This paper considers a regional quadratic performance analysis and state feedback synthesis method based on a polytopic approach for linear time invariant systems with saturation nonlinearities. In particular, a domain of quadratic performance is defined as a region of initial states in order to satisfy a quadratic performance. In the analysis case, the polytopic approach is theoretically less conservative than both the circle criterion and the existing linear analysis. However, in the synthesis case, the synthesis condition with the polytopic approach is theoretically equivalent to one with the circle criterion or the linear analysis in the meaning of the same achievable domains or quadratic performance levels in spite of obtaining different state feedback controllers respectively. Finally, this paper proposes a synthesis method making use of effectiveness of the polytopic approach.
This paper propose new design method of discrete-time sliding-mode preview reptitive servo systems. First, an augmented error system which contains periodicity signal, is introduced. Next, the construction of an linear slide mode servo system using augmented system is shown. Then, the preview feed forward compensation based on the optimal control theory is applied. This method not only has the advantage of conventional preview repetition servo system, but has the robustness it is ineffective by the conventional method. The computer experimental results on LDM position control system show the effectiveness of proposed design method.
Knowledge discovery in databases (KDD) or data mining involves fitting models to or determining patterns from high dimensional data sets, and extraction of correlation rules plays an important role. This paper proposes a new approach to knowledge discovery with linear model estimation, in which principal component analysis (PCA) is performed by selecting variables. The proposed algorithm is a hybrid of fuzzy clustering and PCA based on lower rank approximation of data matrix, in which the relative responsibilities of the variables are estimated by using possibilistic constraint for memberships. The proposed algorithm is also enhanced to a local PCA model that can be used for data mining by performing both of linear model estimation and stratified sampling.
We consider a class of discrete-time state-reset systems with a state-reset mechanism, where some elements of the state vector is reset to particular values when the elements exceeds a prescribed threshold. For the state-reset system we derive the state invariant set. Furthermore, as an example of state-reset systems, we also consider a network-type decentralized control system and show that the invariant set of the state estimation error exists.