@incollection{Borgeest2019, author = {Borgeest, Kai}, title = {Safe Development Environments for Radiation Tracing Robots}, series = {Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics}, booktitle = {Handbook of Research on Advanced Mechatronic Systems and Intelligent Robotics}, publisher = {IGI Global}, address = {Hershey, PA}, isbn = {9781799801375}, doi = {doi.org/10.4018/978-1-7998-0137-5.ch006}, pages = {126 -- 138}, year = {2019}, abstract = {Robots can substitute for men in radioactively-contaminated areas. This is a suitable field to deploy robots for measurements, repair, or clearance, but development and test of such robots could be dangerous, because radiation sources need to be handled. To avoid these hazards in development or public demonstrations, safe alternatives to radiation samples have been sought using an already existing robot (EtaBot). One proposed solution is an optical substitution ("light follower"), the other one a fully-digital simulation of the contaminated area and the robot movement inside it using a hardware-in-the-loop simulator (HiL).}, subject = {Mobiler Roboter}, language = {en} } @incollection{BorgeestSchneider2014, author = {Borgeest, Kai and Schneider, Peter Josef}, title = {Comparison of Control Strategies by the Example of the Cooling Fan Control of a Mobile Machine}, series = {Handbook of research on advancements in robotics and mechatronics}, booktitle = {Handbook of research on advancements in robotics and mechatronics}, publisher = {IGI Global}, address = {Hershey, PA, USA}, isbn = {978-1-4666-7388-5}, pages = {500 -- 526}, year = {2014}, abstract = {In order to compare different control strategies the cooling system of a mobile machine has been chosen. The example control problem was to run the cooling system for m control variables and with n≤m correction variables in a way to minimize power in order to save energy and to reduce fan noise while maintaining sufficient cooling. The plant is nonlinear. Three different kinds of controllers have been investigated in several variations, i.e. fuzzy control, PI(D) and model predictive control (MPC). 14 different criteria have been used for evaluation. In many respects a linear controller with fuzzy prediction proved best, in particular the prediction model can handle nonlinear properties of the plant.}, subject = {Fuzzy-Regelsystem}, language = {en} }