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A Fault-Tolerant Intelligent Robotic Control System
This research project involves development of path planning and graphical user interface software at an operator workstation and a manipulator control system which translates the commands from the user workstation to control the robot arm.
Sixth NASA Glenn Research Center Propulsion Control and Diagnostics (PCD) Workshop
The Intelligent Control and Autonomy Branch at NASA Glenn Research Center hosted the Sixth Propulsion Control and Diagnostics Workshop on August 22-24, 2017. The objectives of this workshop were to disseminate information about research being performed in support of NASA Aeronautics programs; get feedback from peers on the research; and identify opportunities for collaboration. There were presentations and posters by NASA researchers, Department of Defense representatives, and engine manufacturers on aspects of turbine engine modeling, control, and diagnostics.
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.
Integrated health monitoring and controls for rocket engines
Current research in intelligent control systems at the Lewis Research Center is described in the context of a functional framework. The framework is applicable to a variety of reusable space propulsion systems for existing and future launch vehicles. It provides a 'road map' technology development to enable enhanced engine performance with increased reliability, durability, and maintainability. The framework hierarchy consists of a mission coordination level, a propulsion system coordination level, and an engine control level. Each level is described in the context of the Space Shuttle Main Engine. The concept of integrating diagnostics with control is discussed within the context of the functional framework. A distributed real time simulation testbed is used to realize and evaluate the functionalities in closed loop.
Integrated health monitoring and controls for rocket engines
Current research in intelligent control systems at the Lewis Research Center is described in the context of a functional framework. The framework is applicable to a variety of reusable space propulsion systems for existing and future launch vehicles. It provides a 'road map' technology development to enable enhanced engine performance with increased reliability, durability, and maintainability. The framework hierarchy consists of a mission coordination level, a propulsion system coordination level, and an engine control level. Each level is described in the context of the Space Shuttle Main Engine. The concept of integrating diagnostics with control is discussed within the context of the functional framework. A distributed real time simulation testbed is used to realize and evaluate the functionalities in closed loop.
Intelligent command and control systems for satellite ground operations
The Georgia Tech portion of the Intelligent Control Center project includes several complementary activities. Two major activities entail thesis level research; the other activities are either support activities or preliminary explorations (e.g., task analyses) to support the research. The first research activity is the development of principles for the design of active interfaces to support monitoring during real-time supports. It is well known that as the operator's task becomes less active, i.e., more monitoring and less active control, there is concern that the operator will be less involved and less able to rapidly identify anomalous or failure situations. The research project to design active monitoring interfaces is an attempt to remediate this undesirable side-effect of increasingly automated control systems that still depend ultimately on operator supervision. The second research activity is the exploration of the use of case-based reasoning as a way to accumulate operator experience and make it available in computational form.
Fault analysis of multichannel spacecraft power systems
The NASA Marshall Space Flight Center proposes to implement computer-controlled fault injection into an electrical power system breadboard to study the reactions of the various control elements of this breadboard. Elements under study include the remote power controllers, the algorithms in the control computers, and the artificially intelligent control programs resident in this breadboard. To this end, a study of electrical power system faults is being performed to yield a list of the most common power system faults. The results of this study will be applied to a multichannel high-voltage DC spacecraft power system called the large autonomous spacecraft electrical power system (LASEPS) breadboard. The results of the power system fault study and the planned implementation of these faults into the LASEPS breadboard are described.
A reinforcement learning-based architecture for fuzzy logic control
This paper introduces a new method for learning to refine a rule-based fuzzy logic controller. A reinforcement learning technique is used in conjunction with a multilayer neural network model of a fuzzy controller. The approximate reasoning based intelligent control (ARIC) architecture proposed here learns by updating its prediction of the physical system's behavior and fine tunes a control knowledge base. Its theory is related to Sutton's temporal difference (TD) method. Because ARIC has the advantage of using the control knowledge of an experienced operator and fine tuning it through the process of learning, it learns faster than systems that train networks from scratch. The approach is applied to a cart-pole balancing system.
A study of fault injection in multichannel spacecraft power systems
NASA/Marshall Space Flight Center proposes to implement fault injection into an electrical power system breadboard to study the reactions of the various control elements of this breadboard. Among the elements to be studied are the remote power controllers, the algorithms in the control computers, and the artificially intelligent control programs resident in this breadboard. To this end, a study of electrical power is being performed to yield a list of the most common power system faults. The results of this study are being applied to a multichannel high-voltage DC spacecraft power system called the Large Autonomous Spacecraft Electrical Power System Breadboard. Some of the reactions of the breadboard to some of the faults which have been encountered are presented along with the results of this study.
Applications of Payload Directed Flight
Next generation aviation flight control concepts require autonomous and intelligent control system architectures that close control loops directly around payload sensors in manner more integrated and cohesive that in traditional autopilot designs. Research into payload directed flight control at NASA Ames Research Center is investigating new and novel architectures that can satisfy the requirements for next generation control and automation concepts for aviation. Tighter integration between sensor and machine requires definition of specific sensor-directed control modes to tie the sensor data directly into a vehicle control structures throughout the entire control architecture, from low-level stability- and control loops, to higher level mission planning and scheduling reasoning systems. Payload directed flight systems can thus provide guidance, navigation, and control for vehicle platforms hosting a suite of onboard payload sensors. This paper outlines related research into the field of payload directed flight; and outlines requirements and operating concepts for payload directed flight systems based on identified needs from the scientific literature.'
