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This article deals with the control chassis dynamics of a light electric vehicle. In the frame of Global Chassis Control, it proposes a hierarchical control approach focuses on compensating the chassis dynamics against driver disturbances. It is based on a supervisory structure consists of two main levels, a global controller for regulating chassis variables and a local controller to each suspension system. Both controllers are designed using a fractional-order robust controller, namely CRONE controller, to provide an optimized solution for the performance-robustness tradeoff. The main objective is to improve ride comfort for passengers while respecting road holding and handling criteria. In contrast to other studies, random road disturbances are considered to investigate the limits of the proposed approach. Analysis in frequency and time domain has been done to evaluate the performance and robustness of the designed controller. Simulation results based on a full nonlinear 14 degree of freedom vehicle model show that the proposed strategy can effectively improve the ride quality in the field of interest where a significant performance in driver disturbance rejection is reported. [ABSTRACT FROM AUTHOR]

Model predictive control (MPC) in automotive active safety applications has gained great success in recent years. Detailed vehicle models along with a receding horizon control scheme seek the optimal control distribution among actuators for enhanced performances while satisfying system constraints. To promote the re-usability and scalability of MPC-based vehicular active safety systems, an agent-based MPC (AMPC) is proposed in our previous study for a modularized control architecture. In this paper, implementation of the control scheme is demonstrated on an all-wheel-drive test platform. Both centralized and agent-based MPCs for vehicular stability are compared for their control performances as well as computational costs on embedded hardware. It is shown from experimental results that agent-based MPC is more flexible and computationally efficient in handling vehicle active safety challenges while gives no compromise to control performances compared to their holistically formulated counterparts. [ABSTRACT FROM AUTHOR]

Longitudinal velocity, lateral velocity, and front wheel steering angle are crucial states for vehicle active safety features. However, direct measurement of these variables requires expensive measurement instruments and can be easily affected by vehicle nonlinear dynamics, outliers and noise pollution. Moreover, unknown inputs bring great challenges to their accurate estimation. Therefore, this paper develops a novel robust unscented M-estimation-based filter (RUMF) for vehicle state estimation with unknown driver steering torque. The nonlinear system model is constructed based on the vehicle dynamics model. Unscented transformation (UT) and statistical linearization are implemented to transform the nonlinear process and measurement function into a linear-like regression form with data redundancy to achieve outlier suppression. Then, an M-estimation-based iterated algorithm is designed to address process uncertainty and innovation and observation outliers for robust vehicle state estimation. The iteratively reweighted least-squares (IRLS) method based on the M-estimation methodology is utilized for unknown input estimation. Simulations and experimental results compared with extended Kalman filter (EKF) and particle filter (PF) have verified the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Appropriate vehicle lateral stability control is the key to ensure vehicle driving safety, whereas accurate lateral stability recognition can help improve the performance of vehicle control. In this article, the vehicle stability recognition and coordinated control are studied. Firstly, the vehicle dynamic model is established, through vehicle simulation software Carsim, the attribute dataset representing the vehicle lateral stability is further obtained. Then, clustering by fast search and find of density peaks method (CFSFDP) based procedure for the classification of the lateral stability as ‘Absolutely stability’, ‘Nearly stability’ and ‘Hardly stability’ is applied. The brain emotional learning network combined with genetic algorithm (GA-BEL) model is used to train datasets to recognize vehicle stability categories during driving. For the different recognition results, a coordinated control strategy based on active front steering control (AFS) and direct yaw moment control (DYC) is designed finally. Through the Carsim/Simulink co-simulations and Hardware-in-the-Loop tests under several typical driving conditions, the superiorities of the stability recognition method and the coordinated control strategy proposed in this paper are verified. [ABSTRACT FROM AUTHOR]

A novel longitudinal, lateral, and vertical integrated stability control system is proposed for distributed electric vehicle in this paper. The integrated stability control system is based on model predictive control and takes advantage of its multi-objective optimization feature to takes manipulability, stability and comfort requirements into consideration at the same time, and realize the optimal longitudinal $/$ lateral $/$ vertical forces distribution with constraints overall considered. The advantage of the proposed integrated control system is that it can solve the control objective conflict of the cooperative motion control subsystems and the interference of the actuators compared to distributed control system. Finally, the simulation and comparison analysis are given in several different driving conditions under Carsim and Matlab $/$ Simulink environment. Results show that an improvement of over 80 $\%$ in the pitch angle and roll angle is realized compared to the simple control strategy. [ABSTRACT FROM AUTHOR]

