Set-membership LPV model identification of vehicle lateral dynamics

Set-membership identification of a Linear Parameter Varying (LPV) model describing the vehicle lateral dynamics is addressed in the paper. The model structure, chosen as much as possible on the ground of physical insights into the vehicle lateral behavior, consists of two single-input single-output {LPV} models relating the steering angle to the yaw rate and to the sideslip angle. A set of experimental data obtained by performing a large number of maneuvers is used to identify the vehicle lateral dynamics model. Prior information on the error bounds on the output and the time-varying parameter measurements are taken into account. Comparison with other vehicle lateral dynamics models is discussed

The Crewman's Associate for Path Control (CAPC): an automated driving function

Army Tank Automotive Command, Warren, Mich.http://deepblue.lib.umich.edu/bitstream/2027.42/1134/2/88210.0001.001.pd

Use of numerical optimisation to determine on-limit handling behaviour of race cars.

The aim of this research is to use numerical optimisation to investigate the on-limit behaviour of an open wheel downforce type race car using the best compromise of modelling accuracy and computational effort. The current state of lap simulation methods are identified, and the GG speed diagram is described. The use of constrained optimisation, which is a form of optimal control, is used to develop the methods described in this thesis. A seven degree of freedom vehicle model validated by other researchers is used for method validation purposes, and is extended, where possible, to make the modelling of vehicle components more physically significant, without adversely affecting the computational time. This research suggests a quasi steady state approach that produces a GG speed diagram and circuit simulation tool that is capable of optimising vehicle parameters and subsystems in addition to the prevailing control vector of steer and throttle response. The use of numerical optimisation to optimise the rear differential hydraulic pressure and the roll stiffness distribution to maximise vehicle performance is demonstrated. The optimisation of the rear differential hydraulic pressure showed a very small improvement in vehicle performance in combined high speed braking and cornering, but highlighted the ability of the differential to affect the cornering behaviour of the vehicle. The optimisation of the roll stiffness distribution research showed that a significant improvement in the lateral acceleration capability of the vehicle could be achieved at all vehicle speeds between 20 and 80m/s, especially in combined braking and cornering. In addition, a parameter sensitivity study around a realistic Formula One vehicle setup was conducted, looking at the sensitivity of vehicle mass, yaw inertia, tyres, centre of gravity location and engine torque to vehicle performance. An investigation into the importance of the path finding calculation is also reported

Optimal Vehicle Motion Control to Mitigate Secondary Crashes after an Initial Impact.

Statistical data of road traffic fatalities show that fatalities in multiple-event crashes are higher than in single-event crashes. Most vehicle safety systems were developed to mitigate first crash events. Few active safety systems can deal with subsequent crash events. After a first crash event, drivers may not react in a timely or correct manner, which can have devastating consequences. Production active safety systems such as Electronic Stability Control (ESC) may not react to a first crash event properly unless such events are within their design specifications. The goal of this thesis is to propose control strategies that bring the vehicle state back to regions where drivers and ESC can easily take over the control, so that the severity of possible subsequent (secondary) crashes can be reduced. Because the most contributing causes of fatal secondary crashes are large lateral deviations and heading angle changes, the proposed algorithms consider both lateral displacement and heading of the vehicle. To characterize the vehicle motion after a crash event, a collision force estimation method and a vehicle motion prediction scheme are proposed. The model-based algorithm uses sensing information from the early stage of a collision process, so that the collision force can be predicted and the desired vehicle state can be determined promptly. The final heading angles are determined off-line and results are stored in a look-up table for faster implementation. Linear Time Varying Model Predictive Control (LTV-MPC) method is used to obtain the control signals, with the key tire nonlinearities captured through linearization. This algorithm considers tire force constraints based on the combined-slip tire model. The computed high-level control signals are realized through a control allocation problem which maps vehicle motion commands to tire braking forces. For real-time implementation, a rule-based control strategy is obtained. Several rules were constructed, and results under the rule-based control are similar to those under the optimal control (LTV-MPC) method while avoiding heavy on-board computations. Lastly, this thesis proposes a preemptive steering control concept. By assessing the expected strength of an imminent collision force from another vehicle, a preemptive steering control is applied to mitigate the imminent impact.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111343/1/bjukim_1.pd

