A Durbin-Watson serial correlation test for ARX processes via excited adaptive tracking

We propose a new statistical test for the residual autocorrelation in ARX adaptive tracking. The introduction of a persistent excitation in the adaptive tracking control allows us to build a bilateral statistical test based on the well-known Durbin-Watson statistic. We establish the almost sure convergence and the asymptotic normality for the Durbin-Watson statistic leading to a powerful serial correlation test. Numerical experiments illustrate the good performances of our statistical test procedure

A New Recursive Least-Squares Method with Multiple Forgetting Schemes

We propose a recursive least-squares method with multiple forgetting schemes to track time-varying model parameters which change with different rates. Our approach hinges on the reformulation of the classic recursive least-squares with forgetting scheme as a regularized least squares problem. A simulation study shows the effectiveness of the proposed method

Application of flexible recipes for model building, batch process optimization and control

Unlike the traditionally fixed recipes in batch process operation, flexible recipes allow the adjustment of some of its relevant recipe items. These adjustments can either be predefined in cases of planned experimentation, or suggested by a formal process optimization or control algorithm on the basis of actual information. In both the response surface methodology and the simplex evolutionary operation (EVOP), some well-known methods for empirical model building and process optimization, flexible recipes are involved. Another application of flexible recipes arises in a feedforward quality control strategy of batch processes when variations in market or process conditions are known a priori. The experimental results of these strategies are presented for the batchwise production of benzylalcohol on a pilotplant scale. Experiments have been performed to obtain a reliable model of the yield. On the basis of this model, better process conditions have been suggested, which substantially deviate from the final simplex resulted from experiments within simplex EVOP. Finally, an adaptive feedforward control strategy has been applied for a priori known disturbances in the process inputs

Performance Improvement of QPSK Signal Predetection EGC Diversity Receiver

This paper proposes a modification of quadrature phase-shift-keying (QPSK) signal diversity reception with predetection equal gain combiner (EGC). The EGC combining is realized by using the constant modulus algorithm (CMA). Carrier synchronization is performed by the phase locked loop (PLL). Comparative analysis of the modified and ordinary diversity receiver in the presence of carrier frequency offset in the additive white Gaussian noise (AWGN) channel, as well as in Rician fading channel is shown. The proposed diversity receiver allows significant frequency offset compared to the diversity receiver that uses only PLL, and the error probability of the proposed receiver is very close to the error probability of the receiver with only PLL and zero frequency offset. The functionality of the proposed diversity receiver, as well as its properties is experimentally verified on a system based on universal software radio peripheral (USRP) hardware. The performed comparison confirms the expected behavior of the system

Identification of Evolving Rule-based Models.

An approach to identification of evolving fuzzy rule-based (eR) models is proposed. eR models implement a method for the noniterative update of both the rule-base structure and parameters by incremental unsupervised learning. The rule-base evolves by adding more informative rules than those that previously formed the model. In addition, existing rules can be replaced with new rules based on ranking using the informative potential of the data. In this way, the rule-base structure is inherited and updated when new informative data become available, rather than being completely retrained. The adaptive nature of these evolving rule-based models, in combination with the highly transparent and compact form of fuzzy rules, makes them a promising candidate for modeling and control of complex processes, competitive to neural networks. The approach has been tested on a benchmark problem and on an air-conditioning component modeling application using data from an installation serving a real building. The results illustrate the viability and efficiency of the approach. (c) IEEE Transactions on Fuzzy System

Recursive least squares for online dynamic identification on gas turbine engines

Online identification for a gas turbine engine is vital for health monitoring and control decisions because the engine electronic control system uses the identified model to analyze the performance for optimization of fuel consumption, a response to the pilot command, as well as engine life protection. Since a gas turbine engine is a complex system and operating at variant working conditions, it behaves nonlinearly through different power transition levels and at different operating points. An adaptive approach is required to capture the dynamics of its performance

Biomimetic bluff body drag reduction by self-adaptive porous flaps

The performances of an original passive control system based on a biomimetic approach are assessed by investigating the flow over a bluff-body. This control device consists in a couple of flaps made from the combination of a rigid plastic skeleton coated with a porous fabric mimicking the shaft and the vane of the bird's feathers, respectively. The sides of a square cylinder have been fitted with this system so as to enable the flaps to freely rotate around their leading edge. This feature allows the movable flaps to self-adapt to the flow conditions. Comparing both the uncontrolled and the controlled flow, a significant drag reduction (up to 22%) has been obtained over a broad range of Reynolds number. The investigation of the mean flow reveals a noticeable modification of the flow topology at large scale in the vicinity of the controlled cylinder accounting for the increase of the pressure base in comparison with the natural flow. Meanwhile, the study of the relative motion of both flaps points out that their dynamics is sensitive to the Reynolds number. Furthermore, the comparative study of the flow dynamics at large scale suggest a lock-in coupling of the flap motion and the vortex shedding.Comment: 19 pages, 17 figures, submitted to Comptes-Rendus de l' Acad\'emie des Sciences (M\'ecanique

Self-Adapting Soft Sensor for On-Line Prediction

When it comes to application of computational learning techniques in practical scenarios, like for example adaptive inferential control, it is often difficult to apply the state-of-the-art techniques in a straight forward manner and usually some effort has to be dedicated to tuning either the data, in a form of data pre-processing, or the modelling techniques, in form of optimal parameter search or modification of the training algorithm. In this work we present a robust approach to on-line predictive modelling which is focusing on dealing with challenges like noisy data, data outliers and in particular drifting data which are often present in industrial data sets. The approach is based on the local learning approach, where models of limited complexity focus on partitions of the input space and on an ensemble building technique which combines the predictions of the particular local models into the final predicted value. Furthermore, the technique provides the means for on-line adaptation and can thus be deployed in a dynamic environment which is demonstrated in this work in terms of an application of the presented approach to a raw industrial data set exhibiting drifting data, outliers, missing values and measurement noise