Road Preview Research Articles

A simulation study on visual preview control of vehicle magnetorheological suspension

Abstract At present, the boom in intelligent vehicles, especially automotives, has brought magnetorheological (MR hereinafter) suspensions into further focus. Generally, traditional MR suspensions work with acceleration sensors to respond with a non-negligible time delay issue due to the inductive property of MR damper (MRD hereinafter), which hinders their full performance especially at high-speed driving with a powerless “blind zone”. To address this issue, this study proposes a simulation study on visual preview control for vehicle MR suspension based on MPC (Model Predictive Control), since the road preview is able to compensate the time delay due to the future road information perceived in advance. First, a camera with a computer vision algorithm is employed to preview road information of target type, target size and distance to wheels. Besides, the error correction of radial and tangential distortions of camera lens are investigated, and the trajectory of wheels are predicted by a cycloid model because MR suspension only needs to respond to the road targets that the wheels will pass over. Further, the area in front of the vehicle is divided into a recognition zone and action zone, and the road targets within action zone (<1.5 m) are calculated more precisely for a prepared immediate response from the MR suspension. Then, the performance of a simulating vehicle MR suspension is investigated through a joint simulation by the automotive simulation software CarSim and the model-solving Simulink. The evident results show that the MR suspension with visual preview control performs the best in ride comfort (mean square values of sprung mass acceleration is 82% and 52% lower compared to a passive suspension and a MR suspension with skyhook control, respectively). Therefore, the visual preview control strategy of MR suspension is able to enhance the performance on the discrete road conditions.

Model Predictive Control for Speed-Dependent Active Suspension System with Road Preview Information.

This paper proposes a model predictive control (MPC) scheme based on linear parameter variation to enhance the damping control of speed-dependent active suspensions. The controller is developed by introducing a speed-dependent term, specifically front- and rear-wheel time delays, to the half-car model using the Padé approximation. Subsequently, the model is augmented with time-varying parameter dependence. An adaptive Kalman filter based on variance matching is employed to estimate system states affected by imprecise sensor measurement noise. Finally, a set of explicit control laws incorporating road preview information and available vehicle speed are determined offline using multi-parameter linear programming (mp-LP), simplifying online implementation to searching for optimal solutions in a lookup table. Simulation results demonstrate a significant improvement in active suspension control under changing vehicle speeds compared to passive control.

Explicit model predictive control of magnetorheological suspension for all-terrain vehicles with road preview

The integration of magnetorheological (MR) semi-active suspension systems in all-terrain vehicles (ATV) has garnered significant attention due to their ability to enhance damping performance and off-road capabilities. However, traditional control strategies result in poor control accuracy and limited vibration reduction effects when facing complex road excitations and impact disturbances. With technological advancements, enhanced vehicle environmental perception and road sensing capabilities have made it possible to implement model predictive control (MPC) for vehicle suspensions. Nevertheless, traditional MPC is limited in vehicle suspension applications due to its high computational complexity. To address these issues, this study introduces an explicit model predictive control based on road preview (EMPC-P). Firstly, road data obtained through a non-contact measurement method enables the system to perceive road excitation information in advance. Subsequently, a 7 Degree-of-Freedom (7-DOF) suspension model incorporating road excitations is constructed. By adhering to system constraints and employing a multiparameter optimization method, the control problem based on rolling optimization is transformed into an explicit polyhedral system. The offline precomputation of control state relations enhances the computational efficiency of the control system. Through this approach, the designed EMPC allows the vehicle suspension system to make optimal control decisions quickly and accurately in response to complex driving conditions, thus improving the damping effect of the system. Through a combined approach of simulation and experimental validation, the designed EMPC-P controller is compared with the Skyhook controller under preview and non-preview states, respectively. Empirical testing confirms that the EMPC-P exhibits superior damping effects, significantly improving vehicle ride comfort and handling stability.

Model Predictive Control of a Semi-Active Vehicle-Mounted Vibration Isolation Platform.

