Pure Perception Motion Control based on Stochastic Nonlinear Model Predictive Control
Author:
Funder
CAPES
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Artificial Intelligence,Industrial and Manufacturing Engineering,Mechanical Engineering,Control and Systems Engineering,Software
Link
http://link.springer.com/content/pdf/10.1007/s10846-019-01141-8.pdf
Reference35 articles.
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2. Barreto, S.J.C.L., Conceição, A.S., Dórea, C E T, Martinez, L., de Pieri, E.R.: Design and implementation of model-predictive control with friction compensation on an omnidirectional mobile robot. IEEE/ASME Trans. Mech. 19(2), 467–476 (2014). https://doi.org/10.1109/TMECH.2013.2243161
3. Bascetta, L., Ferretti, G., Matteucci, M., Bossi, M.: Lft-based mpc control of an autonomous vehicle. IFAC-PapersOnLine 49(15), 7–12 (2016). https://doi.org/10.1016/j.ifacol.2016.07.597
4. Basso, G.F., Amorim, T.G.S.D., Brito, A.V., Nascimento, T.P.: Kalman filter with dynamical setting of optimal process noise covariance. IEEE Access 5, 8385–8393 (2017). https://doi.org/10.1109/ACCESS.2017.2697072
5. Blackmore, L., Ono, M., Bektassov, A., Williams, B.C.: A probabilistic particle-control approximation of chance-constrained stochastic predictive control. IEEE Trans. Robot. 26(3), 502–517 (2010). https://doi.org/10.1109/TRO.2010.2044948
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