Abstract: A method and system for performing model-based multi-objective asset optimization and decision-making is provided. The method includes building at least two predictive models for an asset. The building includes categorizing operational historical data via at least one of: controllable variables, uncontrollable variables, output objectives, and constraints. The building also includes selecting at least two output objectives or constraints, and identifying at least one controllable or uncontrollable variable suitable for achieving the at least two output objectives or constraints. The method also includes validating each predictive model and performing multi-objective optimization using the predictive models. The multi-objective optimization includes specifying search constraints and applying a multi-objective optimization algorithm. The method further includes generating a Pareto Frontier, and selecting a Pareto optimal input-output vector.

Abstract: The present invention discloses a method and system for controlling a power supply by delivering stable output voltage independent of load. The control system includes a sensor for sensing a state variable, a fuzzy logic controller for generating a control action, and an actuator for regulating the power supply in accordance with the control action. The fuzzy logic controller generates a fuzzy logic knowledge base using low-level fuzzy logic controllers and a mode selector in one embodiment and a fuzzy logic proportional integral controller in another embodiment. The generated fuzzy logic knowledge base is compiled in a look-up table and is used to quickly and efficiently control a power supply device such as a resonant converter.

Abstract: A method for automated control of a steam turbine during cyclic operation includes the steps of comparing current turbine loading with an anticipated load profile to generate a load error signal. Anticipated electrical load profiles correspond to the time of day, day of the week, month of the year, and historical data; operator input can vary such historical profiles as appropriate for unusual situations. The predicted load profile is categorized with respect to selected composite electrical load cycle profiles; weighted operating constraints are generated by means of a fuzzy inference calculation using electrical cycle profiles selected to correspond to the anticipated electrical load profile.

Abstract: A fuzzy logic based navigation module for use on a semi-autonomous vehicle computes the safety of moving from predefined local regions to adjacent local regions for the purpose of planning a safe path for the vehicle to take to reach its intended destination. Image understanding software provides the input to the system in the form of values for linguistic variables for each local region which impact the safe movement of the vehicle. Using a five level hierarchial rule base structure, the fuzzy inference process computes intermediate and final safety values which are affected not only by local conditions but also by nearby conditions in order to allow a minimum clearance for the vehicle.

Abstract: The invention is directed to an automated, real-time, rule-based reasoning with uncertainty system and method for practicing same. The system has a three layer structure comprising of representation, inference and control layers. Rule firings and conclusions are computed in the inference layer in a manner to propagate uncertainty information through the system in accordance with predetermined formulas designated by the control layer. Rule structures and conclusions and uncertainty information associated therewith are represented to the user in the representation layer. The control layer includes a planning mechanism which determines the fastest plan for reaching the conclusion and generates prioritized alternate plans which, if time permits, will be executed in accordance with their respective priorities.

Abstract: The invention is directed to an automated rule-based reasoning with uncertainty system and method for practicing same. The system has a three layer structure composed of representation, inference and control layers. Rule firings and conclusions are computed in the inference layer in a manner to propagate uncertainty information through the system in accordance with predetermined formulas designated by the control layer. Rule structures and conclusions and uncertainty information associated therewith are represented to the user in the representation layer.