Dominik Moser

Cooperative adaptive cruise control applying stochastic linear model predictive control strategies

In this paper a cooperative adaptive cruise control approach using stochastic, linear model predictive control strategies is presented. The presented approach deals with an urban traffic environment where vehicle to vehicle and vehicle to infrastructure communication systems are available. The goal is the minimization of a vehicles fuel consumption in a vehicle-following scenario. This is achieved by minimizing a piecewise linear approximation of the vehicles fuel consumption map. By means of a conditional Gaussian model the probability distribution of the upcoming velocity of the preceding vehicle is estimated based on current measurements and upcoming traffic light signals. The predicted distribution function of the predecessors velocity is used in two ways for stochastic model predictive control. On the one hand, individual chance constraints are introduced and subsequently reformulated to obtain an equivalent deterministic model predictive control problem. On the other hand, samples are drawn from the prediction model and used for a randomized optimization approach. Finally, the two developed stochastic control strategies are evaluated and compared against a deterministic model predictive control approach by means of a virtual traffic simulation. The evaluation of the controllers show a significant reduction of the fuel consumption compared to the predecessor while increasing safety and driving comfort.

Flexible Spacing Adaptive Cruise Control Using Stochastic Model Predictive Control

This paper proposes a stochastic model predictive control (MPC) approach to optimize the fuel consumption in a vehicle following context. The practical solution of that problem requires solving a constrained moving horizon optimal control problem using a short-term prediction of the preceding vehicle’s velocity. In a deterministic framework, the prediction errors lead to constraint violations and to harsh control reactions. Instead, the suggested method considers errors, and limits the probability of a constraint violation. A conditional linear Gauss model is developed and trained with real measurements to estimate the probability distribution of the future velocity of the preceding vehicle. The prediction model is used to evaluate two different stochastic MPC approaches. On the one hand, an MPC with individual chance constraints is applied. On the other hand, samples are drawn from the conditional Gaussian model and used for a scenario-based optimization approach. Finally, both developed control strategies are evaluated and compared against a standard deterministic MPC. The evaluation of the controllers shows a significant reduction of the fuel consumption compared with standard adaptive cruise control algorithms.

Scenario model predictive control for robust adaptive cruise control in multi-vehicle traffic situations

Considering multi-lane and multi-vehicle scenarios common adaptive cruise control (ACC) systems often face the problem of sudden and uncomfortable control actions when surrounding vehicles change the lane leading to a switch in the target vehicle of the ACC. Probabilistic modeling of the lane change behavior of surrounding traffic participants allows to predict such lane changes. This enables anticipatory control actions to avoid hard braking maneuvers and hence increases driving comfort and economy. This paper presents a scenario model predictive control (SCMPC) which estimates the lane change tendency of surrounding drivers by drawing a number of scenarios from a stochastic lane change prediction model. The model itself is identified based on real driving data. Simulation results show the advantages of the proposed control strategy by means of comparison to a common PI controlled ACC system.

Torque control of a diesel engine by an eigenpressure based approach

Although the application of cylinder pressure sensors to obtain insight into the combustion process is not a novel topic itself, the recent availability of inexpensive in-cylinder pressure sensors has prompt an upcoming interest for the utilization of the cylinder pressure signal within engine control and monitoring. By the use of techniques, like principle component analysis, it is possible to reduce the high amount of data in the pressure signal during one cycle whilst preserving as much as possible of the fundamental information. Up to now this extracted information, the so called features, were mainly used for modeling tasks and virtual sensors. In this work a converse approach is proposed, namely to directly control these features to desired values by controlling the injection parameters. To this end, the relation between engine torque and features was identified and in addition also models for the relation between features and injection parameters, like the angle and amount of the main injection, were obtained. These models were then used for the active control of the features. The method was implemented on a 2L common rail Diesel engine at the testbench of the JKU Linz and led to initial results in a torque control application.

Quasi-optimal energy management of range extender buses in presence of changing traffic conditions

Buses and other vehicles with regular routes and stop patterns are an important application field for hybrid electric drives. Given initial and final desired state of charge (SOC) of the battery, the optimal distribution of power between both sources, battery and engine, can be computed off-line for a known driving cycle. In the case of a range extender (REX) with an engine switched between two operating points, the solution boils down to a sequence of engine state changes. However, applying this profile to the vehicle under general traffic conditions proves very inefficient, as the required traction power over time will change strongly according to the actual traffic and load situation. Instead, this paper suggests to use a spatial-domain SOC trajectory based on off-line optimization results as reference quantity, for which simulations indicate a smaller sensitivity to varying traffic conditions. The paper shows the a posteriori computation of an energy efficient control sequence as well as an on-line implementation that utilizes model predictive control (MPC) and a short-term prediction of the future power demand. Evaluation is performed using a detailed nonlinear simulation model and real traffic data where the limited loss of optimality due to changes of traffic but also due to the drivers style is confirmed.