[6] G. Rigatos,
Modelling and control for intelligent industrial systems: Adaptive algorithms in robotics and
industrial engineering, Springer, 2011 (monograph). download
Ref. No 4083, Modelling and Control for Intelligent Industrial Systems: Adaptive Algorithms
in Robotics and Industrial Engineering, Duration: 2010-2011 . The project aimed at developing new control and estimation
algorithms for industrial systems with emphasis to robotics. It was aimed to address to major issues arising in intelligent
industrial systems, (i) learning and adaptation in unknown environments, (ii) compensation of model uncertainties as well
as of unknown or stochastic external disturbances. The project’s results have demonstrated that learning can be performed
with the use of gradient-type algorithms (also applied to nonlinear modelling techniques) or with the use of derivative-free
stochastic algorithms. Moreover, the project has shown that the compensation of uncertainties in the model’s parameters as
well as of external disturbances can be performed through stochastic estimation algorithms (usually applied to filtering
and identification problems), and through the design of adaptive and robust control schemes.
