Showing papers on "Vehicle dynamics published in 2012"

Control of Multiple UAVs for Persistent Surveillance: Algorithm and Flight Test Results

Abstract: Interest in control of multiple autonomous vehicles continues to grow for applications such as weather monitoring, geographical mapping fauna surveys, and extra-terrestrial exploration. The task of persistent surveillance is of particular significance in that the target area needs to be continuously surveyed, minimizing the time between visitations to the same region. This distinction from one-time coverage does not allow a straightforward application of most exploration techniques to the problem, though ideas from these methods can still be used. The aerial vehicle dynamic and endurance constraints add additional complexity to the autonomous control problem, whereas stochastic environments and vehicle failures introduce uncertainty. In this work, we investigate techniques for high-level control, that are scalable, reliable, efficient, and robust to problem dynamics. Next, we suggest a modification to the control policy to account for aircraft dynamic constraints. We also devise a health monitoring policy and a control policy modification to improve performance under endurance constraints. The Vehicle Swarm Technology Laboratory-a hardware testbed developed at Boeing Research and Technology, Seattle, WA, for evaluating a swarm of unmanned air vehicles-is then described, and these control policies are tested in a realistic scenario.

Distributed Receding Horizon Control of Vehicle Platoons: Stability and String Stability

Abstract: This paper considers the problem of distributed control of a platoon of vehicles with nonlinear dynamics. We present distributed receding horizon control algorithms and derive sufficient conditions that guarantee asymptotic stability, leader-follower string stability, and predecessor-follower string stability, following a step speed change in the platoon. Vehicles compute their own control in parallel, and receive communicated position and velocity error trajectories from their immediate predecessor. Leader-follower string stability requires additional communication from the lead car at each update, in the form of a position error trajectory. Predecessor-follower string stability, as we define it, implies leader-follower string stability. Predecessor-follower string stability requires stricter constraints in the local optimal control problems than the leader-follower formulation, but communication from the lead car is required only once at initialization. Provided an initially feasible solution can be found, subsequent feasibility of the algorithms are guaranteed at every update. The theory is generalized for nonlinear decoupled dynamics, and is thus applicable to fleets of planes, robots, or boats, in addition to cars. A simple seven-car simulation examines parametric tradeoffs that affect stability and string stability. Analysis on platoon formation, heterogeneity and size (length) is also considered, resulting in intuitive tradeoffs between lead car and following car control flexibility.

Landing a VTOL Unmanned Aerial Vehicle on a Moving Platform Using Optical Flow

Abstract: This paper presents a nonlinear controller for a vertical take-off and landing (VTOL) unmanned aerial vehicle (UAV) that exploits a measurement optical flow to enable hover and landing control on a moving platform, such as, for example, the deck of a sea-going vessel. The VTOL vehicle is assumed to be equipped with a minimum sensor suite [i.e., a camera and an inertial measurement unit (IMU)], manoeuvring over a textured flat target plane. Two different tasks are considered in this paper. The first concerns the stabilization of the vehicle relative to the moving platform that maintains a constant offset from a moving reference. The second concerns regulation of automatic vertical landing onto a moving platform. Rigorous analysis of system stability is provided, and simulations are presented. Experimental results are provided for a quadrotor UAV to demonstrate the performance of the proposed control strategy.

Algorithms for Real-Time Estimation of Individual Wheel Tire-Road Friction Coefficients

Abstract: It is well recognized in the automotive research community that knowledge of the real-time tire-road friction coefficient can be extremely valuable for active safety applications, including traction control, yaw stability control and rollover prevention. Previous research results in literature have focused on the estimation of average tire-road friction coefficient for the entire vehicle. This paper explores the development of algorithms for reliable estimation of independent friction coefficients at each individual wheel of the vehicle. Three different observers are developed for the estimation of slip ratios and longitudinal tire forces, based on the types of sensors available. After estimation of slip ratio and tire force, the friction coefficient is identified using a recursive least-squares parameter identification formulation. The observers include one that utilizes engine torque, brake torque, and GPS measurements, one that utilizes torque measurements and an accelerometer and one that utilizes GPS measurements and an accelerometer. The developed algorithms are first evaluated in simulation and then evaluated experimentally on a Volvo XC90 sport utility vehicle. Experimental results demonstrate the feasibility of estimating friction coefficients at the individual wheels reliably and quickly. The sensitivities of the observers to changes in vehicle parameters are evaluated and comparisons of robustness of the observers are provided.

