Adaptive control and state error prediction of flexible manipulators using radial basis function neural network and dynamic surface control method.

ABSTRACT: This paper introduces a novel control strategy for managing the uncertainties in flexible joint manipulators, incorporating a Radial Basis Function Neural Network (RBFNN) with Adaptive Dynamic Surface Control (ADSC). This strategy innovatively utilizes RBFNN to precisely approximate uncertain system dynamics and integrates a nonlinear damping term to effectively counteract external disturbances, enhancing the overall control accuracy. We have also developed an adaptive law that updates neural network weights and system parameters in real-time, ensuring the system's adaptability to dynamic changes. The application of the Lyapunov method ensures that all signals within the closed-loop system remain semi-globally uniformly bounded, significantly reducing tracking errors. Moreover, we introduc

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC11864562 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature