Compensation of voltage disturbances using PEMFC supported Dynamic Voltage Restorer

The continuity of supply and quality of power are the two main significant aspects of today’s power delivery system. The Dynamic Voltage Restorer (DVR) is a series connected custom power device which improves the quality of power delivered to the consumers. This paper deals with the effectual exploitation of DVR for interconnecting the proton exchange membrane fuel cell (PEMFC) stack to the grid based on optimized proportional integral (PI) and fuzzy logic (FL) Controller. The real coded Genetic algorithm (GA) is used to optimize the PI controller parameters. The PEMFC operated boost converter is used to boost up the fuel cell output voltage to balance the DC side necessities of the voltage source converter (VSC). The proposed DVR provides balanced and unbalanced voltage sag/swell compensation, harmonic reduction as well as an active power injection to the grid. The designed method also protects the sensitive loads from source side power quality disturbances including short term interruption. In addition, the harmonic compensation performance of the proposed work is validated by comparing with the results of the H^∞ controller based DVR under medium level voltage condition. The simulation results from MATLAB/SIMULINK are discussed to prove the effectiveness of the planned method.     

Hydroxyapatite cages with aligned pores for bone grafting – Seeding of human osteoblast-like cells in vitro and their response in dynamic culture mode

Hydroxyapatite (HA) is a highly regarded synthetic bone graft material. Porous HA ceramics blocks are used to substitute harvested natural bone grafts. Being similar to bone mineral, HA material integrates with the host bone through surface osteointegration and slowly resorb along with the natural bone remodeling process. The blocks in use currently have random and tortuous pore structures. The present work explores the usefulness of cage-like HA ceramic design with end-to-end open pores, with the help of in vitro cell culture methods. Such a structure, on implantation, will take up the blood factors and cells and host the bone remodeling process inside the bulk of the cage, leading to early healing. In the study, HA samples with aligned through-pores were prepared and explored in vitro, with a focus on how the pores host the cells inside and to what level the cells maintain their activity. Human osteoblast-like cells (HOS) were used, at different seeding and culturing approaches. Cell seeding was done through (i) conventional large volume cell suspension, (ii) a confined mini chamber with a limited volume of cell suspension, and (iii) placing a concentrated drop of cell suspension directly on top of the scaffold. The third approach gave the best cell adhesion and proliferation, and hence used for further explorations. A dynamic culture system was designed in-house by bifurcating the cell culture wells using vertical inserts, holding the samples horizontally with their ends open to both sides, and making the media flow across using a rocker platform. The HOS cell adhesion, viability and proliferation were tested in the HA cages, in static and dynamic culture conditions, with conventional porous ceramics as the control. The cell infiltration was deeper and the cell viability over a period of 7 days was significantly higher in dynamic culture conditions in the test samples.     

When Does Communication Learning Need Hierarchical Multi-Agent Deep Reinforcement Learning

Abstract

Multi-agent systems need to communicate to coordinate a shared task. We show that a recurrent neural network (RNN) can learn a communication protocol for coordination, even if the actions to coordinate are performed steps after the communication phase. We show that a separation of tasks with different temporal scale is necessary for successful learning. We contribute a hierarchical deep reinforcement learning model for multi-agent systems that separates the communication and coordination task from the action picking through a hierarchical policy. We further on show, that a separation of concerns in communication is beneficial but not necessary. As a testbed, we propose the Dungeon Lever Game and we extend the Differentiable Inter-Agent Learning (DIAL) framework. We present and compare results from different model variations on the Dungeon Lever Game.     

On one boundary problem for nonlinear stationary controlled system

The object of study is nonlinear stationary controlled system of ordinary differential equations with constant disturbance in the right part. The problem of constructing the synthesising control function providing the transfer of this system from the initial state to the origin is considered. The sufficiently simple for numerical implementation algorithm of solution of the above-mentioned problem is obtained. It is shown that for local null controllability of the considered system, it is sufficient that the conditions of the Kalman's type were satisfied. In addition, the estimates restricting the choice of initial conditions and external disturbances under which the transfer is guaranteed are obtained. The main idea of the method of construction of the desired control function consists in reducing the original problem to stabilisation of a special kind linear non-stationary system and solving the Cauchy problem for an auxiliary system of ordinary differential equations closed by stabilising control. The simplicity of the realisation of this algorithm is determined by the construction of the auxiliary system and its stabilisation that could be obtained by analytical methods. The effectiveness of the method is illustrated by solving the problem of crane control and its numerical simulation.     

Usability of a cloud-based collaborative learning framework to improve learners’ experience

Computer-Supported Collaborative Learning (CSCL) is concerned with how Information and Communication Technology (ICT) might facilitate learning in groups which can be co-located or distributed over a network of computers such as Internet. CSCL supports effective learning by means of communication of ideas and information among learners, collaborative access of essential documents, and feedback from instructors and peers on learning activities. As the cloud technologies are increasingly becoming popular and collaborative learning is evolving, new directions for development of collaborative learning tools deployed on cloud are proposed. Development of such learning tools requires access to substantial data stored in the cloud. Ensuring efficient access to such data is hindered by the high latencies of wide-area networks underlying the cloud infrastructures. To improve learners’ experience by accelerating data access, important files can be replicated so a group of learners can access data from nearby locations. Since a cloud environment is highly dynamic, resource availability, network latency, and learner requests may change. In this paper, we present the advantages of collaborative learning and focus on the importance of data replication in the design of such a dynamic cloud-based system that a collaborative learning portal uses. To this end, we introduce a highly distributed replication technique that determines optimal data locations to improve access performance by minimizing replication overhead (access and update). The problem is formulated using dynamic programming. Experimental results demonstrate the usefulness of the proposed collaborative learning system used by institutions in geographically distributed locations.     

