Autonomous Power Control Adaptive Protection for Distributed Microgrids
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An adaptive-control switching buck regulator - Implementation, analysis, and design
Describing-function techniques and averaging methods have been employed to characterize a multiloop switching buck regulator by three functional blocks: power stage, analog signal processor, and pulse modulator. The model is employed to explore possible forms of pole-zero cancellation and the adaptive nature of the control to filter parameter changes. Analysis-based design guidelines are provided including a suggested additional RC-compensation loop to optimize regulator performances such as stability, audiosusceptibility, output impedance, and load transient response.
Lee, F. C.↗
Extended Lyapunov stability criterion using a nonlinear algebraic relation with application to adaptive control
A new nonlinear stability criterion is developed by use of a class of Lyapunov functionals for model-reference adaptive systems (MRAS). Results are compared with traditional results, and a comparative design technique is used to illustrate its function in improving the transient response of an MRAS controller. For a particular system structure and class of input signals, the new stability criterion is shown to include traditional sufficiency stability conditions as a special case. An example is cited to illustrate the use of the nonlinear criterion and its definite advantages in helping improve the adaptive error transient response of a system. Analysis of results is effected by use of a linearization technique on the resulting adaptive equations.
Colburn, B. K.↗
Survey of adaptive control using Liapunov design
A survey was made of the literature devoted to the synthesis of model-tracking adaptive systems based on application of Liapunov's second method. The basic synthesis procedure is introduced and a critical review of extensions made to the theory since 1966 is made. The extensions relate to design for relative stability, reduction of order techniques, design with disturbance, design with time variable parameters, multivariable systems, identification, and an adaptive observer.
Lindorff, D. P.↗
Stable Direct Adaptive Control of Linear Infinite-dimensional Systems Using a Command Generator Tracker Approach
A command generator tracker approach to model following contol of linear distributed parameter systems (DPS) whose dynamics are described on infinite dimensional Hilbert spaces is presented. This method generates finite dimensional controllers capable of exponentially stable tracking of the reference trajectories when certain ideal trajectories are known to exist for the open loop DPS; we present conditions for the existence of these ideal trajectories. An adaptive version of this type of controller is also presented and shown to achieve (in some cases, asymptotically) stable finite dimensional control of the infinite dimensional DPS.
Balas, M. J.↗
Adaptive Control Law Development for Failure Compensation Using Neural Networks on a NASA F-15 Aircraft
This viewgraph presentation covers the following topics: 1) Brief explanation of Generation II Flight Program; 2) Motivation for Neural Network Adaptive Systems; 3) Past/ Current/ Future IFCS programs; 4) Dynamic Inverse Controller with Explicit Model; 5) Types of Neural Networks Investigated; and 6) Brief example
Burken, John J.↗
An Indirect Adaptive Control Scheme in the Presence of Actuator and Sensor Failures
The problem of controlling a system in the presence of unknown actuator and sensor faults is addressed. The system is assumed to have groups of actuators, and groups of sensors, with each group consisting of multiple redundant similar actuators or sensors. The types of actuator faults considered consist of unknown actuators stuck in unknown positions, as well as reduced actuator effectiveness. The sensor faults considered include unknown biases and outages. The approach employed for fault detection and estimation consists of a bank of Kalman filters based on multiple models, and subsequent control reconfiguration to mitigate the effect of biases caused by failed components as well as to obtain stability and satisfactory performance using the remaining actuators and sensors. Conditions for fault identifiability are presented, and the adaptive scheme is applied to an aircraft flight control example in the presence of actuator failures. Simulation results demonstrate that the method can rapidly and accurately detect faults and estimate the fault values, thus enabling safe operation and acceptable performance in spite of failures.