This article suggests a model-based controller for an all-wheel drive (AWD) vehicle using a novel vehicle dynamics model. Recently, an active type of AWD system that automatically controls the clutch engagement force of the transfer case has become popular. However, its performance has been limited by its rule-based controller, which necessitates the development of a model-based controller. Although the bicycle model has been most widely adopted for upper level controller of vehicle control systems, it is not appropriate for AWD controllers because the model does not have a direct relationship between the states and control inputs. In this sense, this study adopted a tire force-based full car model for the AWD controller. The clutch engagement force limit, which varies depending on the current engagement state, was calculated systematically by considering changeable dynamic behavior that is inherent in AWD vehicle and was used as a valid value for input limit in linear quadratic regulator (LQR) control algorithm. For the real-time application of the designed controller, only data obtained from the controller area network (CAN) of production vehicles were used. Then, the performance of the proposed controller was validated through vehicle experiments that included scenarios of both longitudinal and lateral movements. [ABSTRACT FROM AUTHOR]

This article is motivated by an interesting recent result on antilock braking system (ABS) design using two-time scale (TTS)-based estimation. In this article, the basic method of TTS is extended to estimate not only the disturbances but also their derivatives, improving the accuracy of estimation by an order of magnitude. Further, the problem of ABS is extended to an eight-DOF vehicle model to include the effects of load transfer, steering dynamics, and actuator dynamics. Since analysis showed that the use of basic TTS to ABS design results in a poor transient in the estimation error dynamics, this article proposes a new formulation of the TTS method specifically for ABS. Two new ABS-specific sliding surfaces are proposed to compensate the actuator dynamics where the second sliding surface is proposed as an improvement over the first one. The stability, and convergence of estimation, and tracking errors are analyzed. The proposed methods are validated on MATLAB/Simulink, and CarSim platforms for a D-class sedan car. The results are also validated on an experimental ABS platform in the laboratory. [ABSTRACT FROM AUTHOR]

Safety at high speed is the most important performance of autonomous vehicle. Wherein, yaw stability control (YSC) is a key technology to improve vehicle safety. However, the high-speed vehicles are easy to be out of control in nonlinear region, which will result in serious accidents. This study proposes a model predictive control (MPC) method based on the Takagi-Sugeno (T-S) fuzzy model to realize YSC in the nonlinear region. Firstly, a T-S fuzzy model of vehicle is established based on a two-degree-of-freedom model to describe the vehicle dynamic characteristics in the nonlinear region. Then, a robust MPC is designed and its solution is given in the linear matrix inequalities (LMIs) form. Next, a sliding mode controller (SMC) is proposed to prevent wheel lock in the YSC process. Finally, the proposed strategy is verified in simulations and experiments. The results show that the proposed strategy has similar performance in vehicle stable region with linear MPC, and shows the ability to suppress the instability of the vehicle in the nonlinear region, and its computation burden is acceptable. [ABSTRACT FROM AUTHOR]

Accurate physical modeling of vehicle dynamics requires extensive a priori knowledge of the studied vehicle. In contrast, data-driven modeling approaches require only a set of data that are a good account of the vehicle’s driving envelope. In this brief, we compare, for the first time, the prediction capabilities of both approaches applied to a large-scale real-world driving data set. The data set contains several cornering maneuvers, acceleration, and deceleration stages and was collected over public roads. Linear and nonlinear physical models were identified through nonlinear optimization of their unknown parameters. Closed-form subspace identification methods were used to initialize the estimate of a linear state-space model, and the initialization was then refined through nonlinear optimization. The optimized models were validated against 59 km of independent driving data. The model fits, in the longitudinal velocity, were 68.9% versus 80.2% for the nonlinear physical model and linear data-driven (second-order) model, respectively, and, in the yaw rate, 43.0% versus 63.5%. These results show that, for this vehicle, a simple linear data-driven model outperformed both linear and nonlinear physical models under real-world driving conditions. This has important implications for control design approaches in autonomous vehicles. [ABSTRACT FROM AUTHOR]

