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A robust control system is presented for damping vibrations in a cantilevered beam. The distributed deflections of the beam are sensed using real-time holography. A hybrid processor which uses optical and digital processors is proposed to implement a distributed control algorithm. The controller is designed using the linear-quadratic-Gaussian method to add damping to a finite number of vibrational modes.
Since the beginning of the Space Station Freedom Program (SSFP), the Lewis Research Center (LeRC) has been developed electrical power system test beds to support the overall design effort. Through this time, the SSFP has changed the design baseline numerous times, however, the test bed effort has endeavored to track these changes. Beginning in August 1989 with the baseline and an all DC system, a test bed was developed to support the design baseline. The LeRC power measurement and distribution (PMAD) DC test bed and the changes in the restructure are described. The changes includeed the size reduction of primary power channel and various power processing elements. A substantial reduction was also made in the amount of flight software with the subsequent migration of these functions to ground control centers. The impact of these changes on the design of the power hardware, the controller algorithms, the control software, and a description of their current status is presented. An overview of the testing using the test bed is described, which includes investigation of stability and source impedance, primary and secondary fault protection, and performance of a rotary utility transfer device. Finally, information is presented on the evolution of the test bed to support the verification and operational phases of the SSFP in light of these restructure scrubs.
The effects of different elevation and azimuth drive configurations on DSS-13 antenna performance are presented as well as a study of gearbox stiffness and motor inertia. Small motor inertia and rigid gearboxes would improve the pointing accuracy up to a certain limit. The limit is imposed by critical values of gearbox stiffness and motor inertia introduced in the article. The critical values depend on the lowest structural frequency of the rate-loop model. The tracking performance can be improved by raising gearbox stiffness to the critical stiffness and reducing motor inertia to the critical inertia. An azimuth drive configuration with four driven wheels was also investigated. For the four-wheel drive configuration in azimuth, the cross-coupling effects are reduced and wind disturbance rejection properties improved. Pointing is improved substantially in the cross-elevation but is relatively unaffected in the elevation direction. More significant improvements can be achieved through either structural redesign (stiffening the structure) or new control algorithms or control concepts, which would eliminate the effect of flexible deformations on the antenna pointing accuracy. Although the study is performed for the DSS-13 antenna, the results can be extended for other DSN antennas.
Since the beginning of the Space Station Freedom Program (SSFP), the Lewis Research Center (LeRC) has been developed electrical power system test beds to support the overall design effort. Through this time, the SSFP has changed the design baseline numerous times, however, the test bed effort has endeavored to track these changes. Beginning in August 1989 with the baseline and an all DC system, a test bed was developed to support the design baseline. The LeRC power measurement and distribution (PMAD) DC test bed and the changes in the restructure are described. The changes included the size reduction of primary power channel and various power processing elements. A substantial reduction was also made in the amount of flight software with the subsequent migration of these functions to ground control centers. The impact of these changes on the design of the power hardware, the controller algorithms, the control software, and a description of their current status is presented. An overview of the testing using the test bed is described, which includes investigation of stability and source impedance, primary and secondary fault protection, and performance of a rotary utility transfer device. Finally, information is presented on the evolution of the test bed to support the verification and operational phases of the SSFP in light of these restructure scrubs.
This paper describes the control algorithms and control design process for a generic commercial aircraft engine simulation of a 40,000 lb thrust class, two spool, high bypass ratio turbofan engine. The aircraft engine is a complex nonlinear system designed to operate over an extreme range of environmental conditions, at temperatures from approximately -60 to 120+ F, and at altitudes from below sea level to 40,000 ft, posing multiple control design constraints. The objective of this paper is to provide the reader an overview of the control design process, design considerations, and justifications as to why the particular architecture and limits have been chosen. The controller architecture contains a gain-scheduled Proportional Integral controller along with logic to protect the aircraft engine from exceeding any limits. Simulation results illustrate that the closed loop system meets the Federal Aviation Administration s thrust response requirements
Robotics engineers, ground controllers and International Space Station (ISS) crew have been running successful experiments using Robonaut 2 (R2) on-board the ISS for more than a year. This humanoid upper body robot continues to expand its list of achievements and its capabilities to safely demonstrate maintenance and servicing tasks while working alongside human crewmembers. The next phase of the ISS R2 project will transition from a stationary Intra Vehicular Activity (IVA) upper body using a power/data umbilical, to an IVA mobile system with legs for repositioning, a battery backpack power supply, and wireless communications. These upgrades will enable the R2 team to evaluate hardware performance and to develop additional control algorithms and control verification techniques with R2 inside the ISS in preparation for the Extra Vehicular Activity (EVA) phase of R2 operations. As R2 becomes more capable in assisting with maintenance tasks, with minimal supervision, including repositioning itself to different work sites, the ISS crew will be burdened with fewer maintenance chores, leaving them more time to conduct other activities. R2's developers at the Johnson Space Center (JSC) are preparing the R2 IVA mobility hardware and software upgrades for delivery to the ISS in late 2013. This paper summarizes R2 ISS achievements to date, briefly describes the R2 IVA mobility upgrades, and discusses the R2 IVA mobility objectives and plans.
