In recent years, many different concepts to manage smart distributed systems were proposed and solutions developed. Smart grids and the increasing influence of renewable sources on energy production lead to concerns about grid stability and load balance. Combined Heat and Power (CHP) generators coupled with solar or other renewable sources offer the opportunity to satisfy both electric and thermal power economically. Both electric and thermal demand and supply change continuously, and sources such as solar and wind are not dispatchable or accurately predictable. At the same time, it is essential to use the most efficient and cost effective sources to satisfy the demand. This problem has been studied at the University of Genoa (UNIGE), Italy, using different generators and energy storage device that can supply both electric and thermal energy to consumer buildings. Here the problem is formulated as a constrained Multi-Input Multi-Output (MIMO) problem with sometimes conflicting requests that must be satisfied. The results come from experiments carried out on the test rig located at the Innovative Energy System Laboratories (IESL) of the Thermochemical Power Group (TPG) of UNIGE. This paper compares three different control approaches to manage the distributed generation system: Simplified Management Control (SMC), Model Predictive Control (MPC), and Multi-Commodity Matcher (MCM). Control systems and their control actions are evaluated through economic and performance key indicators. (C) 2015 Elsevier Ltd. All rights reserved.

A Model Predictive Control (MPC) strategy has been suggested and simulated with the empirical dynamic data collected on the Hybrid Performance (HyPer) project facility installed at the National Energy Technology Laboratory (NETL), U.S. Department of Energy, in Morgantown, WV. The HyPer facility is able to simulate gasifier/fuel cell power systems and uses hardware-based simulation approach that couples a 'modified recuperated gas turbine cycle with hardware driven by a solid oxide fuel cell model. Dynamic data was collected by operating the HyPer facility continuously during five days. Bypass valves along with electric load of the system were manipulated and variables such as mass flow, turbine speed, temperature, pressure, among others were recorded for analysis. This work was developed by focusing on a multivariable recursive system identification structure fitting measured transient data. The results showed that real-time or online data is a viable means to provide a dynamic model for controller design.The excursion dynamic data collected between the setup changes of the experiments was processed off-line to determine the feasibility of applying an adaptive Model Predictive Control strategy. One of the strengths of MPC is that it can allow the designer to impose strict limits on inputs and outputs in order to keep the system within known safe bounds. Two identification structures, ARX and a State-Space model, were used to fit the measured data to dynamic models of the HyPer facility. The State-Space identification was very accurate with a second order model. Visual inspection of the tracking accuracy shows that the ARX approach was approximately as accurate as the State Space structure in its ability to reproduce measured data.However, by comparing the Loss Function and the FPE parameters, the State-Space approach gives better results.The MPC proved to be a good strategy to control the HyPer facility. The airflow valves and the electric load were used to control the turbine speed and the cathode airflow. For the ARX/State Space models, the MPC was very robust in tracking set -point variations. The anticipation feature of the MPC was revealed to be a good tool to compensate time delays in the output variables of the facility or to anticipate eventual set -point moves in order to achieve the objectives very quickly. The MPC also displayed good disturbance rejection on the output variables when the fuel flow was set to simulate FC heat effluent disturbances. Different off-design scenarios of operation have been tested to confirm the estimated implementation behavior of the plant-controller dynamics.

In this paper, an assistive robotic device that integrates the consideration of human motion and algorithm of multi-axial control using a dual twisted-string actuation was designed and fabricated. To derive arm trajectories, subjects with different heights were recruited to identify the impacts to arm motions with various physical conditions. In addition to the identification of arm movements, an adaptive robust control (ARC) algorithm was used to compensate for the rotational movement of shoulder joint with an external load attached to the palm of the fabricated robotic device. With the ARC controller, the robotic device demonstrates an excellent tracking and synchronization performance.

Multi-coordination of actuators for a highly integrated, tightly coupled advanced power system was evaluated using the Hybrid Performance (Hyper) project facility at the U.S. Department of Energy's National Energy Technology Laboratory (NETL). A two-by-two scenario in a fuel cell, turbine hybrid power system was utilized as a representative problem in terms of system component coupling during transients and setpoint changes. In this system, the gas turbine electric load is used to control the turbine speed, and the cold air bypass valve regulated fuel cell cathode mass flow.Perturbations in the turbine speed caused by variations in the waste heat from the fuel cell affect the cathode airflow, and the cold-air bypass control action required for constant cathode airflow strongly affects the turbine speed. Previous implementation of two single-input, single-output (SISO) controllers failed to provide acceptable disturbance rejection and setpoint tracking under these highly coupled conditions. A multiple-input, multiple-output (MIMO) controller based on the classic internal model control (IMC) concept was implemented and experimentally tested for the first time using the Hyper project facility.The state-space design of the MIMO configuration, the control law integration into the digital control platform, and the experimental comparison with the SISO case are presented.

