Search NASA⌕ Search

SEARCH · Search NASA

Results for “learning 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 145 records · Page 8

Repetitive control in the presence of multiple periodic disturbances

In this paper, the authors introduce several methods for implementing repetitive control of systems subjected to noninteger multiple periodic disturbances. One method uses a single repetitive controller to learn at the common period of the disturbances. The second employs multiple p-integrator based repetitive controllers operating in parallel. The third method introduces the concept of sequenced repetitive controllers. Cross-talk elimination algorithms are proposed to improve the repetitive control performance in the presence of transients. A novel averaging algorithm is introduced for on-line and off-line control strategies.

Solcz, Edward J.↗

A new learning strategy for the two-time-scale neural controller with its application to the tracking control of rigid arms

A novel fast learning rule with fast weight identification is proposed for the two-time-scale neural controller, and a two-stage learning strategy is developed for the proposed neural controller. The results of the stability analysis show that both the tracking error and the fast weight error will be uniformly bounded and converge to a bounded region which depends only on the accuracy of the slow learning if the system is sufficiently excited. The efficiency of the two-stage learning is also demonstrated by a simulation of a two-link arm.

Cheng, W.↗

Artificial neural networks and approximate reasoning for intelligent control in space

A method is introduced for learning to refine the control rules of approximate reasoning-based controllers. A reinforcement-learning technique is used in conjunction with a multi-layer neural network model of an approximate reasoning-based controller. The model learns by updating its prediction of the physical system's behavior. The model can use the control knowledge of an experienced operator and fine-tune it through the process of learning. Some of the space domains suitable for applications of the model such as rendezvous and docking, camera tracking, and tethered systems control are discussed.

Berenji, Hamid R.↗

Disturbance rejection in repetitive controllers

The p-integrator is a digital controller that learns from previous experience executing a repetitive command in order to improve its performance in future repetitions. This class of repetitive controller is an analog of the integral controller of classical control theory. This paper addresses several issues related to the performance of the p-integrator. This is followed by a derivation that will demonstrate the repetitive disturbance rejection capabilities of the controller. The effect of the tracking performance in practical implementations with prefiltering and postfiltering devices are studied - using an exact and approximate formulation. Relations showing the actual system response during steady state operation with these filters are derived. The results are then extended to include the tracking error performance of systems subjected to repetitive disturbances. Finally, the system response between sample times is developed.

Solcz, Edward J.↗

Learning guide for the terminal configured vehicle advanced guidance and control system mode select panel

This learning guide is designed to assist pilots in taking the PLATO presimulator training course on the advanced guidance and control system mode select panel. The learning guide is divided into five sections. The first section, the introduction, presents the course goals, prerequisites, definition of PLATO activities, and a suggested approach to completing the course. The remaining four sections present the purpose, learning activities and summary of each lesson of the AGCS PLATO course, which consists of (1) AGCS introduction; (2) lower order modes; (3) higher order modes; and (4) an arrival route exercise.

Anderson, M. A.↗

Sound transmission reduction with intelligent panel systems

Experimental and theoretical investigations are performed of the use of intelligent panel systems to control the sound transmission and radiation. An intelligent structure is defined as a structural system with integrated actuators and sensors under the guidance of an adaptive, learning type controller. The system configuration is based on the Active Structural Acoustic Control (ASAC) concept where control inputs are applied directly to the structure to minimize an error quantity related to the radiated sound field. In this case multiple piezoelectric elements are employed as sensors. The importance of optimal shape and location is demonstrated to be of the same order of influence as increasing the number of channels of control.

Fuller, Chris R.↗

The efficacy of using human myoelectric signals to control the limbs of robots in space

This project was designed to investigate the usefulness of the myoelectric signal as a control in robotics applications. More specifically, the neural patterns associated with human arm and hand actions were studied to determine the efficacy of using these myoelectric signals to control the manipulator arm of a robot. The advantage of this approach to robotic control was the use of well-defined and well-practiced neural patterns already available to the system, as opposed to requiring the human operator to learn new tasks and establish new neural patterns in learning to control a joystick or mechanical coupling device.

