On Steady-State Cornering Equilibria for Wheeled Vehicles with Drift

In this work we derive steady-state cornering conditions for a single-track vehicle model without restricting the operation of the tires to their linear region (i.e. allowing the vehicle to drift). For each steady-state equilibrium we calculate the corresponding tire friction forces at the front and rear tires, as well as the required front steering angle and front and rear wheel longitudinal slip, to maintain constant velocity, turning rate and vehicle sideslip angle. We design a linear controller that stabilizes the vehicle dynamics with respect to the steady-state cornering equilibria using longitudinal slip at the front and the rear wheels as the control inputs. The wheel torques necessary to maintain the given equilibria are calculated and a sliding-mode controller is proposed to stabilize the vehicle using only front and rear wheel torques as control inputs

Search-Based Motion Planning for Performance Autonomous Driving

Driving on the limits of vehicle dynamics requires predictive planning of future vehicle states. In this work, a search-based motion planning is used to generate suitable reference trajectories of dynamic vehicle states with the goal to achieve the minimum lap time on slippery roads. The search-based approach enables to explicitly consider a nonlinear vehicle dynamics model as well as constraints on states and inputs so that even challenging scenarios can be achieved in a safe and optimal way. The algorithm performance is evaluated in simulated driving on a track with segments of different curvatures.Comment: Accepted to IAVSD 201

Anytime computation of time-optimal off-road vehicle maneuvers using the RRT*

Incremental sampling-based motion planning algorithms such as the Rapidly-exploring Random Trees (RRTs) have been successful in efficiently solving computationally challenging motion planning problems involving complex dynamical systems. A recently proposed algorithm, called the RRT*, also provides asymptotic optimality guarantees, i.e., almost-sure convergence to optimal trajectories (which the RRT algorithm lacked) while maintaining the computational efficiency of the RRT algorithm. In this paper, time-optimal maneuvers for a high-speed off-road vehicle taking tight turns on a loose surface are studied using the RRT* algorithm. Our simulation results show that the aggressive skidding maneuver, usually called the trail-braking maneuver, naturally emerges from the RRT* algorithm as the minimum-time trajectory. Along the way, we extend the RRT* algorithm to handle complex dynamical systems, such as those that are described by nonlinear differential equations and involve high-dimensional state spaces, which may be of independent interest. We also exploit the RRT* as an anytime computation framework for nonlinear optimization problems.United States. Air Force Office of Scientific Research. Multidisciplinary University Research Initiative (Grant W911NF-11-1-0046)National Science Foundation (U.S.) (Grant CNS-1016213

Time minimization for vehicles passing a given trajectory

Táto práca rieši modelovanie pohybu automobilu a jeho optimálnu rýchlosť v každom bode trajektórie, po ktorej sa pohybuje s cieľom minimalizovať celkový čas prejazdu. Využíva k tomu reálne dáta namerané na Masarykovom okruhu v Brne. Obsahom práce je rozbor pôsobiacich síl na dynamiku vozidla a následne zostavenie matematického modelu. Cieľom práce je najskôr nájsť optimálnu rýchlostnú charakteristiku a následne ju porovnať s reálnymi dátami. Potom model automobilu rozšíriť o nastaviteľné krídlo a zdôvodniť pozitívny vplyv tohto pridaného prvku na minimalizáciu celkového času. Simulácia je prevedená pomocou prostredia programu MATLAB.This bachelor’s thesis deals with model building of automobile movement and it’s optimal speed in every single point of trajectory, whit aim on minimal lap time. It uses a real data, which was measured on Masaryk circuit in Brno. Contents of thesis are analysing effecting forces on vehicle’s dynamic and then building mathematic model. The main purpose is finding optimal speed characterization and compare with real data. Then, we modify model with adjustable wing and present advantage of this customization. Simulation was done by program MATLAB.

Vehicle Stabilization during Critical Cornering Scenarios Using Sliding Surface Control

While effective in improving handling and passenger safety, current vehicle control systems are generally limited to braking or steering control. This project presents an approach which integrates steering and braking actuation to further improve vehicle stability in critical cornering scenarios. A 3D phase portrait visualization tool enables examination of lateral velocity, longitudinal velocity, and yaw rate. This tool is used to determine vehicle stability under different operating conditions to inform the design of a controller. The proposed hierarchical controller defines a path-following function for the desired cornering radius and determines appropriate braking and steering inputs, using sliding surface control, to drive the vehicle to the desired path. A low-complexity vehicle model is used to formulate the sliding surface, while a high-fidelity model is used to determine optimal inputs. Simulations show that the sliding surface controller design is more effective than a baseline steering controller in keeping the vehicle on the roadway. Examination reveals that the complex sequence of braking and steering inputs is only feasible with the addition of a modern vehicle control system. While average drivers lack the ability to effectively employ such complex sequencing, modern control systems are capable of this coordination. When entering corners at speeds within the capability of the vehicle, but beyond the ability of the driver, these control sequences can help maintain stability to avoid an accident

