of a half-car model and the active suspension model is then derived through their mechanical-hydraulic coupling in the cylinders for the model based LQR control. Three controllers have been developed and implemented in Simulink. Two different road excitations have been used to validate the robustness of the designed controllers. The effectiveness of all these three controllers has been verified by the simulation results with considerable roll angle reductions, and the Fuzzy PID controller shows better effect and stability than other two controllers.

Vehicle side slip angle is a critical variable used in car safety systems like Electronic Stability Control. Due to the practical difficulty in direct measurement of side slip angle, accurate estimation of vehicle side slip angle using available signals is becoming important. This paper presents a novel algorithm for estimating the side slip angle of a vehicle in real time using inertial motion sensors. The algorithm uses a J48 decision tree classifier to assist the Extended Kaiman Filter (EKF) predictions of the vehicle side slip angle. The decision tree classifies the inertial data into classes based on the condition the slip angle is expected to be in. Using the class information asserted by the classifier, the error covariance parameter of the EKF is adjusted to compensate for changes in disturbances and nonlinearities. The results show that the decision tree assisted EKF technique presented in this paper is capable of predicting the slip angle with sound accuracy using inertial motion data.

This paper presents a Takagi-Sugeno (T-S) model-based fuzzy control design approach for electrohydraulic active vehicle suspensions considering nonlinear dynamics of the actuator, sprung mass variation, and constraints on the control input. The T-S fuzzy model is first applied to represent the nonlinear uncertain electrohydraulic suspension. Then, a fuzzy state feedback controller is designed for the obtained T-S fuzzy model with optimizedHinfinperformance for ride comfort by using the parallel-distributed compensation (PDC) scheme. The sufficient conditions for the existence of such a controller are derived in terms of linear matrix inequalities (LMIs). Numerical simulations on a full-car suspension model are performed to validate the effectiveness of the proposed approach. The obtained results show that the designed controller can achieve good suspension performance despite the existence of nonlinear actuator dynamics, sprung mass variation, and control input constraints.