Eric Hongtei Tseng

Predictive Control of Autonomous Ground Vehicles With Obstacle Avoidance on Slippery Roads

Two frameworks based on Model Predictive Control (MPC) for obstacle avoidance with autonomous vehicles are presented. A given trajectory represents the driver intent. An MPC has to safely avoid obstacles on the road while trying to track the desired trajectory by controlling front steering angle and differential braking. We present two different approaches to this problem. The first approach solves a single nonlinear MPC problem. The second approach uses a hierarchical scheme. At the high-level, a trajectory is computed on-line, in a receding horizon fashion, based on a simplified point-mass vehicle model in order to avoid an obstacle. At the low-level an MPC controller computes the vehicle inputs in order to best follow the high level trajectory based on a nonlinear vehicle model. This article presents the design and comparison of both approaches, the method for implementing them, and successful experimental results on icy roads.Copyright

Vehicle Sideslip Angle EKF Estimator based on Nonlinear Vehicle Dynamics Model and Stochastic Tire Forces Modeling

This paper presents the extended Kalman filter-based sideslip angle estimator design using nonlinear 5DoF single-track vehicle dynamics model with stochastic modeling of tire forces. Lumped front and rear tire forces have been modeled as first order random walk state variables. The proposed estimator is primarily designed for vehicle sideslip angle estimation ; however it can also be used for estimation of tire forces and cornering stiffness. This estimator design does not rely on linearization of the tire force characteristics and it is robust against the variations of tire parameters and does not require the information on coefficient of friction. The estimator performance has been first analyzed by means of computer simulations using the 10DOF two-track vehicle dynamics model and underlying magic formula tire model, and then validated by running the estimator algorithm off-line on the experimental data sets recorded on the test vehicle.

Bond Graph Modeling of Automotive Transmissions and Drivelines

Abstract The paper presents an overview of the bond graph models of advanced automotive transmission and driveline systems. This includes one-mode and two-mode series-parallel hybrid electric vehicle transmissions, a continuous variable transmission, active-limited slip and torque vectoring differentials in 2WD and 4WD configurations, and electromechanical actuator-based wet and dry clutch actuation systems. It is illustrated that the bond graph method can be effectively used to gain valuable insights about the system dynamics structure and behavior.

Robust vehicle lateral stabilization via set-based methods for uncertain piecewise affine systems: Experimental results

The paper presents the design of a lateral stability controller for ground vehicles based on front steering and four wheels independent braking. The control objective is to track yaw rate and lateral velocity reference signals while avoiding front and rear wheel traction force saturation. Control design is based on an approximate piecewise-affine nonlinear dynamical model of the vehicle. Vehicle longitudinal velocity and driver steering input are modeled as measured disturbances taking values in a compact set. We use a time-optimal control strategy which ensures convergence into a maximal robust control invariant set. This paper presents the controller experimental results on a vehicle equipped with active front steering and differential braking. In particular, tests at high-speed on ice with aggressive driver maneuvers show the effectiveness of the proposed scheme.

Design of Test Rigs for a Dry Dual Clutch and its Electromechanical Actuator

Dual Clutch Transmissions with dry electromechanically actuated clutches have emerged on the market recently. In order to provide their favorable operation in terms of the clutch torque control, it is very important to have a good knowledge on the system behavior related to the actuator dynamics, the dry friction coefficient behavior, and the thermal dynamics. This paper describes two test rigs developed to support the research work on a dry dual clutch with a leverbased electromechanical actuation system. The first test rig (actuation system test rig) provides a basis for a comprehensive multi-step identification of the actuation system parameters and characterization of the overall system behavior. This test rig includes a modified dual clutch assembly including a built-in sensor for the purpose of direct normal force measurement. The second test rig (transmission test rig) is aimed at providing support for more comprehensive characterization of the actuation system under realistic operating conditions and characterization of the clutch torque transfer dynamics/friction coefficient behavior, thermal dynamics, and wear. Both test rigs are computer controlled and include measurements of all main system variables. Functionality of the test rigs is demonstrated by characteristic experimental results.

Torque Phase Shift Control Based on Clutch Torque Estimation

An automatic transmission shift method is presented, in which the torque transfer phase is controlled in closed loop. This is made possible by real-time estimation of the torque transmitted by the off-going and on-coming clutches participating in the shift. Each clutch torque is determined based on measured or estimated input and output shaft torques and accelerations. To illustrate an application of the method, traditional friction elements are used to emulate one-way-clutch function during a power-on upshift.Copyright

On Low Complexity Predictive Approaches to Control of Autonomous Vehicles

In this paper we present low complexity predictive approaches to the control of autonomous vehicles. A general hierarchical architecture for fully autonomous vehicle guidance systems is presented together with a review of two control design paradigms. Our review emphasizes the trade off between performance and computational complexity at different control levels of the architecture. In particular, experimental results are presented, showing that if the controller at the lower level is properly designed, then it can handle system nonlinearities and model uncertainties even if those are not taken into account at the higher level.

Nonlinear Stability Analysis of Vehicle Lateral Motion With a Hybrid Physical/Dynamic Tire/Road Friction Model

We present a nonlinear analysis of vehicle motion using a hybrid physical/dynamic tire/road friction model. The advantage of the proposed LuGre dynamic tire/road friction model is the simple and attractive structural properties for real-time friction estimation and control. Moreover, the model provides a property of capturing coupling effects between the longitudinal and lateral friction forces. We take advantages of these properties and analyze the vehicle lateral motion stability. We have shown that the existence of longitudinal slip affects the lateral motion stability. The quantitative analysis and relationship are also demonstrated through numerical simulation examples.Copyright

A “Smart Tire” System for Tire/Road Friction Estimation

In this paper, we present the development of a tire deformation sensing system that can provide the critical information for estimation of tire/road interaction for mobile robots and vehicles. Polyvinylidene fluoride (PVDF)-based sensor is designed and fabricated to embed on the inner tread surface to measure the rubber tread deformation. Analytical models of the PVDF-based sensing system are presented to capture the tire/road contact information and friction characteristics. The sensed deformation measurements are integrated with the on-board control system through a wireless data transmission module. Experimental results on a skid-steered mobile robot are presented to show the feasibility of the developed sensing system.Copyright

Adaptive Kalman filter-based yaw rate estimation using accelerometers and wheel speed sensors

This paper presents a kinematic yaw rate estimator which combines measurements obtained from a pair of accelerometers and ABS wheel speed sensors of the non-driven wheels. The Extended Kalman Filter methodology has been used for realization of this sensor fusion-based estimator. The estimator adapts to variations in reliability levels of individual sensors. The estimator performance is validated and corresponding estimation errors are analyzed by computer simulations for different driving maneuvers.Copyright