Piotr Duch

The variable, fractional-order discrete-time PD controller in the IISv1.3 robot arm control

In this paper, the discrete differentiation order functions of the variable, fractional-order PD controller (VFOPD) are considered. In the proposed VFOPD controller, a variable, fractional-order backward difference is applied to perform closed-loop, system error, discrete-time differentiation. The controller orders functions which may be related to the controller input or output signal or an input and output signal. An example of the VFOPD controller is applied to the robot arm closed-loop control due to system changes in moment of inertia. The close-loop system step responses are presented.

Order Functions Selection in the Variable-, Fractional-Order PID Controller

In the paper a variable-, fractional-order PID (VFOPID) controller microprocessor realization problems are discussed. In such controllers the variable-, fractional-orders backward differences and sums (VFOBD/S) are used to perform closed-loop system error discrete-time differentiation and integration. In practice all digitally differentiated and integrated signals are noised so there is a necessity of a digital signal pre-filtering. This additionally loads the DSP system. A solution of this problem is proposed. Also the possibilities of the VFOPID controller DSP realizations are presented and compared with the computer simulation results.

Variable-, Fractional-Order Dead-Beat Control of a Robot Arm

In the paper a synthesis method of the variable-, fractional – order dead – beat controller is proposed. It is applied to control of a robot arm described as a simple integrating element. The system structure is presented. The transient characteristic of a closed – loop system with the proposed controller are measured and compared with computer simulations performed for classical controllers.

Modifications of most expressive feature reordering criteria for supervised kernel Principal Component Analysis

The following paper proposes a set of novel feature selection criteria that can be applied to kernel Principal Component Analysis (kPCA) outcome to derive discriminative feature spaces for complex classification problems, such as biometric recognition tasks. The proposed class-separation criteria that are used to evaluate distributions of samples, which are projected onto nonlinear most discriminative directions, are modifications of Fisher Linear Discriminant (FLD). The modifications include reformulation of a basic class separation index that addresses the case of multi-modal class distributions and introduction of information regarding sample distribution skewness into the corresponding feature assessment criterion. It has been shown that class discrimination performance of the proposed scheme is better than in case of an application of a basic FLD scheme.

Likewise - radar fractional-order edge detector

Abstract In this paper a new likewise – radar fractional order edge detector is presented. Proposed edge detector is compared with well-known edge detectors like Sobel operator, Robert cross and with method based on fractional-order derivatives. Based on fractional differentiation a mask for edge detection is created. The main idea of presented algorithm is it works in similar way to radars, namely mask is rotating through 180 degrees in each pixel of the image.

Object Classification Using Support Vector Machines with Kernel-based Data Preprocessing

Abstract The paper explores possibility of improving Support Vector Machine-based classification performance by introducing an input data dimensionality reduction step. Feature extraction by means of two different kernel methods are considered: kernel Principal Component Analysis (kPCA) and Supervised kernel Principal Component Analysis. It is hypothesized that input domain transformation, aimed at emphasizing between-class differences, would facilitate classification problem. Experiments, performed on three different datasets show that one can benefit from the proposed approach, as it provides lower variability in classification performance at similar, high recognition rates.

Maze Exploration Algorithm for Small Mobile Platforms

Abstract An algorithm that enables efficient maze exploration is presented in the paper. The algorithm involves two phases: first the whole maze is explored in an ordered way and then, the shortest possible way out is determined. The algorithm has been derived in a way that combines main advantages of the two known labirynth-exploration algorithms: “Wall follower” and “Trémaux’s algorithm”. The algorithm has been tested using an autonomous vehicle, controlled by Arduino UNO, with two DC engines, ultrasonic sensors and gyroscope. It has been shown that the proposed approach provides a few crucial advantages with respect to already known solutions.

The Concept of Subsystem for Mobile Robot Management From the Operator Console

Abstract The authors in this publication present the concept of control system for the console operator to manage mobile robot platform and its components. Implemented functionality provides the ability to set the robot in different modes. The operator can control the state of robot using simple commands, and is also able to carry more complex tasks. The applied layered software architecture and Object-oriented programming (OOP) allows for easy management of software, maintenance, reusability and its further development.

The Concept of the Modularized Subsystem Performing Mobile Platform Navigation Tasks

Abstract Robotic vehicles autonomy is the subject of many researches performed by various institutions. There is growing tendency to conduct research in the field of autonomous mobile units, unmanned vehicles and robots moving without operator supervision. Authors of this article presents the concept of the modularized subsystem implementing the functionality of the mobile platform navigation in an unknown environment. The concept has been implemented in the real mobile robot.

Enhancement of Temperature Feature as a Result of Double Source Image Fusion

Abstract In this paper a double source images fusion algorithm is presented. Its task is to enhance temperature feature of objects located on the scene. Presented solution is design to be executed in real-time environment. It consists of three stages: in the first part the differences between acquired double source images are examined in order to determine their intersection. Then for each analysed image the contours of all objects located on the scene are determined. This operation is essential to solve disparition issue. In the last stage, based on determined contours and their match coefficient the images are fused. The enhanced temperature feature is displayed on one image acquired from day-light camera.