Lateral Stability Control of In-Wheel-Motor-Driven Electric Vehicles Based on Sideslip Angle Estimation Using Lateral Tire Force Sensors

Abstract: This paper presents a method for using lateral tire force sensors to estimate vehicle sideslip angle and to improve vehicle stability of in-wheel-motor-driven electric vehicles (IWM-EVs) Considering that the vehicle motion is governed by tire forces, lateral tire force measurements give practical benefits in estimation and motion control To estimate the vehicle sideslip angle, a state observer derived from the extended-Kalman-filtering (EKF) method is proposed and evaluated through field tests on an experimental IWM-EV Experimental results show the ability of a proposed observer to provide accurate estimation Moreover, using the estimated sideslip angle and tire cornering stiffness, the vehicle stability control system, making best use of the advantages of IMW-EVs with a steer-by-wire system, is proposed Computer simulation using Matlab/Simulink-Carsim and experiments are carried out to demonstrate the effectiveness of the proposed stability control system Practical application of lateral tire force sensors to vehicle control systems is discussed for future personal electric vehicles

Interacting Multiple Model Filter-Based Sensor Fusion of GPS With In-Vehicle Sensors for Real-Time Vehicle Positioning

Abstract: Vehicle position estimation for intelligent vehicles requires not only highly accurate position information but reliable and continuous information provision as well. A low-cost Global Positioning System (GPS) receiver has widely been used for conventional automotive applications, but it does not guarantee accuracy, reliability, or continuity of position data when GPS errors occur. To mitigate GPS errors, numerous Bayesian filters based on sensor fusion algorithms have been studied. The estimation performance of Bayesian filters primarily relies on the choice of process model. For this reason, the change in vehicle dynamics with driving conditions should be addressed in the process model of the Bayesian filters. This paper presents a positioning algorithm based on an interacting multiple model (IMM) filter that integrates low-cost GPS and in-vehicle sensors to adapt the vehicle model to various driving conditions. The model set of the IMM filter is composed of a kinematic vehicle model and a dynamic vehicle model. The algorithm developed in this paper is verified via intensive simulation and evaluated through experimentation with a real-time embedded system. Experimental results show that the performance of the positioning system is accurate and reliable under a wide range of driving conditions.

Identification and Learning Control of Ocean Surface Ship Using Neural Networks

Abstract: This paper presents the problems of accurate identification and learning control of ocean surface ship in uncertain dynamical environments. Thanks to the universal approximation capabilities, radial basis function neural networks (NNs) are employed to approximate the unknown ocean surface ship dynamics. A stable adaptive NN tracking controller is first designed using backstepping and Lyapunov synthesis. Partial persistent excitation (PE) condition of some internal signals in the closed-loop system is satisfied during tracking control to a recurrent reference trajectory. Under the PE condition, the proposed adaptive NN controller is shown to be capable of accurate identification/learning of the uncertain ship dynamics in the stable control process. Subsequently, a novel NN learning control method which effectively utilizes the learned knowledge without re-adapting to the unknown ship dynamics is proposed to achieve closed-loop stability and improved control performance. Simulation studies are performed to demonstrate the effectiveness of the proposed method.

An Intelligent V2I-Based Traffic Management System

Abstract: Vehicles equipped with intelligent systems designed to prevent accidents, such as collision warning systems (CWSs) or lane-keeping assistance (LKA), are now on the market. The next step in reducing road accidents is to coordinate such vehicles in advance not only to avoid collisions but to improve traffic flow as well. To this end, vehicle-to-infrastructure (V2I) communications are essential to properly manage traffic situations. This paper describes the AUTOPIA approach toward an intelligent traffic management system based on V2I communications. A fuzzy-based control algorithm that takes into account each vehicle's safe and comfortable distance and speed adjustment for collision avoidance and better traffic flow has been developed. The proposed solution was validated by an IEEE-802.11p-based communications study. The entire system showed good performance in testing in real-world scenarios, first by computer simulation and then with real vehicles.

