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.

A method for providing dynamic state information for a coordinate measuring machine that includes a base, a probe head, a machine structure linking the probe head to the base and a drive mechanism that moves the probe head. A dynamic model with first state variables represents an actual state of physical properties of the coordinate measuring machine. The first state variables are provided in a database and the actual state of the coordinate measuring machine is determined using the dynamic model. The state variables are monitored and, based thereon, the change of the state variables is determined. Updated, second state variables are set regarding the determined change of the first state variables. The dynamic model is updated using the second state variables in place of the first state variables, wherein the actual state of the coordinate measuring machine is calculated based on the second state variables.

A method for providing dynamic state information for a coordinate measuring machine that includes a base, a probe head, a machine structure with structural components linking the probe head to the base and a drive mechanism that moves the probe head relative to the base. A dynamic model is defined with actual state variables related to physical properties representing an actual state of the coordinate measuring machine. The actual state is derived by a calculation based on the dynamic model. A filtering process using the dynamic model includes deriving prediction variables based on the state variables that describe an expected proximate state of the coordinate measuring machine, measuring at least one of the state variables and determining observables, deriving successive state variables by comparing the prediction variables with the observables and updating the dynamic model using the successive state variables as the actual state variables.

The present disclosure provides a feedback controller and method for controlling an operation of a device at different control steps. The feedback controller comprises at least one processor, and the memory having instructions stored thereon that, when executed by the at least one processor, causes the feedback controller, for a control step, to collect a measurement indicative of a state of the device at the control step, and execute, recursively until a termination condition is met, a probabilistic solver parameterized on a control input to an actuator operating the device to produce a control input for the control step. The feedback controller is further configured to control the actuator operating the device based on the produced control input.