Stability of nonlinear signal‐based control for nonlinear structural systems with a pure time delay

2019 ◽  
pp. e2365 ◽  
Author(s):  
Ryuta Enokida
Keyword(s):  
Time Delay ◽  
Structural Systems ◽  
Nonlinear Structural ◽  
Nonlinear Signal ◽  
Pure Time Delay

scholarly journals Decoupling Adaptive Smith Prediction Model of Flatness Closed-Loop Control and Its Application

Processes ◽  
2020 ◽  
Vol 8 (8) ◽  
pp. 895
Author(s):  
Mingming Song ◽  
Hongmin Liu ◽  
Yanghuan Xu ◽  
Dongcheng Wang ◽  
Yangyang Huang
Keyword(s):  
Mathematical Model ◽  
Control System ◽  
Time Delay ◽  
Control Method ◽  
Delay System ◽  
Control Performance ◽  
Time Delay System ◽  
Single Loop ◽  
Flatness Control ◽  
Pure Time Delay

Flatness control system is characterized by multi-parameters, strong coupling, pure time delay, which complicate the establishment of an accurate mathematical model. Therefore, a control scheme that combines dynamic decoupling, PI (Proportion and Integral) control and adaptive Smith predictive compensation is proposed. To this end, a dynamic matrix is used to decouple the control system. A multivariable coupled pure time-delay system is transformed into several independent generalized single-loop pure time-delay systems. Then, a PI-adaptive Smith predictive controller is constructed for the decoupled generalized single-loop pure time-delay system. Simulations show that the scheme has a simple and feasible structure, and good control performance. When the mathematical model of the control system is inaccurate, the control performance of adaptive Smith control method is evidently better than that of the ordinary Smith control method. The model is successfully applied to the cold rolling production site through LabVIEW, and the control accuracy is within 5I. This study reveals a new solution to the problem of coupled pure time-delay in flatness control system.

scholarly journals Experimental Validation of Optimal Parameter and Uncertainty Estimation for Structural Systems Using a Shuffled Complex Evolution Metropolis Algorithm

2019 ◽  
Vol 9 (22) ◽  
pp. 4959 ◽  
Author(s):  
Hesheng Tang ◽  
Xueyuan Guo ◽  
Liyu Xie ◽  
Songtao Xue
Keyword(s):  
Optimal Parameter ◽  
Structural Parameters ◽  
Metropolis Algorithm ◽  
Shaking Table ◽  
Parameter Distribution ◽  
Physical Parameters ◽  
Structural Systems ◽  
Model Errors ◽  
Nonlinear Structural ◽  
Shuffled Complex Evolution

The uncertainty in parameter estimation arises from structural systems’ input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is presented. First, the estimation framework is theoretically developed. The SCEM-UA algorithm is employed to search through feasible parameters’ space and to infer the posterior distribution of the parameters automatically. The resulting posterior parameter distribution then provides the most likely estimation of parameter sets that produces the best model performance. The algorithm is subsequently validated through both numerical simulation and shaking table experiment for estimating the parameters of structural systems considering the uncertainty of available information. Finally, the proposed algorithm is extended to identify the uncertain physical parameters of a nonlinear structural system with a particle mass tuned damper (PTMD). The results demonstrate that the proposed algorithm can effectively estimate parameters with uncertainty for nonlinear structural systems, and it has a stronger anti-noise capability. Notably, the SCEM-UA method not only shows better global optimization capability compared with other heuristic optimization methods, but it also has the ability to simultaneously estimate the uncertainties associated with the posterior distributions of the structural parameters within a single optimization run.

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