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A short technical subsection for Gaussian Process learning and Uncertainty propagation is presented as required in applications like Model Predictive Control, machine learning and optimization. ...
Motion control of bio-inspired mobile robotic platforms can prove a challenging problem. In particular, models for the considered type of systems may prove nonlinear, uncertain, and fairly complicated. To address these issues, use of an output predictor-ba ...
In Control System Theory, the study of continuous-time, finite dimensional, underdetermined systems of ordinary differential equations is an important topic. Classification of systems in different categories is a natural initial step to the analysis of a g ...
Microaerobic (oxygen-limited) conditions are critical for inducing many important microbial processes in industrial or environmental applications. At very low oxygen concentrations, however, the process performance often suffers from technical limitations. ...
Chemical incidents are typically caused by loss of control, resulting in runaway reactions or process deviations in different stages of the production. In the case of fed-batch reactors, the problem generally encountered is the accumulation of heat. This i ...
This paper presents an investigation of how ModelPredictiveControl (MPC) and weatherpredictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ve ...
A probabilistic interpretation of model predictive control is presented, enabling extensions to multiple coordinate systems. The resulting controller follows a minimal intervention principle, by learning and retrieving movements through the coordination of ...
Equipped with a third hand under their direct control, surgeons may be able to perform certain surgical interventions alone; this would reduce the need for a human assistant and related coordination difficulties. However, does human performance improve wit ...
We consider the control of a large-scale system composed of state-coupled linear subsystems that can be added or removed offline. In this paper we present Plug-and-Play (PnP) design methods based on Model Predictive Control (MPC) meaning that (i) the desig ...
This paper addresses the control of the blending process in cement industries. This process can be modeled by a nonlinear multivariable system with large parametric uncertainty. Using a specific transformation, a linear parameter varying (LPV) model with s ...
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