When conventional delivery vehicles are driven over complex terrain, large vibrations can seriously affect vehicle-loaded equipment and cargo. Semi-active vehicle-mounted vibration isolation control based on road preview can improve the stability of loaded cargo and instruments by enabling them to have lower vertical acceleration. A combined dynamic model including a vehicle and platform is developed first. In order to obtain a non-linear relationship between damping force and input current, a continuous damping control damper model is developed, and the corresponding external characteristic tests are carried out. Because some conventional control algorithms cannot handle complex constraints and preview information, a model predictive control algorithm based on forward road preview and input constraints is designed. Finally, simulations and real tests of the whole vehicle vibration environment are carried out. The results show that the proposed model predictive control based on road preview can effectively improve vibration isolation performance of the vehicle-mounted platform.

Development of data-based model predictive control for continuous damping and air spring suspension system

This paper presents the development of a data-based model predictive control method for a semi-active suspension system with air springs and continuous damping. A continuous damping controller (CDC) has been devised for the system to alter its damping coefficient in real-time, capable of reducing the impact of external road disturbances. In this research, the damping force has been split up into a nominal force and a controllable additional force, allowing the system to be modelled as a linear time-invariant system, despite the inherent nonlinearity of air spring suspension systems. In consideration of such constraints given by the damper, a model predictive controller (MPC) has been devised with the goal of improving ride comfort. Additionally, Gaussian process regression (GPR) has been used to compensate for output estimation errors arising from model parameter uncertainties. The semi-active suspension system also featured a multichamber air spring with three available modes of stiffness. Hence, a stiffness controller has been designed to select an appropriate mode based on the predicted vehicle states given by the MPC using road preview information and a reduced full-car model. The proposed algorithm has been verified using computer simulations. The results showed that compensations for model errors made with GPR significantly improves ride comfort even in the presence of parameter uncertainties. Additionally, both the damping controller and stiffness mode selector were successfully implemented into an actual test vehicle. Vehicle test results showed the proposed algorithm to be robust and effective in enhancing ride comfort and reducing vehicle pitch motion.

Road preview MPC of semi‐active suspension with magneto‐rheological damper

In this article, a road preview model predictive control scheme for semi-active suspension system with magneto-rheological damper (MRD) is suggested. In order to improve the comprehensive performance of the semi-active suspension, studies of both the actuator and control algorithm have been carried out. For the actuator, e.t. MRD, a cascade control strategy is proposed based on a Hammerstein model, compared with the traditional open-loop control methods, the tracking accuracy of the damping force has been improved. For the control algorithm, in contrast to existing works which define all requirements in a single cost functional and minimize it, in this work a road preview model predictive controller is adopted for semi-active suspension to provide optimal ride comfort by keeping constrained variables within specified limits. The road excitation is a measurable external input rather than an unknown disturbance. Finally, the optimization issue with hard constrains is converted into a quadratic programming problem. Simulation results show that the desired damping force of the MRD is realized by using the cascade control strategy. Meanwhile, vehicles with the proposed road preview model predictive control scheme can achieve better performance compared with a H ∞ $$ {H}_{\infty } $$ /generalized H 2 $$ {H}_2 $$ controller.

On Optimal Control of a Multichamber Air Spring and Experimental Validation over Speed Bumps

This paper deals with a preview-based optimal control of a multichamber suspension over speed bumps. A multichamber suspension is an innovative type of road vehicle suspension whose spring is composed of multiple auxiliary air reservoirs connected to a main pneumatic chamber. A set of controllable valves regulate the airflow among the different chambers, allowing the modulation of the spring equivalent stiffness. Multichamber suspensions are the only existing architecture in the market capable of vertical stiffness regulation. The first goal of this paper is to find an optimal valve position sequence which minimizes the vertical acceleration over some known speed bump profiles, under the hypothesis of perfect road preview. This optimization is carried out in simulation considering a control-oriented model of the multichamber suspension. A sensitivity study is then performed considering different bump shapes and vehicle speeds. The second goal of this work is to apply and validate the control sequence on a real vehicle equipped with commercial multichamber suspensions. Experimental results successfully reported improvements in corner vertical acceleration up to 16% and chassis pitch rate up to 12% compared to the passive benchmark.

