A practical type-3 Fuzzy control for mobile robots: predictive and Boltzmann-based learning

Abstract This study presents an innovative path-following scheme using a new intelligent type-3 fuzzy system for mobile robots. By designing non-singleton FS and incorporating error measurement signals, this is able to handle natural disturbances dynamics uncertainties. To further enhance accuracy, Boltzmann machine (BM) models tracking errors predicts compensators. A parallel supervisor also included in the central controller ensure robustness. The BM model trained contrastive divergence, while adaptive rules extracted from stability theorem train NT3FS. Simulation results chaotic reference signals show that proposed accurate robust, even face of unknown disturbances. Moreover, practical implementation on real-world robot proves feasibility designed controller. watch short video action, visit shorturl.at/imoCH.