We present a framework for robust estimation of the configuration of an articulated robot using a large number of redundant proprioceptive sensors (encoders, gyros, accelerometers) distributed throughout the robot. Our method uses an Unscented Kalman Filter (UKF) to fuse the robot’s sensor measurements. The filter estimates the angle of each joint of the robot, enabling the accurate estimation ...

State estimation is the foundation of any control and decision making in power networks. The first requirement for a secure network is a precise and safe state estimator in order to make decisions based on accurate knowledge of the network status. This paper introduces a new estimator which is able to detect bad data with few calculations without need for repetitions and estimation residual cal...

the main task of this work is related with the design of a class of siso robust control law for theregulation of substrate concentration (cdo) of an industrial activated sludge wastewater plant. the controldesign is related with an uncertainty estimator (reduced order observer) based active control. departing from the tracking error between the desired and the current substrate concentrations t...

the main task of this work is related with the design of a class of siso robust control law for theregulation of substrate concentration (cdo) of an industrial activated sludge wastewater plant. the controldesign is related with an uncertainty estimator (reduced order observer) based active control. departing from the tracking error between the desired and the current substrate concentrations t...

The problem of observer design for nonlinear systems has got great attention in the recent literature. The nonlinear observer has been a topic of interest in control theory. In this research, a modified robust sliding-mode observer (SMO) is designed to accurately estimate the state variables of nonlinear systems in the presence of disturbances and model uncertainties. The observer has a simple ...

Robust predictive control of non-linear systems under state estimation errors and input and state constraints is a challenging problem, and solutions to it have generally involved solving computationally hard non-linear optimizations. Feedback linearization has reduced the computational burden, but has not yet been solved for robust model predictive control under estimation errors and constrain...

Polymerization processes are highly non-linear systems that require strict control of their dynamic operation to be competitive. The unscented Kalman filter is a filtering strategy has shown rewarding performance for state estimation. Besides, filters based on robust statistics have been proposed deal with the presence outliers. However, reported employed only Huber M-estimator as loss function...