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Problems of implementing rule-based expert systems using fuzzy sets are considered. A fuzzy logic software development shell is used that allows inclusion of both crisp and fuzzy rules in decision making and process control problems. Results are given that compare this type of expert system to a human expert in some specific applications. Advantages and disadvantages of such systems are discussed.
Control of nutrient solution parameters is very important for the growth and development of plants grown hydroponically. Protocols involving different nutrient solution replenishment times (e.g. one-week, two-week, or two-day replenishment) provide manual periodic control of the nutrient solution's electrical conductivity (EC). Since plants take-up nutrients as they grow, manual control has a drawback in that EC is not held constant between replenishments. In an effort to correct this problem the Center for Food and Environmental Systems for Human Exploration of Space at Tuskegee University has developed a system for managing and controlling levels of EC over a plant's entire growing cycle. A prototype system is being tested on sweetpotato production using the nutrient film technique (NFT), and it is being compared to a system in which sweetpotatoes are grown using NFT with manual control. NASA has played an important role in the development of environmental control systems. They have become a forerunner in growing plants hydroponically with some control systems through the use of networked data acquisition and control using environmental growth chambers. Data acquisition systems which involve the use of real-time, calibration, set points, user panel, and graphical representation programming provide a good method of controlling nutrient solution parameters such as EC and pH [Bledsoe, 19931]. In NASA's Biomass Production Chamber (BPC) at Kennedy Space Center, control is provided by a programmable logic controller (PLC). This is an industrial controller which combines ladder computer logic which has the ability to handle various levels of electrical power. The controller controls temperature, light and other parameters that affect the plant's environment, in the BPC, the Nutrient Delivery System (NIX), a sub-system of the PLC, controls nutrient solution parameters such as EC, pH, and solution levels. When the nutrient EC measurement goes outside a preset range (120-130 mS/m) a set amount of a stock solution of nutrients is automatically added by a metering pump to bring the EC back into operating range [Fortson, 1992]. This paper describes a system developed at Tuskegee University for controlling the EC of a nutrient solution used for growing sweetpotatoes with an EC controller and a computer with LabView data acquisition and instrumentation software. It also describes the preliminary data obtained from the growth of sweetpotatoes using this prototype control system.
The fuzzy classifier system (FCS) combines the ideas of fuzzy logic controllers (FLC's) and learning classifier systems (LCS's). It brings together the expressive powers of fuzzy logic as it has been applied in fuzzy controllers to express relations between continuous variables, and the ability of LCS's to evolve co-adapted sets of rules. The goal of the FCS is to develop a rule-based system capable of learning in a reinforcement regime, and that can potentially be used for process control.
In principle, formal methods offer many advantages for aerospace software development: they can help to achieve ultra-high reliability, and they can be used to provide evidence of the reliability claims which can then be subjected to external scrutiny. However, despite years of research and many advances in the underlying formalisms of specification, semantics, and logic, formal methods are not much used in practice. In our opinion this is related to three major shortcomings. First, the application of formal methods is still expensive because they are labor- and knowledge-intensive. Second, they are difficult to scale up to complex systems because they are based on deep mathematical insights about the behavior of the systems (t.e., they rely on the "heroic proof"). Third, the proofs can be difficult to interpret, and typically stand in isolation from the original code. In this paper, we describe a tool for formally demonstrating safety-relevant aspects of aerospace software, which largely circumvents these problems. We focus on safely properties because it has been observed that safety violations such as out-of-bounds memory accesses or use of uninitialized variables constitute the majority of the errors found in the aerospace domain. In our approach, safety means that the program will not violate a set of rules that can range for the simple memory access rules to high-level flight rules. These different safety properties are formalized as different safety policies in Hoare logic, which are then used by a verification condition generator along with the code and logical annotations in order to derive formal safety conditions; these are then proven using an automated theorem prover. Our certification system is currently integrated into a model-based code generation toolset that generates the annotations together with the code. However, this automated formal certification technology is not exclusively constrained to our code generator and could, in principle, also be integrated with other code generators such as RealTime Workshop or even applied to legacy code. Our approach circumvents the historical problems with formal methods by increasing the degree of automation on all levels. The restriction to safety policies (as opposed to arbitrary functional behavior) results in simpler proof problems that can generally be solved by fully automatic theorem proves. An automated linking mechanism between the safety conditions and the code provides some of the traceability mandated by process standards such as DO-178B. An automated explanation mechanism uses semantic markup added by the verification condition generator to produce natural-language explanations of the safety conditions and thus supports their interpretation in relation to the code. It shows an automatically generated certification browser that lets users inspect the (generated) code along with the safety conditions (including textual explanations), and uses hyperlinks to automate tracing between the two levels. Here, the explanations reflect the logical structure of the safety obligation but the mechanism can in principle be customized using different sets of domain concepts. The interface also provides some limited control over the certification process itself. Our long-term goal is a seamless integration of certification, code generation, and manual coding that results in a "certified pipeline" in which specifications are automatically transformed into executable code, together with the supporting artifacts necessary for achieving and demonstrating the high level of assurance needed in the aerospace domain.
