A fuzzy neural network approach for predicting and optimizing the dynamic stiffness of the Stewart platform

Abstract This study proposes a fuzzy neural network-based prediction and optimization method to address the challenge of modeling dynamic stiffness in Stewart platforms. Traditional approaches, such as the Newton-Euler method and finite element analysis, often struggle to capture nonlinear characteristics and multivariate coupling effects under complex conditions. To overcome these limitations, this paper constructs a fuzzy neural network framework that integrates fuzzy logic with neural computation. This model selects drive joint position, velocity, acceleration, torque, and external load as input variables. These inputs are mapped into fuzzy subsets through fuzzification. The fuzzy radial basis function network is designed to simulate the nonlinear relationships between input variables and dynamic stiffness. An error back-propagation algorithm is applied to optimize the network weights, and the structure is refined using cross-validation and grid search. The fuzzy rule base is constructed from both expert knowledge and data-driven insights. Experimental validation is conducted under varying working conditions. This includes load variation and angular velocity changes. The proposed method demonstrates higher accuracy and robustness compared to traditional Newton-Euler, finite element, statistical regression, and reinforcement learning models. The average mean square error under most scenarios is significantly reduced. This paper also highlights the limitations of current fuzzy rule adaptability under unknown disturbances. Future work aims to enhance model generalizability through self-learning mechanisms and simplify computational complexity for real-time applications. Overall, this study contributes a reliable and adaptive approach to improving dynamic stiffness prediction for Stewart platforms, offering insights for broader applications in multi-degree-of-freedom robotic systems.