Adaptive sliding mode tracking control for a flexible air-breathing hypersonic vehicle

Abstract: This paper is concerned with the adaptive sliding mode control (ASMC) design problem for a flexible air-breathing hypersonic vehicle (FAHV). This problem is challenging because of the inherent couplings between the propulsion system, the airframe dynamics and the presence of strong flexibility effects. Due to the enormous complexity of the vehicle dynamics, only the longitudinal model is adopted for control design in the present paper. A linearized model is established around a trim point for a nonlinear, dynamically coupled simulation model of the FAHV, then a reference model is designed and a tracking error model is proposed with the aim of the ASMC problem. There exist the parameter uncertainties and external disturbance in the model, which are not necessary to satisfy the so-called matched condition. A robust sliding surface is designed, and then an adaptive sliding mode controller is designed based on the tracking error model. The proposed controller can drive the error dynamics onto the predefined sliding surface in a finite time, and guarantees the property of asymptotical stability without the information of upper bound of uncertainties as well as perturbations. Finally, simulations are given to show the effectiveness of the proposed control methods.

Autonomous Vehicle Control at the Limits of Handling

Abstract: Racecar drivers have the ability to operate a vehicle at its friction limit without losing control. If autonomous vehicles or driver assistance systems had similar capabilities, many fatal accidents could be avoided. To advance this goal, an autonomous racing controller is designed to gain insights into vehicle control at the friction limits. A bicycle model and a ‘ g-g ’ diagram are used to mimic racecar drivers’ internal vehicle model. Lanekeeping steering feedback and wheel slip feedback controllers are used to imitate drivers making steering and throttle corrections according to the vehicle responses. Experimental results on a low friction surface demonstrate that the controller can robustly track a path while operating at the limits of tyre adhesion and provide insights for the future development of vehicle safety systems.

Innovation by in-wheel-motor drive unit

Abstract: The in-wheel-motor (IWM) will be the most important key technology in the near future to be used by electric vehicles (including fuel cell vehicles). In the past 100 years of the internal combustion engine, several kinds of vehicle packages have been developed, for example, front-engine front-wheel drive, front-engine rear-wheel drive, mid-engine rear-wheel drive, and rear-engine rear-wheel drive. However, a conclusive solution for the best package has not been found. Combining electric drive and IWM enables both good vehicle dynamics and a roomy interior. In addition, the responsiveness of IWM raises the performance of the dynamic control to an even higher level.

Path planning in time dependent flow fields using level set methods

Abstract: We develop and illustrate an efficient but rigorous methodology that predicts the time-optimal paths of ocean vehicles in continuous dynamic flows. The goal is to best utilize or avoid currents, without limitation on these currents or on the number of vehicles. The methodology employs a new modified level set equation to evolve a front from the starting point of a vehicle until it reaches the desired goal location, combining flow advection with nominal vehicle motion. The optimal path of the vehicle is then obtained by solving a particle tracking equation backward in time. The computational cost of this method increases linearly with the number of vehicles and geometrically with spatial dimensions. The methodology is applicable to any continuous flow and in scenarios with multiple vehicles. Present illustrations consist of the crossing of a canonical uniform jet and its validation using a classic optimization solution, as well as swarm formation in more complex time varying 2D flow fields, including jets, eddies and forbidden regions.

Efficient algorithms for collision avoidance at intersections

Abstract: We consider the problem of synthesising the least restrictive controller for collision avoidance of multiple vehicles at an intersection. The largest set of states for which there exists a control that avoids collisions is known as the maximal controlled invariant set. Exploiting results from the scheduling literature we prove that, for a general model of vehicle dynamics at an intersection, the problem of checking membership in the maximal controlled invariant set is NP-hard. We then describe an algorithm that solves this problem approximately and with provable error bounds. The approximate solution is used to design a supervisor for collision avoidance whose complexity scales polynomially with the number of vehicles. The supervisor is based on a hybrid algorithm that employs a dynamic model of the vehicles and periodically solves a scheduling problem.