In this article, we aim to investigate the path following control problem for four-wheel-independent-drive electric vehicles with consideration of modeling errors and complex driving scenarios. In order to guarantee operational safety and robustness, a supertwisting second-order sliding mode (SOSM) control strategy is employed to suppress the heavy chattering issue existing in the traditional sliding mode control (SMC). To degrade the lumped disturbance, a nonlinear disturbance observer (NDOB) technique is proposed to estimate the disturbance and compensate the modeling error. Then, a composite control strategy, which combines the SMC algorithm and NDOB technique is proposed based on stability analysis of the corresponding closed-loop system. Finally, hardware-in-the-loop experimental results show that the supertwisting SOSM control algorithm with the NDOB technique can achieve better tracking performance and suppress the chattering phenomenon. [ABSTRACT FROM AUTHOR]

Active safety systems are of significant importance for autonomous vehicles operating in safety-critical situations like an obstacle-avoidance manoeuvre with high vehicle speed or poor road condition. However, a conventional electronic stability control system, may not always yield desired path following and yaw stability performance in such circumstances merely through brake intervention. This paper pursues a detailed investigation on utilising model predictive control (MPC) and torque vectoring for path following and yaw stability control of over-actuated autonomous electric vehicles (AEVs) in dangerous double lane change manoeuvre. The control problem of the AEV is formulated based on MPC by utilising active front steering and torque vectoring, and constraints are imposed explicitly on yaw rate and sideslip angle to ensure yaw stability. Four MPC-based controllers are designed based on double-track vehicle models. Specifically, they include two one-level controllers, i.e. one with torque vectoring and one with equal torque allocation, and two two-level controllers, i.e. one with optimisation-based torque allocation and one with rule-based allocation. These controllers are assessed extensively, with respect to passing velocity, tracking accuracy, tyre utilisation and robustness. The effect of horizon length on the control performance and computational efficiency is also investigated. [ABSTRACT FROM AUTHOR]

We present a switched adaptive observer for the estimation of the tire extended braking stiffness (XBS) during ABS braking scenarios. The design of the observer is based on Burckhardt’s friction model and on a model of the wheel dynamics in which the XBS appears as one of the state variables. Global asymptotic stability of the estimation error is established, provided that certain dwell time and persistency of excitation conditions hold. The approach is validated under constant road conditions with experimental data from a tire-in-the-loop test rig and under varying road conditions via numerical simulations on a two-axle vehicle model that includes dynamic load transfer and tire relaxation length. [ABSTRACT FROM AUTHOR]

In this article, the problem of control of antilock braking systems under significant uncertainties and unknown relationship between tire-road friction coefficient and wheel slip ratio for a two-axle vehicle model is considered. Two methods based on a disturbance observer and sliding mode control are proposed. In the first method, a conventional sliding surface is used while in the second method a new nonlinear sliding surface is proposed as an improvement over the first method. The performance under the proposed methods is assessed analytically and by MATLAB simulation under different road conditions. The proposed methods are validated further on CarSim platform. [ABSTRACT FROM AUTHOR]

Development of compact and easy to use mathematical models of articulated vehicles for the motion planning, control, and localization purposes becomes more and more important in the era of intelligent transportation systems, especially when there exists a need of reliable predictions of motion for multi-body (semi-)automated freight and public transportation vehicles of various kinematic structures. We propose a modular algorithmic approach to kinematic modelling of nonholonomic (multi-)articulated buses, including the N-trailer vehicles as a special case, comprising a car-like prime-mover passively interconnected with arbitrary number of segments (wagons/trailers) equipped with fixed or steerable wheels, and with various locus of a driving axle in a kinematic chain. Kinematic models are valid under an assumption of a pure rolling of all the vehicle wheels (no skid/slip motion), which is practically justified for the low-speed maneuvering conditions. The proposed approach leads to compact nonlinear models which, thanks to their modular construction, preserve clear geometrical interpretation of velocity couplings between the vehicle segments. Derivations of kinematic models for popular structures of articulated and bi-articulated urban buses are presented for various driving-axle locus and steering capabilities. Experimental model validation, conducted with a full-scale wagon-driven articulated bus, illustrates utility of the approach. [ABSTRACT FROM AUTHOR]