On Pre-Emptive In-Wheel Motor Control for Reducing the Longitudinal Acceleration Oscillations Caused by Road Irregularities

Road irregularities induce vertical and longitudinal vibrations of the sprung and unsprung masses, which affect vehicle comfort. While the vertical dynamics and related compensation techniques are extensively covered by the suspension control literature, the longitudinal dynamics on uneven road surfaces are less frequently addressed, and are significantly influenced by the tires and suspension systems. The relatively slow response of internal combustion engines does not allow any form of active compensation of the effect of road irregularities. However, in-wheel electric powertrains, in conjunction with pre-emptive control based on the information on the road profile ahead, have some potential for effective compensation, which, however, has not been explored yet. This paper presents a proof-of-concept nonlinear model predictive control (NMPC) implementation based on road preview, which is preliminarily assessed with a simulation model of an all-wheel drive electric vehicle with in-wheel motors, including a realistic tire model for ride comfort simulation. The major improvement brought by the proposed road preview controller is evaluated through objective performance indicators along multiple maneuvers, and is confirmed by the comparison with two benchmarking feedback controllers from the literature.

Road Preview Control for Active Suspension System

Road Preview Control for Active Suspension System

Active vehicle suspension control using road preview model predictive control and radial basis function networks

Active suspension systems in road vehicles are applied in order to mitigate the road-induced chassis vertical accelerations more effectively than standard passive suspensions, thus increasing comfort and handling. Such systems are greatly assisted by road preview schemes, consisting of special sensors usually based on laser scanners (e.g. LiDAR sensors), which detect road irregularities ahead of the vehicle and feed this information to a control system, designed to manipulate the active suspension accordingly. In this paper, a model predictive controller (MPC) with road preview incorporating radial basis function (RBF) models, is presented as a control scheme for a full car active suspension system. The employed RBF models can efficiently approximate the nonlinear behavior of the suspension system, thus improving performance over linear MPC methods. Special care is taken to alleviate the increased computational complexity entailed in the RBF models, in order to ensure that online implementation of the controller is feasible. The proposed scheme is evaluated on a detailed simulated full car model under various road excitation types, while making use of a realistic approach for incorporating LiDAR road scanner noise. Comparisons to a passive suspension system, as well as a standard MPC controller with a fully linear plant model, demonstrate the performance potential of using RBF prediction models in a road preview MPC context.

Design of a linear motor-based shaker rig for testing driver's perceived ride comfort

In autonomous vehicles the driver will become a passenger. By ensuring a high level of ride comfort through active suspensions, the driver's ability to perform various tasks such as reading, drawing and texting can be enhanced. A high-performance shaker rig can conveniently be used to test the ride comfort improvement by means of various active suspensions under laboratory conditions. The paper deals with the design of such a rig, which employs a linear electric servomotor to impose accurately controlled vertical vibrations of driver seat. The reference time profiles of seat acceleration, velocity and displacement are generated off-line by using a half-car vehicle model and LQR control with a road preview option. The selection of linear motor comes from assessment of three characteristic shaker rig drive designs (hydraulic and two electric ones). To ensure high-precision seat motion, the proposed shaker control system includes: acceleration feedforward and feedback control loops, a state controller-like compensator of accelerometer offset to prevent drift of seat position and velocity, and feedforward compensation of linear motor cogging force mode. The designed shaker rig application is demonstrated through a case study related to ride comfort evaluation of various active suspension configurations and a drawing task. The drawing task results are employed to determine the root-mean-square vertical seat acceleration threshold, below which the active suspension drawing task performance remains similar to that obtained under standstill conditions.

