Search NASA⌕ Search

SEARCH · Search NASA

Results for “PID control”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Testing of the Lunar Reconnaissance Orbiter Attitude Control System Re-Design Without a Gyro

The Lunar Reconnaissance Orbiter (LRO) was launched in 2009 and, with itsseven science instruments, has made numerous contributions to our understandingof the moon. LRO is in an elliptical, polar lunar orbit and nominally maintainsa nadir orientation. There are frequent slews off nadir to observe various sciencetargets. LRO attitude control system (ACS) has two star trackers and a gyro forattitude estimation in an extended Kalman filter (EKF) and four reaction wheelsused in a proportional-integral-derivative (PID) controller. LRO is equipped withthrusters for orbit adjustments and momentum management. In early 2018, thegyro was powered off following a fairly rapid decline in the laser intensity on theX axis. Without the gyro, the EKF has been disabled. Attitude is provided by asingle star tracker and a coarse rate estimate is computed by a back differencingof the star tracker quaternions. Slews have also been disabled. A new rate estimationapproach makes use of a complementary filter, combining the quaterniondifferentiated rates and the integrated PID limited control torque (with reactionwheel drag and feedforward torque removed). The filtered rate estimate replacesthe MIMU rate in the EKF, resulting in minimal flight software changes. The paperwill cover the preparation and testing of the new gyroless algorithm, both inground simulations and inflight.

Halverson, Julie↗
NASA NTRS1996

Nonlinear Multi-Window Controllers

In many control designs the error signal is split into parallel channels which are tailored to be significant over distinct frequency ranges and are then recombined. Often, at least one channel contains nonlinear elements, The most common example is a PID controller with an anti-windup nonlinearity in the I-channel. A more general example using higher-order compensators is presented, which is a thermal controller for a spacecraft-mounted telescope.

PID↗
NASA NTRS1990

Two Axis Pointing System (TAPS) attitude acquisition, determination, and control

The Two Axis Pointing System (TAPS) is a 2 axis gimbal system designed to provide fine pointing of Space Transportation System (STS) borne instruments. It features center-of-mass instrument mounting and will accommodate instruments of up to 1134 kg (2500 pounds) which fit within a 1.0 by 1.0 by 4.2 meter (40 by 40 by 166 inch) envelope. The TAPS system is controlled by a microcomputer based Control Electronics Assembly (CEA), a Power Distribution Unit (PDU), and a Servo Control Unit (SCU). A DRIRU-II inertial reference unit is used to provide incremental angles for attitude propagation. A Ball Brothers STRAP star tracker is used for attitude acquisition and update. The theory of the TAPS attitude determination and error computation for the Broad Band X-ray Telescope (BBXRT) are described. The attitude acquisition is based upon a 2 star geometric solution. The acquisition theory and quaternion algebra are presented. The attitude control combines classical position, integral and derivative (PID) control with techniques to compensate for coulomb friction (bias torque) and the cable harness crossing the gimbals (spring torque). Also presented is a technique for an adaptive bias torque compensation which adjusts to an ever changing frictional torque environment. The control stability margins are detailed, with the predicted pointing performance, based upon simulation studies. The TAPS user interface, which provides high level operations commands to facilitate science observations, is outlined.