Holistic Vehicle Control Using Learning MPC

In recent years, learning MPC schemes have been introduced to address these challenges of traditional MPC. They typically leverage different machine learning techniques to learn the system dynamics directly from data, allowing it to handle model uncertainty more effectively. Besides, they can adapt to changes by continuously updating the learned model using real-time data, ensuring that the controller remains effective even as the system evolves. However, there are some challenges for the existing learning MPC techniques. Firstly, learning-based control approaches often lack interpretability. Understanding and interpreting the learned models and their learning and prediction processes are crucial for safety critical systems such as vehicle stability systems. Secondly, existing learning MPC techniques rely solely on learned models, which might result in poor performance or instability if the model encounters scenarios that differ significantly from the training data. Thirdly, existing learning MPC techniques typically require large amounts of high-quality data for training accurate models, which can be expensive or impractical in the vehicle stability control domain. To address these challenges, this thesis proposes a novel hybrid learning MPC approach for HVC. The main objective is to leverage the capabilities of machine learning algorithms to learn accurate and adaptive models of vehicle dynamics from data, enabling enhanced control strategies for improved stability and maneuverability. The hybrid learning MPC scheme maintains a traditional physics-based vehicle model and a data-based learning model. In the learned model, a variety of machine-learning techniques can be used to predict vehicle dynamics based on learning from collected vehicle data. The performance of the developed hybrid learning MPC controller using torque vectoring (TV) as the actuator is evaluated through the Matlab/Simulink and CarSim co-simulation with a high-fidelity Chevy Equinox vehicle model under a series of harsh maneuvers. Extensive real-world experiments using a Chevy Equinox electric testing vehicle are conducted. Both simulation results and experimental results show that the developed hybrid learning MPC approach consistently outperforms existing MPC methods with better yaw rate tracking performance and smaller vehicle sideslip under various driving conditions

Model Predictive Control System Design of a passenger car for Valet Parking Scenario

A recent expansion of passenger cars’ automated functions has led to increasingly challenging design problems for the engineers. Among this the development of Automated Valet Parking is the latest addition. The system represents the next evolution of automated system giving the vehicle greater autonomy: the efforts of most automotive OEMs go towards achieving market deployment of such automated function. To this end the focus of each OEM is on taking part to this competitive endeavor and succeed by developing a proprietary solution with the support of hardware and software suppliers. Within this framework the present work aims at developing an effective control strategy for the considered scenarios. In order to reach this goal a Model Predictive Control approach is employed taking advantage of previous works within the automotive OEM in the automated driving field. The control algorithm is developed in a Simulink® simulation according to the requirements of the application and tested; results show the control strategy successfully drives the vehicle on the predefined path

Lap time simulation for racing car design

Racing teams use numerous computational tools (CAD, FEA, CFD) to aid in the design of racing cars and the development of their performance. Computer simulation of racing car handling through Lap Time Simulation (LTS) packages complements these tools. It also allows teams to examine the effect of different vehicle parameter setups to optimise vehicle performance. In similarity with the automotive industry, time is limited and rapid development of new ideas and technology is essential. Thus, the use of a more sophisticated computer simulation would allow a team to gain a significant advantage over their competitors. As LTS are computationally intensive,previous packages have simulateda full lap using a quasi-static method which splits the path of the vehicle into segments. An analysis is then made of the vehicle at each segment point using the external forces acting on the vehicle. Due to the constant acceleration(i.e. steady state) assumption across each segment, this method does not take into account the effect of roll, pitch and yaw inertia as well as damping and tyre lag effects. Another aspect that is not accounted for is the variation in the fastest effective vehicle path along the circuit (i.e.racing line) due to change in driver control inputs or vehicle parameters. The overall aim of this thesis is to develop a transient LTS methodology, which adopts a strategy to vary the racing line taken in order to address the problems found with the existing quasi-static LTS packages. In parallel an investigation of the accuracy of vehicle models in relationship to racing car performance has been developed. The thesis begins with a study of racing car modelling techniques and a review of current LTS packages. A description is then given of the collection of vehicle handlingd ataf rom an actualr acingc ar (alongw ith attaining a vehicle parametesr et) and the measured results displayed and discussed. The creation of two vehicle models, a simple and sophisticated version, is detailed and the measured results are compared to the simulated results of each vehicle model. It was found that the simple vehicle model does not fully represent the actual vehicle's lateral dynamic behaviour, although its steady state response was deemed to be accurate. The sophisticated vehicle model was seen to not only accurately predict the full range of lateral dynamic behaviour of the actual vehicle, but also the actual vehicle's longitudinal and combined lateral and longitudinal dynamic behaviour. To further investigate LTS techniques, a comparison study was made between various simulation approaches which indicated that the transient approach, although more complicated and time consuming, allows for more accurate tuning of a greater number of vehicle parameters. Finally, the creation of two simulation packages has been detailed and case studies are presented to provide further insight into the look and feel of the packages. The first package is a quasi-static approach based LTS package, where a case study is made into the sensitivity of overall lap time to a range of vehicle parameters. The second is a transient approach based simulation package which optimises the driver controls,varying the racing line taken by the vehicle and ensuring the manoeuvre is completed in the quickest time for that vehicle parameter set. This final Manoeuvre Time Minimisation package fulfils the overall aim of the thesis and a case study is made into the effect of front damping value on manoeuvre completion time

