Semi-active Pneumatic Devices for Control of MDOF Structures

This paper presents a study of a predictive control system used to improve the behavior of a multistorey structure under strong earthquakes. The structure and application of supplemental controlled stiffness devices (CSD) are discussed. Instantaneous optimal control is used to obtain the control forces at each time step during an earthquake according to the predicted values of the displacements and the velocities of each storey. A linear autoregressive model with exogenous input (ARX) is used to predict the displacements and the velocities of the frame in order to overcome the time delay problem in the control system. Control of the forces developed in the CSD is possible, owing to the mechanical structure of the device. This device can be efficiently used as a controller because the forces developed in it are independent of the structure's displacements or velocities. Its efficiency is demonstrated in a numerical simulation of a seven‐storey building. The simulation shows that the behavior of a structure with the proposed predictive control system is significantly improved compared with that of an uncontrolled structure subjected to the same earthquakes. In this example, the displacements and velocities in the structure were predicted according to an ‘occurring’ earthquake by the ARX model and were used to obtain the optimal control forces (predictive control). These forces yielded a response of the structure with CSD that was similar to the response obtained with control forces, which were calculated from a ‘known’ complete history of the earthquake displacement and velocity values (instantaneous optimal control). Copyright © 2003 John Wiley & Sons, Ltd.

A study on interaction control for seismic response of parallel structures

<div class="section abstract"><div class="htmlview paragraph">This paper presents an advanced control system design for an engine cooling system in an internal combustion engine (ICE) vehicle. Building upon our previous work, we have derived models for crucial temperatures within the engine, including combustion wall temperature, coolant-out temperature, block temperature, as well as temperatures in external components such as heat exchangers and radiator. To accurately predict these temperatures in a rapid manner, we have utilized a lumped parameter concept with a mean-value approach. This approach allows for precise temperature estimation while maintaining computational efficiency. Given the complexity of the cooling system, we have proposed a linear time-varying (LTV) model predictive control (MPC) system to regulate the temperatures. This control system linearizes the model at each time step and applies linear MPC over the control and prediction horizons. By doing so, we effectively control the highly nonlinear and time-delayed system. Simulation results demonstrate the superiority and effectiveness of the proposed advanced engine cooling system. The control system can successfully regulate the temperatures within their desired range, showcasing its capability to optimize engine performance and ensure efficient cooling.</div></div>

In this paper, the characteristics of forces in active control systems connected to adjacent levels of a building are analyzed. The following characteristics are observed: (1) active control can provide significantly superior supplemental damping to a building, but causes a small frequency shift; (2) the linear quadratic regulator (LQR)-based control force is composed of an elastic restoring force component and a damping force component, where the damping force is almost identical to the total control force, however, the elastic restoring force is very small; and (3) the active control forces prevent motion most of the time during the entire control process. These three characteristics imply that active control systems connected to adjacent levels of a building behave like passive damping devices with adjustable parameters, namely damping characteristics in an active control, which is the mechanism used by semi-active control devices to reach similar performance as active control systems. Two indices are defined to quantify the damping characteristics of control forces in active control systems. These two indices can also be used to quantify the capacity of semi-active control to achieve the performance of active control. Based on the above observations, two principles are founded for optimization of parameters of semi-active control devices and passive dampers. The first is that the maximum output force of a semi-active or passive device to be designed is identical to an active device, called “design principle”. The other is the response equivalent principle, which states that the response of a building with semi-active or passive devices is the same as with active devices when the same maximum output force is applied. The design procedure for semi-active control devices and passive dampers is described in detail. Finally, numerical simulations of two benchmark problems is conducted to demonstrate the damping characteristics of active control and investigate the capacity of semi-active control to achieve the same performance as active control.