Azzolini, John D.↗
NASA NTRS2001

Additive Runge-Kutta Schemes for Convection-Diffusion-Reaction Equations

Additive Runge-Kutta (ARK) methods are investigated for application to the spatially discretized one-dimensional convection-diffusion-reaction (CDR) equations. First, accuracy, stability, conservation, and dense output are considered for the general case when N different Runge-Kutta methods are grouped into a single composite method. Then, implicit-explicit, N = 2, additive Runge-Kutta ARK2 methods from third- to fifth-order are presented that allow for integration of stiff terms by an L-stable, stiffly-accurate explicit, singly diagonally implicit Runge-Kutta (ESDIRK) method while the nonstiff terms are integrated with a traditional explicit Runge-Kutta method (ERK). Coupling error terms are of equal order to those of the elemental methods. Derived ARK2 methods have vanishing stability functions for very large values of the stiff scaled eigenvalue, z(exp [I]) goes to infinity, and retain high stability efficiency in the absence of stiffness, z(exp [I]) goes to zero. Extrapolation-type stage-value predictors are provided based on dense-output formulae. Optimized methods minimize both leading order ARK2 error terms and Butcher coefficient magnitudes as well as maximize conservation properties. Numerical tests of the new schemes on a CDR problem show negligible stiffness leakage and near classical order convergence rates. However, tests on three simple singular-perturbation problems reveal generally predictable order reduction. Error control is best managed with a PID-controller. While results for the fifth-order method are disappointing, both the new third- and fourth-order methods are at least as efficient as existing ARK2 methods while offering error control and stage-value predictors.

Kennedy, Christopher A.↗
NASA NTRS2002

Additive Runge-Kutta Schemes for Convection-Diffusion-Reaction Equations

Additive Runge-Kutta (ARK) methods are investigated for application to the spatially discretized one- dimensional convection-diffusion-reaction (CDR) equations. Accuracy, stability, conservation, and dense-output are first considered for the general case when N different Runge-Kutta methods are grouped into a single composite method. Then, implicit-explicit, (N = 2), additive Runge-Kutta (ARK(sub 2)) methods from third- to fifth-order are presented that allow for integration of stiff terms by an L-stable, stiffly-accurate explicit, singly diagonally implicit Runge-Kutta (ESDIRK) method while the nonstiff terms are integrated with a traditional explicit Runge-Kutta method (ERK). Coupling error terms of the partitioned method are of equal order to those of the elemental methods. Derived ARK(sub 2) methods have vanishing stability functions for very large values of the stiff scaled eigenvalue, z['] yields -infinity, and retain high stability efficiency in the absence of stiffness, z['] yield 0. Extrapolation-type stage- value predictors are provided based on dense-output formulae. Optimized methods minimize both leading order ARK(sub 2) error terms and Butcher coefficient magnitudes as well as maximize conservation properties. Numerical tests of the new schemes on a CDR problem show negligible stiffness leakage and near classical order convergence rates. However, tests on three simple singular-perturbation problems reveal generally predictable order reduction. Error control is best managed with a PID-controller. While results for the fifth-order method are disappointing, both the new third- and fourth-order methods are at least as efficient as existing ARK(sub 2) methods.

Kennedy, Christopher A.↗
NASA NTRS1993

Design issues for a reinforcement-based self-learning fuzzy controller

Fuzzy logic controllers have some often cited advantages over conventional techniques such as PID control: easy implementation, its accommodation to natural language, the ability to cover wider range of operating conditions and others. One major obstacle that hinders its broader application is the lack of a systematic way to develop and modify its rules and as result the creation and modification of fuzzy rules often depends on try-error or pure experimentation. One of the proposed approaches to address this issue is self-learning fuzzy logic controllers (SFLC) that use reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of self-learning fuzzy controller is highly contingent on the design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for the application to chemical process are discussed and its performance is compared with that of PID and self-tuning fuzzy logic controller.

Yen, John↗
NASA NTRS1990

An expert system to perform on-line controller tuning

An expert system which tunes a Proportional-Integral-Derivative (PID) controller on-line for a single-input-single-output multiple-lag process with dead time is described. The expert system examines features of the previous transient responses and their corresponding sets of controller parameters. It determines a new set of controller gains to obtain a more desirable time response. This technique can be used to determine and implement a different set of PID gains for each operating regime and, once in steady state, the system can be used to find optimal parameters for load disturbance rejection. The expert system can be applied to any system of the specified form (aerospace, industrial, etc.) and can be expanded to include additional process models.

