295 results about "Plant models" patented technology

A control method for optimizing or enhancing an operation of a power plant that includes thermal generating units for generating electricity. The power plant may include multiple possible operating modes differentiated by characteristics of operating parameters. The method may include tuning a power plant model so to configure a tuned power plant model. The method may further include simulating proposed operating modes of the power plant with the tuned power plant model. The simulating may include a simulation procedure that includes: defining a second operating period; selecting the proposed operating modes from the possible operating modes; with the tuned power plant model, performing a simulation run for each of the proposed operating modes whereby the operation of the power plant during the second operating period is simulated; and obtaining simulation results from each of the simulation runs.

A controller design for switching between m linear multivariable controllers each of whom stabilizes a linear plant has been presented. A Youla-Kucera factorization was exploited in the interest of obtaining a closed-loop system that is exponentially stable for any switching signal σ(t) in the absence of plant model uncertainty. Robustness to practical model uncertainty was also considered and lower and upper bounds on the tolerable magnitude of unstructured additive plant uncertainty were presented. Numerical example demonstrated that the two controller degrees of freedom in the proposed controller design could be used to separately modify the closed-loop steady-state (with respect to σ(t)) performance and the switching transients.

The present invention involves a Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a plant such as, for example, an internal combustion engine or an automobile suspension system. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and / or output variables; the type of fuzzy inference model (e.g., Mamdani, Sugeno, Tsukamoto, etc.); and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual plant model of the controlled plant. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.

Systems and methods that facilitate messaging capabilities within a plant (e.g., a unified plant model - UPM) via employing a message engine that normalizes messaging of various messaging protocols and formats, wherein various systems of such plant can map to the message engine; and provide a consistent interface where events are sent / received consistently across such system. Such messaging engines can facilitate communication ( e . g ., via preferred channels) to other services / products, wherein the configuration and management of messaging is consistent across the system.

A Soft Computing (SC) optimizer for designing a Knowledge Base (KB) to be used in a control system for controlling a motorcycle is described. In one embodiment, a simulation model of the motorcycle and rider control is used. In one embodiment, the simulation model includes a feedforward rider model. The SC optimizer includes a fuzzy inference engine based on a Fuzzy Neural Network (FNN). The SC Optimizer provides Fuzzy Inference System (FIS) structure selection, FIS structure optimization method selection, and teaching signal selection and generation. The user selects a fuzzy model, including one or more of: the number of input and / or output variables; the type of fuzzy inference; and the preliminary type of membership functions. A Genetic Algorithm (GA) is used to optimize linguistic variable parameters and the input-output training patterns. A GA is also used to optimize the rule base, using the fuzzy model, optimal linguistic variable parameters, and a teaching signal. The GA produces a near-optimal FNN. The near-optimal FNN can be improved using classical derivative-based optimization procedures. The FIS structure found by the GA is optimized with a fitness function based on a response of the actual plant model of the controlled plant. The SC optimizer produces a robust KB that is typically smaller that the KB produced by prior art methods.