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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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Design of attitude control system for a sounding rocket is established by using a control loop which incorporates a lead network to convert position output into a feedback signal that is approximately proportional to position, plus a constant times rate.
Because application of conventional optimal linear regulator theory to flight controller design requires the capability of measuring and/or estimating the entire state vector, it is of interest to consider procedures for computing controls which are restricted to be linear feedback functions of a lower dimensional output vector and which take into account the presence of measurement noise and process uncertainty. Therefore, a stochastic linear model that was developed is presented which accounts for aircraft parameter and initial uncertainty, measurement noise, turbulence, pilot command and a restricted number of measurable outputs. Optimization with respect to the corresponding output feedback gains was performed for both finite and infinite time performance indices without gradient computation by using Zangwill's modification of a procedure originally proposed by Powell. Results using a seventh order process show the proposed procedures to be very effective.
A control system for a permanent magnet motor driven by a multiphase line commutated inverter is provided with integration for integrating the back EMF of each phase of the motor. This is used in generating system control signals for an inverter gate logic using a sync and firing angle (alpha) control generator connected to the outputs of the integrators. A precision full wave rectifier provides a speed control feedback signal to a phase delay rectifier via a gain and loop compensation circuit and to the integrators for adaptive control of the attenuation of low frequencies by the integrators as a function of motor speed. As the motor speed increases, the attenuation of low frequency components by the integrators is increased to offset the gain of the integrators to spurious low frequencies.
A control algorithm that can locally stabilize a specific class of multi-input multi-output nonautonomous nonlinear dynamical systems while satisfying individual input constraints is developed. The proposed Lyapunov-based state-feedback control law inherently accounts for the actuator amplitude saturation limits without the need for computationally expensive real-time optimization techniques. In addition to the control law, a formal definition for the local “controllable region” within which the controller can asymptotically drive the system states to the origin and satisfy the input saturation limits is also presented. The nonautonomous nature of the system dynamics implies that the “controllable region” continuously evolves with time. Therefore, a sufficient condition to maintain the system states within the “controllable region” is proposed in this work to make practical implementation feasible. The effectiveness of the controller is tested for a specific control problem arising in tokamaks, which are toroidal devices that use strong magnetic fields to confine a plasma (hot ionized gas). Here, the primary emphasis of tokamak research is to regulate the plasma properties around predetermined values to achieve stable plasma confinement. Nonlinear simulations show that the proposed controller can achieve the desired plasma control objectives in a DIII-D tokamak scenario.
The results of optimal control theory are used to synthesize a feedback filter. The feedback filter is used to force the output of the filtered frequency response to match that of a desired optimal frequency response over a finite frequency range. This matching is accomplished by employing a nonlinear programing algorithm to search for the coefficients of the feedback filter that minimize the error between the optimal frequency response and the filtered frequency response. The method is applied to the synthesis of an active flutter-suppression control law for an aeroelastic wind-tunnel model. It is shown that the resulting control law suppresses flutter over a wide range of subsonic Mach numbers. This is a promising method for synthesizing practical control laws using the results of optimal control theory.
A state-variable feedback approach is utilized for active control of rotorcraft vibration. Fuselage accelerations are passed through undamped second-order filters with resonant frequencies at N/rev. The resulting outputs contain predominantly the N/rev vibration components, phase shifted by 180 deg, and are used to drive the blade pitch to cancel this component of fuselage vibration. The linear-quadratic-gaussian (LQG) method is used to design a feedback control system utilizing these filtered accelerations. The design is based on a nine-degree-of-freedom linear model of the Rotor System Research Aircraft (RSRA) in hover and is evaluated on a nonlinear blade-element simulation of the RSRA for this flight condition. The system is shown to essentially eliminate vibrations at N/rev in all axes. The required blade-pitch amplitude is within the capability of conventional actuators at the N/rev frequency.
A method and apparatus for converting the intensity of an unknown optical signal (B) into an electrical signal in digital form utilizes two elongated optical attenuators (11, 13), one for the unknown optical signal from a source (10) and one for a known optical signal (A) from a variable source (12), a plurality of photodetectors (e.g., 17, 18) along each attenuator for detecting the intensity of the optical signals, and a plurality of comparators (e.g., 21) connected to the photodetectors in pairs to determine at what points being compared the attenuated known signal equals the attenuated unknown signal. The intensity of the unknown relative to the known is thus determined by the output of a particular comparator. That output is automatically encoded to a relative intensity value in digital form through a balancing feedback control (24) and encoder (23). The digital value may be converted to analog form in a D-to-A converter (27) and used to vary the source of the known signal so that the attenuated intensity of the known signal at a predetermined point (comparator 16) equals the attenuated intensity of the unknown signal at the predetermined point of comparison. If the known signal is then equal to the unknown, there is verification of the analog-to-digital conversion being complete. Otherwise the output of the comparator indicating equality at some other point along the attenuators will provide an output which is encoded and added, through an accumulator comprised of a register (25) and an adder (26), to a previous relative intensity value thereby to further vary the intensity of the known signal source. The steps are repeated until full conversion is verified.
A formal statement of the optimal control problem which includes the interval of dicretization as an optimization parameter, and extend this to include selection of a control algorithm as part of the optimization procedure, is formulated. The performance of the scalar linear system depends on the discretization interval. Discrete-time versions of the output feedback regulator and an optimal compensator, and the use of these results in presenting an example of a system for which fast partial-state-feedback control better minimizes a quadratic cost than either a full-state feedback control or a compensator, are developed.