This paper addresses the development and Hardware-in-the-Loop (HiL) testing of an explicit nonlinear model predictive controller (eNMPC) for an antilock braking system (ABS) for passenger cars, actuated using an electro-hydraulic braking unit. The control structure includes a compensation strategy to guard against the performance degradation due to actuation dead times, identified by experimental tests. The eNMPC is run on an automotive rapid control prototyping unit, which shows its real-time capability with comfortable margin. A validated high-fidelity vehicle simulation model is used for the assessment of the ABS on a HiL rig equipped with the braking system hardware. The eNMPC is tested in seven emergency braking scenarios, and its performance is benchmarked against a proportional-integral-derivative (PID) controller. The eNMPC results show: 1) the control system robustness with respect to the variations in tire-road friction condition and initial vehicle speed; and 2) consistent and significant improvement of the stopping distance and wheel slip reference tracking, with respect to the vehicle with the PID ABS. [ABSTRACT FROM AUTHOR]

This paper proposes a real-time nonlinear model predictive control (NMPC) strategy for direct yaw moment control (DYC) of distributed drive electric vehicles (DDEVs). The NMPC strategy is based on a control-oriented model built by integrating a single track vehicle model with the Magic Formula (MF) tire model. To mitigate the NMPC computational cost, the continuation/generalized minimal residual (C/GMRES) algorithm is employed and modified for real-time optimization. Since the traditional C/GMRES algorithm cannot directly solve the inequality constraint problem, the external penalty method is introduced to transform inequality constraints into an equivalently unconstrained optimization problem. Based on the Pontryagin's minimum principle (PMP), the existence and uniqueness for solution of the proposed C/GMRES algorithm are proven. Additionally, to achieve fast initialization in C/GMRES algorithm, the varying predictive duration is adopted so that the analytic expressions of optimally initial solutions in C/GMRES algorithm can be derived and gained. A Karush-Kuhn-Tucker (KKT) condition based control allocation method distributes the desired traction and yaw moment among four independent motors. Numerical simulations are carried out by combining CarSim and Matlab/Simulink to evaluate the effectiveness of the proposed strategy. Results demonstrate that the real-time NMPC strategy can achieve superior vehicle stability performance, guarantee the given safety constraints, and significantly reduce the computational efforts. [ABSTRACT FROM AUTHOR]

This work considers the problem of position and position-uncertainty estimation for atonomous vehicles during power black-out, where it cannot be assumed that any position data is accessible. To tackle this problem, the position estimation will instead be performed using power separated and independent measurement devices, including one inertial 6 Degrees of Freedom (DOF) measurement unit, four angular wheel speed sensors and one pinion angle sensor. The measurement unit's sensors are initially characterized in order to understand conceptual limitations of the inertial navigation and also to be used in a filtering process. Measurement models are then fused together with vehicle dynamics process models using the architecture of an Extended Kalman Filter (EKF). Two different EKF filter concepts are developed to estimate the vehicle position during a safe stop; one simpler filter for smooth manoeuvres and a complex filter for aggressive manoeuvres. Both filter designs are tested and evaluated with data gathered from an experimental vehicle for selected manoeuvres of developed safe-stop scenarios. The experimental results from a set of use-case manoeuvres show a trend where the size of the position estimation errors significantly grows above an initial vehicle speed of 70 km/h. This paper contributes to develop vehicle dynamics models for the purpose of a blind safe stop. [ABSTRACT FROM AUTHOR]

This paper introduces a new torque vectoring (TV) algorithm with regards to enhancing the cornering performance of the electronic-four-wheel drive (e-4WD) vehicles. The proposed TV algorithm aims to distribute the in-wheel motor (IWM) torques to the left and right wheels assisting the vehicle in following the driver's intended trajectories. The proposed TV algorithm first included a yaw rate controller reference for neutral-steering which leads to the improvement of vehicle cornering agility. Thereafter, in-vehicle sensors and a standalone global positioning system (GPS) combined with a smooth sliding mode controller (SMC) were used based on a vehicle bicycle model to generate desired yaw moment values to track the reference. Finally, a novel torque distribution algorithm using the Daisy-chaining allocation, which is suitable for redundant actuator configuration, was utilized. Noteworthy, the Daisy-chaining allocation algorithm takes into account a torque operation area with the characteristics of IWM. In this manner, based on the vehicle bicycle model and using the CARSIM interface, the simulations, evaluation and verifications of the proposed control technique were done. Thereafter, considering a real-car based experiments with various driving scenarios, the effectiveness of the proposed TV algorithm was evaluated. It was confirmed that some evaluation factors in terms of the cornering performance were improved. The following main contributions makes the proposed TV algorithm be a meaningful solution to enhance the cornering performance of e-4WD vehicles: 1) improvement of both smoothness and convergence rate of control action, 2) a practical and intuitive way to distribute IWM torques, and 3) high applicability to mass-produced vehicles. [ABSTRACT FROM AUTHOR]