Litt, Jonathan S.↗
NASA NTRS1990

An expert system to perform on-line controller tuning

An expert system which tunes a proportional-integral-derivative (PID) controller online for a single-input-single-output multiple-lag process with dead time is described. The expert system examines features of the previous transient responses and their corresponding sets of controller parameters. It determines a new set of controller gains to obtain a more desirable time response. This technique can be used to determine and implement a different set of PID gains for each operating regime and, once in steady state, the system can be used to find optimal parameters for load disturbance rejection. The expert system can be applied to any system of the specified form (aerospace, industrial, etc.) and can be expanded to include additional process models.

Litt, Jonathan↗
NASA NTRS1987

Teleoperated position control of a PUMA robot

A laboratory distributed computer control teleoperator system is developed to support NASA's future space telerobotic operation. This teleoperator system uses a universal force-reflecting hand controller in the local iste as the operator's input device. In the remote site, a PUMA controller recieves the Cartesian position commands and implements PID control laws to position the PUMA robot. The local site uses two microprocessors while the remote site uses three. The processors communicate with each other through shared memory. The PUMA robot controller was interfaced through custom made electronics to bypass VAL. The development status of this teleoperator system is reported. The execution time of each processor is analyzed, and the overall system throughput rate is reported. Methods to improve the efficiency and performance are discussed.

Austin, Edmund↗
NASA NTRS1990

Terminology and concepts of control and Fuzzy Logic

Viewgraphs on terminology and concepts of control and fuzzy logic are presented. Topics covered include: control systems; issues in the design of a control system; state space control for inverted pendulum; proportional-integral-derivative (PID) controller; fuzzy controller; and fuzzy rule processing.

Aldridge, Jack↗
NASA NTRS1993

On identified predictive control

Self-tuning control algorithms are potential successors to manually tuned PID controllers traditionally used in process control applications. A very attractive design method for self-tuning controllers, which has been developed over recent years, is the long-range predictive control (LRPC). The success of LRPC is due to its effectiveness with plants of unknown order and dead-time which may be simultaneously nonminimum phase and unstable or have multiple lightly damped poles (as in the case of flexible structures or flexible robot arms). LRPC is a receding horizon strategy and can be, in general terms, summarized as follows. Using assumed long-range (or multi-step) cost function the optimal control law is found in terms of unknown parameters of the predictor model of the process, current input-output sequence, and future reference signal sequence. The common approach is to assume that the input-output process model is known or separately identified and then to find the parameters of the predictor model. Once these are known, the optimal control law determines control signal at the current time t which is applied at the process input and the whole procedure is repeated at the next time instant. Most of the recent research in this field is apparently centered around the LRPC formulation developed by Clarke et al., known as generalized predictive control (GPC). GPC uses ARIMAX/CARIMA model of the process in its input-output formulation. In this paper, the GPC formulation is used but the process predictor model is derived from the state space formulation of the ARIMAX model and is directly identified over the receding horizon, i.e., using current input-output sequence. The underlying technique in the design of identified predictive control (IPC) algorithm is the identification algorithm of observer/Kalman filter Markov parameters developed by Juang et al. at NASA Langley Research Center and successfully applied to identification of flexible structures.

Bialasiewicz, Jan T.↗
NASA NTRS1992

Design issues of a reinforcement-based self-learning fuzzy controller for petrochemical process control

Fuzzy logic controllers have some often-cited advantages over conventional techniques such as PID control, including easier implementation, accommodation to natural language, and the ability to cover a wider range of operating conditions. One major obstacle that hinders the broader application of fuzzy logic controllers is the lack of a systematic way to develop and modify their rules; as a result the creation and modification of fuzzy rules often depends on trial and error or pure experimentation. One of the proposed approaches to address this issue is a self-learning fuzzy logic controller (SFLC) that uses reinforcement learning techniques to learn the desirability of states and to adjust the consequent part of its fuzzy control rules accordingly. Due to the different dynamics of the controlled processes, the performance of a self-learning fuzzy controller is highly contingent on its design. The design issue has not received sufficient attention. The issues related to the design of a SFLC for application to a petrochemical process are discussed, and its performance is compared with that of a PID and a self-tuning fuzzy logic controller.