Vehicle path optimisation and controllability on the limit using optimal control techniques

Vehicle behaviour near the limit of adhesion is studied using linear optimal . control techniques and relatively simple vehicle models. Both time-invariant and time-varying approaches are used. Controllability is applied as a post-processing tool to analyse the resultant vehicle behaviour. First, a 4WS controller is developed using a linear time-invariant method, with a reference model control structure. Two handling objectives are defined, which are thought to provide predictable dynamics. Advantages of using a reference model control are clearly shown. With a developed control structure, it is shown that the prescribed target dynamics is achieved, provided tyre forces are available. It is also found that the controller is robust to small changes in the various vehicle parameter values. As a next step, time-varying modelling approach was used in order to better represent the vehicle operating conditions through the various dynamic range, including the limit of adhesion. An iterative vehicle path optimisation problem is formulated using a linear time-varying control approach. The validity of the optimisation method is studied against the steady-state simulation result at the limit of adhesion. It is shown that the method is capable of finding a trajectory in the vicinity of the friction limit, where the front tyres are used fully whilst retaining some margin at the rears. However, a couple of Issues are discovered. First, due to the quadratic nature of the road geometry cost function, the trajectory could get locked if the vehicle runs very close to the edge of the road. Hence, the . optimisation needs to be formulated such that the level of "optimality" on the trajectory remains consistent throughout the manoeuvre at each iteration. Secondly, it is found that inappropriate control demands are produced if the system matrix becomes poorly conditioned near the limit. This results in optimisation failure. In order to understand the mechanism of this failure, controllability of linear timevarying system was analysed and its properties were discussed in detail. First, the calculation methods of the controllability gramian matrix are investigated and some practical limitations are found. The gramian matrix is then used to define an open loop control sequence. It is found that the damping of the system has a significant influence on the control strategy. Subsequently, "the moving controllability window of a fixed time period" is found to provide the most relevant information of changing dynamics through the time. The study showed that the failure of the optimisation in the vicinity of the friction limit was indeed due to lack of controllability and the optimisation method itself was functioning correctly. The vehicle path optimisation problem is then extended to include longitudinal dynamics, enabling simulation of more general manoeuvres. The single corner simulation showed that the optimisation converges to an "out-in-out" path, with iterative solution improving continuously in a first order manner. Simulations with various controller settings showed that the strategy is reasonably robust provided that the changes in parameter settings are kept within a reasonable magnitude. It is also confirmed that the optimisation is able to drive a vehicle close to the limit under different types of operations required, i.e. braking, cornering and acceleration. The study was then performed with slightly more complex road geometry in order to investigate if the· optimisation is capable of prioritising certain· part of the manoeuvre in order to achieve better overall result. Unfortunately, this problem could not be solved successfully. The optimisation concentrated on the latter part of the manoeuvre as it had higher sensitivity to the final cost. This resulted in clearly sub-optimal overall performance. Finally, relatively simple study is conducted to investigate the correlation between various vehicle settings and optimisation results. Using the path optimisation problem formulation, iris found that the more oversteer vehicles are able to achieve better· result with more margin left in rear tyre force capacity. The handling objective functions used for the 4 WS controller is also calculated for the resultant trajectories. It is found that the neutral steer cost had a strong correlation, whereas the linearity cost showed no noticeable correlation. The controllability analysis was applied on the various vehicle settings using step steer simulation. It showed that more understeering vehicle retains higher controllability throughout the dynamics range. It is also found that higher inertia gives better controllability near the limit, however, it gives less controllability at more moderate operating conditions