Automotive control systems

Abstract: Preface Part I. Introduction and Background: 1. Introduction 2. Automotive control system design process 3. Review of engine modeling 4. Review of vehicle dynamics 5. Human factors and driver modeling Part II. Powertrain Control Systems: 6. Air-to-fuel ratio control 7. Control of spark timing 8. Idle speed control 9. Transmission control 10. Control of hybrid vehicles 11. Modeling and control of fuel cells for vehicles Part III. Vehicle Control Systems: 12. Cruise and headway control 13. Antilock brake systems and traction control 14. Vehicle stability control 15. Four wheel steering 16. Active suspensions Part IV. Intelligent Transportation Systems (ITS): 17. Overview of ITS 18. Preventing collisions 19. Automated highway systems (AHS) and platooning 20. Lateral active safety systems and automated steering Appendix A. Review of control theory fundamentals Appendix B. Two-mass three DOF vehicle lateral/yaw/roll model.

Vehicle dynamics control of four in-wheel motor drive electric vehicle using gain scheduling based on tyre cornering stiffness estimation

Abstract: This paper focuses on the vehicle dynamic control system for a four in-wheel motor drive electric vehicle, aiming at improving vehicle stability under critical driving conditions. The vehicle dynamics controller is composed of three modules, i.e. motion following control, control allocation and vehicle state estimation. Considering the strong nonlinearity of the tyres under critical driving conditions, the yaw motion of the vehicle is regulated by gain scheduling control based on the linear quadratic regulator theory. The feed-forward and feedback gains of the controller are updated in real-time by online estimation of the tyre cornering stiffness, so as to ensure the control robustness against environmental disturbances as well as parameter uncertainty. The control allocation module allocates the calculated generalised force requirements to each in-wheel motor based on quadratic programming theory while taking the tyre longitudinal/lateral force coupling characteristic into consideration. Simulations under a variety of driving conditions are carried out to verify the control algorithm. Simulation results indicate that the proposed vehicle stability controller can effectively stabilise the vehicle motion under critical driving conditions.

Design and Evaluation on Electric Differentials for Overactuated Electric Ground Vehicles With Four Independent In-Wheel Motors

Abstract: This paper discusses the design and evaluation of electric differentials (ED) for overactuated electric ground vehicles (EGVs) with four independent in-wheel motors. Three patterns of ED, namely, 1) front ED, 2) rear ED, and 3) all-wheel ED, are designed and discussed based on vehicle performances during normal cornering and circling maneuvers. Through both simulation and experimental results, the three different ED can achieve almost the same vehicle performances in terms of the EGV sideslip angle, yaw rate, and trajectory. Moreover, when an ED is applied to only one pair of wheels, i.e., either the front or rear pair, the other pair of wheels can be utilized to estimate (the passive tires are adopted as sensors under normal driving conditions) the EGV longitudinal speed and yaw rate and consequently generate the reference wheel speeds for the ED wheels. Thus, sensors for measuring the vehicle speed and yaw rate to generate the reference wheel speeds in the design of ED, such as the all-wheel ED design for the EGV, may become unnecessary. Both simulation and experimental results validate the designs of the three ED.

Multi-sources model and control algorithm of an energy management system

Abstract: This paper presents the multi-sources energy models and ruled based feedback control algorithm of an energy management system (EMS) for light electric vehicle (LEV), i.e., scooters. The multiple sources of energy, such as a battery, fuel cell (FC) and super-capacitor (SC), EMS and power controller, DC machine and vehicle dynamics are designed and modeled using MATLAB/SIMULINK. The developed control strategies continuously support the EMS of the multiple sources of energy for a scooter under normal and heavy power load conditions. The performance of the proposed system is analyzed and compared with that of the ECE-47 test drive cycle in terms of vehicle speed and load power. The results show that the designed vehicle’s speed and load power closely match those of the ECE-47 test driving cycle under normal and heavy load conditions. This study’s results suggest that the proposed control algorithm provides an efficient and feasible EMS for LEV.

