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

An Observer-Based Type-3 Fuzzy Control for Non-Holonomic Wheeled Robots

“…To better show the effectiveness of the suggested controller, the root-mean-square of tracking error (RMSE) is compared with some other related controllers, such as predictive controller [39], type-3 fuzzy controller (T3-FLC) [40], and SMC [7]. The results are shown in Table 2.…”