A Feedback Linearization Design for the Control of Vehicle's Lateral Dynamics

issues of controllability and stabilization design for vehicle's lateral dynamics are presented. Based on the assumption of constant driving speed, a second-order nonlinear lateral dynamical model is obtained. It is observed that saddle node bifurcation will appear in vehicle dynamics with respect to the variation of the front wheel steering angle, which might result in spin and/or system instability. In order to possibly prevent the occurrence of such an instability, the controllability of vehicle dynamics at the saddle node bifurcation point is discussed. This leads to the design of a direct state feedback control law for system stabilization. Two-Parameter bifurcation analysis of system behavior is also obtained to classify the regime of the effective control gains for system stabilization. Numerical simulations for an example model demonstrate the effectiveness of analytical results.

SIDESLIP ANGLE MEASUREMENT, EXPERIMENTAL CHARACTERIZATION AND EVALUATION OF THREE DIFFERENT PRINCIPLES

Set-Based Actuator Reconfguration Analysis for the Integrated Control of Lateral Vehicle Dynamics

In this paper, we propose a new adaptive side slip observer using a simple combined vehicle dynamics. Though there are many ways to estimate the side slip angle, some problems still exist in the popular estimation techniques based on the kinematic model, the dynamic model, and the combined kinematic-dynamic model. Therefore we propose the adaptive side slip observer. A combined model of linearized roll motion and lateral vehicle dynamics was used to involve the roll motion in the second-order lateral vehicle dynamics. Furthermore, a time delay was also applied to the combined model, reflecting the reaction time between the steering angle and the side slip angle. We designed an H∞ discrete-time slip angle observer using linear matrix inequality to reduce the infinite norm between a set of disturbance inputs and the estimation error. The performance of the proposed method was validated via simulations and experiments.

This paper develops a multiple fixed model blending based adaptive parameter identification scheme for multi-input multi-output (MIMO) systems with polytopic parameter uncertainty. The developed identification scheme is proven to be asymptotically stable for uncertain linear time-invariant MIMO systems, and is shown to provide fast adaptation for even uncertain linear time-varying (LTV) systems. Furthermore, utilizing the proposed parameter identification scheme, a linear-quadratic (LQ) optimal multiple model adaptive control (MMAC) scheme is developed for linear MIMO systems with polytopic uncertainties. The proposed MMAC scheme is applied to tracking control of uncertain lateral vehicle dynamics. A set of simulation test results are presented to verify the stability and effectiveness of the proposed MMAC scheme.

The paper introduces the method of assessment of vehicle manoeuvres through a set of global cost functions. The corresponding cost functions can be derived for different domains like longitudinal and lateral dynamics, driving comfort and other. The procedures of the computation of the cost functions include in general: (1) Selection of vehicle dynamics parameters relevant to the domain; (2) Transformation the appointed parameter to the dimensionless form; (3) Definition of weighting factors for each of the appointed parameters with taking into account that the weighting factors can be variable depending on the type of the vehicle manoeuvre as well as on the driving conditions; (4) Calculation of the cost function for the selected domain; (5) Calculation of a global cost function in the case of the integrated assessment of the manoeuvre through several domains of the vehicle dynamics. The described procedures are discussed in the paper as applied to the domain of lateral vehicle dynamics. The parameters chosen for the calculation of the corresponding cost function are the lateral acceleration a y , the yaw rate dψ/dt, and the sideslip angle β. To transform these parameters to a dimensionless form, the procedure is proposed that uses the function of root mean square of deviations between reference and actual values for each variable. This procedure implements also an original method of definition of reference values for lateral acceleration a y and yaw rate dψ/dt. The method is based on the variation of understeer characteristic of the baseline vehicle with the aim to extend the linear region and to reduce the understeer gradient as well as to increase the maximum level of lateral acceleration. The validation of the developed methods and procedures is illustrated by way of model-in-the-loop simulation. The test programme covers several standard manoeuvres—steady-state circle, slalom and avoidance manoeuvre—performed for a simulator, medium-sized passenger car. The numerical values of the cost functions for each manoeuvre are introduced and analyzed. The further applications of the developed technique can be: (1) Assessment of vehicle dynamics based on criterions of performance and stability; (2) Optimization of vehicle dynamics control systems; (3) Choice of proper control strategies/tuning of control gains and resolution of critical control situations by simultaneous operation of several systems like ABS, TCS, TV/vehicle dynamics control.KeywordsVehicle dynamicsCost functionsStabilityWeighting factorsSimulation