Design and Control of Large Collections of Learning Agents
The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either too brittle, too hard to use, or not scalable to large systems. The 'Collective Intelligence' project at NASA/Ames provides an elegant, machine-learning approach to address these problems. This approach mathematically defines some essential properties that a reward system should have to promote coordinated behavior among reinforcement learners. This work has focused on creating additional key properties and algorithms within the mathematics of the Collective Intelligence framework. One of the additions will allow agents to learn more quickly, in a more coordinated manner. The other will let agents learn with less knowledge of their environment. These additions will allow the framework to be applied more easily, to a much larger domain of multi-agent problems.
Variable Emissivity Through MEMS Technology
This paper discusses a new technology for variable emissivity (vari-e) radiator surfaces, which has significant advantages over traditional radiators and promises an alternative design technique for future spacecraft thermal control systems. All spacecraft rely on radiative surfaces to dissipate waste heat. These radiators have special coatings, typically with a low solar absorptivity and a high infrared-red emissivity, that are intended to optimize performance under the expected heat load and thermal sink environment. The dynamics of the heat loads and thermal environment make it a challenge to properly size the radiator and often require some means of regulating the heat rejection rate of the radiators in order to achieve proper thermal balance. Specialized thermal control coatings, which can passively or actively adjust their emissivity offer an attractive solution to these design challenges. Such systems would allow intelligent control of the rate of heat loss from a radiator in response to heat load and thermal environmental variations. Intelligent thermal control through variable emissivity systems is well suited for nano and pico spacecraft applications where large thermal fluctuations are expected due to the small thermal mass and limited electric resources. Presently there are three different types of vari-e technologies under development: Micro ElectroMechanical Systems (MEMS) louvers, Electrochromic devices, and Electrophoretic devices. This paper will describe several prototypes of micromachined (MEMS) louvers and experimental results for the emissivity variations measured on theses prototypes. It will further discuss possible actuation mechanisms and space reliability aspects for different designs. Finally, for comparison parametric evaluations of the thermal performances of the new vari-e technology and standard thermal control systems are presented in this paper.
Artificial intelligence in process control: Knowledge base for the shuttle ECS model
The general operation of KATE, an artificial intelligence controller, is outlined. A shuttle environmental control system (ECS) demonstration system for KATE is explained. The knowledge base model for this system is derived. An experimental test procedure is given to verify parameters in the model.
A review of the field of artificial intelligence and its possible applications to nasa objectives final report
Artificial intelligence - control, data gathering, and data analyzing systems design
A Boltzmann machine for the organization of intelligent machines
In the present technological society, there is a major need to build machines that would execute intelligent tasks operating in uncertain environments with minimum interaction with a human operator. Although some designers have built smart robots, utilizing heuristic ideas, there is no systematic approach to design such machines in an engineering manner. Recently, cross-disciplinary research from the fields of computers, systems AI and information theory has served to set the foundations of the emerging area of the design of intelligent machines. Since 1977 Saridis has been developing an approach, defined as Hierarchical Intelligent Control, designed to organize, coordinate and execute anthropomorphic tasks by a machine with minimum interaction with a human operator. This approach utilizes analytical (probabilistic) models to describe and control the various functions of the intelligent machine structured by the intuitively defined principle of Increasing Precision with Decreasing Intelligence (IPDI) (Saridis 1979). This principle, even though resembles the managerial structure of organizational systems (Levis 1988), has been derived on an analytic basis by Saridis (1988). The purpose is to derive analytically a Boltzmann machine suitable for optimal connection of nodes in a neural net (Fahlman, Hinton, Sejnowski, 1985). Then this machine will serve to search for the optimal design of the organization level of an intelligent machine. In order to accomplish this, some mathematical theory of the intelligent machines will be first outlined. Then some definitions of the variables associated with the principle, like machine intelligence, machine knowledge, and precision will be made (Saridis, Valavanis 1988). Then a procedure to establish the Boltzmann machine on an analytic basis will be presented and illustrated by an example in designing the organization level of an Intelligent Machine. A new search technique, the Modified Genetic Algorithm, is presented and proved to converge to the minimum of a cost function. Finally, simulations will show the effectiveness of a variety of search techniques for the intelligent machine.
Linear quadratic servo control of a reusable rocket engine
A design method for a servo compensator is developed in the frequency domain using singular values. The method is applied to a reusable rocket engine. An intelligent control system for reusable rocket engines was proposed which includes a diagnostic system, a control system, and an intelligent coordinator which determines engine control strategies based on the identified failure modes. The method provides a means of generating various linear multivariable controllers capable of meeting performance and robustness specifications and accommodating failure modes identified by the diagnostic system. Command following with set point control is necessary for engine operation. A Kalman filter reconstructs the state while loop transfer recovery recovers the required degree of robustness while maintaining satisfactory rejection of sensor noise from the command error. The approach is applied to the design of a controller for a rocket engine satisfying performance constraints in the frequency domain. Simulation results demonstrate the performance of the linear design on a nonlinear engine model over all power levels during mainstage operation.
Future applications of artificial intelligence to Mission Control Centers
Future applications of artificial intelligence to Mission Control Centers are presented in the form of the viewgraphs. The following subject areas are covered: basic objectives of the NASA-wide AI program; inhouse research program; constraint-based scheduling; learning and performance improvement for scheduling; GEMPLAN multi-agent planner; planning, scheduling, and control; Bayesian learning; efficient learning algorithms; ICARUS (an integrated architecture for learning); design knowledge acquisition and retention; computer-integrated documentation; and some speculation on future applications.