Vehicle Steering Systems - Hardware-in-the-Loop Simulator, Driving Preferences, and Vehicle Intervention

The steering system is a critical component of all ground vehicles regardless of their propulsion source. Chassis directional control is provided by the steering system, which in turn relays valuable feedback about the road and vehicle behavior. As the primary feedback channel to the driver, the steering system also delivers the initial perception of a vehicle\u27s handling and responsiveness to the consumer. Consequently, the steering system is an important aspect of the vehicle\u27s evaluation and purchasing process, even if drivers are unaware of its direct influence in their decision making. With automobile purchases potentially hinging on the steering system, a need exists for a better understanding of steering preference through a focused research project. In this investigation, driver steering preferences have been studied using an advanced hardware-in-the-loop automobile steering simulator. Additionally, vehicle run-off-road situations have been studied, which occur when some of the vehicle wheels drift off the road surface and the driver recovers through steering commands. The Clemson University steering simulator underwent three significant generations of refinements to realize a state-of-the-art automotive engineering tool suitable for human subject testing. The first and third generation refinements focused on creating an immersive environment, while the second generation introduced the accurate reproduction of steering feel found in hydraulic systems and real-time adjustable steering feel. This laboratory simulator was the first known validated driving simulator developed for the sole purpose of supporting driver steering preference studies. The steering simulator successfully passed all validation tests (two pilot studies) leading to an extensive demographics-based driver preference study with 43 subjects. This study reflected the following preliminary trends: Drivers who used their vehicles for utility purposes preferred quicker steering ratios and heavier efforts in residential, country, and highway environments. In contrast, car enthusiasts preferred quick steering ratios in residential and country environments and light steering effort on the highway. Finally, rural drivers preferred quicker steering ratios on country roads. These relationships may be used to set steering targets for future vehicle developments to accurately match vehicles to their intended market segments. The second research aspect was the development of an objective steering metric to evaluate a driver\u27s steering preference. In past simulator studies, driver feedback has been gathered extensively using written questionnaires. However, this delays the testing procedure and introduces an outside influence that may skew results. Through the data collected in this project, a robust objective steering preference metric has been proposed to gather steering preferences without directly communicating with the driver. The weighted steering preference metric demonstrated an excellent correlation with survey responses of -0.39 regardless of steering setting. This global steering preference metric used a combination of yaw rate, longitudinal acceleration, and lateral acceleration. The objective data was further dissected and it was discovered that changes made to the steering ratio resulted in a correlation of -0.55 between the objective data and subjective response from the test subjects. This substantial correlation relied on the longitudinal acceleration, left front tire angle, and throttle position. Beyond steering preferences, vehicle safety remains a major concern for automotive manufacturers. One important type of crash results from the vehicle leaving the road surface and then returning abruptly due to large steering wheel inputs: road runoff and return. A subset of run-off-road crashes that involves a steep hard shoulder has been labeled \u27shoulder induced accidents\u27. An active steering controller was developed to mitigate these \u27shoulder induced accidents\u27. A cornering stiffness estimation technique, using a Kalman filter, was coupled with a full state feedback controller and \u27driver intention\u27 module to create a safe solution without excessive intervention. The concept was designed to not only work for shoulder induced accidents, but also for similar road surface fluctuations like patched ice. The vehicle crossed the centerline after 1.0s in the baseline case; the controller was able to improve this to 1.3s for a 30% improvement regardless of driver expertise level. For the case of an attentive driver, the final heading angle of the vehicle was reduced by 47% from 0.48 rad to 0.255 rad. These laboratory investigations have clearly demonstrated that advancements in driver preference and vehicle safety may be realized using simulator technology. The opportunity to apply these tools should result in better vehicles and greater safety of driver and occupants. With the development of the objective steering preference metric, future research opportunities exist. For prior steering preference research, the feedback loop has typically required interaction with the subject to rate a setting before continuing. However, the objective steering preference metric allows this step to be automated, opening the door for the development of an automatic tuning steering system

Eleventh Annual Conference on Manual Control

Human operator performance and servomechanism analyses for manual vehicle control tasks are studied