Recurrent emotional contagion for the crowd evacuation of a cyber-physical society

The tracking performance of an actuation transfer system in a real‐time hybrid simulation (RTHS) frequently faces accuracy and robustness challenges under constraints and complicated environments with uncertainties. This study proposes a novel control approach based on the deep deterministic policy gradient algorithm in reinforcement learning (RL) combined with feedforward (FF) compensation, which emphasizes the implementation of shaking table control and substructure RTHS. The proposed method first describes the control plant within the RL environment. Then, the agent is trained offline to develop optimized control policies for interaction with the environment. A series of validation tests were conducted to assess the performance of the proposed method, starting with the dynamic testing of underwater shaking table control and then a virtual RTHS benchmark problem. For complex systems, such as controlling the underwater shaking table, the proposed algorithm, FF, and adaptive time series (ATS) compensation methods are compared under various water depths and motions. The results show better performance and wider broadband frequency applicability under different shaking table dynamic‐coupling effects. Next, a controller based on the proposed method was designed by extending the virtual RTHS via the configuration of the control plant and substructure division, as provided in the RTHS benchmark problem. The proposed RL controller also improved the tracking accuracy and robustness of conventional FF compensators against unmodeled dynamics and perturbation uncertainties. This controller can be extended to further advanced control strategies as a component of model‐based control methods.

Reinforcement learning control method for real‐time hybrid simulation based on deep deterministic policy gradient algorithm

Boundary coordination algorithm for real-time hybrid test of high-speed maglev train-guideway coupling vibration

The accuracy of numerical substructures is important in online hybrid tests for assessing structural seismic performance. The recently developed online model updating (OMU) technique can improve the accuracy of numerical substructures by identifying and updating parameters using measurements from physically tested substructures. However, the updated mechanical model often depends on the loading pattern, particularly for models with coupled behaviors in multiple directions. Ignoring this influence may result in a large discrepancy between the simulation results and real performance. In this paper, an OMU method with multiple inputs and considering coupled boundary conditions is proposed to identify the parameters of a complex mechanical model for laminated rubber bearings, where the horizontal performance is significantly influenced by the vertical force. In this method, the horizontal and vertical responses serve as inputs for an unscented Kalman filter to identify the parameters of the mechanical model, and the vertical response provides additional information and constraints. Realistic boundary conditions are achieved by the vertical force-displacement switching control and horizontal displacement control. One of the isolators of a six-story steel moment frame was chosen as the physical substructure. A sophisticated mechanical model was selected to simulate the remaining isolators in the numerical substructure, and the parameters were updated online. The results of the hybrid test indicate that the proposed method provides higher accuracy for estimating the post-yield stiffness ratio than the traditional method. The coefficient of variation was 50% lower, and the convergence efficiency was almost five times higher, demonstrating the effectiveness and accuracy of the proposed method. 

Online Model Updating Method with Multiple Inputs Considering Realistic Boundary Conditions in Hybrid Tests

The accuracy of real-time hybrid simulation (RTHS) is greatly influenced by the inevitable time delay and amplitude error due to the control plant dynamics. Several tracking controllers have been implemented to improve the overall performance, and among them, model predictive control (MPC) loses its prediction advantage due to the characteristic of real-time command calculation of RTHS. In this study, an improved tracking controller based on MPC controller combined with a polynomial-based forward reference prediction (MPC-RP) is proposed according to the principle of providing future data insight. First, the proposed controller is described, and the basic implementation procedure is presented. Then, validation tests were carried out to evaluate the tracking performance of the proposed controller based on the virtual RTHS benchmark problem. The results show that the MPC-RP controller has an effective delay compensation performance and a good amplitude error regulation capacity. It is also demonstrated that the MPC-RP controller has a great robust performance concerning control plant uncertainties, which ensures highly improved accuracy of RTHS.

Performance study of model predictive control with reference prediction for real-time hybrid simulation

HSAEP: a new platform to evaluate hybrid simulation algorithms

Adaptive compound control for the real-time hybrid simulation of high-speed railway train–bridge coupling vibration

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HSAEP: a new platform to evaluate hybrid simulation algorithms

Adaptive compound control for the real-time hybrid simulation of high-speed railway train–bridge coupling vibration