Constraint-based planning and control for safe, semi-autonomous operation of vehicles

Abstract: This paper presents a new approach to semi-autonomous vehicle hazard avoidance and stability control, based on the design and selective enforcement of constraints. This differs from traditional approaches that rely on the planning and tracking of paths. This emphasis on constraints facilitates “minimally-invasive” control for human-machine systems; instead of forcing a human operator to follow an automation-determined path, the constraint-based approach identifies safe homotopies, and allows the operator to navigate freely within them, introducing control action only as necessary to ensure that the vehicle does not violate safety constraints. The method evaluates candidate homotopies based on “restrictiveness”, rather than traditional measures of path goodness, and designs and enforces requisite constraints on the human's control commands to ensure that the vehicle never leaves the controllable subset of a desired homotopy. Identification of these homotopic classes in off-road environments is performed using geometric constructs. The goodness of competing homotopies and their associated constraints is then characterized using geometric heuristics. Finally, input limits satisfying homotopy and vehicle dynamic constraints are enforced using threat-based feedback mechanisms to ensure that the vehicle avoids collisions and instability while preserving the human operator's situational awareness and mental models. The methods developed in this work are shown in simulation and experimentally demonstrated in safe, high-speed teleoperation of an unmanned ground vehicle.

Neural-Adaptive Output Feedback Control of a Class of Transportation Vehicles Based on Wheeled Inverted Pendulum Models

Abstract: The wheeled inverted pendulum (WIP) models have been widely applied in the transportation vehicles formed by a mobile wheeled inverted pendulum system with an operator (demonstrated in Fig. 1 ). In this paper, we focus on the study of nonlinear control design for the WIP model-based vehicles, for which accurate dynamics could not be obtained beforehand due to the presence of uncertainties caused by the human operator as well as the vehicle. We develop an output feedback adaptive neural network (NN) control incorporating a linear dynamic compensator to achieve stable dynamic balance and tracking of the desired given trajectories. Comparison simulation studies demonstrate guaranteed tracking performance and stable dynamics balance in the presence of uncertainties and thus verify the efficiency of the developed nonlinear controller.

Modern techniques for condition monitoring of railway vehicle dynamics

Abstract: A modern railway system relies on sophisticated monitoring systems for maintenance and renewal activities. Some of the existing conditions monitoring techniques perform fault detection using advanced filtering, system identification and signal analysis methods. These theoretical approaches do not require complex mathematical models of the system and can overcome potential difficulties associated with nonlinearities and parameter variations in the system. Practical applications of condition monitoring tools use sensors which are mounted either on the track or rolling stock. For instance, monitoring wheelset dynamics could be done through the use of track-mounted sensors, while vehicle-based sensors are preferred for monitoring the train infrastructure. This paper attempts to collate and critically appraise the modern techniques used for condition monitoring of railway vehicle dynamics by analysing the advantages and shortcomings of these methods.

Torque blending and wheel slip control in EVs with in-wheel motors

Abstract: Among the many opportunities offered by electric vehicles (EVs), the design of power trains based on in-wheel electric motors represents, from the vehicle dynamics point of view, a very attractive prospect, mainly due to the torque-vectoring capabilities. However, this distributed propulsion also poses some practical challenges, owing to the constraints arising from motor installation in a confined space, to the increased unsprung mass weight and to the integration of the electric motor with the friction brakes. This last issue is the main theme of this work, which, in particular, focuses on the design of the anti-lock braking system (ABS). The proposed structure for the ABS is composed of a tyre slip controller, a wheel torque allocator and a braking supervisor. To address the slip regulation problem, an adaptive controller is devised, offering robustness to uncertainties in the tyre–road friction and featuring a gain-scheduling mechanism based on the vehicle velocity. Further, an optimisation framework ...

