An apparatus for controlling a system, comprising: 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; a sensor to measure a signal to produce a sequence of measurements indicative of a state of the system; a processor 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; 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, characterised in that the processor for performing an iteration of the joint estimate of the first and the second Student-t distributions is configured to: estimate, using the motion model, the state of the system, current values of the first Student-t distribution, and current values of the second Student-t distribution based on a previous estimation of the state of the system, previous values of the first Student-t distribution, and previous values of the second Student-t distribution; and updating, using the measurements and the measurement model in response to receiving current values of the measurements, the current estimation of the state of the system, the current values of the first Student-t distribution, and the current values of the second Student-t distribution. 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; a sensor to measure a signal to produce a sequence of measurements indicative of a state of the system; a processor 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; and 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; 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, 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, characterised in that the processor for performing an iteration of the joint estimate of the first and the second Student-t distributions is configured to: estimate, using the motion model, the state of the system, current values of the first Student-t distribution, and current values of the second Student-t distribution based on a previous estimation of the state of the system, previous values of the first Student-t distribution, and previous values of the second Student-t distribution; and updating, using the measurements and the measurement model in response to receiving current values of the measurements, the current estimation of the state of the system, the current values of the first Student-t distribution, and the current values of the second Student-t distribution. estimate, using the motion model, the state of the system, current values of the first Student-t distribution, and current values of the second Student-t distribution based on a previous estimation of the state of the system, previous values of the first Student-t distribution, and previous values of the second Student-t distribution; and