Design and Robust Performance Evaluation of a Fractional Order PID Controller Applied to a DC Motor

This paper proposes a methodology for the quantitative robustness evaluation of PID controllers employed in a DC motor. The robustness analysis is performed employing a 2~3 factorial experimental design for a fractional order proportional integral and derivative controller(FOPID), integer order proportional integral and derivative controller(IOPID)and the Skogestad internal model control controller(SIMC). The factors assumed in experiment are the presence of random noise,external disturbances in the system input and variable load. As output variables, the experimental design employs the system step response and the controller action. Practical implementation of FOPID and IOPID controllers uses the MATLAB stateflow toolbox and a NI data acquisition system. Results of the robustness analysis show that the FOPID controller has a better performance and robust stability against the experiment factors.

Crack Detection and Localization on Wind Turbine Blade Using Machine Learning Algorithms: A Data Mining Approach

Wind turbine blades are generally manufactured using fiber type material because of their cost effectiveness and light weight property however,blade get damaged due to wind gusts,bad weather conditions,unpredictable aerodynamic forces,lightning strikes and gravitational loads which causes crack on the surface of wind turbine blade.It is very much essential to identify the damage on blade before it crashes catastrophically which might possibly destroy the complete wind turbine.In this paper,a fifteen tree classification based machine learning algorithms were modelled for identifying and detecting the crack on wind turbine blades.The models are built based on computing the vibration response of the blade when it is excited using piezoelectric accelerometer.The statistical,histogram and ARMA methods for each algorithm were compared essentially to suggest a better model for the identification and localization of crack on wind turbine blade.

Comparative Study on Tree Classifiers for Application to Condition Monitoring ofWind Turbine Blade through Histogram Features Using Vibration Signals: A Data-Mining Approach

Wind energy is considered as a alternative renewable energy source due to its low operating cost when compared with other sources.The wind turbine is an essential system used to change kinetic energy into electrical energy.Wind turbine blades,in particular,require a competitive condition inspection approach as it is a significant component of the wind turbine system that costs around 20-25 percent of the total turbine cost.The main objective of this study is to differentiate between various blade faults which affect the wind turbine blade under operating conditions using a machine learning approach through histogram features.In this study,blade bend,hub-blade loose connection,blade erosion,pitch angle twist,and blade cracks were simulated on the blade.This problem is formulated as a machine learning problem which consists of three phases,namely feature extraction,feature selection and feature classification.Histogram features are extracted from vibration signals and feature selection was carried out using the J48 decision tree algorithm.Feature classification was performed using 15 tree classifiers.The results of the machine learning classifiers were compared with respect to their accuracy percentage and a better model is suggested for real-time monitoring of a wind turbine blade.

An execution control method for the Aerostack aerial robotics framework

Execution control is a critical task of robot architectures which has a deep impact on the quality of the final system. In this study, we describe a general method for execution control, which is a part of the Aerostack software framework for aerial robotics, and present technical challenges for execution control and design decisions to develop the method. The proposed method has an original design combining a distributed approach for execution control of behaviors(such as situation checking and performance monitoring) and centralizes coordination to ensure consistency of the concurrent execution. We conduct experiments to evaluate the method. The experimental results show that the method is general and usable with acceptable development efforts to efficiently work on different types of aerial missions. The method is supported by standards based on a robot operating system(ROS) contributing to its general use, and an open-source project is integrated in the Aerostack framework. Therefore, its technical details are fully accessible to developers and freely available to be used in the development of new aerial robotic systems.

TML:a language to specify aerial robotic missions for the framework Aerostack

Purpose–The purpose of this paper is to describe the specification language TML for adaptive mission plans that the authors designed and implemented for the open-source framework Aerostack for aerial robotics.Design/methodology/approach–The TML language combines a task-based hierarchical approach together with a more flexible representation,rule-based reactive planning,to facilitate adaptability.This approach includes additional notions that abstract programming details.The authors built an interpreter integrated in the software framework Aerostack.The interpreter was validated with flight experiments for multi-robot missions in dynamic environments.Findings–The experiments proved that the TML language is easy to use and expressive enough to formulate adaptive missions in dynamic environments.The experiments also showed that the TML interpreter is efficient to execute multi-robot aerial missions and reusable for different platforms.The TML interpreter is able to verify the mission plan before its execution,which increases robustness and safety,avoiding the execution of certain plans that are not feasible.Originality/value–One of the main contributions of this work is the availability of a reliable solution to specify aerial mission plans,integrated in an active open-source project with periodic releases.To the best knowledge of the authors,there are not solutions similar to this in other active open-source projects.As additional contributions,TML uses an original combination of representations for adaptive mission plans(i.e.task trees with original abstract notions and rule-based reactive planning)together with the demonstration of its adequacy for aerial robotics.