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.

Microstructure and mechanical properties analysis of β-tricalcium phosphate/carbon nanotubes scaffold based on rapid prototyping

β-TCP ceramic scaffolds were fabricated with selective laser sintering (SLS) in this work. Carbon nanotubes (CNTs) were mixed with porous β-TCP matrix to enhance the mechanical performance of the bone tissue engineering scaffolds. Scaffold reconstruction and microstructure analysis were fulfilled based on micro-computed tomography (Micro-CT) scanning data. Results show that the strength of scaffold mixed with 0.2% CNTs reaches 0.819 MPa which has been improved by 85.7% compared with that without CNTs. Micro-CT analysis shows that the scaffold has a good interconnectivity, and pore size mainly distributes in the two regions of 60-340 μm and 500-620 μm.

Review:Recent advances in nonlinear control technologies for shape memory alloy actuators

This paper reviews recent developments in nonlinear control technologies for shape memory alloy (SMA) actuators in robotics and their related applications. SMA possesses large hysteresis, low bandwidth, slow response, and non-linear behavior, which make them difficult to control. The fast response of the SMA actuator mostly depends upon, (1) type of controller, (2) rate of addition and removal of heat, and (3) shape or form of the actuator. Though linear controllers are more desirable than nonlinear ones, the review of literature shows that the results obtained using nonlinear controllers were far better than the former one. Therefore, more emphasis is made on the nonlinear control technologies taking into account the intelligent controllers. Various forms of SMA actuator along with different heating and cooling methods are presented in this review, followed by the nonlinear control methods and the control problems encountered by the researchers.

Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems

In this paper, neural networks are used to approximately solve the finite-horizon constrained input H-infinity state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game. The game value function is approximated by a neural network with time- varying weights. It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded L2 gain. The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line. The effectiveness of the method is shown on the Rotational/Translational Actuator benchmark nonlinear control problem.

APL-based flexibility analysis of manufacturing grid

With the characteristics of diversity, randomness, concurrency and decomposability, tasks in manufacturing field are very complicated, and so manufacturing grid (MG) should have considerable flexibility to deal with this problem. With the definition of node and arc, MG structure is converted into a small-world network. Given construction cost constraint, the problem of shortest task waiting time is transformed into the constrained optimization problem, and a corresponding ?exibility analysis model based on average path length (APL) is proposed, and the premise of arc-length and node-distance are defined. The results of application example show that the analysis model is effiective.

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.

Big Data Analysis in Smart Manufacturing: A Review

The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.

Photonic Crystal Fiber Based Sensor for Detecting Binary Liquid Mixture

Detection of liquid mixture of different volume ratio is very important in industrial purposes. The paper reports a sensing mechanism of binary liquid mixture for different volume fraction, based on the measurement of refractive index of the mixture. Here, a highly sensitive liquid filled core Photonic Crystal Fiber structure has been proposed to detect liquid mixture solution. Numerical investigation of the proposed structure is carried out by employing full vectorial Finite Element Method (FEM).

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.

Incentive regulation of banks on third party logistics enterprises in principal-agent-based inventory financing

In inventory financing,asymmetric information between banks and the third party logistics enterprises may incur moral risks,often causing economic losses of banks.To effectively solve this problem,a pure incentive scheme and a regulatory incentive scheme are designed with the principal-agent theory.By comparison,it is found that the pure incentive model is not applicable to practical conditions,and regulatory incentive model can not only solve practical problems substantially but also outbalance pure incentive model under certain conditions.The research results from example analysis given in this paper offer theoretical instruction and a practical method for effective regulation of banks.

Adaptive dynamic programming for online solution of a zero-sum differential game

This paper will present an approximate/adaptive dynamic programming(ADP) algorithm,that uses the idea of integral reinforcement learning(IRL),to determine online the Nash equilibrium solution for the two-player zerosum differential game with linear dynamics and infinite horizon quadratic cost.The algorithm is built around an iterative method that has been developed in the control engineering community for solving the continuous-time game algebraic Riccati equation(CT-GARE),which underlies the game problem.We here show how the ADP techniques will enhance the capabilities of the offline method allowing an online solution without the requirement of complete knowledge of the system dynamics.The feasibility of the ADP scheme is demonstrated in simulation for a power system control application.The adaptation goal is the best control policy that will face in an optimal manner the highest load disturbance.

Quantitative Progress Evaluation of Post-stroke Patients Using a Novel Bimanual Cable-driven Robot

Rehabilitation is the most effective way to reduce motor impairments in post-stroke patients.This process demands several hours with a specialized therapist.Given resources and personnel shortages,the literature reports a high interest in robotic assisted rehabilitation solutions.Recently,cable-driven robotic architectures are attracting significant research interest for post-stroke rehabilitation.However,the existing cable-driven robots are mostly unilateral devices allowing the rehabilitation only of the most affected limb.This leaves unaddressed the rehabilitation of bimanual activities,which are predominant within the common Activities of Daily Living(ADL).Thus,this paper presents a specific novel design to achieve bimanual rehabilitation tasks that has been named as BiCAR robot.Specifically,this paper provides a full insight on the BiCAR system as well as on its dedicated developed software BiEval.In particular,BiEval software has been developed as based on a serious game strategy and a virtual reality environment to track the patient exercising duration,motion ranges,speeds,and forces over time for achieving a quantitative assessment of the rehabilitation progress.Finally,the paper presents the BiCAR/BiEval capabilities by referring to a pilot Randomized Controlled Trial(RCT).The clinical trials have been used to validate the BiCAR/BiEval in terms of engineering feasibility and user acceptance to achieve an innovative cost-oriented integrated hardware/software device for the bimanual assistive rehabilitation of post-stroke patients.

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.