Clark, Jane E.↗

Flight Test of the F/A-18 Active Aeroelastic Wing Airplane

A viewgraph presentation of flight tests performed on the F/A active aeroelastic wing airplane is shown. The topics include: 1) F/A-18 AAW Airplane; 2) F/A-18 AAW Control Surfaces; 3) Flight Test Background; 4) Roll Control Effectiveness Regions; 5) AAW Design Test Points; 6) AAW Phase I Test Maneuvers; 7) OBES Pitch Doublets; 8) OBES Roll Doublets; 9) AAW Aileron Flexibility; 10) Phase I - Lessons Learned; 11) Control Law Development and Verification & Validation Testing; 12) AAW Phase II RFCS Envelopes; 13) AAW 1-g Phase II Flight Test; 14) Region I - Subsonic 1-g Rolls; 15) Region I - Subsonic 1-g 360 Roll; 16) Region II - Supersonic 1-g Rolls; 17) Region II - Supersonic 1-g 360 Roll; 18) Region III - Subsonic 1-g Rolls; 19) Roll Axis HOS/LOS Comparison Region II - Supersonic (open-loop); 20) Roll Axis HOS/LOS Comparison Region II - Supersonic (closed-loop); 21) AAW Phase II Elevated-g Flight Test; 22) Region I - Subsonic 4-g RPO; and 23) Phase II - Lessons Learned

Voracek, David↗

Intelligent flight control systems

The capabilities of flight control systems can be enhanced by designing them to emulate functions of natural intelligence. Intelligent control functions fall in three categories. Declarative actions involve decision-making, providing models for system monitoring, goal planning, and system/scenario identification. Procedural actions concern skilled behavior and have parallels in guidance, navigation, and adaptation. Reflexive actions are spontaneous, inner-loop responses for control and estimation. Intelligent flight control systems learn knowledge of the aircraft and its mission and adapt to changes in the flight environment. Cognitive models form an efficient basis for integrating 'outer-loop/inner-loop' control functions and for developing robust parallel-processing algorithms.

Stengel, Robert F.↗

Feedback Implementation of Zermelo's Optimal Control by Sugeno Approximation

This paper proposes an approach to implement optimal control laws of nonlinear systems in real time. Our methodology does not require solving two-point boundary value problems online and may not require it off-line either. The optimal control law is learned using the original Sugeno controller (OSC) from a family of optimal trajectories. We compare the trajectories generated by the OSC and the trajectories yielded by the optimal feedback control law when applied to Zermelo's ship steering problem.

Clifton, C.↗

Aircraft Control Using Engine Thrust: A History of Learning TOC Real-Time

A history of learning the operation of Throttles Only Control (TOC) to control an aircraft in real time using engine thrust is shown. The topics include: 1) Past TOC Accidents/Incidents; 2) 1972: DC-10 American Airlines; 3) May 1974: USAF B-52H; 4) April 1975: USAF C-5A; 5) April 1975: USAF C-5A; 6) 1981: USAF B-52G; 7) August 1985: JAL 123 B-747; 8) JAL 123 Survivor Story; 9) JAL 123 Investigation Findings; 10) July 1989: UAL 232 DC-10; 11) UAL 232 DC-10; 12) Eastwind 517 B-737; 13) November 2003: DHL A-300; 14) Historically, TOC has saved lives; 15) Automated Throttles-Only Control; 16) PCA Project; 17) Propulsion-Controlled Aircraft; 18) MD-11 PCA System and Flight Test Envelope; 19) MD-11 Simulation, PCA ILS-Soupled Landing Dispersion; 20) Throttles-Only Pitch and Roll Control Power; 21) PCA in Commercial Fleet; 22) Fall 2005: PCAR Project; 23) PCAR Background - TOC; and 24) PCAR Background - TOC.

Cole, Jennifer H.↗

Lessons Learned and Flight Results from the F15 Intelligent Flight Control System Project

A viewgraph presentation on the lessons learned and flight results from the F15 Intelligent Flight Control System (IFCS) project is shown. The topics include: 1) F-15 IFCS Project Goals; 2) Motivation; 3) IFCS Approach; 4) NASA F-15 #837 Aircraft Description; 5) Flight Envelope; 6) Limited Authority System; 7) NN Floating Limiter; 8) Flight Experiment; 9) Adaptation Goals; 10) Handling Qualities Performance Metric; 11) Project Phases; 12) Indirect Adaptive Control Architecture; 13) Indirect Adaptive Experience and Lessons Learned; 14) Gen II Direct Adaptive Control Architecture; 15) Current Status; 16) Effect of Canard Multiplier; 17) Simulated Canard Failure Stab Open Loop; 18) Canard Multiplier Effect Closed Loop Freq. Resp.; 19) Simulated Canard Failure Stab Open Loop with Adaptation; 20) Canard Multiplier Effect Closed Loop with Adaptation; 21) Gen 2 NN Wts from Simulation; 22) Direct Adaptive Experience and Lessons Learned; and 23) Conclusions