In this paper, a testbed for studying longitudinal and lateral vehicle dynamics is proposed. The testbed consists of a treadmill and a scaled remote-controlled car with four independently controlled brushless DC motors with front steering. The car is equipped with four power converters suitable for field-oriented torque control. The parameters of the scaled vehicle are chosen using the Buckingham PI theorem to achieve dynamic similitude to a full-size vehicle. Such a testbed allows testing of different control algorithms for vehicle dynamics. In this paper, a model predictive steering controller is implemented. Simulations are performed on both the full-scale vehicle in IPG CarMaker and the scaled vehicle in Matlab/Simulink environment. In addition, preliminary experimental results on the scaled vehicle are included to show the real-time suitability of the proposed control algorithm.

In recent years, the automotive industry has been developing applied research to meet customer’s needs; considering safety, vehicle comfort and energetic efficiency. In particular, automotive tires have a prominent position in this research area, ensuring good vehicle handling, comfort and safety. In the vehicle dynamics performance, excellent gripping and reduced rolling resistance in the tires are crucial to maximize the energetic efficiency in a reliable way. However, to ensure such a level of reliability in vehicle operation, the inherent uncertainties of tires, as well as other factors subject to variability must be taken into account in the vehicle design. In this way, the present paper analyzes in detail the effect of variations in some parameters such as ambient temperature, ground conditions, vertical load, speed and tire inflation on the analysis of vehicle dynamics. A Metamodeling approach associated with the Monte Carlo Simulation was employed to develop the mathematical models to analyze the effect of uncertain parameters on the tire rolling resistance; traction, centripetal and lateral forces, using experimental data from the literature, in the longitudinal and lateral vehicle dynamics. Therefore, the present research brings as an innovation an integrated approach to the input parameters of the system with the rolling resistance through the developed metamodels. There was a substantial variability of up to 15% both up and down in the Maximum Traction Force of a vehicle in response to variations in the vehicle’s weight and the coefficient of tire rolling resistance. In contrast, the Lateral Force exhibited a greater variability, with a 25 downward and 10% upward variation associated with the weight and friction coefficient variability of the vehicle. Further investigations into the sensitivity analysis highlight the significant influence of the friction coefficient and temperature on the Traction Forces of the vehicle.

Residual signal generation is a fundamental step in fault diagnosis and fault tolerant control systems. This paper proposes a switched observer approach to generate a road-adaptive vehicle dynamics residual signal. The main contribution in this paper is to generate a residual signal with low false alarm rate. Lateral vehicle dynamics suffers from presence of two types of disturbances, a stochastic white-noise disturbance and a deterministic lateral dynamics one. Kalman filter and parity-space approach are used to generate the residual signals that must feed the fault tolerant control system. The observers were modeled and simulated in Matlab/Simulink environment, they were then applied to a vehicle dynamics scenarios with lateral disturbances using virtual vehicle simulator (CarMaker).

Current lane-based microscopic traffic simulators combine car-following and lane changing logic to describe the (often discrete) lateral vehicle motion on multi-lane road segments. However, the simulated lateral trajectories are physically unplausible and inside-lane behavior such as lane-keeping and curve negotiation cannot be modelled. In this work, we integrate lateral vehicle dynamics and yaw motion into a traffic simulation framework, aiming to describe lateral motion and vehicle interactions with more precision. The resulting framework consists of two coupled layers, an upper tactical level that plans maneuvers such as lane-changing; and a lower operational layer with a control module (steering and acceleration control) that operates in a closed loop with the bicycle model of vehicle dynamics. The feedback mechanism between the layers allows for dynamic trajectory re-planning. Unlike the microscopic traffic models, the proposed framework accounts for lateral vehicle dynamics and yaw motion; provides additional variables such as vehicle heading and front wheel steering angle; and is hence termed as submicroscopic. Case study results demonstrate the power of the framework to include lateral maneuvers such as curve negotiation, corrective steering, lane change abortion and fragmented lane changing. The framework was operationalized to model multi-lane traffic flow consisting of human-driven vehicles. At the macroscopic level, the traffic flow simulation can reproduce phenomena such as capacity drop. Thus the framework preserves the properties of the component models and at the same time describe the continuous 2-D planar movement of vehicles.