Sun, Joy Z.↗
Implementation of an Adaptive Controller System from Concept to Flight Test
The National Aeronautics and Space Administration (NASA) at the Dryden Flight Research Center (DFRC) has been conducting flight-test research using an F-15 aircraft (figure 1). This aircraft has been specially modified to interface a neural net (NN) controller as part of a single-string Airborne Research Test System (ARTS) computer with the existing quad-redundant flight control system (FCC) shown in figure 2. The NN commands are passed to FCC channels 2 and 4 and are cross channel data linked (CCDL) to the other computers as shown. Numerous types of fault-detection monitors exist in the FCC when the NN mode is engaged; these monitors would cause an automatic disengagement of the NN in the event of a triggering fault. Unfortunately, these monitors still may not prevent a possible NN hard-over command from coming through to the control laws. Therefore, an additional and unique safety monitor was designed for a single-string source that allows authority at maximum actuator rates but protects the pilot and structural loads against excessive g-limits in the case of a NN hard-over command input. This additional monitor resides in the FCCs and is executed before the control laws are computed. This presentation describes a "floating limiter" (FL) concept that was developed and successfully test-flown for this program (figure 3). The FL computes the rate of change of the NN commands that are input to the FCC from the ARTS. A window is created with upper and lower boundaries, which is constantly "floating" and trying to stay centered as the NN command rates are changing. The limiter works by only allowing the window to move at a much slower rate than those of the NN commands. Anywhere within the window, however, full rates are allowed. If a rate persists in one direction, it will eventually "hit" the boundary and be rate-limited to the floating limiter rate. When this happens, a persistent counter begins and after a limit is reached, a NN disengage command is generated. The tunable metrics for the FL are (1) window size, (2) drift rate, and (3) persistence counter. Ultimate range limits are also included in case the NN command should drift slowly to a limit value that would cause the FL to be defeated. The FL has proven to work as intended. Both high-g transients and excessive structural loads are controlled with NN hard-over commands. This presentation discusses the FL design features and presents test cases. Simulation runs are included to illustrate the dramatic improvement made to the control of NN hard-over effects. A mission control room display from a flight playback is presented to illustrate the neural net fault display representation. The FL is very adaptable to various requirements and is independent of flight condition. It should be considered as a cost-effective safety monitor to control single-string inputs in general.
Larson, Richard R.↗
Adaptive Control Algorithm to Adjust Settings in Photovoltaic Inverters for Electric Grid Cybersecurity (DERAC) v0.1
Volt-VAR and Volt-Watt functionality in photovoltaic (PV) smart inverters provide mechanisms to ensure system voltage magnitudes and power factors remain within acceptable limits. However, these control functions can become unstable, introducing oscillations in system voltages when not appropriately configured or maliciously altered during a cyberattack. In the event that Volt-VAR and Volt-Watt control functions in a portion of PV smart inverters in a distribution grid are unstable, the proposed adaptation scheme utilizes the remaining and stably-behaving PV smart inverters and other Distributed Energy Resources to mitigate the effect of the instability in real-time. The adaptation mechanism is entirely decentralized, model-free, communication-free, and requires virtually no external configuration. This repository provides code to simulate the algorithm in experiments on the IEEE 37 node test feeder.
Arnold, Daniel↗
Hardware verification of distributed/adaptive control
Adaptive control techniques are studied for their future application to the control of large space structures, where uncertain or changing parameters may destabilize standard control system designs. The approach used is to examine an extended Kalman filter estimator, in which the state vector is augmented with the unknown parameters. The associated Riccatti equation is linearized about the case of exact knowledge of the parameters. By assuming that parameter variations occur slowly, the filter complexity is reduced further yet. Simulations on a two degree-of-freedom oscillator demonstrate the parameter-tracking capability of the filter, and an implementation on the JPL Flexible Beam Facility using an incorrect model shows the adaptive filter/optimal control to be stable where a standard Kalman filter/optimal control design is unstable.
