Recurrent Neural Network–Based Nonlinear Orientation Control of Redundant Stewart Platform

This paper presents a novel Recurrent Neural Network (RNN) controller for redundancy resolution and orientation control of the Stewart platform. The Stewart platform features six prismatic actuators, making it a six-degrees-of-freedom (6-DOF) system. When imposing three-dimensional orientation control, the platform retains a redundancy of 3-DOF, which can be utilized to achieve secondary goals. The key novelty of this study lies in the formulation of a Jacobian-free, gradient-free control strategy that directly solves a constrained nonlinear optimization problem at the angular level, thereby significantly improving computational efficiency and robustness compared with conventional controllers. Specifically, we propose the Beetle Antennae Olfactory Recurrent Neural Network (BAORNN) algorithm, a biologically inspired metaheuristic framework that bypasses the computationally intensive Jacobian inversion typically required in redundancy resolution. The orientation control problem is formulated as a constrained optimization task, incorporating an energy-efficient actuator usage objective and mechanical constraints modeled as inequalities. Theoretical stability and convergence guarantees are established for the proposed BAORNN framework, ensuring reliable operation across a wide range of configurations. To validate the approach, we developed a high-fidelity simulation environment using the Simscape Multibody library in Simulink and conducted extensive experiments across multiple time-varying reference trajectories. Quantitative performance comparisons against a state-of-the-art inverse kinematics controller demonstrate the superior accuracy, convergence speed, and constraint-handling capabilities of our method. Furthermore, we showcase a realistic application scenario by integrating the controller with a chair-mounted Stewart platform for immersive driving and flight simulations, demonstrating the potential for real-world deployment in motion simulation and training systems. In summary, this paper introduces a computationally lightweight, robust, and highly accurate RNN-based controller tailored for redundant Stewart platforms, with proven advantages over traditional Jacobian–based methods.