In this work a new model for the twisted string actuation (TSA) was proposed and results were compared to the obtained data from various strings. Moreover, a wearable device was designed and fabricated, which was actuated by TSA. Joints were actuated by two antagonistic actuators. To simulate the motion of the elbow of the robot and validate the performance of the controller, a control test bed was designed which could capture the angle of the joint as well as the conflicting error due to antagonistic actuation. A master-slave architecture was adopted to control the joint, while an activity of daily living (ADL), adopted from previous work, was set as the desired trajectory. Test results are shown.

A data noise reduction model for a direct-fired fuel cell turbine hybrid power system was evaluated using a hardware-based simulation of an integrated gasifier/fuel cell/turbine hybrid cycle (IGFC), implemented through the Hybrid Performance (HyPer) project at the National Energy Technology Laboratory, U.S. Department of Energy (NETL). The Hyper facility is designed to explore dynamic operation of hybrid systems and quantitatively characterize such transient behaviorThe system is controlled by an embedded real-time control platform provided by Woodward Industrial Control. Every sensor is monitored by the platform, and an overall strategy drives the system from start-up to shut-down. Fuel is regulated by a valve which reacts based on the speed of the turbine. There are three optical encoder sensors which are used to monitor turbine speed, and the average of these three sensors is used as feedback for a PID controller, which works to regulate fuel consumption to the combustor The turbine speed has demonstrated fluctuation in certain conditions, which may be a result of data noise combined with a systemic instability in the flow to the turbine.This research introduces the method of Double Exponential Smoothing as it is applied to data noise reduction in an embedded control platform. An experimental test was conducted to evaluate the performance of the fuel valve speed control when filtered by a real-time Double Exponential Smoothing Algorithm. The results demonstrate, that when compared with traditional filtering techniques, Double Exponential Smoothing offers a significant improvement in both signal volatility and data latency.

In this research the twisting cable mechanism was employed to design and develop a light elbow assistive robot. A particular type of fishing line was experimentally tested for use as the strand material, and the behavior was compared to the proposed model. A correction parameter called effective diameter was derived to adapt the model to the experimental data. To ensure the consistency of the model, hysteresis of two-strand cables was tested. A curve was fitted to the experimental data and the most linear range was selected to be used in the mechanism. Moreover, a single degree of freedom elbow mechanism for flexion and extension was designed and constructed. Ultimately, to ensure the applicability of the mechanism, an ordinary Activity of Daily Living (ADL) was used and the angle of the twisted strand actuator motor as a function of the motor rotation was computed.

Initial startup of a direct-fired fuel cell turbine power system with equivalence ratio control using cathode air bypass valves to minimize thermal shock to the fuel cell was evaluated using the Hybrid Performance (Hyper) project hardware-based simulation facility at the U.S. Department of Energy, National Energy Technology Laboratory. The turbine in the system was started with the minimum possible airflow through the fuel cell cathode from a cold condition using two bypass valves to mitigate thermal shock failure in the fuel cell. The limitation of bypass flow was set by air requirements to maintain a combustor equivalence ratio below 0.6 during turbine windup. A ID distributed fuel cell model operating in real time was used to produce individual cell transient temperature profiles during the course of the turbine start. The results provide insight into the procedural requirements of starting a fully coupled hybrid system.

In this study, a twisted string actuation mechanism was used to develop an assistive robotic system to assist post-stroke patients to regain mobility. The robotic manipulator used in therapy systems can apply forces to aid in specific tasks during goal-directed movements. The device was designed for the one-degree-of-freedom movement of patients' elbow joint. To compensate the moving trajectory, the dynamics of both actuation system and robotic mechanism were identified. An angular trajectory of healthy male adults was used to drive the movement of the proposed robotic system to mimic the locomotion of real human arms. Different physical conditions, including induced torque disturbance and applicable range of actuation, were discussed. A high-gain controller with the consideration of robustness and time-delay to the torque disturbance was synthesized to ensure the control performance.

Drawing of glass fibers is an important industrial process used for manufacture of a variety of materials. A variety of furnaces and machines exist for manufacture of glass fibers, but all share similar problems with control of the fiber diameter and breakage of the fibers during the extrusion process. In many cases, control systems are not configured to monitor the most critical process variable - temperature of molten glass in the furnace, but instead use only furnace crown temperature. This work seeks to develop an estimator-based LQR control model to monitor molten glass temperature and winder speed for good production quality. When a disturbance in ambient temperature and/or molten glass depth happens, the control system still performs as expected.