A polynomial chaos approach to the analysis of vehicle dynamics under uncertainty

Abstract: The ability of ground vehicles to quickly and accurately analyse their dynamic response to a given input is critical to their safety and efficient autonomous operation. In field conditions, significant uncertainty is associated with terrain and/or vehicle parameter estimates, and this uncertainty must be considered in the analysis of vehicle motion dynamics. Here, polynomial chaos approaches that explicitly consider parametric uncertainty during modelling of vehicle dynamics are presented. They are shown to be computationally more efficient than the standard Monte Carlo scheme, and experimental results compared with the simulation results performed on ANVEL (a vehicle simulator) indicate that the method can be utilised for efficient and accurate prediction of vehicle motion in realistic scenarios.

Design and implementation of a hybrid fuzzy logic controller for a quadrotor VTOL vehicle

Abstract: Helicopters have generated considerable interest in both the control community due to their complex dynamics, and in military community because of their advantages over regular aerial vehicles. In this paper, we present the modeling and control of a four rotor vertical take-off and landing (VTOL) unmanned air vehicle known as quadrotor aircraft. This model has been generated using Newton-Euler equations. In order to control the helicopter, classical PD (proportional derivative) and Hybrid Fuzzy PD controllers have been designed. Although fuzzy control of various dynamical systems has been presented in literature, application of this technology to quadrotor helicopter control is quite new. A quadrotor helicopter has nonlinear characteristics where classical control methods are not adequate especially when there are time delays, disturbances and nonlinear vehicle dynamics. On the other hand, Fuzzy control is nonlinear and it is thus suitable for nonlinear system control. Matlab Simulink has been used to test, analyze and compare the performance of the controllers in simulations. For the evaluation of the autonomous flight controllers, some experiments were also performed. For this purpose, an experimental test stand has been designed and manufactured. This study showed that although, both of the classical PD and the Fuzzy PD controllers can control the system properly, the Fuzzy PD controllers performed slightly better than the classical PD controllers, and have benefits such as better disturbance rejection, ease of building the controllers.

Design of Automatic Steering Controller for Trajectory Tracking of Unmanned Vehicles Using Genetic Algorithms

Abstract: In this paper, an efficient strategy is proposed to design the automatic steering controller for trajectory tracking of unmanned vehicles, which is robust with respect to the inherent nonlinearities and uncertainties of vehicles. The proposed automatic steering controller consists of a feedback part and a feedforward part. First, a fuzzy controller is proposed as the feedback part, and the parameters of membership functions and rules are optimized by genetic algorithms (GAs) to guarantee high performance. Then, a feedforward controller is designed to assist the controller when the vehicle is engaged in a curved section of trajectory, which utilizes preview information regarding upcoming curvature of reference trajectory to calculate a preview steering angle so that it offsets the disturbance of curvature. Both simulation and experimental results show that the proposed strategy can robustly track the reference trajectories under various conditions with high accuracy.

Lateral Vehicle Dynamics

Abstract: The first section in this chapter provides a review of several types of lateral control systems that are currently under development by automotive manufacturers and researchers. The subsequent sections in the chapter study kinematic and dynamic models for lateral vehicle motion. Control system design for lateral vehicle applications is studied later in Chapter 3.

Adaptive Output Feedback Design Using Asymptotic Properties of LQG/LTR Controllers

Abstract: This technical note introduces an observer-based adaptive output feedback tracking control design for multi-input-multi-output dynamical systems with matched uncertainties. The reported methodology exploits asymptotic behavior of LQG/LTR regulators. Sufficient conditions for closed-loop stability and uniform ultimate boundedness of the corresponding tracking error dynamics are formulated. This method is valid for systems whose nominal linearized dynamics are controllable and observable. We assume that the number of the system measured outputs (sensors) is greater than the number of the control inputs (actuators) and that the system output-to-input matrix product has full column rank. In this case, the system can be “squared-up” (i.e., augmented) using pseudo-control signals to yield relative degree one minimum-phase dynamics. Since it is known that the “squaring-up” problem is solvable for any controllable observable triplet (A, B, C), the proposed design is applicable to systems whose regulated output dynamics may be non-minimum phase or have a high relative degree. A simulation example is presented to demonstrate key design features.