Hybrid MultiGene Genetic Programming - Artificial neural networks approach for dynamic performance prediction of an aeroengine

In this paper, an H∞ direct output feedback control algorithm is employed to design the control system in reducing structural seismic responses. Control forces are obtained from the multiplication of direct output measurements by a pre-calculated time-invariant feedback gain matrix. To achieve optimal control performance, the strategy to select both control parameters γ and α is developed. Analytical results show that the decrease of γ or the increase of α makes better control performance, but requires larger control forces. It is also found that lower and upper bounds of γ and α exist. The selection beyond these values will cause control system instability. In this paper, analytical expressions of the upper and lower bounds of γ and α are derived. Structural seismic responses can thus be reduced effectively with appropriate selection of γ and α. Moreover, control force execution time delay cannot be avoided. Relatively small delay time not only can render the control ineffective, but also may cause system instability. In this study, explicit formulas to calculate maximum allowable delay time and critical control parameters are derived for the design of a stable control system. Some solutions are also proposed to increase the maximum allowable delay time. The desired control performance can be guaranteed even with time delay.

Tableting is a dry granulation process for compacting powder blends into tablets. In this process, a blend of active pharmaceutical ingredients (APIs) and excipients are fed into the hopper of a rotary tablet press via feeders. Inside the tablet press, rotating feed frame paddle wheels fill powder into dies, with tablet mass adjusted by the lower punch position during the die filling process. Pre-compression rolls press air out of the die, while main compression rolls apply the force necessary for compacting the powder into tablets. In this paper, process variables such as feeder screw speeds, feed frame impeller speed, lower punch position during die filling, and punch distance during main compression have been systematically varied. Corresponding responses, including pre-compression force, ejection force, and tablet porosity have been evaluated to optimize the tableting process. After implementing an open platform communications unified architecture (OPC UA) interface, process variables can be monitored in real-time. To enable in-line monitoring of tablet porosity, a novel UV/Vis fiber optic probe has been implemented into the rotary tablet press. To further analyze the overall process, a data-driven modeling approach is adopted. Data-driven modeling is a valuable alternative to modeling real-world processes where, for instance, first principles modeling is difficult or infeasible. Due to the complex nature of the powder compaction process, several model classes need to be explored. To begin with, linear autoregressive models with exogenous inputs (ARX) have been considered. Thereafter, nonlinear autoregressive models with exogenous inputs (NARX) have been considered. Notably, several experiments have been designed to gather the data required for the development of the model.

The classical step-by-step integration method is commonly used for solving structural dynamic responses, but its performance is highly sensitive to the choice of time step. A large time step can significantly reduce accuracy and stability, while a very small time step greatly diminishes computational efficiency. Data-driven modeling and analysis methods are particularly favored for their potential to replace traditional structural analysis methods. However, traditional data-driven machine learning approaches require substantial amounts of training data to predict structural dynamic responses. Moreover, these methods often function as black boxes, lacking physical interpretability, and acquiring the necessary data can be challenging in practice. To address these limitations, this paper employs a physics-informed neural network (PINN) method for structural dynamic response analysis. The PINN approach incorporates the governing equations of structural dynamics into the neural network’s loss function as physical constraints, endowing the model with physical interpretability. The feasibility and effectiveness of this method are demonstrated through case studies on structural dynamic responses under different excitation scenarios. Numerical results show that the PINN method can accurately analyze structural dynamic responses without relying on system response data, a key advantage over traditional data-driven models. Moreover, unlike the classical step-by-step integration method, the PINN approach operates as a spatial domain optimization process and is independent of time step. Given the critical role of hyperparameters in the performance of the PINN model, this paper systematically investigates the influence of various hyperparameter settings on accuracy and efficiency, uncovering patterns of their impact on structural response analysis. Replication code for this paper will be available on GitHub:https://github.com/duke-iem/AIStructDynSolve.

Since January 2009, two long-range high-frequency (HF) radar systems have been collecting hourly high-spatial-resolution surface current data in the southeastern corner of the Bay of Biscay. The temporal resolution of the HF radar surface currents permits simulating drifter trajectories with the same time step as that of real drifters deployed in the region in 2009. The main goal of this work is to compare real drifter trajectories with trajectories computed from HF radar currents obtained using different methods, including forecast currents. Open-boundary modal analysis (OMA) is applied to the radar radial velocities and then a linear autoregressive model on the empirical orthogonal function (EOF) decomposition of an historical data series is used to forecast OMA currents. Additionally, the accuracy of the forecast method in terms of the spatial and temporal distribution of the Lagrangian distances between observations and forecasts is investigated for a 4-yr period (2009–12). The skills of the different HF radar products are evaluated within a 48-h window. The mean distances between real trajectories and their radar-derived counterparts range from 4 to 5 km for real-time and forecast currents after 12 hours of simulations. The forecast model improves persistence (i.e., the simulations obtained by using the last available OMA fields as a constant variable) after 6 hours of simulation and improves the estimation of trajectories up to 28% after 48 hours. The performance of the forecast is observed to be variable in space and time, related to the different ocean processes governing the local ocean circulation.