Hybrid generation systems have been extensively modeled as a first step toward the development of automatic controls for the system. In most cases, it is impossible to validate mathematical models against real hardware because only a handful of hardware systems exist in the world. Data taken from the existing hardware has demonstrated significant nonlinearity, complex coupling between controlled variables, and sometimes non-intuitive behavior. This work exploits the capability of the HyPer hardware test bed at the National Energy Technology Laboratory (NETL) to generate data from a real recuperated gas turbine coupled with hardware simulations of a fuel cell cathode and appropriate ancillary equipment. Prior work has characterized the system only over a limited range of its operating envelope, due to the inability to manipulate multiple control inputs simultaneously. The work presented here fills the gaps using data from a 34 factorial experiment to generate quasi-continuous response surfaces describing the operating state space of the HyPer system. Polynomial correlation functions have been fitted to the data with excellent agreement. Relationships between the control inputs and critical state variables such as cathode mass flow, cathode temperature, turbine inlet and exhaust temperatures and other key system parameters are presented.

A full factorial experimental design and a replicated fractional factorial design were carried out using the Hybrid Performance (HyPer) project facility installed at the National Energy Technology Laboratory (NETL), U.S. Department of Energy to simulate gasifer/fuel cell/turbine hybrid power systems. The HyPer facility uses hardware in the loop (HIL) technology that couples a modified recuperated gas turbine cycle with hardware driven by a solid oxide fuel cell model. A 34 full factorial design (FFD) was selected to study the effects of four factors: cold-air, hot-air, bleed-air bypass valves, and the electric load on different parameters such as cathode and turbine inlet temperatures, pressure and mass flow. The results obtained, compared with former results where the experiments were made using one-factor-at-a-time (OFAT), show that no strong interactions between the factors are present in the different parameters of the system. This work also presents a fractional factorial design (ffd) 3(4-2) in order to analyze replication of the experiments. In addition, a new envelope is described based on the results of the design of experiments (DoE), compared with OFAT experiments, and analyzed in an off-design integrated fuel cell/gas turbine framework. This paper describes the methodology, strategy, and results of these experiments that bring new knowledge concerning the operating state space for this kind of power generation system.

Control and management of cathode airflow in a solid oxide fuel cell gas turbine hybrid power system was analyzed using the Hybrid Performance (HyPer) hardware simulation at the National Energy Technology (NETL), U.S. Department of Energy. This work delves into previously unexplored operating practices for HyPer, via simultaneous manipulation of bypass valves and the electric load on the generator. The work is preparatory to the development of a Multi-Input, Multi-Output (MIMO) controller for HyPer. A factorial design of experiments was conducted to acquire data for 81 different combinations of the manipulated variables, which consisted of three air flow control valves and the electric load on the turbine generator. From this data the response surface for the cathode airflow with respect to bypass valve positions was analyzed. Of particular interest is the control of airflow through the cathode during system startup and during large load swings. This paper presents an algorithm for controlling air mass flow through the cathode based on a modification of the steepest ascent method.

Video target tracking is being a hot research subject applied in solving a wide range of real world electronic monitoring problems. In this work, a predictive tracking approach is presented by adopting control-based full state observer (FSO) tactics. To remedy the tracking precision inadequacy compromised by using only a FSO, a lowpass filter is designed to correct the tracking trajectory as the case may be. The major merit of the proposed approach lies with avoidance of hypothesis for the stochastic noise uncertainties existing in process' dynamics, as otherwise required with Kalman filtering solution. The presented approach is engineered based on an articulate real-world video sequence set, and later tested on four additional real-world video sequence sets. The results suggest that the tracking quality utilizing the proposed approach is not inferior to that using Kalman filter method, and may thus be rendered as an alternative video target tracking technique.

Management of air flow through the cathode of a hybrid Solid Oxide Fuel Cell/Gas Turbine generation system is of critical importance for the survival of the fragile fuel cell. The cell must be protected from excessive thermal gradients within each cell/stack and from pressure differences between the anode and cathode. While significant modeling of hybrid system performance has been done for the steady state case, only modest attention has been given to startup and shutdown of a hybrid system. Various researchers have performed modeling studies on SOFC during startup, and have concluded that thermal ramp-up times can require anywhere from less than one hour to more than four hours to avoid thermal shock and potential destruction of the fuel cells. For hybrid systems employing single spool turbine/compressor systems, gradual ramping will be difficult because the rotating components must be brought up to full operating speed quickly to avoid stalling the compressor. This paper proposes a strategy for accommodation of the conflicting startup constraints using both experimental data from the NETL HyPer system and simulation approaches.