극한 주행 성능 향상을 위한 타이어 슬립 정보 기반 통합 샤시제어 알고리즘

학위논문 (석사)-- 서울대학교 대학원 : 기계항공공학부, 2016. 8. 이경수.This paper presents a tire slip based integrated chassis control (ICC) algorithm of four-wheel drive(4WD)/ electronic stability control(ESC)/electronic controlled suspension(ECS) for enhanced limit handling. The principal objective of the vehicle dynamic control algorithm for limit handling is to enable agile, steady maneuver at the limits and expand vehicle control capability to maximum. In order to achieve this objective, the ICC consists of three layers - a supervisor, an upper level controller, and a lower level controller. The supervisor determines desired vehicle motions based on driver commands to the vehicle. The upper level controller calculated virtual control inputs based on desired vehicle motion. In the lower level controller, the virtual control inputs are optimally coordinated to each chassis module based on tire combined slip for enhanced limit handling. The performance of ICC has been investigated via closed loop simulation and vehicle experiment. To investigate the ICC algorithm at the limits via closed loop simulation, the lateral driver model, which mimics professional drivers, for limit handling has been developed and utilized. In the developed driver model, body side slip angle incorporates into path tracking error in contrast to common path tracking algorithms. It has been shown that the proposed ICC algorithm effectively keeps stability and maneuverability of the vehicle at the limits.Chapter 1 Introduction 1 1.1 Study Background 1 1.2 Purpose of Research 3 Chapter 2 Vehicle Control System 5 2.1 Vehicle Chassis System 5 Chapter 3 Lateral Driver Model 8 3.1 Overall Algorithm 8 3.2 Comparison and Validation 23 Chapter 4 Development of Integrated Chassis Control Algorithm of ESC and 4WD for Enhanced Limit Handling 32 4.1 Supervisor 32 4.2 Uppel Level Controller 34 4.3 Lower Level Controller: Optimal Coordination 37 4.4 Simulation Results 47 Chapter 5 Development of Integrated Chassis Control Algorithm of ESC, 4WD, ECS and ARS for Enhanced Limit Handling 58 5.1 Supervisor 58 5.2 Upper Level Controller 59 5.3 ECS/ARS Control Allocation 59 5.4 Comparison and validation 64 Chapter 6 Vehicle Tests of 4WD/ESC/ECS Algorithm 71 6.1 Experimental Results 72 Chapter 7 Conclusion & Future Works 73 Bibliography 75 국문 초록 78Maste

A Controls-Oriented Approach For Modeling Professional Drivers During Ultra-High Performance Maneuvers

In the study of vehicle dynamics and controls, modeling ultra-high performance maneuvers (i.e., minimum-time vehicle maneuvering) is a fascinating problem that explores the boundaries of capabilities for a human controlling a machine. Professional human drivers are still considered the benchmark for controlling a vehicle during these limit handling maneuvers. Different drivers possess unique driving styles, i.e. preferences and tendencies in their local decisions and corresponding inputs to the vehicle. These differences in the driving style among professional drivers or sets of drivers are duly considered in the vehicle development process for component selection and system tuning to push the limits of achievable lap times. This work aims to provide a mathematical framework for modeling driving styles of professional drivers that can then be embedded in the vehicle design and development process. This research is conducted in three separate phases. The first part of this work introduces a cascaded optimization structure that is capable of modeling driving style. Model Predictive Control (MPC) provides a natural framework for modeling the human decision process. In this work, the inner loop of the cascaded structure uses an MPC receding horizon control strategy which is tasked with finding the optimal control inputs (steering, brake, throttle, etc.) over each horizon while minimizing a local cost function. Therein, we extend the typical fixed-cost function to be a blended cost capable of optimizing different objectives. Then, an outer loop finds the objective weights used in each MPC control horizon. It is shown that by varying the driver\u27s objective between key horizons, some of the sub-optimality inherent to the MPC process can be alleviated. In the second phase of this work, we explore existing onboard measurements of professional drivers to compare different driving styles. We outline a novel racing line reconstruction technique rooted in optimal control theory to reconstruct the driving lines for different drivers from a limited set of measurements. It is demonstrated that different drivers can achieve nearly identical lap times while adopting different racing lines. In the final phase of this work, we use our racing line technique and our cascaded optimization framework to fit computable models for different drivers. For this, the outer loop of the cascaded optimization finds the set of objective weights used in each local MPC horizon that best matches simulation to onboard measurements. These driver models will then be used to optimize vehicle design parameters to suit each driving style. It will be shown that different driving styles will yield different parameters that optimize the driver/vehicle system