A matrix converter system having a current control mode operation is provided. The matrix converter system includes a matrix converter that includes a plurality of switches. A unity current reference vector having an amplitude of one is determined that defines an angle and frequency of an output current vector as a function of a reference phase angle, which is a function of the output current vector and an output voltage vector representing feedback signals of a multiphase signal output to a load. The unity current reference vector is used to control the matrix converter, to cause the output current vector to be aligned with the unity current reference vector. A unity voltage reference vector having an amplitude of one is provided that defines a reference angle and frequency as a function of a zero phase angle. The output voltage vector and the unity voltage reference vector are aligned to determine the reference phase angle as an angle between the aligned output voltage vector and the output current vector.
We consider the problem of decoherence in a Bose – Einstein condensate (BEC) of noninteracting atoms during interferometric probing with a classical monochromatic external field. The condensate is located in one of the arms of the Mach – Zehnder interferometer, while part of the radiation from the interferometer output is fed back to the input, thereby closing the coherent feedback loop. A more general setting of the problem is also considered, in which the condensate is located in a system of interferometers ‘nested’ into each other, while part of the output radiation of each of them is also fed back to its input, closing the so-called multiloop feedback. The possibility of effective control of decoherence rates of various matrix elements is shown. The application of feedback of the proposed type to BEC in a double-well potential is investigated. It is found that this feedback allows efficient control of the distribution of atoms between the wells. (paper)
Open-loop solutions of dynamical optimization problems can be numerically computed usingexisting software packages. The computed time histories of the state and control variables, formultiple sets of end conditions can then be used to train a neural network to 'recognize' the optimal,nonlinear feedback relation between the states and controls of the system. The 'learned' network canthen be used to output an approximate optimal control given a full set (or a partial set) of measuredsystem states. With simple neural networks, we have successfully demonstrated the efficacy of theproposed approach using a minimum-time orbit injection problem. The usefulness and limitations ofthis novel approach on real-life optimal guidance and control problems, with many state and control variables as well as path inequality constraints, remain to be seen.
Hydraulic servo, which quickly diverts fluid to either of two actuators, controls the flow rates and pressures within a hydraulic system so that the output force of the servo system is independent of the velocity of the mechanism which the system actuates. This servo is a dynamic feedback control device.
This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires a networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.
Automatic feedback control for long-term frequency stabilization of He-Ne laser at center frequency of atomic resonance
Kalman-filter "observer" algorithm used in estimating unmeasured output variables of turbofan engine. Unmeasured output variables needed along with measured output variables as feedback data to characterize state of aircraft-and-engine system for control purposes. Subsystem or processor implementing Kalman-filter algorithm incorporated into adaptive digital control system, called "performance-seeking" control system, developed to compute engine-control settings to optimize performance of aircraft under given pseudo-steady-state cruise conditions. Basic concept of algorithm not restricted to aircraft-engine application and can be extended to internal-combustion engines in land vehicles.
We present ongoing work in the Autonomy Incubator at NASA Langley Research Center (LaRC) exploring the efficacy of a data set aggregation approach to reinforcement learning for small unmanned aerial vehicle (sUAV) flight in dense and cluttered environments with reactive obstacle avoidance. The goal is to learn an autonomous flight model using training experiences from a human piloting a sUAV around static obstacles. The training approach uses video data from a forward-facing camera that records the human pilot's flight. Various computer vision based features are extracted from the video relating to edge and gradient information. The recorded human-controlled inputs are used to train an autonomous control model that correlates the extracted feature vector to a yaw command. As part of the reinforcement learning approach, the autonomous control model is iteratively updated with feedback from a human agent who corrects undesired model output. This data driven approach to autonomous obstacle avoidance is explored for simulated forest environments furthering autonomous flight under the tree canopy research. This enables flight in previously inaccessible environments which are of interest to NASA researchers in Earth and Atmospheric sciences.
This paper examines the problem of real-time optimization of networked systems and develops online algorithms that steer the system towards the optimal trajectory without explicit knowledge of the system model. The problem is modeled as a dynamic optimization problem with time-varying performance objectives and engineering constraints. The design of the algorithms leverages the online zero-order primal-dual projected-gradient method. In particular, the primal step that involves the gradient of the objective function (and hence requires networked systems model) is replaced by its zero-order approximation with two function evaluations using a deterministic perturbation signal. The evaluations are performed using the measurements of the system output, hence giving rise to a feedback interconnection, with the optimization algorithm serving as a feedback controller. The paper provides some insights on the stability and tracking properties of this interconnection. Finally, the paper applies this methodology to a real-time optimal power flow problem in power systems, and shows its efficacy on the IEEE 37-node distribution test feeder for reference power tracking and voltage regulation.
A comprehensive program to develop a prototype model of a standardized multiple output power supply for use in space flight applications is described. The prototype unit was tested and evaluated to assure that the design would provide near optimum performance for the planned application. The prototype design used a dc-to-dc converter incorporating reqenerative current feedback with a time-ratio controlled duty cycle to achieve high efficiency over a wide variation of input voltage and output loads. The packaging concept uses a mainframe capable of accommodating up to four inverter/regulator modules with one common input filter and housekeeping module. Each inverter/regulator module provides a maximum of 100 watts or 10 amperes. Each module is adaptable to operate at any voltage between 4.0 volts and 108 volts. The prototype unit contains +5, + or - 15 and +28 volt modules.