| 1 |
Stable Gaussian process based tracking control of Euler-Lagrange systems
Beckers T, Kulic D, Hirche S
Automatica, 103, 390, 2019 |
| 2 |
Data-driven approximate Q-learning stabilization with optimality error bound analysis
Li YQ, Yang CZ, Hou ZS, Feng YJ, Yin CK
Automatica, 103, 435, 2019 |
| 3 |
Synchronization control for reaction-diffusion FitzHugh-Nagumo systems with spatial sampled-data
Chen S, Lim CC, Shi P, Lu ZY
Automatica, 93, 352, 2018 |
| 4 |
Stochastic MPC with offline uncertainty sampling
Lorenzen M, Dabbene F, Tempo R, Allgower F
Automatica, 81, 176, 2017 |
| 5 |
Novel iterative neural dynamic programming for data-based approximate optimal control design
Mu CX, Wang D, He HB
Automatica, 81, 240, 2017 |
| 6 |
Model-based reinforcement learning for approximate optimal regulation
Kamalapurkar R, Walters P, Dixon WE
Automatica, 64, 94, 2016 |
| 7 |
Efficient model-based reinforcement learning for approximate online optimal control
Kamalapurkar R, Rosenfeld JA, Dixon WE
Automatica, 74, 247, 2016 |
| 8 |
Iterative learning control for the systematic design of supersaturation controlled batch cooling crystallisation processes
Sanzida N, Nagy ZK
Computers & Chemical Engineering, 59, 111, 2013 |
| 9 |
Virtual Reference Feedback Tuning for non-minimum phase plants
Campestrini L, Eckhard D, Gevers M, Bazanella AS
Automatica, 47(8), 1778, 2011 |
| 10 |
Fixed-order H-infinity controller design for nonparametric models by convex optimization
Karimi A, Galdos G
Automatica, 46(8), 1388, 2010 |