Analytical and Experimental Evaluation of Various Active Suspension Alternatives for Superior Ride Comfort and Utilization of Autonomous Vehicles

Abstract Autonomous vehicles (AVs) give the driver opportunity to engage in productive or pleasure-related activities, which will increase AV’s utility and value. It is anticipated that many AVs will be equipped with active suspension extended with road disturbance preview capability to provide the necessary superior ride comfort resulting in almost steady work or play platform. This article deals with assessing the benefits of introducing various active suspensions and related linear quadratic regulator (LQR) controls in terms of improving the work/leisure ability. The study relies on high-performance shaker rig-based tests of a group of 44 drivers involved in reading/writing, drawing, and subjective ride comfort rating tasks. The test results indicate that there is a threshold of root-mean-square vertical acceleration, below which the task execution performance is similar to that corresponding to standstill conditions. For the given, relatively harsh road disturbance profile, only the fully active suspension with road preview control can suppress the vertical acceleration below the above critical superior comfort threshold. However, when adding an active seat suspension, the range of chassis suspension types for superior ride comfort is substantially extended and can include semi-active suspension and even passive suspension in some extreme cases that can, however, lead to excessive relative motion between the seat and the vehicle floor. The design requirements gained through simulation analysis, and extended with cost and packaging requirements related to passenger car applications, have guided design of two active seat suspension concepts applicable to the shaker rig and production vehicles.

Incremental Model Predictive Control of Active Suspensions With Estimated Road Preview Information From a Lead Vehicle

Abstract In preview-based vehicle suspension applications, the preview of the road profile is highly dependent on the preview sensors. In some scenarios such as heavy traffic situations, the preview of road profile can only be estimated by other vehicles because the view of the preview sensors may be blocked by other vehicles. The estimated preview road information can contain errors, which thus requires the controller to have a good robust performance. In this paper, an incremental model predictive control (MPC) strategy for active suspension systems along with a road profile estimator using preview information from a lead vehicle is proposed. The efficacy of the proposed strategy is experimentally validated on two scaled-down active suspension stations with comparison to two conventional active suspension control approaches.

A Shaker Rig-Based Testing of Perceived Ride Comfort for Various Configurations of Active Suspensions

Abstract Benefits of introducing active suspension in autonomous vehicles in terms of improving the driver's work/leisure ability can be tested under laboratory conditions using a high-performance shaker rig. In this paper, five different suspension configurations, including passive, semi-active, and fully active suspensions (FAS) and related optimal controls with or without road preview information, are implemented in vehicle dynamics simulator. They are experimentally examined having the driver in the loop to obtain objective and subjective measures of ride comfort and ability to do certain tasks. In total, 44 drivers of various age, gender, and driving experience performed three different tasks while being driven on shaker-rig (reading and writing, drawing, and subjective grading). The examination results show that FAS with road preview control offers highest ride comfort improvement, which can be perceived by the driver and greatly improves his/her ability to text and particularly draw.

A Review of Intelligent Road Preview Methods for Energy Management of Hybrid Vehicles

Abstract Due to the shortage of fuel resources and concerns of environmental pressure, vehicle electrification is a promising trend. Hybrid vehicles are suitable alternatives to traditional vehicles. Travelling information is essential for hybrid vehicles to design the optimal control strategy for fuel consumption minimization and emissions reduction. In general, there are two ways to provide the information for the energy management strategy (EMS) design. First is extracting terrain information by utilizing global positioning system (GPS) and intelligent transportation system (ITS). However, this method is difficult to be implemented currently due to the computational complexity of extracting information. This leads to the second method which is predicting future vehicle speed and torque demand in a certain time horizon based on current and previous vehicle states. To support optimal EMS development, this paper presents a comprehensive review of prediction methods based on different levels of trip information for the EMS of hybrid electric vehicle (HEV) and plug-in hybrid electric vehicle (PHEV).

The Effects of Yaw and Sway Motion Cues in Curve Driving Simulation

This paper investigates the importance of yaw and sway motion cues in curve driving simulation. While such motion cues are known to enhance simulation realism, their function in supporting realistic driver behavior in simulators is still largely unknown. A human-in-the-loop curve driving experiment was performed in the SIMONA Research Simulator at TU Delft, in which eight participants were asked to follow a winding road’s center-line, while being subject to wind disturbances. Four motion conditions were tested: 1) no motion, 2) yaw only, 3) sway only, and 4) both yaw and sway; each was tested with 5 m and 100 m road preview for correspondence with earlier work. Results show that visual road preview is essential for adequate road-following. Although the effects of yaw and sway cues are much smaller, sway motion feedback allows for improved disturbance-rejection performance, while yaw motion feedback results in reduced control activity. These distinctly different effects suggest that both motion cues are important for evoking realistic driving behavior in simulators.

