Meta-heuristic algorithms for the control tuning of omnidirectional mobile robots

The growth in usage of efficient mobile robots in engineering has motivated the search for new alternatives to improve the control tuning task. In this article, Cartesian space proportional–derivative control tuning for omnidirectional mobile robots is established under an offline dynamic optimization approach wherein the minimization of the tracking error and energy consumption are considered simultaneously, providing efficient performance in real tests. A statistical study of the performance of twelve different meta-heuristic algorithms and one gradient technique indicates that using the fittest solution in the meta-heuristic optimization process through generations allows finding more suitable controller parameters. Also, real tests with each of the best control gains obtained using algorithms are realized as a laboratory prototype. Analysis of laboratory tests indicate that, statistically, 75% of comparisons with the best control gains exhibit different performance functions in spite of having only slightly different control gains.