In this paper, a new approach is proposed to solve a multi-objective economic-emission scheduling problem in microgrids (MGs) by simultaneously minimizing the energy and emission costs of the MG with various distributed energy resources (DERs). The proposed approach is an extension of a computationally effective multiobjective optimization technique, Pareto concavity elimination transformation (PaCcET). The proposed approach, referred to as Fuzzified-PaCcET, employs a fuzzy logic controller to dynamically revise crossover and mutation rates in the original PaCcET leading to the faster convergence of the solution. The proposed approach finds the best Pareto front, also referred to as a Non-dominated set (NDS) of solutions, instead of finding a single optimal solution. In order to find the solutions on concave areas of the Pareto front, an iterative objective space transformation is performed in the PaCcET algorithm to allow a linear combination of objective functions (in the transformed objective space). The proposed Fuzzified-PaCcET-based scheduling is implemented on a MG with various dispatchable and non-dispatchable DERs to find the set of optimal solutions according to the total fuel cost of DERs, as well as the most optimum environmental cost. In order to extract the best compromise solution (BCS) among NDS of solutions, a fuzzy-based method is implemented. The comparison of the simulation results of the Fuzzified-PaCcET with that of PaCcET shows that Fuzzified-PaCcET can generate better solution with less computational burden.
Report describes solar heating system composed of warm-air solar collector, logic control unit, and switching and transport unit, that meets government standards for installation in residential dwellings. Text describes system operation and performance specifications complemented by comprehensive set of subcomponent design drawings.
A strategy for autonomous navigation of a robotic vehicle across hazardous terrain involves the use of a measure of traversability of terrain within a fuzzy-logic conceptual framework. This navigation strategy requires no a priori information about the environment. Fuzzy logic was selected as a basic element of this strategy because it provides a formal methodology for representing and implementing a human driver s heuristic knowledge and operational experience. Within a fuzzy-logic framework, the attributes of human reasoning and decision- making can be formulated by simple IF (antecedent), THEN (consequent) rules coupled with easily understandable and natural linguistic representations. The linguistic values in the rule antecedents convey the imprecision associated with measurements taken by sensors onboard a mobile robot, while the linguistic values in the rule consequents represent the vagueness inherent in the reasoning processes to generate the control actions. The operational strategies of the human expert driver can be transferred, via fuzzy logic, to a robot-navigation strategy in the form of a set of simple conditional statements composed of linguistic variables. These linguistic variables are defined by fuzzy sets in accordance with user-defined membership functions. The main advantages of a fuzzy navigation strategy lie in the ability to extract heuristic rules from human experience and to obviate the need for an analytical model of the robot navigation process.
