Rosario Toscano

Robust synthesis of a PID controller by uncertain multimodel approach

This paper presents an effective method to design a PID (or PI) controller for nonlinear systems where desirable robustness and performance properties must be maintained across a large range of operating conditions. For this purpose, an uncertain multimodel of the original nonlinear system is used. The uncertainties affecting the system are treated as stochastic matrices. Based on this multimodel representation a robust PID controller can be designed in order to obtain acceptable performance for all operating conditions. Numerical examples show the practical applicability of the proposed method.

A Kalman Optimization Approach for Solving Some Industrial Electronics Problems

This paper is concerned with solving nonconvex optimization problems arising in various engineering sciences. In particular, we focus on the design of a robust flux estimator of induction machines and the optimal design of on-chip spiral inductors. To solve these problems, a recently developed optimization method, called the heuristic Kalman algorithm (HKA), is employed. The principle of HKA is to explicitly consider the optimization problem as a measurement process designed to give an estimate of the optimum. A specific procedure, based on the Kalman estimator, was developed to improve the quality of the estimate obtained through the measurement process. The main advantage of HKA, compared to other stochastic optimization methods, lies in the small number of parameters that need to be set by the user. Based on HKA a simple but effective design strategy for robust flux estimator and on-chip spiral inductors is developed. Numerical studies are conducted to demonstrate the validity of the proposed design procedure.

Towards an adapted lean system – a push-pull manufacturing strategy

Abstract The direct transplantation or imitation of lean production has led to difficulties of applying a number of lean principles and practices. Thus diffusion of one of the main lean principles, just in time production, which refers to producing only “what is really needed, when it is needed, and in the amount needed”, seems to be limited. To date, some of the these companies produce more than their customers really need. This method of production enables them not only to amortize the high changeover costs over a large number of products, but also to benefit from commercial opportunities. However these companies are exposed to financial losses related to storage costs and risks of non-sale. To provide decision elements for determining the best production strategy, we have developed a model for calculating the optimal quantity to be produced. Moreover, we suggest using a fuzzy aggregation system to optimise the consideration of the risk of non-sale. This new approach defines the limits to not be exceed by taking into consideration the drawbacks linked to the risks of non-sale.

Robustness analysis and synthesis of a multi-PID controller based on an uncertain multimodel representation

This paper presents an effective method for robustness analysis and synthesis of a multi-PID controller for non-linear systems where desirable robustness and performance properties must be maintained across a large range of operating conditions. The robustness analysis problem is solved using an uncertain multimodel of the original non-linear system. The model of uncertainties used is an interval matrix modeled by a stochastic matrix which gives poor conservatism in the analysis of stability robustness. Moreover, the robust stability margin is interpreted as a smallest interval matrix that causes instability. This stability margin is evaluated using a random search algorithm. Simulation studies are used to demonstrate the effectiveness of the proposed method.

A new stochastic inverse identification of the mechanical properties of human skin

The study of the mechanical properties of human skin is a key point to better understand surgery, skin ageing and pathologies. As the skin is a living tissue, it must be studied in vivo, hence analytical solutions are really difficult to obtain. In this study, a new stochastic inverse method for the identification of its mechanical properties is proposed. The developed optimization method is first presented. It is based on an iterative stochastic approach which ensures the identification of a global extremum. The suction actual case study is then analysed through comparisons between experimental data and finite element models of this test. Only the elastic components of the skin are considered here. The solutions for the recursive least squares and Gauss-Newtons problems are finally compared with the proposed approach to conclude this study and to briefly present our future works.

A methodological approach ball bearing damage prediction under fretting wear conditions.

