The goal of this paper was to design a simple Service Oriented Supervisory Control and Data Acquisition System (SCADA) that can be used to manage, control and visualize multiple heterogeneous SCADA systems. A three-tier System architecture was designed consisting of Data Access, Service and Presentation Layers. Clients on the presentation layer can communicate with the system backend via SOAP XML or RESTful JSON Web services. The full entity relationship diagram of the database schema is presented in the paper. Message sequence charts are explained in detail for polling and event based notification of setpoint changes. At the end of the paper a simple Web user interface was introduced to demonstrate how modern Web technologies like AJAX and JQuery can be used to build interactive SCADA user interfaces.

This paper presents a generalized Multirotor Aerial Vehicle (MAV) modeling framework which includes rigid body dynamics, gyroscopic effect and motor dynamics. We illustrate how this model can be used to derive any MAV platform constructed with an arbitrary number of rotors by using the quadrotor case as an example. Based on this result, we design the first Modelica-based MAV simulator. We validate the proposed design by using a simple altitude and attitude stabilization control system through a Modelica simulation setup.

This paper presents a fault-tolerant PD tracking system for Multirotor Aerial Vehicles (MAV) based on a novel Recursive Least Squares (RLS) Fault Detection and Isolation (FDI) algorithm utilized to diagnose propulsion system faults. As a test platform we investigate an octorotor model, including rigid body dynamics, the gyroscopic effect and motor dynamics. A hover configuration control is extended into an adaptive, fault-tolerant PD tracking controller. The approach is validated within a simulation study that includes a severe triple rotor fault scenario.

This paper presents a detailed octorotor model derivation. The full derivation of the rigid octorotor body dynamics based on the Newton-Euler approach including the Gyroscopic effect and motor dynamics is given. We also discuss a generalization of the model thus making it applicable to any symmetric and balanced multirotor aerial vehicle (MAV) system with even number of rotors. Finally, simple stabilization control is designed and compared with the state of the art results.

In this paper, we present an algorithm for fully autonomous exploration and mapping of an unknown indoor robot environment. This algorithm is based on the active SLAM (simultaneous localization and mapping) approach. The mobile robot equipped with laser sensor builds a map of an environment, while keeping track of its current location. Autonomy is introduced to this system by automatically setting goal points so that either previously unknown space is mapped, or known landmarks are revisited in order to increase map accuracy. Final aim is to maximize both map coverage and accuracy. The proposed procedure is experimentally verified on Pioneer 3-DX mobile robot in real environment, using ROS framework for implementation.

It is well known that the process of tuning a fuzzy logic controller is almost always a very complex task, which is time consuming, very laborious and often requires expert knowledge of the controlled system. Mapping of fuzzy logic controller's parameters (rule base and membership functions of input(s) and output(s)) into a performance measure in a closed analytical form is near impossible to get, and thus the use of any classical optimization method is automatically ruled out. Knowing this, genetic algorithms with a fitness function in a form of cumulative response error represent a good choice of the optimization method. This approach enables the use of offline optimization of membership functions' parameters (which are being coded into chromosomes). Sugeno-Takagi fuzzy logic controllers with a proportional and a derivative component, and also with a fixed rule base are used in this approach. Experimental results of both simulations and validations on real systems are given in this paper and they show the good performance of this 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 addresses the challenges of the disturbance observer (DOB) algorithms faced with highly nonlinear electromechanical systems which are dealing with high resolution and high speed operations. It describes the synthesis of robust and stable controllers and their applications in controlling azimuth and elevation angles of the helicopter model CE 150 supplied by Humosoft. Description of the helicopter, including its mechanical characteristics and mathematical model, is given in the paper. Tracking error, transient performances, power consumption and motor strains are used for the validation of control quality. Implementation of the control system on the experimental setup is also explained. MATLAB and Simulink are used as tools for developing the simulation model of the helicopter system. Obtained simulations are showing that developed controllers provide significantly improved results even in the presence of unknown and unpredictable inputs (disturbance and noise), unpredictable and unknown dynamics, external forces (torques) and change of the system parameters.

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 the problems of identification and control of nonlinear helicopter model. The helicopter model is achieved by closed loop identification based on grey-box structure through two steps. The first step considers experiment design to minimize the number of parameters to be estimated. It employs a genetic algorithm for parameter value estimation. In the second step, a criteria function was defined for evaluation of model fitness. After that, linearization of nonlinear model was performed and linear state-space model was obtained for use in controller synthesis. The main contribution of this paper is design of loop shaping controller which is capable to control a nonlinear helicopter model. Advantage of this controller is its simpler linear structure and simpler synthesis. The quality of obtained helicopter model and effectiveness of proposed controller are verified both in simulation and experimental modes.

Determining the position of a mobile robot in every time instant from sensor data is the fundamental problem in mobile robotics. This paper considers a localization of holonomous mobile robot solved in this paper using two different approaches: odometry localization and landmark based localization. In both cases the robot is placed in known environment with landmarks whose coordinates were also known. Detecting the landmarks was done by using the Microsoft Kinect camera. For odometry localization four encoders were used. Data acquired from encoders and camera is fused together employing extended Kalman filter in order to get more accurate estimation of position and orientation. Obtained experimental results prove that using encoders without any additional measurements is not enough for getting reliable estimation of robots position. Odometry localization produced an error that accumulates over time, while in the case of landmark based localization, the error is kept inside acceptable limits.