This study deals with a new integrated control sys-tem using active torque vectoring (ATV) and electronic stability control (ESC) for enhancement of vehicle directional stability and steerability. In this system, the stabilizing yaw moment is calculated using a novel constrained nonlinear controller with both input and state constraints. The proposed controller is designed using the prediction of continuous nonlinear vehicle model. It guarantees the vehicle directional stability by restricting the side slip angle in the admissible range when the yaw rate tracks its desired response for improved steerability. The calculated yaw moment is generated as driving and braking forces by integrated ATV and ESC systems. According to the integration policy, the required yaw moment is initially generated by ATV in the rear wheels to keep the vehicle driving performance. Because of limitation of ATV, if more yaw moment is required, the ESC system applies asymmetric braking pressure to front wheels to compensate the scarcity of ATV. By the proposed strategy, the small reduction of vehicle velocity is achieved and the vehicle drivability performance is maintained. In order to determine the full capacity of ATV, the active differential dynamics is considered in this paper. Simulation studies are conducted to evaluate the efficiency of proposed constrained controller in comparing with the unconstrained controller and a conventional nonlinear model predictive controller (NMPC) developed in the recent papers using 14-DOF vehicle model. By analyzing the results, it is clear that the proposed controller is much faster and easy to solution and implementation. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive steering robust nonlinear control method based on driver behavior is presented for an unmanned driving robotic vehicle (UDRV), in order to achieve accurate and stable steering control of UDRV. Considering modeling uncertainties and unknown nonlinear external disturbances, a UDRV nonlinear dynamics model is established. A driver behavior model is established considering a coordinate transformation model, a driver virtual path planning model, and a driver desired yaw rate model. On the basis of this, an adaptive steering robust nonlinear controller for UDRV is presented, consisting of an adaptive robust backstepping controller and a nonlinear disturbance observer (NDO). The NDO is designed to compensate for modeling uncertainties and unknown nonlinear external disturbances. The adaptive steering robust nonlinear controller for UDRV is designed through backstepping method and NDO compensation. The stability of control system is proved. Finally, double lane change experiments are conducted. Comparison analysis results among the proposed control method, other existed control method, and human driver demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Due to the strong nonlinearity, coupling characteristics, external disturbance, and complex driving conditions, it is difficult to establish an accurate mathematical model for the autonomous ground vehicle (AGV). This requires the AGV path following controller to have strong robustness. In this paper, a robust AGV path following control strategy that is based on nonsingular terminal sliding mode (NTSM) and active disturbance rejection control (ADRC) is presented. First, the complex path following problem is simplified to a simple yaw angle tracking problem by constructing a desired yaw angle function that satisfies that the displacement deviation of AGV converges to zero when the actual yaw angle approaches the desired yaw angle. Second, an NTSM-ADRC controller is designed for the system, which uses the extended state observer to estimate and compensate the unmodeled dynamics and unknown external perturbations of the system in real time. In order to improve response characteristics of the controller, the nonlinear error feedback control law is designed by combining the NTSM and exponential approximation law. In contrast to the existing work, the improved controller can use the simple two-degree-of-freedom linear vehicle dynamic model to provide good performance in a range of driving conditions. Finally, the CarSim–Simulink simulation results of typical conditions show that the proposed control strategy can make the AGV follow the reference path quickly and accurately while ensuring the stability of the vehicle and has strong robustness. [ABSTRACT FROM AUTHOR]

This paper focuses on the controller design for path-tracking problem of autonomous ground vehicles (AGVs) by employing a multi-constraint nonlinear predictive control (NMPC) schema. It is aimed to improve the transient performance of the vehicle and to consider a rollover prevention criterion in the proposed method. The path-tracking problem is transformed into the yaw stabilization issue, and a feedback control law with input saturation is developed to decrease the steady-state errors. Furthermore, the yaw-rate signal is generated for the desired path-tracking performance. The major contributions of the present paper are, first, developing a neural network autoregressive with exogenous input system to assist in obtaining an accurate and explicit model in order to contribute to the control of the system over the prediction horizon; second, describing a Frenet–Serret differential geometry based path-following agenda and developing AGV dynamic model by incorporating the vehicle vertical mode of motion to prevent vehicle rollover during critical maneuvers, and finally, achieving an enhanced yaw stabilization and transient tracking performance considering saturated input signal by employing the proposed system identification algorithm. The effectiveness of the proposed control system is verified by comparing with the traditional NMPC method by employing a high fidelity CarSim/MATLAB framework. [ABSTRACT FROM AUTHOR]

