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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Theoretical studies of decision making and stochastic processes are discussed. Several approaches are described for an improved performing control method. It is shown that control performance is highly dependent on the knowledge of the unknown parameters in the system.
Method controls reaction temperature and rate of reaction of sodium and silicon tetrafluoride by alternately adding measured amounts of reactants. Technique used in large reactor, where heat dissipation becomes serious problem, to control reactor temperatures. Highly efficient method, which would utilize almost 100 percent of raw materials.
The objectives of this program were: to verify the model of off schedule compressor variable geometry in the T700 turboshaft engine nonlinear model; to evaluate the use of the pseudo-random binary noise (PRBN) technique for obtaining engine frequency response data; and to design a high performance power turbine speed governor using modern control methods. Reduction of T700 engine test data generated at NASA-Lewis indicated that the off schedule variable geometry effects were accurate as modeled. Analysis also showed that the PRBN technique combined with the maximum likelihood model identification method produced a Bode frequency response that was as accurate as the response obtained from standard sinewave testing methods. The frequency response verified the accuracy of linear models consisting of engine partial derivatives and used for design. A power turbine governor was designed using the Linear Quadratic Regulator (LQR) method of full state feedback control. A Kalman filter observer was used to estimate helicopter main rotor blade velocity. Compared to the baseline T700 power turbine speed governor, the LQR governor reduced droop up to 25 percent for a 490 shaft horsepower transient in 0.1 sec simulating a wind gust, and up to 85 percent for a 700 shaft horsepower transient in 0.5 sec simulating a large collective pitch angle transient.
The combination of an inherently robust asynchronous (induction) electrical machine with the rapid control of energy provided by a high frequency resonant AC link enables the efficient management of higher power levels with greater versatility. This could have a variety of applications from launch vehicles to all-electric automobiles. These types of systems utilize a machine which is operated by independent control of both the voltage and frequency. This is made possible by using an indirect field-oriented control method which allows instantaneous torque control in all four operating quadrants. Incorporating the AC link allows the converter in these systems to switch at the zero crossing of every half cycle of the AC waveform. This zero loss switching of the link allows rapid energy variations to be achieved without the usual frequency proportional switching loss. Several field-oriented control systems were developed by LeRC and General Dynamics Space Systems Division under contract to NASA. A description of a single motor, electromechanical actuation system is presented. Then, focus is on a conceptual design for an AC electric vehicle. This design incorporates an induction motor/generator together with a flywheel for peak energy storage. System operation and implications along with the associated circuitry are addressed. Such a system would greatly improve all-electric vehicle ranges over the Federal Urban Driving Cycle (FUD).
This paper describes recent advances in structural quieting technology as applied to active truss structures intended for high precision space based optics applications. The active structure incorporates piezoelectric active members which exert control forces internal to the structure and thereby improve the structure's dimensional stability. The control architecture involves two layers of feedback control. The first utilizes collocated measurements of force and velocity at the active member to achieve active damping, the second utilizes noncollocated measurements of acceleration at the location of a simulated optical component to achieve structural stabilization. The local control loops are based on the concept of impedance matching, the global control loops are designed using robust control methods. These two levels of control are intended to operate simultaneously; however, in this paper each approach is applied individually. The combined implementation is left for future work.
The combination of an inherently robust asynchronous (induction) electrical machine with the rapid control of energy provided by a high frequency resonant ac link enables the efficient management of higher power levels with greater versatility. This could have a variety of applications from launch vehicles to all-electric automobiles. These types of systems utilize a machine which is operated by independent control of both the voltage and frequency. This is made possible by using an indirect field-oriented control method which allows instantaneous torque control all four operating quadrants. Incorporating the ac link allows the converter in these systems to switch at the zero crossing of every half cycle of the ac waveform. This zero loss switching of the link allows rapid energy variations to be achieved without the usual frequency proportional switching loss. Several field-oriented control systems were developed under contract to NASA.
Most magnetic bearing control schemes use a bias current with a superimposed control current to linearize the relationship between the control current and the force it delivers. With the existence of the bias current, even in no load conditions, there is always some power consumption. In aerospace applications, power consumption becomes an important concern. In response to this concern, an alternative magnetic bearing control method, called Adaptive Variable Bias Control (AVBC), has been developed and its performance examined. The AVBC operates primarily as a proportional-derivative controller with a relatively slow, bias current dependent, time-varying gain. The AVBC is shown to reduce electrical power loss, be nominally stable, and provide control performance similar to conventional bias control. Analytical, computer simulation, and experimental results are presented in this paper.
