Peter Ortner

Predictive Control of a Diesel Engine Air Path

This brief addresses the model-based control of the air path of diesel engines in terms of an optimal control problem with input constraints which can be solved using model predictive algorithms. A multilinear model identified from data and a switched controller design are used to cope with the nonlinearity of the engine. Experimental results on a production engine confirm that the proposed control method strongly improves the dynamics of the air path and enormously reduces the parameterization work if compared with the conventional approach. To obtain improvements in emissions as well, the new controller approach cannot simply be plugged in at the site of the conventional one, but new set points must be determined. After such a redesign, improvements of 50% in terms of nitrogen oxides and of 10% in terms of particulate matter have been recorded without a net consumption increase, the main price being the increased activity of the turbocharger vane and especially of the exhaust gas recirculation valve

Predictive control of a real-world Diesel engine using an extended online active set strategy

Abstract In order to meet tight emission limits Diesel engines are nowadays equipped with additional hardware components like an exhaust gas recirculation valve and a variable geometry turbocharger. Conventional engine control units use two SISO control loops to regulate the exhaust gas recirculation valve and the variable geometry turbocharger, although their effects are highly coupled. Moreover, these actuators are subject to physical constraints which seems to make an advanced control approach like model predictive control (MPC) the method of choice. In order to deal with MPC sampling times in the order of milliseconds, we employed an extension of the recently developed online active set strategy for controlling a real-world Diesel engine in a closed-loop manner. The results show that predictive engine control based on online optimisation can be accomplished in real-time – even on cheap controller hardware – and leads to increased controller performance.

MPC for a diesel engine air path using an explicit approach for constraint systems

The air path of an internal combustion engine is a classical example of MIMO system with actuator constraints and high dynamic requirements. While the classical approach consists in using simple, decoupled heuristic controllers and empirical limitations, this paper proposes to state the problem in terms of an optimal control problem with input constraints and to solve it in a model based environment. To this end, a recently developed controller design - explicit MPC - is used to calculate the explicit solution of the state feedback control law offline and to store it in tables for online controller selection. The combination of plant and switched controller leads to a piecewise linear and discrete system - a special form of hybrid system. The approach has been tested on a production diesel engine yielding impressive improvements in terms of soot while compared with the basic application.

Nonlinear Model Predictive Control of a Diesel Engine Airpath

Abstract In this paper we show the Nonlinear Model Predictive Control (NMPC) of an airpath of a diesel engine based on a Linear Parameter Varying (LPV) model. We used databased LPV modelling with real data from a dynamical engine test bench in order to obtain a nonlinear model of high quality. Because of the nonlinearity of the model the quadratic program (QP) of the NMPC needs to be set up afresh at each sampling instant, which is the main difference to standard linear MPC. For solving the QP efficiently, we employ the recently developed online active set as implemented in the software package qpOASES. We tested our controller in simulation on the LPV model identified on a mean value model and results show that the NMPC has a better tracking performance in terms of boost pressure and fresh air mass flow compared to the standard linear MPC approach also under the influence of a model plant mismatch.

Chances and Challenges in Automotive Predictive Control

Recent years have witnessed an increased interest in model predictive control (MPC) for fast applications. At the same time, requirements on engines and vehicles in terms of emissions, consumption and safety have experienced a similar increase. MPC seems a suitable method to exploit the potentials of modern concepts and to fulfill the automotive requirements since most of them can be stated in the form of a constrained multi input multi output optimal control problem and MPC provides an approximate solution of this class of problems. In this introductory chapter, we analyze the rationale, the chances and the challenges of this approach. This chapter does not intend to review all the literature, but to give a flavor of the challenges and chances offered by this approach.

Model based nonlinear observers for torque estimation on a combustion engine test bench

Torque is an important quantity in combustion engine test bench control but unfortunately very difficult to measure. Observers can be used as an alternative to sensors to estimate the torque based on a nonlinear model and the available measured output signals, engine speed and dynamometer speed. In this paper three nonlinear observers are designed for a combustion engine test bench simulator including combustion oscillations as well as noisy measurements and disturbed inputs. The well known extended Kalman filter (EKF), a high gain observer (HGO) and a sliding mode observer (SMO) are compared in terms of the quadratic estimation error, computing time and convergence rate.

A Design Framework for Nonlinear Predictive Engine Control

Abstract Model predictive control (MPC) has been proposed several times for automotive control, with promising results, mostly based on a linear MPC approach. However, as most automotive systems are nonlinear, nonlinear MPC (NMPC) would be an interesting option. Unfortunately, an optimal control design with a generic nonlinear model usually leads to a complex, non convex problem. Against this background, this paper proposes a control system design based on a nonlinear system identification using a quasi linear parameter varying (LPV) structure, which is then used in a NMPC design framework. This paper presents the approach and the application to a well studied system, the air path of a Diesel engine.

Control oriented modeling of a water brake dynamometer

Water brakes combine high power ratings with a low moment of inertia and in case of high power ratings they are a good alternative to other braking systems. Despite these advantages water brakes are not widely used in dynamic testing as their nonlinearities make them hard to control. Mathematical models of hydrodynamic dynamometers are presented in this paper. A first principles approach is compared with a data-based model and a gray box model. The first principles model is hard to parametrize. In contrast a purely databased linear model is easy to tune but is not able to extrapolate. To increase the extrapolation ability it gets necessary to use a gray box approach which combines the simple structure of a first principles model with a data-based part. The resulting gray box model is best suited to the plant, of simple structure and can be used for the design of a model-based controller.

On the Use of Parameterized NMPC in Real-time Automotive Control

Automotive control applications are very challenging due to the presence of constraints, nonlinearities and the restricted amount of computation time and embedded facilities. Nevertheless, the need for optimal trade-off and efficient coupling between the available constrained actuators makes Nonlinear Model Predictive Control (NMPC) conceptually appealing. From a practical point of view however, this control strategy, at least in its basic form, involves heavy computations that are often incompatible with fast and embedded applications. Addressing this issue is becoming an active research topics in the worldwide NMPC community. The recent years witnessed an increasing amount of dedicated theories, implementation hints and software. The Control Parametrization Approach (CPA) is one option to address the problem. The present chapter positions this approach in the layout of existing alternatives, underlines its advantages and weaknesses. Moreover, its efficiency is shown through two real-world examples from the automotive industry, namely:

The SkyObserver Project: Autonomous Swarms in Civil Emergency and Disaster Management

Reduced costs of Unmanned Aircraft Systems (UAS) including sensors combined with increased on-board computational power expand the area of application of UAS from strictly military operations to civil emergency and disaster management. The SkyObserver project is aimed to develop a tool for emergency management based on an autonomous swarm to deal with various tasks in the case of a civil hazard. In these and similar tasks the deployment and exploration problems are critical challenges in the coordination and path planning of an autonomous swarm. Additionally robustness and scalability requirements have to be taken into account. This paper gives an overview of available and suitable methods and algorithms solving the deployment and exploration problems in context of civil operations. Simulation results show the capabilities as well as robustness and scalability of the selected methods.