Self-learning adaptive neuro-fuzzy approximation of robust control behavior in electric power steering systems

Abstract

Data training algorithms based on Artificial Intelligence (AI) often encounter overfitting, underfitting, or bias issues. This article presents the design of a hybrid self-learning algorithm to address the above challenges. The proposed approach is developed by integrating fuzzy logic and neural network structures into an Adaptive Network-Based Fuzzy Inference System (ANFIS), which leverages the strengths of both components. This integration is considered a key contribution of the study. Compared to conventional training algorithms, the proposed ANFIS demonstrates high training accuracy while maintaining strong interpolation and prediction capabilities, even under varying conditions. The model is designed with three inputs and one output, trained using data derived from a high-performance robust controller for Electric Power Steering (EPS) systems. Simulation results show that the training error of the proposed ANFIS remains below 1.7% in well-trained cases and under 6.1% in interpolation scenarios. Moreover, the algorithm maintains a prediction error of less than 9.3% when applied to scenarios outside the training domain. The issue of overfitting is significantly resolved, unlike in the case of the Backpropagation Neural Network (BPNN), which is used as a benchmark for comparison. Overall, the proposed algorithm significantly improves data training accuracy and generalization performance.