An energy-saving nonlinear position control strategy for electro-hydraulic servo systems

The electro-hydraulic servo system (EHSS) demonstrates numerous advantages in size and performance compared to other actuation methods. Oftentimes, its utilization in industrial and machinery settings is limited by its inferior efficiency. In this paper, a nonlinear backstepping control algorithm with an energy-saving approach is proposed for position control in the EHSS. To achieve improved efficiency, two control valves including a proportional directional valve (PDV) and a proportional relief valve (PRV) are used to achieve the control objectives. To design the control algorithm, the state space model equations of the system are transformed to their normal form and the control law through the PDV is designed using a backstepping approach for position tracking. Then, another nonlinear set of laws is derived to achieve energy-saving through the PRV input. This control design method, based on the normal form representation, imposes internal dynamics on the closed-loop system. The stability of the internal dynamics is analyzed in special cases of operation. Experimental results verify that both tracking and energy-saving objectives are satisfied for the closed-loop system.     

Delay compensated control of the Stefan problem and robustness to delay mismatch

This paper presents a control design for the one‐phase Stefan problem under actuator delay via a backstepping method. The Stefan problem represents a liquid‐solid phase change phenomenon which describes the time evolution of a material's temperature profile and the interface position. The actuator delay is modeled by a first‐order hyperbolic partial differential equation (PDE), resulting in a cascaded transport‐diffusion PDE system defined on a time‐varying spatial domain described by an ordinary differential equation (ODE). Two nonlinear backstepping transformations are utilized for the control design. The setpoint restriction is given to guarantee a physical constraint on the proposed controller for the melting process. This constraint ensures the exponential convergence of the moving interface to a setpoint and the exponential stability of the temperature equilibrium profile and the delayed controller in the norm. Furthermore, robustness analysis with respect to the delay mismatch between the plant and the controller is studied, which provides analogous results to the exact compensation by restricting the control gain.     

Improved Input and State Estimation for Linear Stochastic Systems with Direct Feedthrough

This paper is concerned with the problem of joint input and state estimation for linear stochastic systems with direct feedthrough. Based on the fact that each unknown input between any two time steps is always bounded, a novel improved algorithm is proposed. Compared with existing results, this algorithm can effectively enhance estimation accuracy. Moreover, the stability of the algorithm is also discussed. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed approach.     

Temperature- and frequency-dependent hysteresis scaling behaviour and phase transitions in a [001]c 0.73Pb(Mg1/3Nb2/3)O3-0.27PbTiO3 single crystal

The dielectric properties and bipolar polarization-electric field (P-E) and strain-electric field (S-E) dynamic hysteresis of a relaxor [001]_c 0.73Pb(Mg_1/3Nb_2/3)O₃-0.27PbTiO₃ (PMN-0.27PT) single crystal were investigated to reveal more details of the temperature-induced phase transitions. Different linear scaling relations for ferroelectric hysteresis area <A>, coercive field E_c, saturation polarization P_s and remnant polarization P_r versus temperature τ were measured in different temperature regions. For each measurement frequency f, all hysteresis parameters were found to decrease linearly with temperature in the temperature range of the single rhombohedral (R) phase or tetragonal (T) phase, and the rate of decrease in the T phase was observed to be much larger than the corresponding rate in the R phase. In the temperature range near the R-T phase transition, the exponent α in the power law <A>∝f ^α for the R phase was found to be smaller than that for the T phase, and the magnitude of α depended strongly on temperature when the crystal was in the R-T coexisting phase state. Our experimental and theoretical results indicate that the difference in the activation energy and dipole moment in the R and T phases may lead to the observed discrepancy for the P-E and S-E hysteresis behaviour in different temperature regions.     

Artificial bee colony based algorithm for maximum power point tracking (MPPT) for PV systems operating under partial shaded conditions

Artificial bee colony (ABC) algorithm has several characteristics that make it more attractive than other bio-inspired methods. Particularly, it is simple, it uses fewer control parameters and its convergence is independent of the initial conditions. In this paper, a novel artificial bee colony based maximum power point tracking algorithm (MPPT) is proposed. The developed algorithm, does not allow only overcoming the common drawback of the conventional MPPT methods, but it gives a simple and a robust MPPT scheme. A co-simulation methodology, combining Matlab/Simulink™ and Cadence/Pspice™, is used to verify the effectiveness of the proposed method and compare its performance, under dynamic weather conditions, with that of the Particle Swarm Optimization (PSO) based MPPT algorithm. Moreover, a laboratory setup has been realized and used to experimentally validate the proposed ABC-based MPPT algorithm. Simulation and experimental results have shown the satisfactory performance of the proposed approach.