Using BELBIC based optimal controller for omni-directional three wheel robots model identified by LOLIMOT

In this paper, an intelligent controller is applied to control omni-directional robots motion. First, the dynamics of the three wheel robots, as a nonlinear plant with considerable uncertainties, is identified using an efficient algorithm of training, named LoLiMoT. Then, an intelligent controller based on brain emotional learning algorithm is applied to the identified model. This emotional learning is based on a computational model of limbic system in the mammalian brain. The Brain Emotional Learning Based Intelligent Controller (BELBIC), using the concept of LQR control is adopted for the omni-directional robots. The performance of this multi objective control is illustrated with simulation results based on real world data. This approach can be utilized directly to the robots in the future.

Keywords:

Reinforcement Learning , Emotional Learning , Neurofuzzy identification