Esteban R. Gelso

Minimal Structurally Overdetermined Sets for Residual Generation: A Comparison of Alternative Approaches

The issue of residual generation using structural analysis has been studied by several authors. Structural analysis does not permit to generate the analytical expressions of residuals since the model of the system is abstracted by its structure. However, it determines the set of constraints from which residuals can be generated and it provides the computation sequence to be used. This paper presents and compares four recently proposed algorithms that solve this problem.

Including a Battery State of Health model in the HEV component sizing and optimal control problem

This paper studies convex optimization and modelling for component sizing and optimal energy management control of hybrid electric vehicles. The novelty in the paper is the modeling steps required to include a battery wear model into the convex optimization problem. The convex modeling steps are described for the example of battery sizing and simultaneous optimal control of a series hybrid electric bus driving along a perfectly known bus line. Using the proposed convex optimization method and battery wear model, the city bus example is used to study a relevant question: is it better to choose one large battery that is sized to survive the entire lifespan of the bus, or is it beneficial with several smaller replaceable batteries which could be operated at higher c-rates?

SQualTrack: A Tool for Robust Fault Detection

One of the techniques used to detect faults in dynamic systems is analytical redundancy. An important difficulty in applying this technique to real systems is dealing with the uncertainties associated with the system itself and with the measurements. In this paper, this uncertainty is taken into account by the use of intervals for the parameters of the model and for the measurements. The method that is proposed in this paper checks the consistency between the systems behavior, obtained from the measurements, and the models behavior; if they are inconsistent, then there is a fault. The problem of detecting faults is stated as a quantified real constraint satisfaction problem, which can be solved using the modal interval analysis (MIA). MIA is used because it provides powerful tools to extend the calculations over real functions to intervals. To improve the results of the detection of the faults, the simultaneous use of several sliding time windows is proposed. The result of implementing this method is semiqualitative tracking (SQualTrack), a fault-detection tool that is robust in the sense that it does not generate false alarms, i.e., if there are false alarms, they indicate either that the interval model does not represent the system adequately or that the interval measurements do not represent the true values of the variables adequately. SQualTrack is currently being used to detect faults in real processes. Some of these applications using real data have been developed within the European project advanced decision support system for chemical/petrochemical manufacturing processes and are also described in this paper.

Structural Analysis Extended with Active Fault Isolation - Methods and Algorithms

Isolability of faults is a key issue in fault diagnosis whether the aim is maintenance or active fault-tolerant control. It is often encountered that while faults are detectable, they are only group-wise isolable from a usual diagnostic point of view. However, active injection of test signals on system inputs can considerably enhance fault isolability. This paper investigates this possibility of active fault isolation from a structural point of view. While such extension of the structural analysis approach was suggested earlier, algorithms and case studies were needed to explore this theory. The paper develops algorithms for investigation of the possibilities of active structural isolation and it offers illustrative examples and a larger case study to explore the properties of active structural isolability ideas.

Automotive Threat Assessment Design for Combined Braking and Steering Maneuvers

The active safety systems available in passenger cars today automatically deploy automated safety interventions in situations where the driver is in need of assistance. In this paper, we consider the process of determining whether such interventions are needed. In particular, we design a threat assessment method that evaluates the risk that the vehicle will either leave the road or its maneuverability will be significantly reduced within a finite time horizon. The proposed threat assessment method accounts for combined braking and steering maneuvers, which results in a nonlinear dynamical vehicle behavior. We formulate the threat assessment problem as a nonconvex constraint satisfaction problem (CSP) and implement an algorithm that solves it through interval-based consistency techniques. Experimental validation of the proposed approach indicates that constraint violation can be predicted while avoiding the detection of false threats.

Robust fault detection using consistency techniques with application to an automotive engine

Abstract Monitoring of the air intake system of an automotive engine is important to meet emission related legislative diagnosis requirements. In this paper, the problem of fault detection in the air intake system is stated as a constraint satisfaction problem over continuous domains with a big number of variables and constraints. This problem can be solved using Consistency Techniques. Consistency techniques are shown to be particularly efficient for checking the consistency of the Analytical Redundancy Relations (ARRs), dealing with uncertain measurements and parameters, and using experimental data.

Constraint satisfaction techniques under uncertain conditions for fault diagnosis in nonlinear dynamic systems

The speed of fault isolation is crucial for the design and reconfiguration of fault tolerant control (FTC). In this paper the fault isolation problem is stated as a constraint satisfaction problem (CSP) and solved using constraint propagation techniques. The proposed method is based on constraint satisfaction techniques and uncertainty space refining of interval parameters. In comparison with other approaches based on adaptive observers, the major advantage of the presented method is that the isolation speed is fast even taking into account uncertainty in parameters, measurements and model errors and without the monotonicity assumption. In order to illustrate the proposed approach, a case study of a nonlinear dynamic system is presented.

Air-Path Model Predictive Control of a Heavy-Duty Diesel Engine with Variable Valve Actuation

Abstract In this paper, a model-based strategy for the air-path, fuel injection timing, and fuel pressure control of a heavy-duty Diesel engine is presented. The engine system is a six-cylinder Diesel engine, equipped with a dual-stage fixed geometry turbo system and a variable valve actuation (VVA) system. The VVA operates on the intake valves, and realizes a late Miller combustion cycle. The control strategy implemented is a multilinear - model predictive control (MPC), which manipulates the intake valve hold and closure timing, the fuel injection timing, and the needle opening pressure. The MPC objectives are: (i) to keep NOx emissions under a reference level, (ii) to keep the air-fuel ratio over a certain reference, while (iii) minimizing fuel consumption under other constraints. The use of a model predictive control strategy is motivated by the fact that the system is a multi-input multi-output system, with several constraints applied to it. This paper presents the applied control strategy and simulation results illustrating the potential of the proposed control. The simulation results show that the control strategy is applicable, and that the fuel consumption is minimized, but also that further refinements are required.

Threat assessment for driver assistance systems using interval-based techniques

We present a threat assessment algorithm for driver assistance systems in which mathematical vehicle and driver models are used to explicitly account for the vehicle and driver behavior. For the application considered in this paper, requirements that the vehicle stays on the road while operating in a stable operating region are expressed as constraints on the vehicle states which need to be satisfied over a finite time horizon. The threat assessment problem is then formulated as a constraint satisfaction problem, solved through interval-based techniques considering bounded uncertainty in the measurements. Experimental results demonstrate the algorithms ability to detect unsafe operation in advance.