RISE-Based Integrated Motion Control of Autonomous Ground Vehicles With Asymptotic Prescribed Performance

This article investigates the integrated lane-keeping and roll control for autonomous ground vehicles (AGVs) considering the transient performance and system disturbances. The robust integral of the sign of error (RISE) control strategy is proposed to achieve the lane-keeping control purpose with rollover prevention, by guaranteeing the asymptotic stability of the closed-loop system, attenuating systematic disturbances, and maintaining the controlled states within the prescribed performance boundaries. Three contributions have been made in this article: 1) a new prescribed performance function (PPF) that does not require accurate initial errors is proposed to guarantee the tracking errors restricted within the predefined asymptotic boundaries; 2) a modified neural network (NN) estimator which requires fewer adaptively updated parameters is proposed to approximate the unknown vertical dynamics; and 3) the improved RISE control based on PPF is proposed to achieve the integrated control objective, which analytically guarantees both the controller continuity and closed-loop system asymptotic stability by integrating the signum error function. The overall system stability is proved with the Lyapunov function. The controller effectiveness and robustness are finally verified by comparative simulations using two representative driving maneuvers, based on the high-fidelity CarSim-Simulink simulation

A methodology for the design of robust rollover prevention controllers for automotive vehicles: Part 2-Active steering

In this paper we apply recent results from robust control to the problem of rollover prevention in automotive vehicles. Specifically, we exploit the results of Pancake, Corless and Brockman, which provide controllers to robustly guarantee that the peak magnitudes of the performance outputs of an uncertain system do not exceed certain values.We use the dynamic Load Transfer Ratio LTRd as a performance output for rollover prevention, and design active-steering based rollover controllers to keep the magnitude of this quantity below a certain level, while we use control input u as an additional performance output to limit the maximum amount of control effort. We present numerical simulations to demonstrate the efficacy of our controllers

A methodology for the design of robust rollover prevention controllers for automotive vehicles: Part 1-Differential Braking

In this paper we apply recent results from robust control to the problem of rollover prevention in automotive vehicles. Specifically, we exploit the results of Pancake, Corless and Brockman, which provide controllers to robustly guarantee that the peak values of the performance outputs of an uncertain system do not exceed certain values. We introduce a new measure of performance for rollover prevention, the Load Transfer Ratio LTRd , and design differential-braking based rollover controllers to keep the value of this quantity below a certain level; we also obtain controllers which yield robustness to variations in vehicle speed. We present numerical simulations to demonstrate the efficacy of our controllers

Center of Gravity Estimation and Rollover Prevention Using Multiple Models & Controllers

In this paper, we present a methodology based on multiple models and switching for realtime estimation of center of gravity (CG) position and rollover prevention in automotive vehicles. Based on a linear vehicle model in which the unknown parameters appear nonlinearly, we propose a novel sequential identification algorithm to determine the vehicle parameters rapidly in real time. The CG height estimate is further coupled with a switching controller to prevent untripped rollover in automotive vehicles. The efficacy of the proposed switched multi model/controller estimation and control scheme is demonstrated via numerical simulations

Topics in Automotive Rollover Prevention: Robust and Adaptive Switching Strategies for Estimation and Control

The main focus in this thesis is the analysis of alternative approaches for estimation and control of automotive vehicles based on sound theoretical principles. Of particular importance is the problem rollover prevention, which is an important problem plaguing vehicles with a high center of gravity (CG). Vehicle rollover is, statistically, the most dangerous accident type, and it is difficult to prevent it due to the time varying nature of the problem. Therefore, a major objective of the thesis is to develop the necessary theoretical and practical tools for the estimation and control of rollover based on robust and adaptive techniques that are stable with respect to parameter variations. Given this background, we first consider an implementation of the multiple model switching and tuning (MMST) algorithm for estimating the unknown parameters of automotive vehicles relevant to the roll and the lateral dynamics including the position of CG. This results in high performance estimation of the CG as well as other time varying parameters, which can be used in tuning of the active safety controllers in real time. We then look into automotive rollover prevention control based on a robust stable control design methodology. As part of this we introduce a dynamic version of the load transfer ratio (LTR) as a rollover detection criterion and then design robust controllers that take into account uncertainty in the CG position. As the next step we refine the controllers by integrating them with the multiple model switched CG position estimation algorithm. This results in adaptive controllers with higher performance than the robust counterparts. In the second half of the thesis we analyze extensions of certain theoretical results with important implications for switched systems. First we obtain a non-Lyapunov stability result for a certain class of linear discrete time switched systems. Based on this result, we suggest switched controller synthesis procedures for two roll dynamics enhancement control applications. One control design approach is related to modifying the dynamical response characteristics of the automotive vehicle while guaranteeing the switching stability under parametric variations. The other control synthesis method aims to obtain transient free reference tracking of vehicle roll dynamics subject to parametric switching. In a later discussion, we consider a particular decentralized control design procedure based on vector Lyapunov functions for simultaneous, and structurally robust model reference tracking of both the lateral and the roll dynamics of automotive vehicles. We show that this controller design approach guarantees the closed loop stability subject to certain types of structural uncertainty. Finally, assuming a purely theoretical pitch, and motivated by the problems considered during the course of the thesis, we give new stability results on common Lyapunov solution (CLS) existence for two classes of switching linear systems; one is concerned with switching pair of systems in companion form and with interval uncertainty, and the other is concerned with switching pair of companion matrices with general inertia. For both problems we give easily verifiable spectral conditions that are sufficient for the CLS existence. For proving the second result we also obtain a certain generalization of the classical Kalman-Yacubovic-Popov lemma for matrices with general inertia