As more and more grid-forming (GFM) inverter-based resources replace traditional fossil-fueled synchronous generators as the GFM sources in microgrids, the existing microgrid energy management systems (EMSs) need to be updated to control and coordinate multiple GFM inverters that consider system control objectives under different microgrid connection states. For each state, we formulate an optimization problem and apply a real-time feedback-based control algorithm; all together, the control algorithms seamlessly connect the states into a generic EMS. We showcase the EMS on a real-world simulation of a microgrid under the different states to demonstrate its operational effectiveness.
An algorithm for design via parameter optimization has been developed for linear-time-invariant control systems based on the model reference adaptive control concept. A cost functional is defined to evaluate the system response relative to nominal, which involves in general the error between the system and nominal response, its derivatives and the control signals. A program for the practical implementation of this algorithm has been developed, with the computational scheme for the evaluation of the performance index based on Lyapunov's theorem for stability of linear invariant systems.
Tethered spacecraft are particularly well suited to serve as isolation platforms for space-borne observatories. It has previously been shown that, due to the relatively large tether force, conventional means of performing attitude control for tethered satellites are inefficient for any mission with pointing requirements more stringent than about 1 deg. A particularly effective method of implementing attitude control for tethered satellites is to use the tether tension force to generate control moments by moving the tether attach point relative to the subsatellite center of mass. This paper presents the development of a precision pointing control algorithm for tethered satellites and the simulation of the control system with laboratory hardware. The control algorithm consists of a linear quadratic regulator feedback law and a Kalman filter. The control algorithm has been shown to regulate the vehicle orientation to within 0.60 arcsec rms. This level of precision was achieved only after including a mass center estimator and accurately modeling the effects of the nonlinear attach point motion actuator.
This paper presents a 2D axisymmetric combined conduction and radiation model of a multizone crystal growth furnace. The model is based on a programmable multizone furnace (PMZF) designed and built at NASA Lewis Research Center for growing high quality semiconductor crystals. A novel feature of this model is a control algorithm which automatically adjusts the power in any number of independently controlled heaters to establish the desired crystal temperatures in the furnace model. The control algorithm eliminates the need for numerous trial and error runs previously required to obtain the same results. The finite element code, FIDAP, used to develop the furnace model, was modified to directly incorporate the control algorithm. This algorithm, which presently uses PID control, and the associated heat transfer model are briefly discussed. Together, they have been used to predict the heater power distributions for a variety of furnace configurations and desired temperature profiles. Examples are included to demonstrate the effectiveness of the PID controlled model in establishing isothermal, Bridgman, and other complicated temperature profies in the sample. Finally, an example is given to show how the algorithm can be used to change the desired profile with time according to a prescribed temperature-time evolution.
The performance seeking control algorithm is designed to continuously optimize the performance of propulsion systems. The performance seeking control algorithm uses a nominal model of the propulsion system and estimates, in flight, the engine deviation parameters characterizing the engine deviations with respect to nominal conditions. In practice, because of measurement biases and/or model uncertainties, the estimated engine deviation parameters may not reflect the engine's actual off-nominal condition. This factor has a necessary impact on the overall performance seeking control scheme exacerbated by the open-loop character of the algorithm. The effects produced by unknown measurement biases over the estimation algorithm are evaluated. This evaluation allows for identification of the most critical measurements for application of the performance seeking control algorithm to an F100 engine. An equivalence relation between the biases and engine deviation parameters stems from an observability study; therefore, it is undecided whether the estimated engine deviation parameters represent the actual engine deviation or whether they simply reflect the measurement biases. A new algorithm, based on the engine's (steady-state) optimization model, is proposed and tested with flight data. When compared with previous Kalman filter schemes, based on local engine dynamic models, the new algorithm is easier to design and tune and it reduces the computational burden of the onboard computer.