The industrial demand for higher reliability of various components is one of the main flywheels of the research and development in the field of modelling of complex phenomena. There is a need to characterize the wear behaviour of the interface under fretting wear conditions in ball bearing application. Pre-treated experimental data was used to determine the wear of contacting surfaces as a criterion of damage that can be useful for a life-time prediction. The benefit of acquired knowledge can be crucial for the industrial expert systems and the scientific feature extraction that cannot be underestimated. Wear is a very complex and partially-formalized phenomenon involving numerous parameters and damage mechanisms. To correlate the working conditions with the state of contacting bodies and to define damage mechanisms different techniques are used. The use of our approaches in the prediction of the response of the system to different test conditions is validated. Two physical models, based on Archard and Energetic approach, are compared with artificial neural network model and genetic programming. Decisive factors for a comparison of used AI techniques are their: performance, generalization capabilities, complexity and time-consumption. Optimization of the structure of the model is done to reach high robustness of field applications. Finally, application of the wear level information to forecast a probability of damage is presented.

Parameterization of a fuzzy classifier for the diagnosis of an industrial process

Abstract The aim of this paper is to present a classifier based on a fuzzy inference system. For this classifier, we propose a parameterization method, which is not necessarily based on an iterative training. This approach can be seen as a pre-parameterization, which allows the determination of the rules base and the parameters of the membership functions. We also present a continuous and derivable version of the previous classifier and suggest an iterative learning algorithm based on a gradient method. An example using the learning basis IRIS, which is a benchmark for classification problems, is presented showing the performances of this classifier. Finally this classifier is applied to the diagnosis of a DC motor showing the utility of this method. However in many cases the total knowledge necessary to the synthesis of the fuzzy diagnosis system (FDS) is not, in general, directly available. It must be extracted from an often-considerable mass of information. For this reason, a general methodology for the design of a FDS is presented and illustrated on a non-linear plant.

Mixed H2/H∞ residual generator design via Heuristic Kalman Algorithm

Abstract This paper presents a simple but effective synthesis strategy for observers based faults detection in linear time-invariant (LTI) systems which are simultaneously affected by two classes of unknown inputs: Noises having fixed spectral densities and unknown finite energy disturbances. The problem of designing such an observer, also called a residual generator, is formulated as a mixed H 2 / H ∞ optimization problem. This is done to obtain an optimal residual generator, i.e. with minimal sensitivity to unknown inputs. Unfortunately, there is no known solution to this difficult optimization problem. Finding such a residual generator is known to be computationally intractable via the conventional techniques. This is mainly due to the non-convexity of the resulting optimization problem. To solve this kind of problem easily and directly, without using any complicated mathematical manipulations, we utilize the Heuristic Kalman Algorithm (HKA) for the resolution of the underlying constrained non-convex optimization problem. A numerical example is given to illustrate the advantage of the mixed H 2 / H ∞ optimization approach against techniques based on optimization of H 2 or H ∞ criteria.

Using Haptic Forces Feedback for Immersive and Interactive Simulation in Industrial Context

In a world in continuous evolution of information technology and computer science, virtual reality VR is a very important technological tool in several areas such as industrial simulations. In this paper, we will present some cases of the uses of VR, mainly in the industrial area, and in ergonomic tests and evaluations. In this work, we will present our approach that allows the use of haptic force feedback using a subjective method. In the first part, we will start with the state of the art by presenting a new approach based on a set of acquisitions in the mobile rolling operation in the real world and in the industrial context. In addition to that, we will study operator muscular forces exercised over a rolling mobile operation to displace it from one point to another. In particular, we will introduce how we can integrate a subjective method of calculating forces in a VR simulation for a realistic operator interaction and mobile behavior. We will also use certain techniques of virtual reality to immerse the operator, with the system of the relative forces exerted and other information. This paper comprises three main parts: the first one is a general study of the haptic force feedback techniques in VR applications haptic rendering. The second part presents the modules, the architecture and how we can integrate these techniques in our application. The final part is a discussion of the developed application and its results with some perspectives.

Dynamic Preventive Maintenance, Optimization of Time between Overhaul

We present in this paper an method for evaluating the reliability in real time applied to the optimization of preventive maintenance and evaluation of the parameters of dependability. This approach is based on a function Z (t), which assesses the damage from the history of real operating conditions. This assessment is used to calculate the residual reliability, and can then be used to optimize the preventive maintenance and in particular optimize the time between overhaul (TBO). This approach can be used to take more realistic decisions about preventive change and thus led to a better risk management.