New, more-efficient designs for aircraft jet engines are not capable of supplying the large quantities of bleed air necessary to provide pressurization and air conditioning for the environmental control systems (ECS) of the next generation of large passenger aircraft. System analysis and engineering have determined that electrically-driven ECS can help to maintain the improved fuel efficiencies; and electronic controllers and induction motors are now being developed in a NASA/NPD SBIR Program to drive both types of ECS compressors. Previous variable-speed induction motor/controller system developments and publications have primarily focused on field-oriented control, with large transient reserve power, for maximum acceleration and optimum response in actuator and robotics systems. The application area addressed herein is characterized by slowly-changing inputs and outputs, small reserve power capability for acceleration, and optimization for maximum efficiency. This paper therefore focuses on the differences between this case and the optimum response case, and shows the development of this new motor/controller approach. It starts with the creation of a new set of controller requirements. In response to those requirements, new control algorithms are being developed and implemented in an embedded computer, which is integrated into the motor controller closed loop. Buffered logic outputs are used to drive the power switches in a resonant-technology, power processor/motor-controller, at switching/resonant frequencies high enough to support efficient high-frequency induction motor operation at speeds up to 50,000-RPA
Loop heat pipes (LHPs) have been used for thermal control of several NASA and commercial orbiting spacecraft. The LHP operating temperature is governed by the saturation temperature of its compensation chamber (CC). Most LHPs use the CC temperature for feedback control of its operating temperature. There exists a thermal resistance between the heat source to be cooled by the LHP and the LHP's CC. Even if the CC set point temperature is controlled precisely, the heat source temperature will still vary with its heat output. For most applications, controlling the heat source temperature is of most interest. A logical question to ask is: "Can the heat source temperature be used for feedback control of the LHP operation?" A test program has been implemented to answer the above question. Objective is to investigate the LHP performance using the CC temperature and the heat source temperature for feedback control
Feedback logic is given that produces satisfactory penetration of an aircraft through a severe downburst. The logic has two parts: (1) A switch that calls for full throttle and tight climb-rate-hold feedback logic when the magnitude of headwind-rate exceeds a threshold level; this switch stays on until re-set by the pilot after penetration of the downburst. (2) A tight climb-rate-hold autopilot, using feedback of pitch-angle to angle-of-attack. After penetration, recovery to steady climbing flight is aided by using feedback of airspeed deviation to throttle. Insights on control strategy are gained from an analysis of energy changes during flight through a downburst. Closed-loop simulations are given for a 727 aircraft penetrating severe downbursts on take-off. Boeing's recommendation of constant pitch angle was confirmed as an excellent feedback control strategy.
This invention relates to a reconfigurable fuzzy cell comprising a digital control programmable gain operation amplifier, an analog-to-digital converter, an electrically erasable PROM, and 8-bit counter and comparator, and supporting logic configured to achieve in real-time fuzzy systems high throughput, grade-of-membership or membership-value conversion of multi-input sensor data. The invention provides a flexible multiplexing-capable configuration, implemented entirely in hardware, for effectuating S-, Z-, and PI-membership functions or combinations thereof, based upon fuzzy logic level-set theory. A membership value table storing 'knowledge data' for each of S-, Z-, and PI-functions is contained within a nonvolatile memory for storing bits of membership and parametric information in a plurality of address spaces. Based upon parametric and control signals, analog sensor data is digitized and converted into grade-of-membership data. In situ learn and recognition modes of operation are also provided.
A data processing apparatus includes one or more host processors with first processing units, one or more caches with second processing unit, a non-cache memory having a third processing unit and a reorder buffer operable to maintain data order during execution of a program of instructions. An instruction scheduler routes instructions to the processing units. Data coherence is maintained by control logic that blocks access to data locations in use by a selected processing unit other than the selected processing unit until data associated with the data locations are released from the reorder buffer. Data stored in the cache is written to the memory if it is already in a modified state, otherwise the state is set to the modified state. A memory controller may be used to restrict access to memory locations to be operated on.
Increased penetration of photovoltaics and electrification of traditionally gas appliances are exacerbating existing challenges in cost-effectively balancing electricity grid supply and demand. Decarbonization without incurring expensive transmission and distribution system capacity increases requires shifting building loads from peak demand times to peak renewable production times. Grid operators are evaluating new ways of encouraging load shifting, including using time-varying price structures to provide a financial incentive. If devices incorporate price-responsive controls, a price profile could be designed to yield a wide variety of load curves as needed to optimize grid functionality. Heat pump water heaters (HPWHs) are an ideal device for price-responsive controls because the storage tank enables them to optimize the timing of electricity consumption without impacting hot water delivery service. This paper presents work demonstrating how price-responsive controls for HPWHs can provide different load profiles, as needed to stabilize the grid, in response to different price profiles. HPWH manufacturers now include web API and CTA-2045 communication capabilities which enable sending load shaping control signals. Pilot studies and preliminary programs have utilized these capabilities with uniform control strategies to reduce 4-9 PM electricity consumption. However, no studies have developed flexible controls capable of both a) responding to constantly varying price profiles and b) customizing logic to match the needs of each HPWH. Berkeley Lab's CalFlexHub project is pioneering price-driven load flexibility by developing and deploying cost-minimizing controls utilizing setpoint setting signals for fleets of HPWHs in response to varying price profiles. Control development is based on simulations using the Flexible Heat Pump Water Heater Performance Predictor which captures the control decisions of a residential, integrated HPWH manufacturer’s on-board controller. The proposed cost-reducing controls respond to constantly changing price profiles, providing the ability to change the price profile to generate load curves as needed to maintain grid stability. Preliminary simulations studying the load shaping capabilities of price-responsive controls on a fleet of 60 HPWHs have demonstrated an average of a) 135.4% increases in load during low-price periods, b) >36.8% reductions in electricity peak-price period, and c) 6.2% electricity cost savings.
