Jasmin Velagic

Adaptive fuzzy ship autopilot for track-keeping

An adaptive fuzzy gain autopilot for ship track-keeping is developed. This autopilot is composed of Sugeno fuzzy type autopilot in an ordinary feedback loop and adjustable scaling factors mechanism in an additional feedback loop. The adjustment mechanism represents a fuzzy controller that changes scaling factors of the base fuzzy autopilot. The control system for the track-keeping is completely described. For the track-keeping problem, the maneuver of way-point turning and ship guiding through a complex path (trajectory) are presented. The influence of sea current and wave disturbances on track-keeping performance was also considered. Simulation results obtained by the Sugeno fuzzy type autopilot are first presented. Then, those results are compared with ones obtained by an adaptive fuzzy autopilot.

A 3-level autonomous mobile robot navigation system designed by using reasoning/search approaches

This paper describes how soft computing methodologies such as fuzzy logic, genetic algorithms and the Dempster-Shafer theory of evidence can be applied in a mobile robot navigation system. The navigation system that is considered has three navigation subsystems. The lower-level subsystem deals with the control of linear and angular volocities using a multivariable PI controller described with a full matrix. The position control of the mobile robot is at a medium level and is nonlinear. The nonlinear control design is implemented by a backstepping algorithm whose parameters are adjusted by a genetic algorithm. We propose a new extension of the controller mentioned, in order to rapidly decrease the control torques needed to achieve the desired position and orientation of the mobile robot. The high-level subsystem uses fuzzy logic and the Dempster-Shafer evidence theory to design a fusion of sensor data, map building, and path planning tasks. The fuzzy/evidence navigation based on the building of a local map, represented as an occupancy grid, with the time update is proven to be suitable for real-time applications. 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. Particular attention is paid to detection of the robots trapped state and its avoidance. One of the main issues in this paper is to reduce the complexity of planning algorithms and minimize the cost of the search. The performance of the proposed system is investigated using a dynamic model of a mobile robot. Simulation results show a good quality of position tracking capabilities and obstacle avoidance behavior of the mobile robot.

A mobile robot based system for fully automated thermal 3D mapping

Display Omitted We propose a fully autonomous system for 3D thermal modeling of buildings.A robot finds the positions for data acquisition using 3D sensor placement planning.Data from a laser scanner, a thermal camera, and a photo camera are automatically joined into one full model.Post-processing prepares the data for inspection in a viewer and points out interesting parts in the environment to experts. It is hard to imagine living in a building without electricity and a heating or cooling system these days. Factories and data centers are equally dependent on a continuous functioning of these systems. As beneficial as this development is for our daily life, the consequences of a failure are critical. Malfunctioning power supplies or temperature regulation systems can cause the close-down of an entire factory or data center. Heat and air conditioning losses in buildings lead to a large waste of the limited energy resources and pollute the environment unnecessarily. To detect these flaws as quickly as possible and to prevent the negative consequences constant monitoring of power lines and heat sources is necessary. To this end, we propose a fully automatic system that creates 3D thermal models of indoor environments. The proposed system consists of a mobile platform that is equipped with a 3D laser scanner, an RGB camera and a thermal camera. A novel 3D exploration algorithm ensures efficient data collection that covers the entire scene. The data from all sensors collected at different positions is joined into one common reference frame using calibration and scan matching. In the post-processing step a model is built and points of interest are automatically detected. A viewer is presented that aids experts in analyzing the heat flow and localizing and identifying heat leaks. Results are shown that demonstrate the functionality of the system.

The Project ThermalMapper - Thermal 3D Mapping of Indoor Environments for Saving Energy

Abstract Heat and air conditioning losses in buildings and factories lead to a large amount of wasted energy. The Action Plan for Energy Efficiency of the Commission of the European Communities (2008) estimates that the largest cost-effective energy savings potential lies in residential (≈ 27%) and commercial (≈ 30%) buildings. Imagine a technology that creates a precise digital 3D model of heat distribution and heat flow enabling one to detect all sources of wasted energy and to modify buildings to reach these savings. This paper presents our overall approach to map indoor environments with thermal data in 3D.

Efficient Path Planning Algorithm for Mobile Robot Navigation with a Local Minima Problem Solving

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 robots 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.

Identification and control of 2DOF nonlinear helicopter model using intelligent methods

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.

Nonlinear Motion Control of Mobile Robot Dynamic Model

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

Design of fuzzy logic control of permanent magnet DC motor under real constraints and disturbances

This paper presents a design of the fuzzy logic control for a permanent magnet DC motor. The main objective is to achieve a robust controller under disturbances and unmodeled dynamics acting, such as load torque, dead zone, measurement noise and nonlinearities. The whole system contains the DC motor, driver, tachogenerator, external load and microprocessor based system (dSPACE CLP1004). This system is considered such as black box. The fuzzy controller is designed in simulation mode first. The model of whole system was obtained through identification procedure. Then this fuzzy controller is included into a real physical control structure. Control performance of the fuzzy controller in both simulation and experimental modes are compared under mentioned constraints. Also, results obtained by the fuzzy controller are compared with the same obtained by PID controller.

Genetic algorithm based identification of a nonlinear 2DOF helicopter model

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.

Design and implementation of fuzzy logic controllers for helicopter elevation and azimuth controls

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.