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In wind tunnel tests, the problems arise of determining the wall pressure distribution, calculating the wall contour, and controlling adjustment of the walls. This report shows how these problems have been solved for the high speed wind tunnel of the Technical University of Berlin.
A crucial requirement of the protection system for multi-terminal high-voltage DC (MTDC) transmission is that it is capable of selectively isolating the faulty area from the healthy part of the network using DC circuit breakers (DCCBs), while ensuring continuous operation of converter stations in the healthy part of the network. But in the half-bridge modular multilevel converters (HB-MMCs) based MTDC system, since HB-MMCs do not have fault current absorption capability, when a DC fault occurs, the rising fault currents can quickly reach the blocking threshold within a few milliseconds and disrupt the operation of the MMCs in the healthy part. To facilitate fault-ride-through capability of MTDC system, large DC reactors are often considered in series with DCCBs to reduce the rate of rise of the fault current and prevent blocking the MMCs in the healthy part of the DC networks; however, large DC reactors prohibitively increase the cost of the system, introduce stability issues and can create post-fault oscillations. This paper presents an adaptive fault current-limiting control method for MMCs to avoid their blocking and enable the continuous operation of the healthy part of MTDC systems. The method contains two parts: The first part is based on circulating current feedforward control, which emulates virtual reactors in each arm of an MMC, and is immediately activated when the fault current starts to increase to reduce the rate of rise of the fault current. The second part temporarily bypasses all the submodules when the fault current exceeds a preset threshold, complementing the fault current-limiting effect of the first part. Neither part requires fault detection signals, and they are automatically activated during faults. Simulation case studies of a four-terminal bipolar MMC-based high-voltage DC system are presented to demonstrate the effectiveness of the proposed control methods.
The NASA highly integrated digital electronic control (HIDEC) program is structured to conduct flight research into the benefits of integrating an aircraft flight control system with the engine control system. A brief description of the HIDEC system installed on an F-15 aircraft is provided. The adaptive engine control system (ADECS) mode is described in detail, together with simulation results and analyses that show the significant excess thrust improvements achievable with the ADECS mode. It was found that this increased thrust capability is accompanied by reduced fan stall margin and can be realized during flight conditions where engine face distortion is low. The results of analyses and simulations also show that engine thrust response is improved and that fuel consumption can be reduced. Although the performance benefits that accrue because of airframe and engine control integration are being demonstrated on an F-15 aircraft, the principles are applicable to advanced aircraft such as the advanced tactical fighter and advanced tactical aircraft.
An eclectic approach was taken to a study of the integration of digital flight and propulsion controls for helicopters. The basis of the evaluation was the current Gen Hel simulation of the UH-60A Black Hawk helicopter with a model of the GE T700 engine. A list of flight maneuver segments to be used in evaluating the effectiveness of such an integrated control system was composed, based on past experience and an extensive survey of the U.S. Army Air-to-Air Combat Test data. A number of possible features of an integrated system were examined and screened. Those that survived the screening were combined into a design that replaced the T700 fuel control and part of the control system in the UH-60A Gen Hel simulation. This design included portions of an existing pragmatic adaptive fuel control designed by the Chandler-Evans Company and an linear quadratic regulator (LQR) based N(p) governor designed by the GE company, combined with changes in the basic Sikorsky Aircraft designed control system. The integrated system exhibited improved total performance in many areas of the flight envelope.
Report describes experimental study of use of active structural members to excite and measure vibrations as small as microns in truss structure. Part of continuing effort to develop active vibration-suppressing control system adapting itself to changing and/or partly unknown dynamical characteristics of truss structure in outer space. Some aspects of control concept and potential terrestrial applications described in "Two Techniques For Suppressing Vibrations In Structures" (NPO-17889).
The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: Overview of Adaptive Systems and V&V Processes/Methods.
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Inactivity, like bed rest (BR), causes insulin resistance (IR) and bone loss even in healthy subjects. High protein intake seems to mitigate this IR but might exacerbate bone loss. We hypothesized that high protein intake (animal:vegetable protein ratio: 60:40), isocaloric, compared to the control group plus high potassium intake would prevent IR without affecting bone turnover. After a 20-day ambulatory adaptation to controlled confinement and diet, 16 women participated in a 60-day, 6 deg head-down-tilt BR and were assigned randomly to one of the two groups. Control subjects (CON, n=8) received 1g/kg body mass/d dietary protein. Nutrition subjects (NUT, n=8) received 1.45g/kg body mass/d dietary protein plus 7.2g branched chain amino acids per day during BR. All subjects received 1670 kcal/d. Bed rest decreased glucose disposal by 35% (p<0.05) in CON. Isocaloric high protein intake prevented insulin resistance, but exacerbated bed rest induced increase in bone resorption markers C-telopeptide (> 30%) and Ntelopeptide (>20%) (both: p<0.001). Bone formation markers were unaffected by high protein intake. We conclude from these results that high protein intake might positively affect glucose tolerance, but might also foster bone loss. Further long-duration studies are mandatory before high protein intake for diabetic patients, who have an increased fracture risk, might be recommended.
