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This work presents a data driven control strategy able to track a set point without steady‐state error. The control sequence is computed as an affine combination of past control signals, which belong to a set of trajectories stored in a process historian database. This affine combination is computed so that the variance of the tracking error is minimised. It is shown that offset free control, that is zero mean tracking error, is achieved under the assumption that the state is measurable, the underlying dynamics are linear and the trajectories of the database share the same error dynamics and are in turn offset free. The proposed strategy learns the underlying controller stored in the database while maintaining its offset free tracking capability in spite of differences in the reference, disturbances and operating conditions. No training phase is required and newly obtained process data can be easily taken into account. The proposed strategy, related to direct weight optimisation learning techniques, is tested on a process control trainer. [ABSTRACT FROM AUTHOR]

In this paper, we study and compare different types of generalized barycentric coordinates in detail, including Wachspress, discrete harmonic and mean value coordinates for convex, n-sided polygons. Contour lines are computed in each barycentric coordinate method, and curvature plots of these contour line curves are visualized. Moreover, different distortions of uniform patterns are also shown, providing exact visual method to compare these methods. To overcome the shortcomings of different generalized barycentric computations, affine combination of methods is provided. [ABSTRACT FROM AUTHOR]

Affine combination takes a weighted sum of the outputs of two adaptive filters subjected to the same input whose weighting factor is a parameter calculated using an adaptive mechanism (adaptive filters) in order to achieve a better performance. This study operationalized the behavior of Algorithm Accelerator Version γ (ARγ ) in Affine combination in order to achieve greater efficiency and better performance than the algorithm presented in its basic configuration; likewise identify the effects of the Affine Combination Algorithm Accelerator Version γ (ARγ ) adaptive algorithm. The methodological design and operational plan are part of a research process with a descriptive, explanatory and directed experimental answer why the phenomenon occurs (application of adaptive filters in Affine Combination ) and under what conditions it occurs. [ABSTRACT FROM AUTHOR]

In this paper, we propose an approach to the transient and steady-state analysis of the affine combination of one fast and one slow adaptive filters. The theoretical models are based on expressions for the excess mean-square error (EMSE) and cross-EMSE of the component filters, which allows their application to different combinations of algorithms, such as least mean-squares (LMS), normalized LMS (NLMS), and constant modulus algorithm (CMA), considering white or colored inputs and stationary or nonstationary environments. Since the desired universal behavior of the combination depends on the correct estimation of the mixing parameter at every instant, its adaptation is also taken into account in the transient analysis. Furthermore, we propose normalized algorithms for the adaptation of the mixing parameter that exhibit good performance. Good agreement between analysis and simulation results is always observed. [ABSTRACT FROM AUTHOR]

This paper studies the statistical behavior of an affine combination of the outputs of two least mean-square (LMS) adaptive filters that simultaneously adapt using the same white Gaussian inputs. The purpose of the combination is to obtain an LMS adaptive filter with fast convergence and small steady-state mean-square deviation (MSD). The linear combination studied is a generalization of the convex combination, in which the combination factor λ(n) is restricted to the interval (0,1). The viewpoint is taken that each of the two filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of optimal affine combining coefficients which minimizes the mean-square error (MSE). First, the optimal unrealizable affine combiner is studied and provides the best possible performance for this class. Then two new schemes are proposed for practical applications. The mean-square performances are analyzed and validated by Monte Carlo simulations. With proper design, the two practical schemes yield an overall MSD that is usually less than the MSDs of either filter. [ABSTRACT FROM AUTHOR]

Abstract: Generalizing the result in Lemma of Baksalary and Baksalary [J.K. Baksalary, O.M. Baksalary, Commutativity of projectors, Linear Algebra Appl. 341 (2002) 129–142], Baksalary et al. [J.K. Baksalary, O.M. Baksalary, T. Szulc, Linear Algebra Appl. 354 (2002) 35–39] have shown that if P 1 and P 2 are orthogonal projectors, then, in all nontrivial situations, a product of any length having P 1 and P 2 as its factors occurring alternately is equal to another such product if and only if P 1 and P 2 commute, in which case all products involving P 1 and P 2 reduce to the orthogonal projector P 1 P 2 (= P 2 P 1). In the present paper, further generalizations of this property are established. They consist in replacing a product of the type specified above, appearing on the left-hand side (say) of the equality under considerations, by an affine combination of two or three such products. Comments on the problem when the number of components in a combination exceeds three are also given. [Copyright &y& Elsevier]