Article, 2018

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

IET Science Measurement & Technology, ISSN 1751-8830, 1751-8822, Volume 12, 5, Pages 678-683, 10.1049/iet-smt.2017.0193

Contributors

Almasi, Omid Naghash 0000-0002-0117-493X [1] Khooban, Mohammad Hassan (Corresponding author) [2] Behzad, Hamid [3]

Affiliations

  1. [1] Cammisa and Wipf Consulting Engineers, San Francisco, CA, 94107, USA
    2. [NORA names: United States; America, North; OECD];
  2. [2] Aalborg University
    3. [NORA names: AAU Aalborg University; University; Denmark; Europe, EU; Nordic; OECD];
  3. [3] University of Shahrood
    4. [NORA names: Iran; Asia, Middle East]

Abstract

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.

Keywords

Inc., LS-SVR, MIMO identification, OMNI, OPAL-RT, Phantom Omni, Phantom Omni robots, SensAble, V-fold cross-validation, adaptive particle swarm optimisation, algorithm, annealing, applications, complementary stages, criteria, cross-validation, fidelity, fidelity of physical simulation, firefly algorithm, flexibility, flexibility of numerical simulation, hardware-in-the-loop, hybrid, hybrid optimisation strategy, identification, least squares support vector regression, method, model, model selection, model selection problem, multiple-input multiple-output, numerical simulations, optimal tuning parameters, optimisation, optimisation strategy, parameters, particle swarm optimisation, performance, phantom, physical simulation, problem, procedure, proposed algorithm, regression, robot, robot model, selection, selection problem, simplex method, simulated annealing, simulation, stage, strategies, support vector regression, swarm optimisation, tuning parameters, vector regression

Data Provider: Digital Science

LINKS
-

Matching Records in NORA

SUBJECTS
+

METRICS
+