Yesil, Inverse-model predictive control based on INFUMO-BB-BC optimization, in: The 10th IFAC Workshop on Adaptation and Learning in Control and Signal Processing - ALCOSP 2010, Antalya, Turkey.Oblak, S., Kumbasar, T., Skrjanc, I., Yesil, E.: Inverse-model predictive control based ...
However, these robots can be difficult to control due to their high-dimensional nonlinear dynamics and actuator constraints. This article presents two controllers for tensegrity spine robots, using model-predictive control (MPC) and inverse statics (IS) optimization. The controllers introduce two ...
This technique turns out to be relevant in explicit model predictive control (MPC) design in terms of reducing the prediction horizon to at most two steps. In view of practical applications, typically leading to problems that are not directly invertible, we show how to adapt the inverse ...
Karer, G., Mušič, G., Škrjanc, I., Zupančič, B.: Hybrid fuzzy model-based predictive control of temperature in a batch reactor. Computers and Chemical Engineering 31, 1552–1564 (2007) CrossRef Karer, G., Mušič, G., Škrjanc, I., Zupančič, B.: Feedfor...
Nonlinear Model Predictive Control, Chapter in Nonlinear Model Predictive Control: Challenges and Opportunities, Birkhauser Verlag (2000), pp. 23-44 CrossrefGoogle Scholar Mayne, 2000 Mayne, D., C. Rao J. Rawlings and P. Scokaert (2000). Constrained model predictive control: Stability and optim...
Free-for-all: community-built test set, new problems welcome! Maros-Meszaros test set: a standard test set with problems designed to be difficult. Model predictive control: model predictive control problems arising e.g. in robotics.About Inverse kinematics test set to benchmark QP solvers Resou...
Galvan IM, Zaldivar JM (1998) Application of recurrent neural networks in batch reactors - Part II: Nonlinear inverse and predictive control of the heat transfer fluid temperature. Chem Eng Process 37(2):149-161Galvan,I.M.Application of Recurrent Neural Networks in Batch Reactors: Part Ⅱ: ...
“Seq+Graph+Descriptor_Opt” showed the highest mean R2 values at 0.697, 0.556 and 0.900, indicating that our constructed multi-modal polymer representations obviously improved the accuracy and stability of the predictive model for few-shot polymers (More results concluded in Supplementary Fig.13and ...
Optimal control, John Wiley & Sons, New York, USA (1995) ISBN 0-471-03378-2 Google Scholar Magni and Scattolini, 2007 L. Magni, R. Scattolini Assessment and Future Directions of Nonlinear Model Predictive Control/2007 of Lecture Notes in Control and Information Sciences (358), Springer-Verl...
Precision control coefficient ∊∊; Output: New outputs βo for DMUo ; 1: Calculate θo, the Illustrative example We now introduce an example to explain our inverse non-radial DEA model based on inverse SBM model (19). Consider the same data set on Table 1, we firstly estimate the ...