Stewart Forward Solution Algorithm Based on ZOA-BPNN-Newton and Workspace Analysis

Intelligent algorithms for solving Stewart platform's forward kinematics face challenges in training time, iteration count and accuracy. To overcome these limitations, this paper proposes an innovative solution algorithm based on the combination of BP neural network using Zebra Optimization Algorithm and Newton iteration method. The algorithm effectively overcomes the limitations of the traditional techniques in initial value setting and multi-solution processing, decreases the number of iterations by 69%, reduces the translational positioning error to below 10^-6mm, and controls the rotational positioning error within 0.0045°, which greatly improves the solution accuracy and efficiency. In addition, a method for position and posture traversal and constraint verification is proposed to construct a platform workspace envelope model, which clarifies the reachable posture range of the platform under specific structural parameters and constraint conditions. It provides important references for the optimization design, performance evaluation, and task planning of the Stewart platform.