A set of PID tuning rules for four plant models used currently in self-tuning controllers is developed. The four models are: 1st and 2nd order plants with delays, multiple time constant, Strejc model. Critically damped responses are assumed, i.e. no overshoot and short settling time. Oscillatory responses can also be obtained for low-order models. Classical techniques of polezero cancellation, Pad? approximation and root locus are employed. The rules are "handbooklike", what means that they explicitly express PID settings in terms of plant parameters, making self-tuning applications possible. Settling time estimates are also given.

This paper aims to contribute in solving the problem of model-based body motion estimation by using data coming from visual sensors. We consider the case of state estimation in jump Markov nonlinear systems. The Interacting Multiple Model (IMM) algorithm is specially designed to track accurately targets whose state and/or measurement (assumed to be linear) models changes during motion transition. However, when these models are nonlinear, the IMM algorithm must be modified in order to guarantee an accurate track. In this paper we propose to compare the results given by an IMM algorithm Extended Kalman filter based (IMM-EKF) versus those given by an IMM algorithm Unscented Kalman filter based (IMM-UKF) in tracking target assumed to be highly maneuverable.

The paper summarizes the research on parameterization of LMS-based adaptive control algorithms for active noise control (ANC) systems. These single- and multi-channel systems were built in enclosures to create three-dimensional zones of quiet (3D ZoQs) by controlling electro-acoustic plants characterized by complicated dynamics and strong nonstationarities. On the basis of results of simulation as well as real-world experiments it was shown how the electroacoustic plant modeling errors, adaptation parameter and controller filter order choice influence the ANC system performance, also in nonstationary conditions. These observations were used to formulate some parameterization rules and hints for systems controlling electro-acoustic plants. It was shown that properly parameterized ANC systems create large adaptive, spatially moving 3D ZoQs.

This paper presents a theory and an experimental evaluation of robust control algorithm for permanent magnet synchronous machine (PMSM) speed drive fed by voltage source inverter. A simple control model is first obtained by performing field orientation through a nominal linearizing state feedback. Then, the robust control algorithm based on pole placement is applied to the PMSM in order to deal with plant uncertainties. The adopted method uses the notion of geometric stability and the multi-model approach in order to synthesis the state regulation by robust pole placement. To show the validity of our control approach, DSP based experimental results are presented. These results are very satisfactory and prove the feasibility of our control algorithm.

The paper explores the potential applications of a controller designed in accordance with the pole placement rule. A 2DOF model representing a quarter-vehicle suspension is considered. However, this is only a theoretical model, and the dynamic properties of a active element are neglected. The study investigates how the variations of the gains matrix K elements should affect the dynamic behavior of an active vibration control system.

In this paper a novel class of filters designed for the removal of impulsive noise in color images is presented. The proposed filter family is based on the kernel function which controls the noise suppression properties of the proposed filtering scheme. The comparison of the new filtering method with the standard techniques used for impulsive noise removal indicates its superior noise reduction capabilities and excellent structure preserving properties.