Connected and automated vehicles (CAVs) have the potential to improve many aspects of the current transportation systems such as safety, mobility, and energy efficiency. In order to evaluate the benefits and impacts of a CAV, the CAV control algorithm is typically implemented on vehicles simulated in a traffic microsimulation environment. However, traffic microsimulation usually lacks detailed vehicle and powertrain dynamics, making it challenging to fully understand how a CAV control algorithm will perform and respond on an actual vehicle. Whether the same benefits measured in the simulation will also be observed in real-world remains an open question. One potential approach to fill in this gap is to conduct a co-simulation of traffic microsimulation with detailed vehicle and powertrain dynamics models, often developed in MATLAB Simulink. However, current microsimulation tools such as VISSIM and SUMO do not have a ready-to-use interface for co-simulation with vehicle dynamics and Simulink. Also, even if such an interface exists, it will be tool-specific, making it challenging to shift from one tool to another or test CAV controls in different tools. There are needs for tool-agnostic co-simulation as different microsimulation tools have their pros and cons, and researchers often need to use different tools based on the purposes of the simulation, project needs, and applications. In this work, Flexible Interface for X-in-the-loop Simulation (FIXS) is developed that can support the co-simulation of microsimulation, CAV control algorithm, and vehicle dynamics model in Simulink. Enabled by the FIXS, the benefit and performance of a CAV control algorithm can be better understood with the consideration of vehicle responses and dynamics. The connection to VISSIM and SUMO is handled internally by the interface, and users can easily switch tools by changing a configuration file. The co-simulation capability is demonstrated for a VISSIM eco-approach and departure CAV scenario and a SUMO cooperative merging scenario for both a passenger CAV and a class 8 heavy-duty connected and automated trucks.
Radar data quality control is a major component to the Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) effort. The quality control algorithm utilizes several adjustable height and reflectivity threshold parameters to remove non-precipitation echoes from ground-based radar data. Spurious radar reflectivity returns not removed during the quality control process may create biases in ground validation rainfall products used to evaluate rainfall measurements retrieved from aboard the TRMM satellite. To better evaluate the performance of the quality control algorithm, WSR-88D radar data from one primary GV site, Melbourne, Florida, are used to determine the seasonal characteristics of non-meteorological radar echoes in cast central Florida during the first year of the TRMM mission. It is demonstrated that unique spurious echo regimes develop throughout the year, leading to different levels of successful non-meteorological echo removal by the quality control algorithm. In addition to the type of non- precipitation echoes present, the success of the algorithm is also greatly dependent upon the amount of precipitation present. Less aggressive attempts to remove non-meteorological echoes must be employed when rain is observed so as not to remove these echoes as well. Therefore, precipitation statistics from ground validation monthly rainfall products will be utilized to further document the quality control algorithm performance. Moreover, these precipitation statistics will be used to demonstrate the relationship between dominant spurious echo regimes and rainfall amount. Finally, monthly statistics of contaminated ground validation radar data will be calculated and the effect on rainfall accumulation products will be discussed.
Automated spacecraft docking operations are being performed using a full scale motion based simulator and an optical sensor. This presentation will discuss the work in progress at TRW and MSFC facilities to study the problem of automated proximity and docking operations. The docking sensor used in the MSFC Optical Sensor and simulation runs are performed using the MSFC Flat Floor Facility. The control algorithms and six degrees of freedom (6DOF) simulation software were developed at TRW and integrated into the MSFC facility. Key issues being studied are the quantification of docking sensor requirements and operational constraints necessary to perform automated docking maneuvers, control algorithms capable of performing automated docking in the presence of sensitive and noisy sensor data, and sensor technologies for automated proximity and docking operations. As part of this study the MSFC sensor characteristics were analyzed and modeled so that off line simulation runs can be performed for control algorithm testing. Our goal is to develop and demonstrate full 6DOF docking capabilities with actual sensors on the MSFC motion based simulator. We present findings from actual docking simulation runs which show sensor and control loop performance as well as problem areas which require close attention. The evolution of various control algorithms using both phase plane and Clohessy-Wiltshire techniques are discussed. In addition, 6DOF target acquisition and control strategies are described.