A prototype of a versatile, observer-based magnetic bearing controller for aerospace flywheels was successfully developed and demonstrated on a magnetic bearing test rig (see the photograph) and an actual flywheel module. The objective of this development included a fast, yet low risk, control development process, and a robust, high-performance controller for a large variety of flywheels. This required a good system model, an efficient development procedure, and a model-based controller that addressed the key problems associated with flywheel and bearing imbalance, sensor error, and vibration. The model used in this control development and tuning procedure included the flexible rotor dynamics and motor-induced vibrations. Such a model was essential for low-risk scheduling of speed-dependent control parameters and for reliable evaluation of novel control strategies. The successfully tested control prototype utilized an extended Kalman filter to estimate the true rotor principal-axis motion from the raw sensor position feedback. For control refinement, the extended Kalman filter also estimated and eliminated the combined effects of mass-imbalance and sensor runouts from the input data. A key advantage of the design based on the extended Kalman filter is its ability to accurately estimate both the rotor's principal-axis position and gyroscopic rates with the least amount of phase lag. This is important for control parameter scheduling to dampen the gyroscopic motions. Because of large uncertainties in the magnetic bearing and imbalance characteristics, this state-estimation scheme alone is insufficient for containing the rotor motion within the desired 1-mil excursion radius. A nonlinear gain adjustment based on an estimation of the principal-axis orbit size was needed to provide a coarse (nonoptimal), but robust, control of the orbit growth. Control current minimization was achieved with a (steepest gradient) search of synchronous errors in the principal-axis position input data. Actual flywheel tests of this observer-based controller (developed entirely in-house) at the NASA Glenn Research Center showed that the model correctly predicted the rotor orbit growth as a function of rotational speed, and it demonstrated the capability of gain adjustments to arrest this growth. Data from these tests on an actual flywheel module spun to 26,000 rpm proved that the controller was able to contain the shaft motion to within much less than 0.5 mils of radial excursion with axis currents less than 300 mA in root-mean-square estimate. The test speed range was limited because of thermal expansion concerns for this particular flywheel unit, not because of any deficiency in the controller. Simulations for this unit indicated that the controller should be robust up to its top operating speed of 60,000 rpm. Aside from these important achievements, and most significantly, it took less than 1 week to adapt this controller from the simple test rig to the actual flywheel and to demonstrate full five-axis levitation and control. This demonstration showed that both the controller and the model-based development and tuning framework are easily adaptable to a wide range of rotors and bearing configurations and, hence, are capable of reducing design risks and costs for many future flywheel technology developments.
This talk will present overviews of various intelligent control technologies currently being developed and studied at the NASA Ames Research Center as applicable to Unmanned Aerial Vehicles (UAVs), Mars flyers, and to the next generation of flight controllers for manned aircraft. The approaches being examined include: (a) direct adaptive dynamic inverse controller, (b) adaptive critic-based optimal trajectory generator; (c) optimal allocation technique based on linear programming; (4) immunized maneuvering using autopilot building blocks. These approaches can utilize, but do not require, fault detection and isolation information. Piloted and unmanned simulation studies are performed to examine if the intelligent flight control techniques adequately: 1) match flying qualities of modem fly-by-wire flight controllers under nominal conditions; 2) improve performance under failure conditions when sufficient control authority is available; and 3) achieve intelligent maneuvering capabilities for unmanned vehicles. Results obtained so far will be presented and discussed.
The Dryden Flight Research Center V&V working group and NASA Ames Research Center Automated Software Engineering (ASE) group collaborated to prepare this report. The purpose is to describe V&V processes and methods for certification of neural networks for aerospace applications, particularly adaptive flight control systems like Intelligent Flight Control Systems (IFCS) that use neural networks. This report is divided into the following two sections: 1) Overview of Adaptive Systems; and 2) V&V Processes/Methods.
Recent developments in the renewable energy sector have seen an unprecedented growth in residential photovoltaic (PV) installations. However, high PV penetration levels often lead to overvoltage problems in low-voltage (LV) distribution feeders. Smart inverter control such as active power curtailment (APC)-based overvoltage control can be implemented to overcome these challenges. The APC technique utilizes a constant droop-based approach which curtails power rigidly, which can lead to significant energy curtailment in the LV distribution feeders. In this paper, different variations of the APC technique with linear, quadratic, and exponential droops have been analyzed from the point-of-view of energy curtailment for a LV distribution network in North America. Further, a combinatorial approach using various droop-based APC methods in conjunction with adaptive dynamic programming (ADP) as a supplementary control scheme has also been proposed. The proposed approach minimizes energy curtailment in the LV distribution network by adjusting the droop gains. Simulation results depict that ADP in conjunction with exponential droop reduces the energy curtailment to approximately 50% compared to using the standard linear droop.
