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Non-linear MIMO identification of a Phantom Omni using LS-SVR with a hybrid model selection

Non-linear MIMO identification of a Phantom Omni using LS-SVR with a hybrid model selection

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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.

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