In this paper, we propose a steering control scheme for vehicle dynamics by using sliding mode control approach. The vehicle dynamics is known to exhibit a saddle-node bifurcation phenomenon while operating at some front wheel steering angle, which might incur spin and/or instability for vehicle systems and then cause traffic accidents. By using the proposed sliding mode control law, the vehicle dynamics will be stabilized at the bifurcation point and the stability of the vehicle system will also be guaranteed after the appearance of bifurcation phenomenon. Simulation results for an example vehicle model demonstrate the success of the proposed design.

Vehicle dynamics estimation has been the subject of study for some years now. If on-board vehicle control systems can be provided with information such as side slip angle, lateral force etc. then stability of the vehicle can be improved. To estimate these dynamic variables different observers have been used e.g., sliding mode, fuzzy logic, neural networks etc. In this article the authors propose an extended Kalman lter to estimate vehicle side slip angle. Roll angle is estimated using vertical loads as input. First, a linear Kalman lter is used to lter out the vertical forces and estimate roll angle. This information is then used to estimate the vehicle side slip angle. To take into account the nonlinearities concerning lateral vehicle dynamics, Pacejka magic formula is used to model lateral forces. Estimated results are then compared with simulations, showing good accuracy.

This paper presents various sliding mode techniques starting from a conventional first order SMC and then integral SMC, Super twisting algorithm and real twisting algorithm) for the tracking control of the lateral dynamics of an autonomous underwater vehicle (AUV). Moreover the controllability and observability analysis of the lateral dynamics is given and a Slotine observer [J, -J. E Slotine, J. K Hedrick, E. A. Misawa 1987] has also been proposed for it. Based on the simulation results a comparison of various controllers is made. The higher order SMC provides a chattering free tracking and a model free control which is robust to parametric variations.

This paper proposes a dynamic event triggering mechanism whose triggering characteristics can be designed for a robust sliding mode controller to solve the lateral motion control problem of intelligent electric vehicle in the presence of bounded matched disturbances. The motivation of current work mainly aims to alleviate unnecessary computing and communication of energy-constrained vehicle control systems by a large margin while ensuring its stability and robustness. The main contributions of the paper are summarized as follows. We design a continuous-time robust sliding mode controller which is proved to have the capability to effectively suppress the bounded matched disturbances. We derive a new dynamic event triggering rule, under which the positive minimum value and the evolution characteristics of the inter-event times are able to be designed. We analyze the stability via the Lyapunov theory, exclude Zeno behavior and prove that the semi-global robust event-separation property holds for the above constructed event-triggered control system theoretically. We numerically evaluate the effectiveness of our theoretical results and the advantages of the proposed method which is highlighted in an overtaking case for the intelligent electric vehicle. To the best of our knowledge, this is the first paper which studies lateral motion control of vehicle with the consideration of the robustness not only for the stability but also for the positive minimum inter-event times.

This paper addresses the problem of lateral dynamics estimation of a vehicle and critical situations detection. For this purpose, two cascaded unknown input observers are designed which achieve finite time convergence. The system describing the lateral vehicle dynamics is decomposed into two sub-systems which are described with linear parameter varying models (LPV) according to the longitudinal velocity. For each sub-system, an unknown input observer is designed by the use of the relative degree of the considered unknown inputs with respect to the outputs. Theoretically, the observers are designed to ensure, firstly, asymptotic state estimation error convergence and secondly, finite time convergence by the use of the geometric homogeneity concept. A strategy for critical situations detection is presented by comparing the estimated nonlinear forces to the linear version obtained from a linear model of the forces and the estimated lateral velocity. Simulation results are provided to illustrate the proposed observer structure and the critical situations detection.