The core electronics subsystems on unmanned spacecraft, which have been sent over the last 20 years to investigate the moon, Mars, Venus, and Mercury, have progressed through an evolution from simple fixed controllers and analog computers in the 1960's to general-purpose digital computers in current designs. This evolution is now moving in the direction of distributed computer networks. Current Voyager spacecraft already use three on-board computers. One is used to store commands and provide overall spacecraft management. Another is used for instrument control and telemetry collection, and the third computer is used for attitude control and scientific instrument pointing. An examination of the control logic in the instruments shows that, for many, it is cost-effective to replace the sequencing logic with a microcomputer. The Unified Data System architecture considered consists of a set of standard microcomputers connected by several redundant buses. A typical self-checking computer module will contain 23 RAMs, two microprocessors, one memory interface, three bus interfaces, and one core building block.
This document is a user's guide for the Power Autonomy Research and Development Environment (PARDE) software package. PARDE is a version of NASA's Autonomous Power Control (APC) software that can be used to evaluate fault management and automatic power system reconfiguration algorithms in a relevant system without having to fully develop all the supporting software. Software items included are a set of C++ class source files representing simplified fault management and reconfiguration logic, a power system simulation representing a notional architecture for NASA's Gateway vehicle, a web-based graphical user interface for running and testing the simulation and APC, a Docker-based automatic setup script for a development environment, and a user's guide.
The tether control law to retrieve the satellite was modified in order to have a smooth retrieval trajectory of the satellite that minimizes the thruster activation. The satellite thrusters were added to the rotational dynamics computer code and a preliminary control logic was implemented to simulate them during the retrieval maneuver. The high resolution computer code for modelling the three dimensional dynamics of untensioned tether, SLACK3, was made fully operative and a set of computer simulations of possible tether breakages was run. The distribution of the electric field around an electrodynamic tether in vacuo severed at some length from the shuttle was computed with a three dimensional electrodynamic computer code.
A set of preliminary entry guidance and autopilot software formulations is presented for use in the Mission Control Center (MCC) entry processor. These software formulations meet all level B requirements. Revision 2 incorporates the modifications required to functionally simulate optimal TAEM targeting capability (OTT). Implementation of this logic in the MCC must be coordinated with flight software OTT implementation and MCC TAEM guidance OTT. The entry guidance logic is based on the Orbiter avionics entry guidance software. This MCC requirements document contains a definition of coordinate systems, a list of parameter definitions for the software formulations, a description of the entry guidance detailed formulation requirements, a description of the detailed autopilot formulation requirements, a description of the targeting routine, and a set of formulation flow charts.
Outlined here is a novel hybrid architecture that uses fuzzy logic to integrate neural networks and knowledge-based systems. The author's approach offers important synergistic benefits to neural nets, approximate reasoning, and symbolic processing. Fuzzy inference rules extend symbolic systems with approximate reasoning capabilities, which are used for integrating and interpreting the outputs of neural networks. The symbolic system captures meta-level information about neural networks and defines its interaction with neural networks through a set of control tasks. Fuzzy action rules provide a robust mechanism for recognizing the situations in which neural networks require certain control actions. The neural nets, on the other hand, offer flexible classification and adaptive learning capabilities, which are crucial for dynamic and noisy environments. By combining neural nets and symbolic systems at their system levels through the use of fuzzy logic, the author's approach alleviates current difficulties in reconciling differences between low-level data processing mechanisms of neural nets and artificial intelligence systems.