2018 (vol. 28) - Number 1

*I. Duleba, I. Karcz-Duleba:*

A comparison of methods solving repeatable inverse kinematics for robot manipulators

*A. Niederliński:*

A new approach for modelling uncertainty in expert systems knowledge bases

*T.T. Tuan, L.D. Tufa, M.I.A. Mutalib, N.M. Ramli :*

Optimal operation of a process by integrating dynamic economic optimization and model predictive control formulated with empirical model

*M. Kaleta:*

Network winner determination problem

*A. Kowalewski:*

Extremal problems for parabolic systems with time-varying lags

*T. Rybus, K. Seweryn, J.Z. S±siadek:*

Application of predictive control for manipulator mounted on a satellite

*T. Kaczorek:*

Reachability and observability of positive discrete-time linear systems with integer positive and negative powers of the state Frobenius matrices

*G. Grassi, A. Ouannas, V-T. Pham:*

A general unified approach to chaos synchronization in~continuous-time systems (with or without equilibrium points) as well as in discrete-time systems

*D. Pazderski:*

A robust smooth controller for a unicycle-like robot

ACS Abstract:

**2008 (Volume 18)**

Number 3

**Compensation of the scan-period irregularities in LQG control systems**

Jan Cvejn(University of Pardubice, Faculty of Electrical Engineering and Informatics, Czech Republic) |

Computer-based control applications, especially if they run under general-purpose opera-ting systems, often exhibit variance of the scan period of processing inputs and outputs. Although this phenomenon is usually neglected when discrete control algorithms are used, it can cause worse performance of the control loop in comparison to theoretical case. In this paper we describe a modified discrete LQG control algorithm that takes disturbances of the scan period into account and partially compensates their effect. This modification concerns both the state estimation and generating the control output. We also show that such a controller can be implemented even on relatively simple hardware platforms if the system dynamics is time-invariant.

**keywords:** LQG controller, stochastic control, hybrid systems

**Remarks about DC motor control**

Jerzy Baranowski, Marek Dlugosz, Wojciech Mitkowski(Faculty of Electrical Engineering, AGH University of Science and Technology, Krakow, Poland) |

**keywords:** DC motor control, linear-quadratic control, minimum-energy control, dead-beat control, dead-beat observer, discrete LQ control, nolinear observer, observer optimization, nonlinear dynamical feedback

**Concepts of learning in assembler encoding**

Tomasz Praczyk(Naval University, Gdynia, Poland) |

**keywords:** evolutionary neural networks, reinforcement learning

Regulation of absorption packed column of CO2 using discrete fuzzy input-output linearization

R. Illoul, S. Bezzaoucha(cole Polytechnique Nationale d'Alger, 10 Avenue Hacen Badi, El-Harrach, Algiers, Algeria) | A. Selatnia(Laboratory of Process Control, Department of Chemical Engineering, Ecole Polytechnique Nationale d'Alger, Algiers, Algeria) |

**keywords:** CO2 absorption packed column, MEA, modeling, fuzzy control, PI regulation, input-output linearization

**Accelerator's supervisory control system based on CANbus**

Mirosław Dach(PSI - Paul Scherrer Institut, Villigen, Switzerland) | Jan Werewka(AGH University of Science and Technology, Department of Automatics, Computer Science Laboratory, Kraków, Poland) |

**keywords:** RT systems, distributed systems, field bus, time analysis, CAN, accelerator

**Stable output feedback model predictive control design: LMI approach**

Vojtech Vesely(URPI, Faculty of Electrical Engineering and IT, Slovak University of Technology, Bratislava, Slovak Republic) | Ruth Bars(Budapest University of Technology and Economics, Department of Automation and Applied Information, Budapest, MTA-BME Control Research Group, Hungary) |

**keywords:** model predictive control, quadratic stability, Lyapunov function

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