Biblio

The limited output of various drives means a challenge in controller design whenever the acceleration need of the "nominal trajectory to be tracked" temporarily exceeds the abilities of the saturated control system. The prevailing control design methods can tackle this problem either in a single theoretical step or in two consecutive steps. In this latter case in the first step the design happens without taking into account the actuator constraints, then apply a saturation compensator if the phenomenon of windup is observed. In the Fixed Point Iteration- based Adaptive Control (FPIAC) that has been developed as an alternative of the Lyapunov function-based approach the actuator saturation causes problems in its both elementary levels: in the kinematic/kinetic level where the desired acceleration is calculated, and in the iterative process that compensates the effects of modeling errors of the dynamic system under control and that of the external disturbances. The here presented approach tackles this problem in both levels by relatively simple considerations. To illustrate the method's efficiency simulation investigations were done in the FPIAC control of a modification of the van der Pol oscillator to which an additional strongly nonlinear term was added.

Alternating direction method of multiplier (ADMM) is a powerful method to solve decentralized convex optimization problems. In distributed settings, each node performs computation with its local data and the local results are exchanged among neighboring nodes in an iterative fashion. During this iterative process the leakage of data privacy arises and can accumulate significantly over many iterations, making it difficult to balance the privacy-utility tradeoff. In this study we propose Recycled ADMM (R-ADMM), where a linear approximation is applied to every even iteration, its solution directly calculated using only results from the previous, odd iteration. It turns out that under such a scheme, half of the updates incur no privacy loss and require much less computation compared to the conventional ADMM. We obtain a sufficient condition for the convergence of R-ADMM and provide the privacy analysis based on objective perturbation.

How to protect users' private data during network data mining has become a hot issue in the fields of big data and network information security. Most current researches on differential privacy k-means clustering algorithms focus on optimizing the selection of initial centroids. However, the traditional privacy budget allocation has the problem that the random noise becomes too large as the number of iterations increases, which will reduce the performance of data clustering. To solve the problem, we improved the way of privacy budget allocation in differentially private clustering algorithm DPk-means, and proposed APDPk-means, a new differential privacy clustering algorithm based on arithmetic progression privacy budget allocation. APDPk-means decomposes the total privacy budget into a decreasing arithmetic progression, allocating the privacy budgets from large to small in the iterative process, so as to ensure the rapid convergence in early iteration. The experiment results show that compared with the other differentially private k-means algorithms, APDPk-means has better performance in availability and quality of the clustering result under the same level of privacy protection.