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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

EGO sensor based robust output control of EGR in diesel engines

In diesel engines, exhaust gas recirculation (EGR) is an efficient method for reducing NO/sub x/. EGR rates are commonly set according to the expected engine behavior, without considering the actual composition of the exhaust gases. In this paper, a new control system for EGR in passenger cars is presented, based on an exhaust gas oxygen (EGO) sensor as indicator of the combustion state as well as on a robust design approach to cope with the parameter variations. The proposed control has been designed for and tested on an Audi TDI engine and has been found to yield a similar performance as the conventional controllers for the standard engine, in spite of the transport delays between combustion process and measurement. Due to the presence of feedback, reduced sensitivity and/or detectability of wear, production irregularities, changes of operating conditions and calibration faults are expected from long-time measurements. >

Gain Scheduled

This paper addresses the modeling and control of the air path system of diesel engines. The underlying issues are critical for the control of the transient exhaust gas fraction pumped into the cylinders, which is known to be a dominant factor to reduce the nitrogen oxides (NO x) emissions. In this paper, we propose a new approach, based on a data-based grey-box linear parameter varying (LPV) model as well as on the gain scheduled Hinfin technique for the controller design. The modeling step is shown to lead naturally to a so-called quasi-LPV structure, which also delivers the scheduling variables to be accounted for. Using this information, gain scheduled Hinfin techniques allow to design a controller which enforces a much better tracking performance than the standard production electronic control unit, while not requiring any calibration work. The performance of the proposed approach is demonstrated by experimental results

H_{\infty}

The interaction between the tire and the road is crucial for determining the dynamic behavior of a road vehicle, and the road-tire contact forces are key variables in the design of traction, braking, and stability control systems. Traditionally, road-tire contact forces are indirectly estimated from vehicle-dynamics measurements (chassis accelerations, yaw-roll rates, suspension deflections, etc.). The emerging of the ldquosmart-tirerdquo concept (tire with embedded sensors and digital-computing capability) has made possible, in principle, a more direct estimation of contact forces. In this field - still in its infancy - the main open problems are the choice of the sensor(s) and the choice of the regressor(s) to be used for force estimation. The objective of this work is to present a new sensor-regressor choice, and to provide some preliminary experimental results, which confirm the validity of this choice. The idea is to use a wheel encoder and an accelerometer mounted directly in the tire. The measurement of the in-tire acceleration is transmitted through a wireless channel. The key innovative concept is to use the phase shift between the wheel encoder and the pulse-like signals provided by the accelerometer as the main regressor for force estimation.

Control for Air Path Systems of Diesel Engines Using LPV Techniques

Dual source systems can request the necessary power supply from two different power sources, with specific operational characteristics and requirements. Exploiting the corresponding combination possibilities opens new optimization potentialities for several practical applications, as in the case of hybrid vehicles. This paper discusses the optimization strategy to be followed to obtain an absolute minimum for the case of an integral equality constraint on one of the two sources and a fixed time horizon. It derives analytically an algorithm, whose optimality is confirmed by comparisons with the results of extensive numerical optimizations.

New Regressors for the Direct Identification of Tire Deformation in Road Vehicles Via “In-Tire” Accelerometers

A feasible suboptimal approach for the computation of MPC under state dependent constraints using a time varying approximation of the constraints is proposed. The method is stated, sufficient stability conditions are given and appropriate tests are derived. An example offers insight into the implementation of this approach, especially the trade-off between loss of optimality and computational burden.

Optimal control of dual power sources

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.

Feasible suboptimal model predictive control for linear plants with state dependent constraints

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.

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

In this paper the application of a robust inverse tracking method to the test bench control in order to achieve a high tracking performance is presented. This controller consists of a feedforward part which is the inverse realization of the approximate model of the combustion engine test bench and a robustifying feedback controller, which is for compensating the approximation error or unknown input disturbances. The robustifying controller is simply an extension of a central robust stabilizing controller which usually is the solution of a Hamilton Jacobi inequality. To this end, an iterative way to find a solution of this partial differential equation is applied here. Finally, the presented tracking controller for the combustion engine test bench is compared to a standard decoupled control strategy in a simulation environment.

Nonlinear Model Predictive Control of a Diesel Engine Airpath

At continuous casting plants the liquid steel level in the mold has to be stabilized by the mold level controller. Many plants are however perturbed by an effect called dynamic bulging, which is an almost periodic disturbance consisting of the fundamental and harmonics up to the 6th order. Its frequency can be assumed to be approximately known, but can change with a known parameter, given by the casting speed. A measurement, which can be used to estimate the periodic disturbance is available, hence the periodic components of this signal can be observed and predicted with an internal model observer and be applied as disturbance feedforward to the control input. The prediction of the signal is necessary because the plant has a substantial input delay. Since the internal model depends on the frequency, which is available as parameter, LPV techniques are used to show convergence of the observer.