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.

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 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.

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.

In this paper the neural network-based controller is designed for motion control of a mobile robot. This paper treats the problems of trajectory following and posture stabilization of the mobile robot with nonholonomic constraints. For this purpose the recurrent neural network with one hidden layer is used. It learns relationship between linear velocities and error positions of the mobile robot. This neural network is trained on-line using the backpropagation optimization algorithm with an adaptive learning rate. The optimization algorithm is performed at each sample time to compute the optimal control inputs. The performance of the proposed system is investigated using a kinematic model of the mobile robot. Keywords—Mobile robot, kinematic model, neural network, motion control, adaptive learning rate.

The problem of motion planning and control of mobile robots has attracted the interest of researchers in view of its theoretical challenges because of their obvious relevance in applications. From a control viewpoint, the peculiar nature of nonholonomic kinematics and dynamic complexity of the mobile robot makes that feedback stabilization at a given posture cannot be achieved via smooth time-invariant control (Oriolo et al., 2002). This indicates that the problem is truly nonlinear; linear control is ineffective, and innovative design techniques are needed. In recent years, a lot of interest has been devoted to the stabilization and tracking of mobile robots. In the field of mobile robotics, it is an accepted practice to work with dynamical models to obtain stable motion control laws for trajectory following or goal reaching (Fierro & Lewis, 1997). In the case of control of a dynamic model of mobile robots authors usually used linear and angular velocities of the robot (Fierro & Lewis, 1997; Fukao et al., 2000) or torques (Rajagopalan & Barakat , 1997; Topalov et al., 1998) as an input control vector. The central problem in this paper is reduction of control torques during the reference position tracking. In the case of dynamic mobile robot model, the position control law ought to be nonlinear in order to ensure the stability of the error that is its convergence to zero (Oriollo et al., 2002). The most authors solved the problem of mobile robot stability using nonlinear backstepping algorithm (Tanner & Kyriakopoulos, 2003) with constant parameters (Fierro & Lewis, 1997), or with the known functions (Oriollo et al., 2002). In (Tanner & Kyriakopoulos, 2003) a combined kinematic/torque controller law is developed using backstepping algorithm and stability is guaranteed by Lyapunov theory. In (Oriollo et al., 2002) method for solving trajectory tracking as well as posture stabilization problems, based on the unifying framework of dynamic feedback linearization was presented. The objective of this chapter is to present advanced nonlinear control methods for solving trajectory tracking as well as convergence of stability conditions. For these purposes we developed a backstepping (Velagic et al., 2006) and fuzzy logic position controllers (Lacevic, et al., 2007). It is important to note that optimal parameters of both controllers are adjusted using genetic algorithms. The novelty of this evolutionary approach lies in automatic obtaining of suboptimal set of control parameters which differs from standard manual adjustment presented in (Hu & Yang, 2001; Oriolo et al., 2002). The considered motion control system of the mobile robot has two levels. The lower level subsystem deals with the

This paper develops a fuzzy logic position controller which membership functions are tuned by genetic algorithm. The main goals are to ensure both successfully velocity and position trajectories tracking between the mobile robot and the reference cart. The proposed fuzzy controller has two inputs and two outputs. The first input represents the distance between the mobile robot and the reference cart. The second input is the angle formed by the straight line defined with the orientation of the robot, and the straight line that connects the robot with the reference cart. Outputs represent linear and angular velocity inputs, respectively. The performance of the fuzzy controller is investigated through comparison with previously developed a mobile robot position controller based on backstepping control algorithm. Simulation results obtained the good quality of both position tracking and torque capabilities with the proposed fuzzy controller. Also, sufficient improvement of torques reduction is achieved in the case of fuzzy controller.

The teleoperation (telerobotic) systems often face two key challenges: the existence of communication delays between the master and slave site as well as the addition of force feedback to improve the user's sense of presence. The first goal of this paper is that the slave manipulator should track the position of the master manipulator and the second goal is that the environmental force acting on the slave, when it contacts a remote environment, be accurately transmitted to the master. For solving both problems we proposed the symmetric impedance matched teleoperation systems with a wave filter in feedback loop. Simulations results using a single-degree of freedom master/slave system are presented showing the performance of the resulting system.

This paper proposes a new reactive planning algorithm for mobile robot navigation in unknown environments. The overall navigation system consists of three navigation subsystems. The lower level subsystem deals with the control of the linear and angular velocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is in the medium level, and it is a nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. The high level subsystem uses the Fuzzy logic and Dempster-Shafer evidence theory to design the fusion of sensor data, map building and path planning tasks. The path planning algorithm is based on a modified potential field method. In this algorithm, the fuzzy rules for selecting the relevant obstacles for robot motion are introduced. Also, suitable steps are taken to pull the robot out of the local minima. A particular attention is paid to detection of the robot's trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.