This effort extends into high frequency (>500 Hz), an earlier developed adaptive control algorithm for the suppression of thermo-acoustic instabilities in a liquidfueled combustor. The earlier work covered the development of a controls algorithm for the suppression of a low frequency (~280 Hz) combustion instability based on simulations, with no hardware testing involved. The work described here includes changes to the simulation and controller design necessary to control the high frequency instability, augmentations to the control algorithm to improve its performance, and finally hardware testing and results with an experimental combustor rig developed for the high frequency case. The Adaptive Sliding Phasor Averaged Control (ASPAC) algorithm modulates the fuel flow in the combustor with a control phase that continuously slides back and forth within the phase region that reduces the amplitude of the instability. The results demonstrate the power of the method - that it can identify and suppress the instability even when the instability amplitude is buried in the noise of the combustor pressure. The successful testing of the ASPAC approach helped complete an important NASA milestone to demonstrate advanced technologies for low-emission combustors.
Control of integrated photovoltaic (PV) plants with energy storage systems (ESSs) has become an important research and development topic in recent times. In this context, a Multi-port Autonomous Reconfigurable Solar (MARS) plant that integrates PV and ESS to alternating current transmission grid and high-voltage direct current (HVdc) link is studied in this paper. With penetration of power electronic based resources in the grid, the grid’s capability to recover from frequency or voltage disturbances are reduced. Therefore, one of the vital objectives of any new grid integrated power electronic resource is to provide advanced control functions like voltage and frequency support to the grid during disturbances. In this research work, a detailed implementation of a synchronverter-based control algorithm of MARS is presented. The proposed control algorithm and the MARS control architecture are evaluated through simulations on PSCAD/EMTDC simulation platform to showcase the performance in different operating conditions. In addition, they are evaluated in Opal-RT offline simulation models which can also be used to perform control-hardware-in-the-loop (cHIL) tests.
Currently, NASA Lewis Research Center is developing magnetic bearings for Space Shuttle Main Engine (SSME) turbopumps. The control algorithms which have been used are based on either the proportional-intergral-derivative control (PID) approach or the linear quadratic (LQ) state space approach. These approaches lead to an acceptable performance only when the system model is accurately known, which is seldom true in practice. For example, the rotor eccentricity, which is a major source of vibration at high speeds, cannot be predicted accurately. Furthermore, the dynamics of a rotor shaft, which must be treated as a flexible system to model the elastic rotor shaft, is infinite dimensional in theory and the controller can only be developed on the basis of a finite number of modes. Therefore, the development of the control system is further complicated by the possibility of closed loop system instability because of residual or uncontrolled modes, the so called spillover problem. Consequently, novel control algorithms for magnetic bearings are being developed to be robust to inevitable parametric uncertainties, external disturbances, spillover phenomenon and noise. Also, as pointed out earlier, magnetic bearings must exhibit good performance at a speed over 30,000 rpm. This implies that the sampling period available for the design of a digital control system has to be of the order of 0.5 milli-seconds. Therefore, feedback coefficients and other required controller parameters have to be computed off-line so that the on-line computational burden is extremely small. The development of the robust and real-time control algorithms is based on the sliding mode control theory. In this method, a dynamic system is made to move along a manifold of sliding hyperplanes to the origin of the state space. The number of sliding hyperplanes equals that of actuators. The sliding mode controller has two parts; linear state feedback and nonlinear terms. The nonlinear terms guarantee that the systems would reach the intersection of all sliding hyperplanes and remain on it when bounds on the errors in the system parameters and external disturbances are known. The linear part of the control drives the system to the origin of state space. Another important feature is that the controller parameter can be computed off-line. Consequently, on-line computational burden is small.
This article proposes a deep learning (DL)-based control algorithm—DL velocity-based model predictive control (VMPC)—for reducing traffic congestion with slowly time-varying traffic signal controls. This control algorithm consists of system identification using DL and traffic signal control using VMPC. For the training process of DL, we established a modeling error entropy loss as the criteria inspired by the theory of stochastic distribution control (SDC) originated by the fourth author. Herein, simulation results show that the proposed algorithm can reduce traffic congestion with a slowly varying traffic signal control input. Results of an ablation study demonstrate that this algorithm compares favorably to other model-based controllers in terms of prediction error, signal varying speed, and control effectiveness.