H. Eric Tseng

MPC-Based Yaw and Lateral Stabilization Via Active Front Steering and Braking

In this paper, we propose a path following Model Predictive Control-based (MPC) scheme utilising steering and braking. The control objective is to track a desired path for obstacle avoidance manoeuvre, by a combined use of braking and steering. The proposed control scheme relies on the Nonlinear MPC (NMPC) formulation we used in [F. Borrelli, et al., MPC-based approach to active steering for autonomous vehicle systems, Int. J. Veh. Autonomous Syst. 3(2/3/4) (2005), pp. 265–291.] and [P. Falcone, et al., Predictive active steering control for autonomous vehicle systems, IEEE Trans. Control Syst. Technol. 15(3) (2007), pp. 566–580.]. In this work, the NMPC formulation will be used in order to derive two different approaches. The first relies on a full tenth-order vehicle model and has high computational burden. The second approach is based on a simplified bicycle model and has a lower computational complexity compared to the first. The effectiveness of the proposed approaches is demonstrated through simulations and experiments.

Predictive control for agile semi-autonomous ground vehicles using motion primitives

This paper presents a hierarchical control framework for the obstacle avoidance of autonomous and semi-autonomous ground vehicles. The high-level planner is based on motion primitives created from a four-wheel nonlinear dynamic model. Parameterized clothoids and drifting maneuvers are used to improve vehicle agility. The low-level tracks the planned trajectory with a nonlinear Model Predictive Controller. The first part of the paper describes the proposed control architecture and methodology. The second part presents simulative and experimental results with an autonomous and semi-autonomous ground vehicle traveling at high speed on an icy surface.

State of the art survey: active and semi-active suspension control

This survey paper aims to provide some insight into the design of suspension control system within the context of existing literature and share observations on current hardware implementation of active and semi-active suspension systems. It reviews the performance envelop of active, semi-active, and passive suspensions with a focus on linear quadratic-based optimisation including a specific example. The paper further discusses various design aspects including other design techniques, the decoupling of load and road disturbances, the decoupling of pitch and heave modes, the use of an inerter as an additional design element, and the application of preview. Various production and near production suspension systems were examined and described according to the features they offer, including self-levelling, variable damping, variable geometry, and anti-roll damping and stiffness. The lessons learned from these analytical insights and related hardware implementations are valuable and can be applied towards future active or semi-active suspension design.

Estimation of land vehicle roll and pitch angles

In this article, two kinematics-based observers are proposed to estimate the vehicle roll and pitch angles by using an inertial measurement unit. The observers are mathematically proven to be stable if the vehicle yaw rate is not zero. With a design variation of the observer gains, the estimated roll or pitch angle is shown to further asymptotically converge to the true value, eliminating possible errors caused by the biases of the acceleration signals. Simulation results show that accurate estimation of both pitch and roll angles can be achieved without the help of external sensors such as global positioning systems, either by using the accelerometer-based reference pitch or roll angle as the maneuver varies, or by using an observer with zero steady-state error property.

A tube-based robust nonlinear predictive control approach to semiautonomous ground vehicles

This paper proposes a robust control framework for lane-keeping and obstacle avoidance of semiautonomous ground vehicles. It presents a systematic way of enforcing robustness during the MPC design stage. A robust nonlinear model predictive controller (RNMPC) is used to help the driver navigating the vehicle in order to avoid obstacles and track the road centre line. A force-input nonlinear bicycle vehicle model is developed and used in the RNMPC control design. A robust invariant set is used in the RNMPC design to guarantee that state and input constraints are satisfied in the presence of disturbances and model error. Simulations and experiments on a vehicle show the effectiveness of the proposed framework.

Robust estimation of road friction coefficient using lateral and longitudinal vehicle dynamics

Vehicle active safety systems stabilise the vehicle by controlling tyre forces. They work well only when the tyre forces commanded by the safety systems are within the friction limit. Therefore, knowledge of the tyre/road friction coefficient can improve the performance of vehicle active safety systems. This study presents four methods to estimate the friction coefficient based on four different excitation conditions: medium lateral excitation, large lateral excitation, small longitudinal excitation, and large longitudinal excitation. For the lateral excitation cases, the estimation is based on vehicle lateral/yaw dynamics and Brush tyre model, whereas for the longitudinal excitation cases, the estimation basis is the relationship between the tyre longitudinal slip and traction force. These four methods are then integrated to increase the working range of the estimator and to improve robustness. The performance of the integrated estimation algorithm is verified through experimental data collected on several surface conditions.

Predictive control of an autonomous ground vehicle using an iterative linearization approach

This paper presents the design of a controller for an autonomous ground vehicle. The goal is to track the lane centerline while avoiding collisions with obstacles. A nonlinear model predictive control (MPC) framework is used where the control inputs are the front steering angle and the braking torques at the four wheels. The focus of this work is on the development of a tailored algorithm for solving the nonlinear MPC problem. Hardware-in-the-loop simulations with the proposed algorithm show a reduction in the computational time as compared to general purpose nonlinear solvers. Experimental tests on a passenger vehicle at high speeds on low friction road surfaces show the effectiveness of the proposed algorithm.

INTEGRATED BRAKING AND STEERING MODEL PREDICTIVE CONTROL APPROACH IN AUTONOMOUS VEHICLES

In this paper we present a Model Predictive Control (MPC) approach for combined braking and steering systems in autonomous vehicles. We start from the result presented in (Borrelli et al. (2005)) and (Falcone et al. (2007a)), where a Model Predictive Controller (MPC) for autonomous steering systems has been presented. As in (Borrelli et al. (2005)) and (Falcone et al. (2007a)) we formulate an MPC control problem in order to stabilize a vehicle along a desired path. In the present paper, the control objective is to best follow a given path by controlling the front steering angle and the brakes at the four wheels independently, while fulfilling various physical and design constraints.

Estimation of road friction for enhanced active safety systems: Algebraic approach

Knowledge of tire friction force capacity, i.e. tire-load frictional coefficient, is important for the control of vehicle active safety systems. In this paper we review several methods for friction estimation and develop two robust and cost effective methods based on a nonlinear least square approach and the peak aligning torque. The methods proposed in this paper utilize simple vehicle lateral dynamics, steering system, and front tire dynamics. The first estimator uses direct calculation based on front tire self-aligning torque and the second method is based on a nonlinear least square method. These estimators are verified with Carsim under various conditions.

Driver Models for Personalised Driving Assistance

We propose a learning-based driver modelling approach which can identify manoeuvres performed by drivers on the highway and predict the future driver inputs. We show how this approach can be applied to provide personalised driving assistance. In a first example, the driver model is used to predict unintentional lane departures and a model predictive controller is used to keep the car in the lane. In a second example, the driver model estimates the preferred acceleration of the driver during lane keeping, and a model predictive controller is implemented to provide a personalised adaptive cruise control. For both applications, we use a combination of real data and simulation to evaluate the proposed approaches.