A nonlinear steady-state thermodynamic model was coupled with linearized dynamic transfer functions to achieve a dynamic description of the NETL HyPer Fuel Cell Gas Turbine (FC/GT) power plant. Nonlinear dynamic models insure accuracy in modeling steady-state behavior over a wide range of operation, but such models are often complex and difficult to implement in real-time using conventional control systems equipment. Conversely, the linearized models provide the ability to predict transient behavior upon which dynamic control systems can be constructed, but are valid only about a narrow operating point. In systems with one or two state variables, it is relatively straightforward to construct controllers that use gain scheduling schemes. But the HyPer system contains many coupled state variables and high degrees of nonlinearity. A method called Real-Time Piecewise Linear Dynamic Modeling (RPLDM) has been implemented to provide both modeling accuracy and real-time performance for the HyPer system over a multi-dimensional hypersurface. Both the nonlinear and the linear constituent models were constructed based on experimental data collected in tests performed on the HyPer system. The models presently consider only the cathode circuit of the fuel cell and contain a recuperated gas turbine system equipped with an electric generator, a simulated fuel cell cathode and various bypass valves for thermal management and system control. The key variables of air temperature, air pressure and mass flow to the cathode of the fuel cell and the turbomachinery have been predicted to within 2% of measured values. This paper presents the modeling technique and comparisons of the model output with experimental data.

Drawing of glass fibers is an important industrial process used for the manufacture of a variety of materials ranging from optical communications cables to fiber filter media. A variety of machines exist for performing the drawing function, but all share similar problems with control of the fiber diameter and breakage of the fibers during the extrusion process. In many cases, control systems are not configured to monitor the most critical process variables, but instead use only furnace crown temperature. Upsets in disturbance variables such as ambient temperature are compensated manually by operators, usually only after significant problem with fiber breakage occur. This work seeks to provide a better understanding of the effects of important process variables on the key quality and production parameters such as fiber diameter and production rates.

This paper presents the study of the effect variations in the heat effluence from a solid oxide fuel cell (SOFC) has on a gas turbine hybrid configuration. The SOFC is simulated through hardware at the U.S. Department of Energy, National Energy Technology Laboratory (NETL). The gas turbine, compressor, recuperative heat exchanger, and other balance of plant components are represented by actual hardware in the Hybrid Performance Test Facility at NETL. Fuel cell heat exhaust is represented by a combustor that is activated by a fuel cell model that computes energy release for various sensed system states System structure is derived by means of frequency response data generated by the sinusoidal oscillation of the combustor fuel valve over a range of frequencies covering three orders of magnitude. System delay and order are obtained from Bode plots of the magnitude and phase relationships between input and output parameters. Transfer functions for mass flow, temperature, pressure, and other states of interest are derived as a function of fuel valve flow, representative of fuel cell thermal effluent. The Bode plots can validate existing analytical transfer functions, provide steady state error detection, give a stability margin criterion for the fuel valve input, estimate system bandwidth, identify any nonminimum phase system behavior, pinpoint unstable frequencies, and serve as an element of a piecewise transfer function in the development of an overall transfer function matrix covering all system inputs and outputs of interest. Further loop shaping techniques and state space representation can be applied to this matrix in a multivariate control algorithm.

Classifying the texture of granules in 2D images has aroused manifold research attention for its technical challenges in image processing areas. This letter presents an aggregate texture identification approach by jointly using Gray Level Co-occurrence Probability (GLCP) and BP neural network techniques. First, up to 8 GLCP-associated texture feature parameters are defined and computed, and these consequent parameters next serve as the inputs feeding to the BP neural network to calculate the similarity to any of given aggregate texture type. A finite number of aggregate images of 3 kinds, with each containing specific type of mineral particles, are put to the identification test, experimentally proving the feasibility and robustness of the proposed method.

The National Energy Technology Laboratory (NETL) has developed a hardware simulator to test the operating characteristics of Solid Oxide Fuel Cell/Gas Turbine (SOFC/GT) hybrid systems. The Hybrid Performance (HyPer) simulator has been described previously, and has contributed to the understanding of SOFC/GT system operation. HyPer contains not only the requisite elements of gas turbine/compressor/generator, recuperator, combustor, and associated piping, but also several air flow control valves that are proposed as system control mechanisms. It is necessary to know how operation of these valves affects the various entities such as cathode air flow, turbine speed, and various temperatures important to the safe and efficient operation of fuel cell/gas turbine hybrid systems. To determine the interactions among key variables, a series of experiments was performed in which the effect of modulating each of the key manipulated variables was recorded. This document outlines the test methods used and presents some of the data from those tests, along with analysis and interpretation of that data in the context of control system design.