112 results about "Model based control" patented technology

A method and system of controlling a gas turbine engine is disclosed. The engine has sensors to detect one or more parameters and actuators adapted to respond to commands. The method includes receiving data from the sensors of the engine for one or more measured or sensed parameters, estimating a state of the engine by estimating one or more unmeasured or unsensed parameters using the data from the sensors and a predictive model of the engine, generating commands for the actuators based on the state using an optimization algorithm; and transmitting the commands to the engine. The system includes a state estimator adapted to estimate a state of the engine by estimating one or more unmeasured or unsensed parameters using data from the sensors of the engine for one or more measured or sensed parameters. The estimator includes a model of the engine. The system also includes a control module adapted to generate commands for the actuators based on the state. The control module includes an optimization algorithm for determining the commands.

A method and apparatus for estimating and/or controlling mercury emissions in a steam generating unit. A model of the steam generating unit is used to predict mercury emissions. In one embodiment of the invention, the model is a neural network (NN) model. An optimizer may be used in connection with the model to determine optimal setpoint values for manipulated variables associated with operation of the steam generating unit.

A method and system of designing the operations and controls of a gas turbine, includes generating an operations model for the gas turbine including at least one objective function and defining operations and control constraints for the operations model of the gas turbine. An online dynamic optimizer/controller dynamically optimizes and controls operation of the gas turbine using model based control based on the operations model and the operations and control constraints. The model based control may include model predictive control.

A method for controlling a gas turbine engine includes: generating model parameter data as a function of prediction error data, which model parameter data includes at least one model parameter that accounts for off-nominal operation of the engine; at least partially compensating an on-board model for the prediction error data using the model parameter data; generating model term data using the on-board model, wherein the on-board model includes at least one model term that accounts for the off-nominal operation of the engine; respectively updating one or more model parameters and one or more model terms of a model-based control algorithm with the model parameter data and model term data; and generating one or more effector signals using the model-based control algorithm.

A method of controlling variable extraction flow in a combustion turbine engine, wherein extraction flow comprises a supply of compressed air extracted from the compressor and supplied to the turbine through extraction conduits, and wherein the extraction conduits includes a variable extraction orifice, the method comprising the steps of: measuring a plurality of turbine engine operating parameters; monitoring, by a control unit, the measured operating parameters of the combustion turbine engine; setting the variable extraction orifices to a setting that allows an approximate maximum level of extraction flow; calculating, by the control unit, at least one calculated operating parameter based upon model-based control and the measured operating parameters, including at least a current turbine inlet temperature and a maximum turbine inlet temperature; and manipulating the setting for the supply of fuel to the combustor such that an increased and/or maximum level of engine output is determined by comparing the values for the current turbine inlet temperature and the maximum turbine inlet temperature.

Embodiments of the invention can provide systems and methods for initializing dynamic model states using a Kalman or similar type filter. In one embodiment, an adaptable model-based control system for controlling a gas turbine engine is provided. The system can include at least one sensor adapted to obtain dynamic-type information about a current state of the engine. Furthermore, the system can include an engine model adapted to receive information from the sensor, and further adapted to reflect the current state of the engine. In addition, the system can include a model filter adapted to initialize the model with at least a portion of the dynamic-type information, wherein at least one value based at least in part on the dynamic-type information is input to the engine model. Moreover, the model can be further adapted to determine an output from the engine model based at least in part on the at least one value. Further, the system can include a controller adapted to determine an engine control action based at least in part on the output from the engine model, and further adapted to output a control command to implement the engine control action.

A robust method of creating process models for use in controller generation, such as in MPC controller generation, adds noise to the process data collected and used in the model generation process. In particular, a robust method of creating a parametric process model first collects process outputs based on known test input signals or sequences, adds random noise to the collected process data and then uses a standard or known technique to determine a process model from the collected process data. Unlike existing techniques for noise removal that focus on clean up of non-random noise prior to generating a process model, the addition of random, zero-mean noise to the process data enables, in many cases, the generation of an acceptable parametric process model in situations where no process model parameter convergence was otherwise obtained. Additionally, process models created using this technique generally have wider confidence intervals, therefore providing a model that works adequately in many process situations without needing to manually or graphically change the model.