The control–structure interaction (CSI) affects the performance and robustness of protective structures in active control system, and it is important to consider the CSI effect to achieve high performance in structural vibration control. In this paper, the electro-mechanical model of the torsional servomotor is derived by establishing dynamic voltage equilibrium equation. Three different active control models (non-CSI, reduced-order CSI, and higher-order CSI model) which considering different accuracy requirements of the electro-mechanical relationship are established to investigate the influence of the CSI on active tuned mass damper (ATMD) control system. The frequency- and time-domain responses of a three-story steel frame structure during different excitations are investigated, and as a comparison, the responses of the uncontrolled and tuned mass damper (TMD) control system are also studied. It can be concluded that the control performance of the active control system is reduced due to the CSI effect, especially at resonance frequency region of the structure (around 2.7 Hz, 8.2 Hz, and 13.2 Hz). The input voltage, velocity of the top floor, velocity of active mass, and higher-order of control forces have an important influence on the servomotor output control force. Furthermore, it can be found that, to achieve the same control effect, the required control voltage has been significantly increased due to the CSI effect.

Modelling and prediction of NO x emission in a coal-fired power generation plant

Each joint of a hydraulic-driven legged robot adopts a highly integrated hydraulic drive unit (HDU), which features a high power–weight ratio. However, most HDUs are throttling-valve-controlled cylinder systems, which exhibit high energy losses. By contrast, pump control systems offer a high efficiency. Nevertheless, their response ability is unsatisfactory. To fully utilize the advantages of pump and valve control systems, in this study, a new type of pump–valve compound drive system (PCDS) is designed, which can not only effectively reduce the energy loss, but can also ensure the response speed and response accuracy of the HDUs in robot joints to satisfy the performance requirements of robots. Herein, considering the force control requirements of energy conservation, high precision, and fast response of the robot joint HDU, a nonlinear mathematical model of the PCDS force control system is first introduced. In addition, pressure–flow nonlinearity, friction nonlinearity, load complexity and variability, and other factors affecting the system are considered, and a novel force control method based on quantitative feedback theory (QFT) and a disturbance torque observer (DTO) is designed, which is denoted as QFT–DTOC herein. This method improves the control accuracy and robustness of the force control system, reduces the effect of the disturbance torque on the control performance of the servo motor, and improves the overall force control performance of the system. Finally, experimental verification is performed using the PCDS performance test platform. The experimental results and quantitative data show that the QFT–DTOC proposed herein can significantly improve the force control performance of the PCDS. The relevant force control method can be used as a bottom-control method for the hydraulic servo system to provide a foundation for implementing the top-level trajectory planning of the robot.

IGISOL control system modernization

Multi-Agent Model-Based Optimization for Future Electrical Grids

In this paper, edge-finishing automation using an active position/force control system is discussed. The architecture for a robust edge-finishing control system is developed, and the implementation of this con trol system on an xy table for the purpose of finishing planar parts is discussed. The control system combines position/force control with a fuzzy logic controller to compensate for part tolerance and fixturing errors. The cutting process is modeled using an empirically derived set of equations that relate tool contact forces and variances to the final chamfer depth that results on the part. The paper demonstrates that tangential force and normal position control is the preferred method for task-level control of edge-finishing processes because kinematic instability that develops when finishing highly curved parts can be avoided. Motion planning at the task level preserves spatial accuracy and prevents this phenomenon from developing. Experimental eval uation of the system on a sample workpiece shows the system is capable of consistent edge finishing in the presence of workpiece position uncertainty.