This paper gives a brief overview of the current state in the ant colony optimization (ACO) field of study. Furthermore, it introduces an alternative pheromone laying strategy for the ACO algorithm. In the paper, the newly introduced strategy is implemented, tested on a model problem and compared with the classical approach. A parameterized problem space generator has been introduced. The generator generates graphs along which ants are allowed to move freely on the Y axis, but constrained to increment the current value on the X axis by one with each move. In this way, a dynamic decision making optimization problem with the goal of minimizing the path from an arbitrary starting node to an arbitrary finish node has been simulated. Using the ACO algorithm, the generated problems are being solved with the classical pheromone laying approach and the modified approach, introduced in this paper. The obtained results unequivocally indicate that the introduced modification has the potential to serve as an improvement for the ACO algorithm in general.

This paper deals with the design and implementation of a discrete dynamic control system using multi-objective dynamic programming. A discrete dynamic system along with the constraints regarding the values of the system's state (output) and the applicable control input has been introduced. Using multi-objective dynamic programming, a control system has been designed which minimizes two objectives while satisfying introduced constraints. The implemented control solution is explained. Experiments have been performed to test the quality of the implemented solution. A single-objective version of the control problem has been tested to provide reference for analysis regarding the quality of the implemented multi-objective control solution. The comparison of the obtained experimental results confirms the effectiveness of the proposed control solution and demonstrates the benefits of the multi-objective approach.

The paper proposes a wireless navigation mobile robot system for both path planning and trajectory execution within an indoor maze environment. This system consists of the mobile robot, trajectory planner, motion controller, visual sensor (CCD camera), ZigBee wireless communication device and a maze terrain. The camera is used to capture images of the mobile robot within the maze. Developed image processing and analyzing algorithms determine the robot's position and orientation based on color markers recognition. Markers are mounted on the top of the robot. Based on this data the implemented navigation system calculates a trajectory for the mobile robot from a starting point to a target point. The proposed navigation system is an upgrade to our previously developed system. Maze encryption and motion planning modules have been added to the previous system. Breadth First Search (BFS) and modified Depth First Search (DFS) algorithms were used for the trajectory calculation. A developed control algorithm calculates control signals in real time. These signals are sent to the robot via modules for wireless communication, causing robot motion along the calculated trajectory and eventually, the completion of the trajectory. The whole control system is realized and experimental results have been obtained. The experimental results confirm the robustness and effectiveness of the implemented control system.

This paper deals with the identification process of an ethane-ethylene distillation column system and introduces a procedure for MIMO system identification using Matlab's IDENT toolbox. An existing, five inputs and three outputs, ninety samples dataset has been analyzed. Four separate datasets are analysed, each having a different level of noise. As a part of the proposed procedure, both nonparametric and parametric identification methods have been implemented and results have been discussed. A comparative analysis of the results for all of the tested model structures has been carried out and the best performing model has been chosen. A Simulink model of the identified system has been implemented based on the best performing parametric model. It has been stimulated with the existing input datasets and the resulting output signals have been compared to the existing output datasets. The obtained results confirm the quality of the obtained model and affirm the correctness of the proposed and implemented procedure.

This paper deals with a design and implementation of a remote position control system for a mobile robot. The system is composed of the mobile robot (controlled object), PC as positioning controller, camera as sensor and ZigBee based wireless communication device. The camera captures images of the mobile robot. Developed image processing algorithms (Matlab platform) determine the robot's position and orientation. Based on this data the implemented system controls the robot's position. Control signals are sent via modules for wireless communication. The whole control system is realized and experimental results have been obtained. The experimental results confirm the robustness and effectiveness of the proposed control system.

This paper deals with a design and implementation of an incubator temperature control system. This system is composed of the incubator, a PLC-based PID controller, HMI (human-machine interface), DC dimmer as actuator and an NTC temperature sensor. A mathematical model of the incubator system with appropriate sensor and drives is derived through identification process based on step responses. Its model is implemented in Simulink program package. The parameters of the PID controller have been adjusted by using a self-tuning toolbox under Matlab/Simulink. The whole control system is realized in both simulation and experimental modes. The robustness and effectiveness of the proposed control system are demonstrated through comparison of simulation and experimental results.