Control is a critical element in many applications and research such as experimental testing in real-time. Linear approaches for control and estimation have been widely applied to real-time hybrid simulation (RTHS) techniques in tracking the physical domain (plant). However, nonlinearities and highly uncertainties of the plant impose challenges that must be properly addressed using nonlinear control procedures. In this study, a controller is developed for such an uncertain nonlinear system by integrating a robust control approach with a nonlinear Bayesian estimator. A sliding mode control methodology synthesizes the nonlinear control law to provide stability and accurate tracking performance, and a particle filter algorithm estimates the full state of the plant using measured signals such as displacement. The Hybrid Simulation Management (HSM) code is developed to implement dynamic systems and the improved nonlinear robust controller. The HSM is integrated in a novel run-time substrate named CyberMech, which is a platform developed to enhance the performance of real-time cyber-physical experiments that supports parallel execution. A set of experiments with a highly uncertain nonlinear dynamic system demonstrates that the combination of advanced control techniques and high performance computation enhances the quality of real-time experimentation and potentially expands RTHS techniques capabilities.

中文翻译：

---

利用并行计算实时控制不确定的非线性系统

控制是许多应用和研究（例如实时实验测试）中的关键要素。用于控制和估计的线性方法已广泛应用于跟踪物理域（工厂）的实时混合仿真 (RTHS) 技术。然而，工厂的非线性和高度不确定性带来了必须使用非线性控制程序正确解决的挑战。在这项研究中，通过将鲁棒控制方法与非线性贝叶斯估计器相结合，为这种不确定的非线性系统开发了一种控制器。滑模控制方法综合了非线性控制律以提供稳定性和精确的跟踪性能，粒子滤波器算法使用位移等测量信号来估计设备的完整状态。开发混合仿真管理 (HSM) 代码以实现动态系统和改进的非线性鲁棒控制器。HSM 集成在名为 Cyber​​Mech 的新型运行时基板中，该平台开发用于增强支持并行执行的实时网络物理实验的性能。一组具有高度不确定性的非线性动态系统的实验表明，先进控制技术和高性能计算的结合提高了实时实验的质量，并有可能扩展 RTHS 技术的能力。这是一个平台，旨在提高支持并行执行的实时网络物理实验的性能。一组具有高度不确定性的非线性动态系统的实验表明，先进控制技术和高性能计算的结合提高了实时实验的质量，并有可能扩展 RTHS 技术的能力。这是一个平台，旨在提高支持并行执行的实时网络物理实验的性能。一组具有高度不确定性的非线性动态系统的实验表明，先进控制技术和高性能计算的结合提高了实时实验的质量，并有可能扩展 RTHS 技术的能力。