Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A Self‐tuning Robust Control System for nonlinear real‐time hybrid simulation
In a real‐time hybrid simulation, a transfer system is used to enforce the interface interaction between computational and physical substructures. A model‐based, multilayer nonlinear control system is developed to accommodate extensive performance variations and uncertainties in a physical substructure. The aim of this work is to extend the application of real‐time hybrid simulation to investigating failure, nonlinearity, and nonstationary behavior. This Self‐tuning Robust Control System (SRCSys) consists of two layers: robustness and adaptation. The robustness layer synthesizes a nonlinear control law such that the closed‐loop dynamics perform as intended under a broad range of parametric and nonparametric uncertainties. Sliding mode control is employed as the control scheme in this layer. Then, the adaptation layer reduces uncertainties at run time through slow and controlled learning of the control plant. The tracking performance of the SRCSys is evaluated in two experiments that have highly uncertain physical specimens.
A Self‐tuning Robust Control System for nonlinear real‐time hybrid simulation
In a real‐time hybrid simulation, a transfer system is used to enforce the interface interaction between computational and physical substructures. A model‐based, multilayer nonlinear control system is developed to accommodate extensive performance variations and uncertainties in a physical substructure. The aim of this work is to extend the application of real‐time hybrid simulation to investigating failure, nonlinearity, and nonstationary behavior. This Self‐tuning Robust Control System (SRCSys) consists of two layers: robustness and adaptation. The robustness layer synthesizes a nonlinear control law such that the closed‐loop dynamics perform as intended under a broad range of parametric and nonparametric uncertainties. Sliding mode control is employed as the control scheme in this layer. Then, the adaptation layer reduces uncertainties at run time through slow and controlled learning of the control plant. The tracking performance of the SRCSys is evaluated in two experiments that have highly uncertain physical specimens.
A Self‐tuning Robust Control System for nonlinear real‐time hybrid simulation
Maghareh, Amin (Autor:in) / Dyke, Shirley J. (Autor:in) / Silva, Christian E. (Autor:in)
Earthquake Engineering & Structural Dynamics ; 49 ; 695-715
01.06.2020
21 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Robust Model Predictive Control for Dynamics Compensation in Real-Time Hybrid Simulation
| DOAJ | 2020
Kalman Filter Based Adaptive Control for Real-Time Hybrid Simulation with Nonlinear Substructure
| Springer Verlag | 2022
Robust adaptive model‐based compensator for the real‐time hybrid simulation benchmark
| Wiley | 2022
Robust decentralized adaptive compensation for the multi-axial real-time hybrid simulation benchmark
| DOAJ | 2024