Here this paper presents a buck converter with sub-nW quiescent power, high efficiency, and a wide dynamic range for ultra-low-power (ULP) IoT SoCs. To optimize the SoC power consumption, the buck converter supports fast dynamic voltage and frequency scaling (DVFS) and enables fast load-transient response (FLTR) through asynchronous control. In addition, the buck converter is fully self-contained with all features integrated on chip including a proposed adaptive deadtime controller. Fabricated in 65nm CMOS, measurement results show the buck converter has an 802pW quiescent power at 1.5V input voltage and a 93% peak efficiency. The measured dynamic range is from 0.5nW to 2.75mW, which is over 6 orders of magnitude. The measured voltage droop is 54mV for a 45nA-to-1mA load current step thanks to the asynchronous load-transient detector. The buck converter achieves the highest efficiency and widest dynamic range among all the state-of-the-art sub-nW switching voltage regulators, which makes it well suited for power management in ULP SoCs.
Proportional control circuit supplies a heater with full current, from a pulsating dc source, to a present temperature and then switches to proportional control for fine temperature regulation. Two resistors and a diode are added to the existing circuit. The circuit can be adapted to control other functions.
Elastomeric materials represent an important class of engineering materials, which are widely used to make components of structures, machinery, and devices for vibration and noise control. Elastomeric material possessing conductive or magnetic properties have been widely used in applications such as conductive and magnetic tapes, sensors, flexible permanent magnets, etc. Our interest in these materials has focussed on understanding and controlling the magnitude and directionality of their response to applied magnetic fields. The effect of magnetic fields on the mechanical properties of these materials has not been the subject of many published studies. Our interest and expertise in controllable fluids have given us the foundation to make a transition to controllable elastomers. Controllable elastomers are materials that exhibit a change in mechanical properties upon application of an external stimuli, in this case a magnetic field. Controllable elastomers promise to have more functionality than conventional elastomers and therefore could share the broad industrial application base with conventional elastomers. As such, these materials represent an attractive class of smart materials, and may well be a link that brings the applications of modern control technologies, intelligent structures and smart materials to a very broad industrial area. This presentation will cover our research work in the area of controllable elastomers at the Thomas Lord Research Center. More specifically, the presentation will discuss the control of mechanical properties and mathematical modeling of the new materials prepared in our laboratories along with experiments to achieve adaptive vibration control using the new materials.
Traffic congestion is an ongoing problem of great interest to researchers from different areas in academia. With the emerging technology for inter-vehicle communication, vehicles have the ability to exchange information with predecessors by wireless communication. In this paper, we present an agent-based model of traffic congestion and examine the impact of having CACC (Cooperative Adaptive Cruise Control) embedded vehicle(s) on a highway system consisting of 4 traffic lanes without overtaking. In our model, CACC vehicles adapt their acceleration/deceleration according to vehicle-to-vehicle inter-communication. We analyze the average speed of the cars, the shockwaves, and the evolution of traffic congestion throughout the lifecycle of the model. The study identifies how CACC vehicles affect the dynamics of traffic flow on a complex network and reduce the oscillatory behavior (stop and go) resulting from the acceleration/deceleration of the vehicles.
Simulation results of adaptive tracking filter application to stabilization of structural bending modes of SI-B launch vehicle
To establish a technical basis for self-regulating microreactors, a model predictive control (MPC) system is investigated to proactively respond to anomalies and disturbances in anticipation of potential deviations from operating setpoints. Due to the difficulty of developing a physics-based surrogate model that can accurately match plant data in various operating conditions, machine learning algorithms are used in MPC, which allow for learning from both simulation and operation data, thus efficiently describing the targeted transient with arbitrary accuracy. However, one of the biggest concerns in applying ML algorithms like artificial neural networks (ANNs) is that the predictive capabilities of ANN are limited by training data. If there are gaps between the training and target domain, the accuracy of an ANN can degrade significantly when it is used to predict unseen data. To improve the predictive capability of ANN and enable a confident use of data-driven MPCs outside the training data, this study proposes an adaptive data-driven MPC framework. The system will monitor the discrepancy between plant responses and surrogate predictions, fine-tune the ANN-based surrogate when a large discrepancy is detected, and continue MPC operation with updated surrogates. The framework is demonstrated on a point kinetic model for microreactors. The hyperparameters of the update strategy, including layers to update, error thresholds, learning rate discount, and number of data points used for fine-tuning, are optimized so the simulated microreactor is able to follow changes in setpoint with the smallest of deviations.