Albert Rosich

Optimal sensor placement for leak location in water distribution networks using genetic algorithms

In this paper, a new approach for sensor placement in water distribution networks (WDN) is proposed. The sensor placement problem is formulated as an integer optimization problem. The optimization criterion consists in minimizing the number of non-isolable leaks according to the isolability criteria introduced. Because of the non-linear integer and large-scale nature of the resulting optimization problem, genetic algorithms (GA) are used as solution approach. To validate the results obtained, they are compared with exhaustive search methods with higher computational cost proving that GA allow to find near-optimal solutions with less computational load. The proposed sensor placement algorithm is combined with a projection-based isolation scheme. However, the proposed methodology does not depend on the isolation method chosen by the user and it could be easily adapted to any other isolation scheme. Experiments on a real network allow to evaluate the performance of such approach.

Optimal sensor placement for model-based fault detection and isolation

The problem of optimal sensor placement for FDI consists in determining the set of sensors that minimizes a pre-defined cost function satisfying at the same time a pre- established set of FDI specifications for a given set of faults. The main contribution of this paper is to propose an algorithm for model-based FDI sensor placement based on formulating a mixed integer optimization problem. FDI specifications are translated into constraints of the optimization problem considering that the whole set of ARRs has been generated, under the assumption that all candidate sensors are installed. To show the effectiveness of this approach, an application based on a two-tanks system is proposed.

Efficient optimal sensor placement for model-based FDI using an incremental algorithm

The problem of optimal sensor placement for FDI consists in determining the set of sensors that minimizes a pre-defined cost function satisfying at the same time a pre-established set of FDI specifications for a given set of faults. Existing approaches are mainly based on formulating an optimization problem once the sets of all possible ARRs has been generated, considering all possible candidate sensors installed. However, the associated computational complexity is exponential with the number of possible sensors. The main goal of this paper is to propose an incremental algorithm for FDI sensor placement that tries to avoid the computational burden. To show the effectiveness of this approach, an application based on a fuel-cell system is proposed.

Fault Diagnosis Based on Causal Computations

This paper focuses on residual generation for model-based fault diagnosis. Specifically, a methodology to derive residual generators when nonlinear equations are present in the model is developed. A main result is the characterization of computation sequences that are particularly easy to implement as residual generators and that take causal information into account. An efficient algorithm, based on the model structure only, which finds all such computation sequences, is derived. Furthermore, fault detectability and isolability performances depend on the sensor configuration. Therefore, another contribution is an algorithm, also based on the model structure, that places sensors with respect to the class of residual generators that take causal information into account. The algorithms are evaluated on a complex highly nonlinear model of a fuel cell stack system. A number of residual generators that are, by construction, easy to implement are computed and provide full diagnosability performance predicted by the model.

Design and simulation of a real-time implementable energy-efficient model-predictive cruise controller for electric vehicles

This paper presents the design of a novel energy-efficient model-predictive cruise controller for electric vehicles as well a simulation model of the longitudinal vehicle dynamics and its energy consumption. Both, the controller and the dynamic model are designed to meet the properties of a series-production electric vehicle whose characteristics are identified and verified by measurements. A predictive eco-cruise controller involves the minimisation of a compromise between terms related to driving speed and energy consumption which are in general both described by nonlinear differential equations. Considering the nonlinearities is essential for a proper prediction of the system states over the prediction horizon to achieve the desired energy-saving behaviour. In this work, the vehicle motion equation is reformulated in terms of the kinetic energy of the moving vehicle which leads to a linear differential equation without loss of information. The energy consumption is modeled implicitly by exploiting the special form of the optimisation problem. The reformulations finally lead to a model-predictive control approach with quadratic cost function, linear prediction model and linear constraints that corresponds to a piecewise linear system behaviour and allows a fast real-time implementation with guaranteed convergence. Simulation results of the MPC controller and the simulation model in closed-loop operation finally provide a proof of concept.

Sensor placement for fault diagnosis performance maximization in Distribution Networks

The success of any diagnosis strategy critically depends on the sensors measuring process variables. This paper presents a strategy based on diagnosability maximization for optimally locating sensors in distribution networks. The goal is to characterize and determine the set of sensors that guarantees a maximum degree of diagnosability taking into account a given sensor configuration cardinality constraint. The strategy is based on the structural model of the system under consideration. Structural analysis is a powerful tool for determining diagnosis possibilities and evaluating whether the number and the location of sensors are adequate in order to meet some diagnosis specifications. The proposed approach is successfully applied to leakage detection in a Drinking Water Distribution Network.

Optimal sensor placement For Fuel Cell System diagnosis using BILP formulation

This paper presents the application of a new methodology for Fault Detection and Isolation (FDI) to a Fuel Cell System. The work is devoted to find an optimal set of sensors for model-based FDI. The novelty is that binary integer linear programming (BILP) is used in the optimization formulation, leading to a reformulation of the detectability and isolability specifications as linear inequality constraints. The approach has been successfully applied to a Fuel Cell System.

Fault-tolerant explicit MPC of PEM fuel cells

In this paper, fault-tolerant explicit MPC control of fuel cell systems is presented. MPC is one of the control methodologies that allows to introduce fault-tolerance more easily. Here, this capability is extended using recent explicit MPC control theory. Explicit MPC control allows to derive offline the control without using optimization. Moreover, it allows to introduce as additional parameters faults since it is based on parametric programming. This makes possible to change in real-time controller parameters without recomputing the MPC controller or having a bank of pre-computed MPC controllers. Finally, the proposed approach is assessed on a known test bench PEM fuel cell system.

A model predictive approach for cyber-attack detection and mitigation in control systems

The paper presents a new approach for control security. Specifically, cyber-attacks on the controller are investigated by means of optimization techniques in order to determine the worst-case scenario. Then, a novel attack detector based on limit checking is introduced. The particularity of this detector is that no specific controller knowledge is necessary. Hence, the vulnerability of the detector can be reduced since no reconfiguration is required (limited accessibility). Finally, the paper shows that the effect of the attacks on the system can be significantly mitigated by applying proper optimal control laws.

Model-based Optimal Sensor Placement Approaches to Fuel Cell Stack System Fault Diagnosis

Abstract The problem of optimal sensor placement for FDI consists in determining the set of sensors that minimizes a pre-defined cost function satisfying at the same time a pre-established set of FDI specifications for a given set of faults. This paper recalls three model-based optimal sensor location approaches: an Incremental search, a Heuristic search and a Binary Integer Linear Programming (BILP) formulation. The main contribution of this paper is a comparative study that addresses efficiency, flexibility and other issues. The performance of the approaches is demonstrated by an application to a fuel cell stack system.