An apparatus for controlling a system includes a memory to store a model of the system including a motion model of the system subject to process noise and a measurement model of the system subject to measurement noise, such that one or combination of the process noise and the measurement noise forms an uncertainty of the model of the system with unknown probabilistic parameters, wherein the uncertainty of the model of the system causes a state uncertainty of the system with unknown probabilistic parameters. The apparatus also includes a sensor to measure a signal to produce a sequence of measurements indicative of a state of the system, a processor to estimate a Gaussian distribution representing the state uncertainty, and a controller to determine a control input to the system using the model of the system with state uncertainty represented by the Gaussian distribution and control the system according to the control input. The processor is configured to estimate, using at least one or combination of the motion model, the measurement model, and the measurements of the state of the system, a first Student-t distribution representing the uncertainties of the model and a second Student-t distribution representing the state uncertainty of the system, the estimation is performed iteratively until a termination condition is met, and fit a Gaussian distribution representing the state uncertainty into the second Student-t distribution.

A system is controlled using particle filter executed to estimate weights of a set of particles based on fitting of the particles into a measurement model, wherein a particle includes a motion model of the system having an uncertainty modeled as a Gaussian process over possible motion models of the system and a state of the system determined with the uncertainty of the motion model of the particle, wherein a distribution of the Gaussian process of the motion model of one particle is different from a distribution of the Gaussian process of the motion model of another particle. Each execution of the particle filter updates the state of the particle according to a control input to the system and the motion model of the particle with the uncertainty and determines particle weights by fitting the state of the particle in the measurement model subject to measurement noise.

In order to provide an estimation apparatus estimating a true value from observation data without determining a state equation for a target to be controlled, the estimation apparatus includes a prediction section and an update section. The prediction section executes linear prediction on time-series data, which includes observation values acquired from a sensor attached to the target to be controlled, to calculate an estimate value before update relating to a state of the target to be controlled. The update section updates the estimate value before update by using the observation value acquired from the sensor. The prediction section may calculate the estimate value before update by weighted linear prediction on the time-series data of the observation values.

Systems and methods presented herein provide for multivariable model predictive control of a multistep plant. In one embodiment, a model predictive controller (MPC) includes a model of the multistep plant. The MPC is operable to linearize at least two steps of the multistep plant into cycle steps based on the model, to process an output signal from the multistep plant, and to independently control the cycle steps based on the output signal to optimize an output of the multistep plant.

In a general aspect, a method is presented for increasing the measurement precision of an optical instrument. The method includes determining, based on optical data and environmental data, a measured value of an optical property measured by the optical instrument. The optical instrument includes an optical path and a sensor configured to measure an environmental parameter. The method also includes determining a predicted value of the optical property based on a model representing time evolution of the optical instrument. The method additionally includes calculating an effective value of the optical property based on the measured value, the predicted value, and a Kalman gain. The Kalman gain is based on respective uncertainties in the measured and predicted values and defines a relative weighting of the measured and predicted values in the effective value.

In a general aspect, a method is presented for increasing the measurement precision of an optical instrument. The method includes determining, based on optical data and environmental data, a measured value of an optical property measured by the optical instrument. The optical instrument includes an optical path and a sensor configured to measure an environmental parameter. The method also includes determining a predicted value of the optical property based on a model representing time evolution of the optical instrument. The method additionally includes calculating an effective value of the optical property based on the measured value, the predicted value, and a Kalman gain. The Kalman gain is based on respective uncertainties in the measured and predicted values and defines a relative weighting of the measured and predicted values in the effective value.

A system is controlled using particle filter executed to estimate weights of a set of particles based on fitting of the particles into a measurement model, wherein a particle includes a motion model of the system having an uncertainty modeled as a Gaussian process over possible motion models of the system and a state of the system determined with the uncertainty of the motion model of the particle, wherein a distribution of the Gaussian process of the motion model of one particle is different from a distribution of the Gaussian process of the motion model of another particle. Each execution of the particle filter updates the state of the particle according to a control input to the system and the motion model of the particle with the uncertainty and determines particle weights by fitting the state of the particle in the measurement model subject to measurement noise.

Systems and methods presented herein provide for multivariable model predictive control of a multistep plant. In one embodiment, a model predictive controller (MPC) includes a model of the multistep plant. The MPC is operable to linearize at least two steps of the multistep plant into cycle steps based on the model, to process an output signal from the multistep plant, and to independently control the cycle steps based on the output signal to optimize an output of the multistep plant.

An apparatus for controlling a system includes a memory to store a model of the system including a motion model of the system subject to process noise and a measurement model of the system subject to measurement noise, such that one or combination of the process noise and the measurement noise forms an uncertainty of the model of the system with unknown probabilistic parameters, wherein the uncertainty of the model of the system causes a state uncertainty of the system with unknown probabilistic parameters. The apparatus also includes a sensor to measure a signal to produce a sequence of measurements indicative of a state of the system, a processor to estimate a Gaussian distribution representing the state uncertainty, and a controller to determine a control input to the system using the model of the system with state uncertainty represented by the Gaussian distribution and control the system according to the control input. The processor is configured to estimate, using at least one or combination of the motion model, the measurement model, and the measurements of the state of the system, a first Student-t distribution representing the uncertainties of the model and a second Student-t distribution representing the state uncertainty of the system, the estimation is performed iteratively until a termination condition is met, and fit a Gaussian distribution representing the state uncertainty into the second Student-t distribution.

A computer programmed method and apparatus is provided for centrally managing sensors and actuators used by a human interactive machine, such as an interactive virtual hologram display machine. The method can be expressed as program code (middleware implementation) stored on a memory of the human interactive machine, and executed by a processor of that machine. The middleware implementation is created using a human interface sensor middleware platform, which acts as an intermediary between sensors in the human interactive machine that provide sensor data, such as accelerometers and motion capture cameras, and actuators in the human interactive machine such as projectors and sound systems. The middleware platform provides mechanisms for reporting and interrogating the protocols used by the sensors and actuators, as well as a standard architecture for creating services used in the middleware implementation.