Eldred, D. B.↗
Temperature controller adapts to fatigue tester
Identical blocks of aluminum, held against front and back of specimen, each contain electrical heaters, liquid nitrogen cavity with input and exhaust tubes, and thermocouple. Thermocouples are connected to control unit, which adjusts specimen temperature during fatigue tests over range of 850 degrees F.
Imig, L. A.↗
A new approach for designing self-organizing systems and application to adaptive control
There is tremendous interest in the design of intelligent machines capable of autonomous learning and skillful performance under complex environments. A major task in designing such systems is to make the system plastic and adaptive when presented with new and useful information and stable in response to irrelevant events. A great body of knowledge, based on neuro-physiological concepts, has evolved as a possible solution to this problem. Adaptive resonance theory (ART) is a classical example under this category. The system dynamics of an ART network is described by a set of differential equations with nonlinear functions. An approach for designing self-organizing networks characterized by nonlinear differential equations is proposed.
Ramamoorthy, P. A.↗
Deeplynx Supervisory Control Adapter
This software is intended to facilitate the sending of data from DeepLynx to some human machine interface (HMI). The HMI would read the data generated by this software and potentially make a physical change on a process which the HMI controls. This software enables a digital twin using DeepLynx to talk to an HMI, thereby making autonomous updates to the physical asset which is being twinned.
Wilsdon, KatherineN [Idaho National Laboratory] (0↗
Voice controlled adaptive manipulator and mobility systems for the severely handicapped
Efforts by NASA and the VA to apply available teleoperator/robot technology to rehabilitate amputees and spinal cord injured patients having severe loss of motor, manipulative, and sensory capabilities in the lower and/or upper extremities are summarized. Techniques developed include the control of a wheelchair by voice control or tongue chinswitch. The voice recognition computer and its operation are also described.
Heer, E.↗
Adapting Control Methods for Autonomous Exploration of Unknown Environments
Proposed missions to explore comets and moons will encounter environments that are hostile and unpredictable.
Autonomous↗
ADAPTIVE STATE VECTOR CONTROL, SECTION 9. ADAPTIVE CONTROLLERS DERIVED BY STABILITY CONSIDERATIONS
Adaptive state vector control, section 9, adaptive controllers derived by stability considerations
STABILITY↗
Modular autopilot design and development featuring bayesian non-parametric adaptive control
According to an embodiment, there is provided an onboard integrated computational system for an unmanned aircraft system (“Stabilis” autopilot). This is an integrated suite of hardware, software, and data-to-decisions services that are designed to meet the needs of business and research developers of UAS. Stabilis is designed to accelerate the development of any UAS platform and avionics system; it does so with hardware modularity and software adaptation. The Stabilis offers multiple technological advantages technological advantages including: Plug-and-adapt functionality; Data-to-decisions capability; and, On board parallelization capability.
97 MATHEMATICS AND COMPUTING↗
An error-controlled adaptive time-stepping method for particle advancement in coupled CFD-DEM simulations
Coupled Computational-Fluid-Dynamics (CFD) and Discrete-Element-Method (DEM) models provide an accurate description of multiphase physical systems where a solid granular particle phase exists in an underlying gaseous continuous medium. The time integration of the granular phase in these simulations is typically handled using an explicit scheme with a constant time-step among all particles that is invariant in time to resolve inter-particle collisions. A locally third-order accurate adaptive time integration technique for particles that employs an embedded locally second-order scheme for error determination is presented in this work. The particle time-step size is dynamically adapted based on solution error, thus leading to significant savings in computational time. The efficacy of our scheme is quantified using four test cases of varying complexity (binary collision, homogeneous cooling system, fluidized bed and hopper discharge). The adaptive time-stepping method exhibits improved performance (~ 2–3 times in most of the cases studied) compared to three commonly used non-adaptive time-step methods (first-order Euler-explicit, second-order Adams-Bashforth and third-order Runge-Kutta schemes), while maintaining the same level of accuracy and parallel scalability.
42 ENGINEERING↗