This paper presents a state feedback adaptive control method for position and velocity control of a direct drive motor. The proposed control scheme allows for integrating heuristic knowledge with mathematical knowledge of a system. It performs well even when mathematical model of the system is poorly understood. The controller consists of an adaptive fuzzy controller and a supervisory controller. The supervisory controller requires only knowledge of the upper bound and lower bound of the system parameters. The fuzzy controller is based on fuzzy basis functions and states of the system. The adaptation law is derived based on the Lyapunov function which ensures that the state of the system asymptotically approaches zero. The proposed controller is applied to a direct drive motor with payload and parameter uncertainty, and the effectiveness is experimentally verified. The real-time performance is compared with simulation results.
This paper presents a state feedback adaptive control method for position and velocity control of a direct drive motor. The proposed control scheme allows for integrating heuristic knowledge with mathematical knowledge of a system. It performs well even when mathematical model of the system is poorly understood. The controller consists of an adaptive fuzzy controller and a supervisory controller. The supervisory controller requires only knowledge of the upper bound and lower bound of the system parameters. The fuzzy controller is based on fuzzy basis functions and states of the system. The adaptation law is derived based on the Lyapunov function which ensures that the state of the system asymptotically approaches zero. The proposed controller is applied to a direct drive motor with payload and parameter uncertainty, and the effectiveness is verified by simulation results.
Disclosed is a novel adaptive control method and system called optimal control modification with normalization and covariance adjustment. The invention addresses specifically to current challenges with adaptive control in these areas: 1) persistent excitation, 2) complex nonlinear input-output mapping, 3) large inputs and persistent learning, and 4) the lack of stability analysis tools for certification. The invention has been subject to many simulations and flight testing. The results substantiate the effectiveness of the invention and demonstrate the technical feasibility for use in modern aircraft flight control systems.
Smart building technologies are a new suite of resources that improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both college curricula and building professionals’ continuing education, there is a lack of systematic instruction on smart building technologies–topics that include smart building concepts, key components, smart building controls, “Internet of Things” (IoT) devices, and how to integrate multiple energy systems including distributed energy resources (DER). This major gap in smart building education prevents stakeholders from understanding and adopting smart building technologies in building design and operations. Slipstream leads a DOE-funded project developing a semester-long smart building curriculum for college students and adapting the contents into 16 training videos for building professionals and the general public. The education and training cover the drivers and benefits of smart building technologies, key building energy systems, the latest sensor technologies and IoT devices, and focus on topics related to smart building controls (i.e., energy management information systems, smart building control platforms, cybersecurity, grid-interactive-efficient buildings (GEBs), smart building control methods, and occupant-centric control. This paper describes the project approach, provides outlines of the training materials, and identifies lessons learned in creating the content. We also suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies in the real world.
The ability to control energy loads such as a building's heating, ventilation, and air conditioning (HVAC) system can help facilitate increased penetration of variable renewable energy sources into the electric grid. To be able to control these HVAC systems more effectively, detailed simulations of the corresponding building physics is becoming increasingly important. These detailed simulations can be complex, nonlinear, and can require immense computational power when used in an advanced control method such as model predictive control (MPC), prompting the need to explore less computationally intensive strategies. In this work, a multi-fidelity approach is proposed to combine samples from a complex, high-fidelity model with a simple, low-fidelity model within the MPC control loop. More specifically, the parameters of a reduced-order, linear building model are periodically updated with knowledge from its high-fidelity counterpart - an EnergyPlus model - in an online fashion using a Gaussian Process surrogate model. Hence, highly accurate predictions of current and future conditions in a building are maintained with a substantially reduced computational burden compared to using the high-fidelity models alone. In other words, this linear parameter varying model preserves the low computational requirements of a low-order linear model while accurately modeling a building's dynamics. This allows a building controller to take highly informed actions without requiring a large computational budget.
