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 describes indoor temperature regulation. Indoor environment temperature regulation is most frequent type of regulation. To achieve this goal a small scale model was built with software support as well. This system model is composed of the incubator, DC controlled dimmer, ME — RedLab data acquisition module as AD/DA converter, a Matlab-based controller, GUI (Graphical User Interface), and an NTC temperature sensor. System identification using step response was carried out to determine a mathematical model of the system which was implemented in Simulink program package. Within the Simulink model P, PI, PID and fuzzy controller were designed. Results acquired during simulation were successfully applied on the real system. Both simulation and experimental results are consistent. It is shown that system developed on simulation level can be translated to the real system which simplifies the regulator design.

This paper explains the whole process of making a system for remote control of a robot arm with five degrees of freedom (DOF). For this purpose a hardware structure was fully designed and implemented. The hardware structure is based on microcontroller PIC16F877A and surrounding architecture that controls movement of different axis of the arm. The arm has no sensors, so the visual information from the camera was used as feedback. Two communications were used to operate robot arm. The first one is realized by serial RS-232 protocol between PC and Microcontroller, and this communication is used to operate the arm. The second communication uses TCP/IP protocol for remote control. The TCP/IP protocol provides communication between server and client computers and sends information of position of robot arm. For interaction with user appropriate GUI is implemented in MATLAB. The main objective of this paper was to obtain fully and precise control of every degree of freedom.

This paper deals with a simple procedure for map building as well as planning mobile robot trajectories. This approach divides the above problem into three stages: first, a design and construction of simple differential wheeled robot with ZigBee wireless communication; second, development of applications for mobile robot and PC which provide 2D map building of robot's environment using IR sensors; and third, development of applications for mobile robot and PC which provide trajectory planning and robot motion control for created maps. PC applications are implemented using Matlab GUI tool. Finally, this paper also shows developed wireless communication protocol for data exchange between mobile robot and PC. The robustness and effectiveness of the proposed system are demonstrated through experimental results.

This paper focuses on problems which arise from network controlled systems within master-slave type of Communications. Since the master and slave are connected over the network, network-induced time-varying delay has negative impact on system stability and control system performance. In order to compensate an influence of delay in NCS we proposed the improvement of two control strategies, Smith predictive and parallel fuzzy-PI controllers. Design of parallel fuzzy-PI controller has no demands for mathematical model of controlled system. Parameters of PI controller are adjusted adaptively depending on the delay. Adaptive Smith predictor is based on the adaptive loop that decreases influence of network-induced delay. It requires a mathematical model of the controlled system. Furthermore, simulations and practical experiments are given to illustrate the effectiveness and robustness of the proposed methods.

This paper explains the whole process of a system design for the remote control of a stepper motor via web server. For this purpose a hardware structure is fully designed and implemented. Two types of communications were used to operate with the stepper motor. The first is realized by serial RS-232 protocol and the second one uses TCP/IP protocol for remote control. The TCP/IP protocol provides communication between server and client computers. The proposed control system is connected to the server. The main objective of this paper was to obtain a precise control of velocity or number of steps of the stepper motor. Validity and effectiveness of the used algorithm were verified through both simulations and experiments.

This paper presents a design of fuzzy logic control for 2DOF laboratory helicopter model (Humusoft CE 150) which represents a nonlinear and highly cross-coupled system. The controller is composed of two fuzzy logic controllers for azimuth and elevation controls. The main objective of the paper is to obtain robust and stable controls for wide range of azimuth and elevation angles changing during the long time flight. The quality and effectiveness of both fuzzy controllers were verified through both simulations and experiments. Also, a comparative analysis of proposed fuzzy and traditional PID controllers is performed.

This paper presents an implementation of soft computing methodologies, like genetic algorithm and fuzzy logic, in identification and control of 2DOF nonlinear helicopter model (Humusoft CE 150). The genetic algorithm is proposed for identification of the physical structure of helicopter system, which contains a helicopter body, main and tail motors and drivers. The quality of helicopter model achieved was validated through simulation and experimental modes. Then, this model is used to elevation and azimuth fuzzy logic Mamdani type controllers design in a simulation mode. The main objective of the paper is to obtain robust and stable controls for wide range of azimuth and elevation angles changing during the long time flight. The robustness and effectiveness of both fuzzy controllers were verified through both simulations and experiments.

Neural networks have been applied very successfully in the identification and control of nonlinear dynamic systems. The paper presents a design of neural network based control system for 2DOF nonlinear laboratory helicopter model (Humusoft CE 150). The main objective of this paper is to develop artificial neural networks to control helicopter's motors, or consequently elevation and azimuth angles. Neural networks are obtained by cloning various type of controllers designed in our previous papers. Those procedures included a cloning linear PID controller, gain scheduling controller and fuzzy controller.

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.

The paper presents a design of 3D simulator for 2DOF laboratory helicopter model (Humusoft CE 150) and its practical implementation in various control system structures. The developed simulator provides testing performance of different control algorithms and visualization of results obtained. The 3D simulator has been designed and developed using the VRML programming language under the Virtual Reality toolbox in Matlab/Simulink. The effectiveness of this simulator are demonstrated during simulation and experiments on the 2DOF helicopter model using different control techniques, such as PID, adaptive gain scheduling and fuzzy controllers. Some of them have been designed in this paper.

This paper demonstrates the effectiveness of a genetic algorithm in identification of the unknown parameters of a nonlinear 2DOF laboratory helicopter model. The mathematical model of physical structure of the helicopter has fourteen unknown parameters which are necessary to be identified. For identification of this model a genetic algorithm is chosen because it enables finding referent results using less number of the experiments in comparison with other identification techniques. After the identification process has been carried out, the unknown parameters are determined and validated through comparisons of the simulation model response and response of the real helicopter system.

The paper discusses the problems of design and implementation of three-dimensional simulator for control of laboratory model helicopter. The research is realized under student project at the Department of Automatic Control and Electronics of Faculty of Electrical Engineering in Sarajevo. For this purpose the HUMUSOFT CE150 laboratory helicopter model was used. In order to verify various control algorithms the 3D simulator is designed. Simulator was developed in Matlab/Simulink environment using the Virtual Reality Modelling Language (VRML). Three-dimensional helicopter was built by using 3D Studio Max script. The quality of this simulator is tested during the helicopter motion with PID controllers.

The purpose of this paper is to analyse and implement PI control for the permanent magnet DC motor. The control algorithm is realised using Siemens S7-200 programmable logic controller (PLC). The complex motor system is composed of DC motor, driver and tachogenerator. The main objective is to achieve a satisfactory time response of the system output under disturbances like death zone, nonlinearity, measurement noise and external load acting. The PI controller is designed in the programming environment on a previously identified nonlinear motor system. Then the PI controller is embedded into the S7-200 PLC. The effectiveness of this controller are tested in both simulation mode and experiments.