Yen, John↗
NASA NTRS2002

Implementation of Adaptive Digital Controllers on Programmable Logic Devices

Much has been made of the capabilities of Field Programmable Gate Arrays (FPGA's) in the hardware implementation of fast digital signal processing functions. Such capability also makes an FPGA a suitable platform for the digital implementation of closed loop controllers. Other researchers have implemented a variety of closed-loop digital controllers on FPGA's. Some of these controllers include the widely used Proportional-Integral-Derivative (PID) controller, state space controllers, neural network and fuzzy logic based controllers. There are myriad advantages to utilizing an FPGA for discrete-time control functions which include the capability for reconfiguration when SRAM- based FPGA's are employed, fast parallel implementation of multiple control loops and implementations that can meet space level radiation tolerance requirements in a compact form-factor. Generally, a software implementation on a Digital Signal Processor (DSP) device or microcontroller is used to implement digital controllers. At Marshall Space Flight Center, the Control Electronics Group has been studying adaptive discrete-time control of motor driven actuator systems using DSP devices. While small form factor, commercial DSP devices are now available with event capture, data conversion, Pulse Width Modulated (PWM) outputs and communication peripherals, these devices are not currently available in designs and packages which meet space level radiation requirements. In general, very few DSP devices are produced that are designed to meet any level of radiation tolerance or hardness. An alternative is required for compact implementation of such functionality to withstand the harsh environment encountered on spacemap. The goal of this effort is to create a fully digital, flight ready controller design that utilizes an FPGA for implementation of signal conditioning for control feedback signals, generation of commands to the controlled system, and hardware insertion of adaptive-control algorithm approaches. Radiation tolerant FPGA's are a feasible option for reaching this goal.

Gwaltney, David A.↗
NASA NTRS2002

Implementation of Adaptive Digital Controllers on Programmable Logic Devices

Much has been made of the capabilities of FPGA's (Field Programmable Gate Arrays) in the hardware implementation of fast digital signal processing. Such capability also makes an FPGA a suitable platform for the digital implementation of closed loop controllers. Other researchers have implemented a variety of closed-loop digital controllers on FPGA's. Some of these controllers include the widely used proportional-integral-derivative (PID) controller, state space controllers, neural network and fuzzy logic based controllers. There are myriad advantages to utilizing an FPGA for discrete-time control functions which include the capability for reconfiguration when SRAM-based FPGA's are employed, fast parallel implementation of multiple control loops and implementations that can meet space level radiation tolerance requirements in a compact form-factor. Generally, a software implementation on a DSP (Digital Signal Processor) or microcontroller is used to implement digital controllers. At Marshall Space Flight Center, the Control Electronics Group has been studying adaptive discrete-time control of motor driven actuator systems using digital signal processor (DSP) devices. While small form factor, commercial DSP devices are now available with event capture, data conversion, pulse width modulated (PWM) outputs and communication peripherals, these devices are not currently available in designs and packages which meet space level radiation requirements. In general, very few DSP devices are produced that are designed to meet any level of radiation tolerance or hardness. The goal of this effort is to create a fully digital, flight ready controller design that utilizes an FPGA for implementation of signal conditioning for control feedback signals, generation of commands to the controlled system, and hardware insertion of adaptive control algorithm approaches. An alternative is required for compact implementation of such functionality to withstand the harsh environment encountered on spacecraft. Radiation tolerant FPGA's are a feasible option for reaching this goal.