Bosworth, John↗

Facts and fiction of learning systems

The methodology that will provide the updated precision for the hardware control and the advanced decision making and planning in the software control is called learning systems and intelligent control. It was developed theoretically as an alternative for the nonsystematic heuristic approaches of artificial intelligence experiments and the inflexible formulation of modern optimal control methods. Its basic concepts are discussed and some feasibility studies of some practical applications are presented.

Saridis, G. N.↗

Application of fuzzy logic-neural network based reinforcement learning to proximity and docking operations: Attitude control results

As part of the RICIS activity, the reinforcement learning techniques developed at Ames Research Center are being applied to proximity and docking operations using the Shuttle and Solar Max satellite simulation. This activity is carried out in the software technology laboratory utilizing the Orbital Operations Simulator (OOS). This report is deliverable D2 Altitude Control Results and provides the status of the project after four months of activities and outlines the future plans. In section 2 we describe the Fuzzy-Learner system for the attitude control functions. In section 3, we provide the description of test cases and results in a chronological order. In section 4, we have summarized our results and conclusions. Our future plans and recommendations are provided in section 5.

Jani, Yashvant↗

Developing a Simulation-Based Training Program for Non-Traditional Caregivers

Simulation-based training enables learning in controlled environments which nevertheless mimic real-world scenarios. It has proven effective in the training of medical personnel and affords rapid assimilation and integration of necessary skills. Non-traditional caregivers often operate in austere environments, where resource and personnel limitations preclude more standard provision of care by highly trained and fully equipped health care teams. In these settings, training time for the caregivers may be limited, with long gaps between time of the training and use of the skills, a limited grasp of the underlying physiology, and unfamiliarity with "medical English" which can render it difficult to communicate concepts to more advanced practitioners when such interaction can take place, as for example, when telemedicine can be used to project medical skills further forward . Methods: Simulation-based training can assist in the familiarization of caregivers to the environment, ensure adequate execution of skills at the appropriate time(s), and allow practice of telemedicine communication patterns between the mentor and caregiver. Results: Scenario-based training can and has been used for initial and sustainment training modules, including self-taught modules for use in the field. Strict identification of the critical concepts is vital, as is development and practice of technically simple procedures wherever possible. Medical devices can off-load tasks from caregivers, as well as to minimize the necessary level of caregiver knowledge, while integrated simulations among all members of the mission team can improve communication and efficiency. Discuss ion: Nontraditional caregivers face unique challenges when learning to provide medical care. Scenario-based curricula allow lesson plans to be tailored to each group's individual needs, as well as being suited for the participation of numerous groups, including the caregiver, evacuation/transport staff, decision-makers, and hospital-based physician.

Bacal, Kira↗

Program Aids Simulation Of Neural Networks

Computer program NETS - Tool for Development and Evaluation of Neural Networks - provides simulation of neural-network algorithms plus software environment for development of such algorithms. Enables user to customize patterns of connections between layers of network, and provides features for saving weight values of network, providing for more precise control over learning process. Consists of translating problem into format using input/output pairs, designing network configuration for problem, and finally training network with input/output pairs until acceptable error reached. Written in C.

Baffes, Paul T.↗

Intelligent control and adaptive systems; Proceedings of the Meeting, Philadelphia, PA, Nov. 7, 8, 1989

Various papers on intelligent control and adaptive systems are presented. Individual topics addressed include: control architecture for a Mars walking vehicle, representation for error detection and recovery in robot task plans, real-time operating system for robots, execution monitoring of a mobile robot system, statistical mechanics models for motion and force planning, global kinematics for manipulator planning and control, exploration of unknown mechanical assemblies through manipulation, low-level representations for robot vision, harmonic functions for robot path construction, simulation of dual behavior of an autonomous system. Also discussed are: control framework for hand-arm coordination, neural network approach to multivehicle navigation, electronic neural networks for global optimization, neural network for L1 norm linear regression, planning for assembly with robot hands, neural networks in dynamical systems, control design with iterative learning, improved fuzzy process control of spacecraft autonomous rendezvous using a genetic algorithm.

Rodriguez, Guillermo↗