The invention relates to a dynamic load simulating device and a dynamic load simulating method for an automobile power system test, and belongs to the technical field of vehicle power system tests. The dynamic load simulating device comprises a control computer, a dynamometer controller, a frequency converter, an alternating current (AC) power dynamometer and a torque flange with a controller. A virtual automobile model-based control algorithm is adopted, a virtual automobile model is driven by an actual measurement torque, and the simulation of the rolling resistance, wind resistance and the inertia resistance of a vehicle is realized under the conditions of not calculating angular acceleration of the dynamometer. The dynamic load simulating device and the dynamic load simulating method have high stability and high simulation precision, are favorable for shortening the development cycle of an automobile power system and providing convenient test environment for the development of the power system. A process that the vehicle acceleration is acquired by differentiating the rotation speed of the automobile power system is avoided in the calculation process, and a phenomenon that accurate differential values are difficult to acquire due to relatively large noise caused by the process of differentiating the rotation speed is prevented.

A method for determining a target path for a model-based control system. The model-based control system includes a directed acyclic graph, where the directed acyclic graph includes a plurality of vertices interconnected by a plurality of edges. The method includes the steps of performing a depth-first search of the directed acyclic graph for the target path. The depth-first search is operative to return an explicit solution or an implicit solution, wherein the implicit solution is determined using a heuristic. The method further includes determining if the depth-first search returned an explicit solution or an implicit solution, and if the depth-first search returned an implicit solution, constructing the target path from the implicit solution. The method may further include constructing a pattern database.

Advanced combustion modes, such as PCCI, operate near the system stability limits. In PCCI, the combustion event begins without a direct combustion trigger in contrast to traditional spark-ignited gasoline engines and direct-injected diesel engines. The lack of a direct combustion trigger encourages the usage of model-based controls to provide robust control of the combustion phasing. The nonlinear relationships between the control inputs and the combustion system response often limit the effectiveness of traditional, non-model-based controllers. Accurate knowledge of the system states and inputs is helpful for implementation of an effective nonlinear controller. A nonlinear controller is developed and implemented to control the engine combustion timing during diesel PCCI operation by targeting desired values of the in-cylinder oxygen concentration, pressure, and temperature during early fuel injection.

A method of controlling a torque biasing system includes determining a torque command, calculating a torque error based on the torque command and a model-based torque. A control signal is generated based on the torque error and the torque biasing system is operated based on the control signal.

A system for controlling the clearance between a turbine blade and the turbine casing that includes an impingement cooling manifold attached to a turbine casing, a temperature sensing device for determining the temperature of the turbine casing, a blower, a control system logic for determining the setting temperature of the casing, and a controller for controlling the blower, wherein the blower forces air onto the impingement cooling manifold to cool the casing towards the setting temperature and control the clearance.

A method and apparatus for designing perturbation signals to excite a number of input variables of a system, in order to test that system for the purpose of obtaining models for the synthesis of a model-based controller. The method begins with providing input parameters of the system. A plurality of binary multi-frequency (BMF) signals are generated based on these input parameters and the frequency spectra of these BMF signals are calculated. One BMF signal is selected out of the set of BMF signals so that the frequency spectrum of the selected BMF signal most closely matches a desired frequency spectrum specified by the input parameters. The selected BMF signal is used as a first perturbation signal for testing the system. The selected BMF signal is also shifted by predetermined amounts of samples to create delayed copies of the original BMF signal to be used as additional perturbation signals.

The present invention relates to a novel means to use a fundamental model of the parts of the brain that are dysfunctional in Parkinson's disease, and related dynamical diseases of the brain, as part of a feedback control system to modulate the signs and symptoms of disease. Fundamental computational models that embody our knowledge of the anatomy, neurons, and dynamics of the parts of the brain we wish to control, and use those models to reconstruct what is inaccessible to our measurements. Through emulation the controller synchronizes to the parts of the brain we wish to observe and track. By passing simultaneous control pulses to both the model controller, as well as the brain, we control both the model and the brain. The detailed framework to embed fundamental models of the brain within a control scheme to control symptoms of Parkinsons and related dynamical diseases of the brain are disclosed.

Simultaneous or independent control of a by-pass valve and a variable-geometry forced induction component on a combustion engine is based on operational parameters measured by various sensors provided as inputs to a control module. Sudden loss of power due to low turbine efficiencies is prevented during transitions between operating conditions of engine speed and load. Excessive peak cylinder pressures are also prevented by controlling engine boost pressure to a permitted limit at high engine speed and load.

A method for controlling a gas turbine engine having a constrained model based control (CMBC) system. The method including obtaining information about a current and previous states of the engine, updating model data information in the CMBC and a parameter estimation system based on the obtained information, and identifying trends in the data based on the information. The method also includes diagnosing the engine, based on the identified trends, determining at least one of a new constraint, objective, initial condition, model characteristic, prediction horizon, and control horizon for the control system based on the diagnosing step if the diagnosing step identified a fault condition, and adapting the CMBC system based on the at least one new constraint, objective, initial condition, model characteristic, prediction and control horizon. The method further includes generating at least on control command based on the adapting and commanding an actuator based on the control command.