A Discrete-Form Double-Integration-Enhanced Recurrent Neural Network for Stewart Platform Control with Time-Varying Disturbance Suppression

The discrete-form control of the Stewart platform is essential for digital implementation in intelligent manufacturing and robotic systems under the context of Industry 4.0, yet its performance is often degraded by unavoidable discrete disturbances. This challenge motivates the development of algorithms with strong disturbance suppression capability. To address this issue, a continuous-form double-integration-enhanced recurrent neural network (CF-DIE-RNN) algorithm incorporating a novel double-integration-enhanced design concept is first developed to improve robustness against time-varying disturbances. For digital hardware applications, a discrete-form double-integration-enhanced RNN (DF-DIE-RNN) algorithm is then constructed by discretizing the CF-DIE-RNN algorithm using a general four-step discretization formula and a one-step forward difference formula based on Taylor expansion. Rigorous theoretical analysis establishes the convergence properties of the proposed algorithm and characterizes its steady-state residual bounds under different disturbance types, revealing its capability to suppress discrete quadratic time-varying disturbances. Numerical and simulation experiments demonstrate that the DF-DIE-RNN algorithm achieves superior disturbance suppression and more accurate trajectory tracking than existing discrete-form RNN algorithms, confirming its effectiveness for discrete-form Stewart platform control.