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    260 F.F. Teles, J.M. Lemos / Biomedical Signal Processing and Control 48 (2019) 255–264
    where U(n) is the output of the bumpless transfer and anti-windup
    system, and U(n−1) is the previous drug effect U.
    The distribution of the models in S is crucial for the system per-
    formance. In this work, an algorithm where the controller bank
    is smaller than the model bank, i.e. where NcN, being Nc the
    Fig. 8. Architecture of the bumpless transfer and anti-windup scheme.
    number of local controllers, is used. Then, clustering algorithms are
    implemented for creating clusters c
    of models. The centroids Mc of
    When changes in the plant are detected, the current controller those clusters are the models from which the local controllers Cc
    are designed.
    is switched to a new one with a higher satisfactory performance.
    However, this ideal control action can be deteriorated by the 2.4.1. Model sensitivity analysis
    switching since a degradation of the transient performance can be
    expected [41]. By swapping from one controller to another, abrupt
    The sensitivity analysis is used to choose from the set of 11
    changes in the manipulated variable U may occur, resulting in parameters of the patient model the 2 most sensitive parameters,
    doses that largely exceed the maximum values allowed. Bumpless since clustering is performed based on them. ∈ IR11 is
    transfer between different controllers can prevent this problem, for
    instance by inserting an integrator common to all the controllers the parameter vector, sensitivity Si can be represented as
    With this insertion, the local controllers have to be redesigned
    by considering an augmented state space composed by x and the
    state of the integrator Ub , resulting in
    From the solution of (20), it ABT263 is concluded that the parameters I
    A B
    An important factor regarding the classification and agglom-
    eration of models is the choice of a good evaluation metric. The
    Vinnicombe metric [45] was chosen to be used, since an evaluation
    of the closed loop response is performed for comparing two distinct
    models. More precisely, if the Vinnicombe metric shows that two
    models are distinct, then a controller that gives satisfactory results
    for one model will behave poorly or even destabilize the other.
    Considering two models Mi and Mj , that are described by a
    transfer function with the same number of inputs and outputs, the
    Vinnicombe metric ıv(Mi , Mj ) is then given by
    When there is no saturation, es is zero having no effect in the operation. If it is not null, the feedback path tries to reset the inte-grator which prevents it from winding up. A value of Tw = 10 is used, since it represents the best tested scenario.
    Despite the inclusion of the bumpless transfer, there may still exist situations in which the system is not able to prevent the occur-rence of peaks, such as a switch between two models that are very distinctive. The bumpless transfer system is capable of smoothing these peaks but not of completely removing them. In this way, a cumulative moving average filter was included, which calculates for each time instant tn , the mean of all the iterations between the first instant t0 and the previous one tn−1 . Thus, the drug effect for the time instant n, U(n) , is given by
    are very distinctive. Thus, values closer to 0 indicate that a con-troller designed for Mi can also stabilize Mj , having also similar closed loops gains.
    Since the plant has two inputs, two Vinnicombe metrics are available. However, almost no differences were found between both metrics, being the first one used.
    After creating a model data set, made of N models whose param-eters I and ˇ were generated by a log-normal distribution, two clustering methods were applied: k-means [46] and complete link-age [47].
    For a test with N = 100 models and Nc = 6 clusters, the number of models Mi in each cluster ci , the mean of distances dc between Mi and its clusters, and the worst case of the same distance dc were computed for both algorithms. The biggest cluster in the k-means had 24 models, whilst the larger one in complete linkage had 69. On the other hand, the smallest one for the first method had 7 clusters, whilst for the second it had only 1. The mean of dc was for the com-plete linkage 147.4% lower than for the k-means, being the worst dc also 91.8% lower for the first method. Aside from that, by increasing