TY - JOUR
T1 - Non-linear MIMO identification of a Phantom Omni using LS-SVR with a hybrid model selection
AU - Almasi, Omid Naghash
AU - Khooban, Mohammad Hassan
AU - Behzad, Hamid
PY - 2018/8
Y1 - 2018/8
N2 - Here, a multiple-input–multiple-output (MIMO) Phantom Omni robot made by SensAble Technologies Inc. is identified by using a least-square support vector regression (LS-SVR). To this end, a two-stage hybrid optimisation strategy combining coupled simulated annealing as a priori optimisation strategy and a derivative-free Simplex method as a complementary stage is proposed to solve the LS-SVR model selection problem. This extra step is a fine-tuning procedure to enhance the optimal tuning parameters and hence lead LS-SVR to a better performance. Generalised v-fold cross-validation is considered as the criterion of LS-SVR model selection problem. The Phantom robot model is implemented via OPAL-RT to assess the performance of the proposed algorithm compared with firefly algorithm and adaptive particle swarm optimisation in solving LS-SVR model selection in practical application of the Phantom robot modelling. Finally, the proposed approach is validated and implemented in the hardware-in-the-loop based on OPAL-RT to integrate the fidelity of physical simulation as well as the flexibility of numerical simulations.
AB - Here, a multiple-input–multiple-output (MIMO) Phantom Omni robot made by SensAble Technologies Inc. is identified by using a least-square support vector regression (LS-SVR). To this end, a two-stage hybrid optimisation strategy combining coupled simulated annealing as a priori optimisation strategy and a derivative-free Simplex method as a complementary stage is proposed to solve the LS-SVR model selection problem. This extra step is a fine-tuning procedure to enhance the optimal tuning parameters and hence lead LS-SVR to a better performance. Generalised v-fold cross-validation is considered as the criterion of LS-SVR model selection problem. The Phantom robot model is implemented via OPAL-RT to assess the performance of the proposed algorithm compared with firefly algorithm and adaptive particle swarm optimisation in solving LS-SVR model selection in practical application of the Phantom robot modelling. Finally, the proposed approach is validated and implemented in the hardware-in-the-loop based on OPAL-RT to integrate the fidelity of physical simulation as well as the flexibility of numerical simulations.
UR - http://www.scopus.com/inward/record.url?scp=85051279312&partnerID=8YFLogxK
U2 - 10.1049/iet-smt.2017.0193
DO - 10.1049/iet-smt.2017.0193
M3 - Journal article
SN - 1751-8822
VL - 12
SP - 678
EP - 683
JO - IET Science, Measurement & Technology
JF - IET Science, Measurement & Technology
IS - 5
ER -