Active safety systems must be used to manipulate the dynamics of autonomous vehicles to ensure safety. To this end, accurate vehicle information, such as the longitudinal and lateral velocities, is crucial. Measuring these states, however, can be expensive, and the measurements can be polluted by noise. The available solutions often resort to Bayesian filters, such as the Kalman filter, but can be vulnerable and erroneous when the underlying assumptions do not hold. With its clear merits in handling nonlinearities and uncertainties, moving horizon estimation (MHE) can potentially solve the problem and is thus studied for vehicle state estimation. This paper designs an unscented Kalman filter, standard MHE, modified MHE, and recursive least squares MHE to estimate critical vehicle states, respectively. All the estimators are formulated based upon a highly nonlinear vehicle model that is shown to be locally observable. The convergence rate, accuracy, and robustness of the four estimation algorithms are comprehensively characterized and compared under three different driving maneuvres. For MHE-based algorithms, the effects of horizon length and optimization techniques on the computational efficiency and accuracy are also investigated. [ABSTRACT FROM AUTHOR]

In this paper, a general control allocation (CA) algorithm is proposed based on a lexicographic optimization (LO) strategy for a vehicle's longitudinal and lateral control. The primary objective of this CA is to distribute the torque adjustments of the lateral controller such that the error between the desired and actual forces and moments at the vehicle's center of gravity is minimized, while maintaining the longitudinal tire slip ratios close to a desired range. Presence of various constraints in practical systems can significantly increase the complexity and effort of properly adjusting the tuning parameters in the objective function. Hence, an LO method is used to prioritize the objectives. Lateral stability of the vehicle has the highest priority and is subject to the constraint of maintaining small tire slip ratios. The second priority of CA is assigned to minimize the adjustment torques. The proposed LO-based approach reduces exhaustive vehicle testing commonly required for tuning of the separately designed controllers and the cost and time of the implementation. In addition, it makes the algorithm easily transferable from one vehicle to another by reducing the number of tuning parameters. Simulation and experimental results are presented to show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The differential drive assist steering (DDAS) system improves the handiness of steering by torque difference between two-side wheels. However, the DDAS and the vehicle stability control (VSC) will interfere with each other due to the same actuators. This paper proposes a hierarchical coordination control approach for the DDAS and VSC. The system is divided into four layers: the parameter estimation layer, the control region division layer, the coordination decision layer, and the control allocation layer. In the control region division layer, the stable area is firstly determined on the $\beta - \dot{\beta }$ phase portrait, and the coordinated control region is obtained by applying the simulated annealing algorithm inwardly and outwardly to the boundary of the stable area. In the coordination decision layer, the direct control strategy of steering hand wheel torque is used to establish the controller of the DDAS based on the fuzzy adaptive PID algorithm. At the same time, the controller of yaw moment is established based on the sliding mode control theory. In the stable region, the DDAS works separately. The VSC works separately in the unstable region. In the coordination region, the Sigmoid function is used to adjust the weight coefficient of the DDAS dynamically. Besides, the control allocation layer includes torque distribution controller and vehicle slip ratio controller. Finally, the vehicle model is established in CarSim, the control strategy, and the wheel motor model are established in Simulink. The joint simulation results of several typical conditions show that the designed control strategy can improve the handling stability of the vehicle effectively while DDAS working even in severe driving condition. [ABSTRACT FROM AUTHOR]

Abstract-The control of an antilock braking system (ABS) is a difficult problem due to its strongly nonlinear and uncertain characteristics. To overcome this difficulty, the integration of graysystem theory and sliding-mode control is proposed in this paper. This way, the prediction capabilities of the former and the robustness of the latter are combined to regulate optimal wheel slip depending on the vehicle forward velocity. The design approach described is novel, considering that a point, rather than a line, is used as the sliding control surface. The control algorithm is derived and subsequently tested on a quarter vehicle model. Encouraged by the simulation results indicating the ability to overcome the stated difficulties with fast convergence, experimental results are carried out on a laboratory setup. The results presented indicate the potential of the approach in handling difficult real-time control problems. [ABSTRACT FROM AUTHOR]