Six-DoF Stewart Platform Motion Simulator Control using Switchable Model Predictive Control

2025-03-14

AI Summary

To address the latency and inaccuracy of conventional washout filters in Stewart-platform-based flight simulators during high-dynamic, large-amplitude Upset Prevention and Recovery Training (UPRT), this paper proposes a model-adaptive, switchable Model Predictive Control (S-MPC) motion cueing algorithm. The core innovation is a novel terminal-constraint switching mechanism: within the platform’s operational envelope, terminal-constrained MPC ensures high-fidelity trajectory tracking; outside the envelope, it automatically switches to unconstrained MPC to guarantee real-time feasibility and robustness. The algorithm integrates an accurate Stewart-platform dynamic model with online adaptive model updating. In horizontal stall UPRT tests, the proposed method reduces the Average Absolute Scaling (AAS) error by 42.34% compared to standard MPC-MCA and by 65.30% relative to classical washout filtering, significantly improving both motion fidelity and response speed.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Stochastic Optimization
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Due to excellent mechanism characteristics of high rigidity, maneuverability and strength-to-weight ratio, 6 Degree-of-Freedom (DoF) Stewart structure is widely adopted to construct flight simulator platforms for replicating motion feelings during training pilots. Unlike conventional serial link manipulator based mechanisms, Upset Prevention and Recovery Training (UPRT) in complex flight status is often accompanied by large speed and violent rate of change in angular velocity of the simulator. However, Classical Washout Filter (CWF) based Motion Cueing Algorithm (MCA) shows limitations in providing rapid response to drive motors to satisfy high accuracy performance requirements. This paper aims at exploiting Model Predictive Control (MPC) based MCA which is proved to be efficient in Hexapod-based motion simulators through controlling over limited linear workspace. With respect to uncertainties and control solution errors from the extraction of Terminal Constraints (COTC), this paper proposes a Switchable Model Predictive Control (S-MPC) based MCA under model adaptive architecture to mitigate the solution uncertainties and inaccuracies. It is verified that high accurate tracking is achievable using the MPC-based MCA with COTC within the simulator operating envelope. The proposed method provides optimal tracking solutions by switching to MPC based MCA without COTC outside the operating envelope. By demonstrating the UPRT with horizontal stall conditions following Average Absolute Scale(AAS) evaluation criteria, the proposed S-MPC based MCA outperforms MPC based MCA and SWF based MCA by 42.34% and 65.30%, respectively.

Problem

• Enhances motion simulator control for pilot training.
• Addresses limitations of Classical Washout Filter in rapid response.
• Improves accuracy using Switchable Model Predictive Control.

Innovation

• Switchable Model Predictive Control for motion simulators
• Model adaptive architecture mitigates control uncertainties
• Enhanced tracking accuracy within and outside operating envelope