Adaptive Rollover Prevention for Automotive Vehicles with Differential Braking

In this paper we present an adaptive controller implementation based on the multiple models, switching, and tuning (MMST) paradigm for preventing untripped rollover in automotive vehicles. Our approach relies on differential-braking to keep the value of the Load Transfer Ratio (LTR) below a threshold. We first employ multiple models to infer the unknown center of gravity height and the suspension parameters of the vehicle, which are subsequently used to switch to the corresponding rollover controller. The proposed multicontroller switched scheme is shown via numerical simulations to result in better performance than its fixed robust counterpart

An automotive vehicle dynamics prototyping platform based on a remote control model car

The use of a modified remote control (RC) model car as a vehicle dynamics testing and development platform is detailed. Vehicle dynamics testing is an important aspect of automotive engineering and it plays a key role during the design and tuning of active safety control systems. Considering the fact that such tests are conducted at great expense, scaled model cars can potentially be used to help with the process to reduce the costs. With this view, we instrument and develop a standard electric RC model car into a vehicle dynamics testing platform. We then implement 2 representative active safety control applications based on this platform, namely an antilock brake system using open-loop pulse brake control and a roll-over prevention system utilizing lateral acceleration feedback. Both applications are presented with sensor measurements and the effectiveness of the suggested control algorithms are demonstrated. © TÜBİTAK

Rollover prevention and path following of a scaled autonomous vehicle using nonlinear model predictive control

Vehicle safety remains an important topic in the automotive industry due to the large number of vehicle accidents each year. One of the causes of vehicle accidents is due to vehicle instability phenomena. Vehicle instability can occur due to unexpected road profile changes, during full braking, obstacle avoidance or severe manoeuvring. Three main instability phenomena can be distinguished: the yaw-rate instability, the rollover and the jack-knife phenomenon. The main goal of this study is to develop a yaw-rate and rollover stability controller of an Autonomous Scaled Ground Vehicle (ASGV) using Nonlinear Model Predictive Control (NMPC). Open Source Software (OSS) known as Automatic Control and Dynamic Optimisation (ACADO) is used to design and simulate the NMPC controller based on an eight Degree of Freedom (8 DOF) nonlinear vehicle model with Pacejka tire model. Vehicle stability limit were determined using load transfer ratio (LTR). Double lane change (DLC) steering manoeuvres were used to calculate the LTR. The simulation results show that the designed NMPC controller is able to track a given trajectory while preventing the vehicle from rolling over and spinning out by respecting given constraints. A maximum trajectory tracking error of 0.1 meters (on average) is reported. To test robustness of the designed NMPC controller to model mismatch, four simulation scenarios are done. Simulation results show that the controller is robust to model mismatch. To test disturbance rejection capability of the controller, two simulations are performed, with pulse disturbances of 0.02 radians and 0.05 radians. Simulations results show that the controller is able to reject the 0.02 radians disturbance. The controller is not able to reject the 0.05 radians disturbance

Vehicle Rollover Stability And Path Planning In Adas Using Model Predictive Control

Advanced Driver Assistance Systems (ADAS) have been developed in recent years to significantly improve safety in driving and assist driver’s response in extreme situations in which quick decisions and maneuvers are required. Common features of ADAS in modern vehicles include automatic emergency braking (AEB), lane keeping assistance (LKA), electric stability control (ESC), and adaptive cruise control (ACC). While these features are developed primarily based on sensor fusion, image processing and vehicle kinematics, the importance of vehicle dynamics must not be overlooked to ensure that the vehicle can follow the desired trajectory without inducing any instability. In many extreme situations such as object avoidance, fast maneuvering of vehicles with high center of gravity might result in rollover instability, an event with a high fatality rate. It is thus necessary to incorporate vehicle dynamics into ADAS to improve the robustness of the system in the path planning to avoid collision with other vehicles or objects and prevent vehicle instability. The objectives of this thesis are to examine the efficacy of a vehicle dynamics model in ADAS to simulate rollover and to develop an active controller using Model Predictive Control (MPC) to manipulate the front-wheel steering and four-wheel differential braking forces, which are related to active steering as well as dynamic stability control for collision avoidance. The controller is designed using the model predictive control approach. A four degree-of-freedom vehicle model is simulated and tested in various scenarios. According to simulation results, the vehicle controller by the MPC controller can track the predicted path within error tolerance. The trajectories used in different simulation scenarios are generated by the MPC controller

Anti-rollover control of a heavy-duty vehicle based on lateral load transfer rate

With the rapid development of the highway network construction and heavy-duty vehicle market, the rollover accidents of heavy-duty vehicle continue to increase. In order to improve rollover stability of vehicle, a four degree of freedom (DOF) heavy-duty vehicle model is established. An anti-rollover control strategy is designed by using differential braking system to control the lateral load transfer rate (LTR). The dynamic simulation of vehicle with and without control is fulfilled in Matlab/Simulink. Then, the vehicle responses under typical angle step input are compared and analyzed with different road surface adhesion coefficient, vehicle speed, steering wheel angle and vehicle load. The results show that the proposed control strategy is able to improve vehicle rollover stability greatly and is also beneficial to vehicle yaw stability. The increase of road surface adhesion coefficient, vehicle speed, steering wheel angle or vehicle load has positive correlation with the rollover control effect