Engineers at NASA's Dryden Flight Research Center have received a patent on an emergency flight-control method implemented by a propulsion-controlled aircraft (PCA) system. Utilizing the preexisting auto-throttle and engine-pressure-ratio trim controls of the airplane, the PCA system provides pitch and roll control for landing an airplane safely without using aerodynamic control surfaces that have ceased to function because of a primary-flight-control-system failure. The installation of the PCA does not entail any changes in pre-existing engine hardware or software. [Aspects of the method and system at previous stages of development were reported in Thrust-Control System for Emergency Control of an Airplane (DRC-96-07), NASA Tech Briefs, Vol. 25, No. 3 (March 2001), page 68 and Emergency Landing Using Thrust Control and Shift of Weight (DRC-96-55), NASA Tech Briefs, Vol. 26, No. 5 (May 2002), page 58.]. Aircraft flight-control systems are designed with extensive redundancy to ensure low probabilities of failure. During recent years, however, several airplanes have exhibited major flight-control-system failures, leaving engine thrust as the last mode of flight control. In some of these emergency situations, engine thrusts were successfully modulated by the pilots to maintain flight paths or pitch angles, but in other situations, lateral control was also needed. In the majority of such control-system failures, crashes resulted and over 1,200 people died. The challenge lay in creating a means of sufficient degree of thrust-modulation control to safely fly and land a stricken airplane. A thrust-modulation control system designed for this purpose was flight-tested in a PCA an MD-11 airplane. The results of the flight test showed that without any operational control surfaces, a pilot can land a crippled airplane (U.S. Patent 5,330,131). The installation of the original PCA system entailed modifications not only of the flight-control computer (FCC) of the airplane but also of each engine-control computer. Inasmuch as engine-manufacturer warranties do not apply to modified engines, the challenge became one of creating a PCA system that does not entail modifications of the engine computers.
A GDCI engine control system provides rapid heating of intake air to the combustion chamber when the engine is cold. The engine uses an intake air pathway that includes a compressor having a compressor inlet and a compressor outlet. A loop fluidly connects the compressor outlet to the compressor inlet. First and second valves are arranged in the loop and are arranged near the compressor outlet and the compressor inlet respectively. First and second passages converge at a first junction, and the first passage fluidly connects the first valve and the first junction. The second passage fluidly connects to the loop at a second junction arranged fluidly between the first and second valves. An intake air heat exchanger is arranged in the first passage and fluidly between the first valve and the first junction.
Performance Seeking Control (PSC) attempts to find and control the process at the operating condition that will generate maximum performance. In this paper a nonlinear multivariable PSC methodology will be developed, utilizing the Fuzzy Model Reference Learning Control (FMRLC) and the method of Steepest Descent or Gradient (SDG). This PSC control methodology employs the SDG method to find the operating condition that will generate maximum performance. This operating condition is in turn passed to the FMRLC controller as a set point for the control of the process. The conventional SDG algorithm is modified in this paper in order for convergence to occur monotonically. For the FMRLC control, the conventional fuzzy model reference learning control methodology is utilized, with guidelines generated here for effective tuning of the FMRLC controller.
A unified identification and control scheme capable of achieving space at form performance objectives under nominal or failure conditions is described. Preliminary results are also presented, showing that the methodology offers much promise for effective robust control of large space structures. The control method is a multivariable, adaptive, output predictive controller called Model Predictive Control (MPC). MPC uses a state space model and input reference trajectories of set or tracking points to adaptively generate optimum commands. For a fixed model, MPC processes commands with great efficiency, and is also highly robust. A key feature of MPC is its ability to control either nonminimum phase or open loop unstable systems. As an output controller, MPC does not explicitly require full state feedback, as do most multivariable (e.g., Linear Quadratic) methods. Its features are very useful in LSS operations, as they allow non-collocated actuators and sensors. The identification scheme is based on canonical variate analysis (CVA) of input and output data. The CVA technique is particularly suited for the measurement and identification of structural dynamic processes - that is, unsteady transient or dynamically interacting processes such as between aerodynamics and structural deformation - from short, noisy data. CVA is structured so that the identification can be done in real or near real time, using computationally stable algorithms. Modeling LSS dynamics in 1-g laboratories has always been a major impediment not only to understanding their behavior in orbit, but also to controlling it. In cases where the theoretical model is not confirmed, current methods provide few clues concerning additional dynamical relationships that are not included in the theoretical models. CVA needs no a priori model data, or structure; all statistically significant dynamical states are determined using natural, entropy-based methods. Heretofore, a major limitation in applying adaptive controllers to LSS problems has been the requirement for accurate models. We feel that MPC coupled with the CVA identification scheme will resolve this limitation. The results are based on research performed under an SBIR grant, administered by the Air Force Office of Scientific Research.
Comparative evaluations of the major types of control systems, such as three-axis-active, spin-stabilized, dual spin, and momentum basis, are performed to determine their applicability for planetary missions in the early 1990s. The key requirements that determine control system selection are science functional, mission orbital, and vehicle configuration characteristics. Five functional groups are given for preliminary control system selection: (1) preliminary survey; (2) detailed remote observation; (3) close scrutiny - deep space; (4) close scrutiny - near sun; (5) sample return. Requirements affecting the selection of attitude/articulation control methods, comparison of the performance capabilities of competing control concepts, criteria for selecting competing methods, comparison of relative complexity, and identification of hardware needs and trends that may affect control system selection are considered.
Second order feedback method for computing optimal control corrections from state perturbations, noting results for nonlinear examples based of van der Pol equation