Potential of Road Preview for Suspension Control under Transient Road Inputs

Abstract: Performance of control systems can be increased significantly with the help of information about future system inputs. Preview control of a vehicle suspension system involves detection of the road unevenness profile ahead of the vehicle. However, preview of the road unevenness through sensors and estimators is a very demanding task. Therefore, the benefit of preview control in suspension control should be investigated in detail in order to justify the need for development of such systems. In this study, the theoretical maximum benefit that can be obtained from a preview controlled semi-active suspension system is calculated through Model Predictive Control (MPC) approach. In addition to this, the accuracy of different road preview techniques is evaluated to demonstrate the state of the art in the road profile preview technology.

Torque-Vectoring-Based Vehicle Control Robust to Driver Uncertainties

Driver-in-the-loop stability is a central issue in vehicle control systems. However, since a general human behavior model to explore it in a quantitative fashion has been lacking, little is known about how the vehicle can be controlled while considering the driver effects. Indeed, applying a control method without considering the driver effects, and instead separating human level and machine dynamic layers, guaranteeing stability of a vehicle, is impossible. Here, a new formulation of the problem that involves a driver model and a linear vehicle model is proposed. Given that practical controllers usually do not have access to the future road preview data, this information is also modeled in terms of bounded uncertainties. The design allows the tools of robust control to stabilize the system, offering an implementable approach to overcome ranges of delay and uncertainties of closed-loop modeling due to the human presence. The formulation can further deepen the understanding of the effects of a driver during vehicle steering. To evaluate the proposed controller, a nonlinear full vehicle model along with a driver model in CarSim are used. The simulations performed for a standard harsh double-lane-change scenario under different driver and vehicle conditions demonstrate that vehicle stability is enhanced using the proposed controller.

Optimal predictive steering control for autonomous runway exits

In this paper, we present a real-time optimal controller, Predictive Steering Control (PSC), to perform high-speed runway exit manoeuvres. PSC is developed based on a time-varying LQR with look-ahead. The aircraft’s ground dynamics are described by a high-fidelity nonlinear model. The proposed controller is compared with an Expert Pilot Model (EPM), which represents a pilot, in several different speed runway exit manoeuvres. With an improved road preview mechanism and optimal feedback gain, the predictive steering controller outperforms the expert pilot’s manual operations by executing the runway exit manoeuvre with a lower track error. To investigate the optimality of PSC, its solution is further optimised using a numerical optimal controller Generalized Optimal Control (GOC). PSC is shown to be close to the final optimal solution. To study robustness, PSC is tested with various aircraft configurations, road conditions and disturbances. The simulation results show that PSC is robust to disturbances within a normal range of operational parameters.

Path‐Tracking Control of a Riderless Bicycle Via Road Preview and Speed Adaptation

AbstractIn this study, a genetic‐fuzzy control system is used to control a riderless bicycle where control parameters can adapt to the speed change of the bicycle. The equations of motion are developed for a bicycle with constraints of rolling‐without‐slipping contact condition between the wheels and ground. This controller consists of two loops: the inner is a roll‐angle‐tracking controller which generates steering torque to control the roll angle while guaranteeing the stability, and the outer is a path‐tracking controller which generates the reference roll angle for the inner loop. The inner loop is a sliding‐mode controller (SMC) designed on the basis of a linear model obtained from a system identification process. By defining a stable sliding surface of error dynamics and an appropriate Lyapunov function, the bicycle can reach the roll‐angle reference in a finite time and follow that reference without chattering. The outer loop determines the proper reference roll‐angle by using a fuzzy‐logic controller (FLC) in which previewing and tracking errors are taken into consideration. The robustness of the proposed controller against speed change and external disturbances is verified by simulations.