Gwaltney, David A.↗
NASA NTRS2016

Feasibility of Varying Geo-Fence Around an Unmanned Aircraft Operation Based on Vehicle Performance and Wind

Managing trajectory separation is critical to ensuring accessibility, efficiency, and safety in the unmanned airspace. The notion of geo-fences is an emerging concept, where distance buffers enclose individual trajectories and areas of operation in order to manage the airspace. Currently, the Air Traffic Management system for commercial travel defines static distance buffers around the aircraft; however, commercial UASs are envisioned to operate in significantly closer proximity to other UAS requiring a geo-fence for spacing operations. The geo-fence size can be determined based on vehicle performance characteristics, state of the airspace, weather, and other unforeseen events such as emergency or disaster response. Calculation of the geo-fence size could be determined as part of pre-flight planning and during real-time operations. A largely non-homogeneous fleet of UASs will be operating in low altitude and will likely be commercially developed. Due to intellectual property concerns, the operators may not provide detailed specifications of the control system to UTM. In addition, the huge variety of UAS makes modeling each control system prohibitive and flight data for these vehicles may not exist. Therefore, a generalized, simple geo-fence sizing algorithm must be developed such that it does not rely on detailed knowledge of the vehicle control system, accounts for the presence of urban winds, and is sufficiently accurate. In this work, two simple models are investigated to determine its feasibility as an adequate means for calculating the geo-fence size. The vehicle data used in this work are provided by UAS manufactures who have partnered with NASA's UTM project and some publicly available websites. The first model utilizes wind data processed from the NOAA HRRR (Hourly Rapid Refresh) product and Sonar Annemometer data provided by San Jose State. The second model utilizes OpenFOAM which is a CFD code used to generate a wind field for flow around a single building. The key vehicle performance parameters can include UAS response time to disturbances, command to actuation latency, control system rate limits, time to recovery to desired path, and aerodynamics. It was found that the first model provides an initial understanding of geo-fence sizing, but does not provide enough accuracy to provide UTM with an efficient means of scheduling vehicles. The results of the second model reveal that modeling UAS controls systems with a linearized plant and gain scheduled PID controller does not allow capture the UAS flight dynamics within a significant envelope of the wind disturbances.

uncertainty↗
NASA NTRS1989

Robust high-performance control for robotic manipulators

Model-based and performance-based control techniques are combined for an electrical robotic control system. Thus, two distinct and separate design philosophies were merged into a single control system having a control law formulation including two distinct and separate components, each of which yields a respective signal componet that is combined into a total command signal for the system. Those two separate system components include a feedforward controller and feedback controller. The feedforward controller is model-based and contains any known part of the manipulator dynamics that can be used for on-line control to produce a nominal feedforward component of the system's control signal. The feedback controller is performance-based and consists of a simple adaptive PID controller which generates an adaptive control signal to complement the nomical feedforward signal.

Seraji, Homayoun↗
NASA NTRS1991

Robust high-performance control for robotic manipulators

Model-based and performance-based control techniques are combined for an electrical robotic control system. Thus, two distinct and separate design philosophies have been merged into a single control system having a control law formulation including two distinct and separate components, each of which yields a respective signal component that is combined into a total command signal for the system. Those two separate system components include a feedforward controller and a feedback controller. The feedforward controller is model-based and contains any known part of the manipulator dynamics that can be used for on-line control to produce a nominal feedforward component of the system's control signal. The feedback controller is performance-based and consists of a simple adaptive PID controller which generates an adaptive control signal to complement the nominal feedforward signal.

Seraji, Homayoun↗
NASA NTRS1991

Attitude control compensator for flexible spacecraft

An attitude control loop for a spacecraft uses a proportional-integral-derivative (PID) controller for control about an axis. The spacecraft body has at least a primary mechanical resonance. The attitude sensors are collocated, or both on the rigid portion of the spacecraft. The flexure attributable to the resonance may result in instability of the system. A compensator for the control loop has an amplitude response which includes a component which rolls off beginning at frequencies below the resonance, and which also includes a component having a notch at a notch frequency somewhat below the resonant frequency. The phase response of the compensator tends toward zero at low frequencies, and tends toward -180.degree. as frequency increases toward the notch frequency. At frequencies above the notch frequency, the phase decreases from +180.degree., becoming more negative, and tending toward -90.degree. at frequencies far above the resonance frequency. Near the resonance frequency, the compensator phase is near zero.

Goodzeit, Neil E.↗