Fault-Tolerant Control for Robotic Systems Using a Wavelet Type-2 Fuzzy Brain Emotional Learning Controller and a TOPSIS-Based Self-organizing Algorithm

To improve control performance in robotic systems with faults and unknown uncertainties, a fault-tolerant control (FTC) system based on a wavelet type-2 fuzzy brain emotional learning controller (WT2FBC) is developed in this research. Type-2 fuzzy logic systems can handle uncertainties better than type-1 fuzzy logic systems, so they can solve some uncertain complex problems more accurate. Given its fast learning and problem-solving capabilities, the proposed WT2FBC is an important fuzzy neural network. The next step is to investigate how a WT2FBC-based FTC system can help solve the puzzles that plague the robotic systems. To achieve FTC, a control system with a computed torque controller and the WT2FBC estimator is provided. If the robotic system ever exhibits faults, it can consult an online fault estimator built on WT2FBC, and then compensates the faults. For more efficient, the total number of fuzzy rules of WT2FBC can be dynamically adjusted using a self-organizing algorithm based on the technique for order of preference by similarity to ideal solution. Simulations of a biped robot and a two-link manipulator robot have demonstrated the effectiveness of the proposed control algorithm.