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Extra resources for Adaptive Control
And in the ideal case, no information is wasted in the iteration process of the IC estimator. This property is not seen in traditional identification algorithms since only partial information and certain stochastic a priori knowledge can be utilized in those algorithms. 2. It does not give single parameter estimate at every step; instead, it gives a (finite or infinite) set of parameter estimates at every step. This property is also unique since traditional identification algorithms always give parameter estimates directly.
High-dimensional Case: In case of d > 2, φ Tθ ≤ c represents a hyperplane which splits the whole space into two half-hyperplanes. Unlike in case of d = 2, the vertices in this case generally cannot be arranged in a certain natural order (such as clock-wise order). In this case, we can use an algorithm AddLinearConstraintND which is listed in Algorithm 2. The idea of this algorithm is to classify the vertices of V first according to their relationship with the hyperplane determined by hyperplane φ Tθ ≤ c .
Randomly taken from uniform distribution 10 U(0, 1). The simulation results for two examples are depicted in Figure 8 and Figure 9, respectively. In each figure, the output sequence and the reference sequence are Δ plotted in the top-left subfigure; the tracking error sequence bottom-left subfigure; the control sequence ut et = yt − yt* is plotted in the is plotted in the top-right subfigure; and the parameter θ together with its upper and lower estimated bounds is plotted in the bottomright subfigure.