Experimental verification of leverage-type stiffness-controllable tuned mass damper using direct output feedback LQR control with time-delay compensation

Yeh S., Lu L., Lin G., Yu T.

Laghate A., Tamizharasi G.

Buildings with stiffness irregularity along elevation cause undesired deformations and damages during an intense earthquake. Therefore, provisions are suggested in design codes to identify such irregularity in buildings at the initial design stage. Typically, stiffness irregularity is defined in building codes based on the variation in lateral stiffness (e.g., India, New Zealand, etc.) or lateral drift (e.g., Peru and Turkey) of adjacent storeys. Thus, it is necessary to identify the finest methods of calculating stiffness irregularity in buildings. Using simple equivalent static analysis, four distinct 2D RC moment frame buildings are used to examine their stiffness irregularity based on four different codes (India, Peru, New Zealand, and Turkey). Responses are studied with and without including infill wall effects as an equivalent strut. In buildings with strut, stiffness irregularity defined based on India, i.e., IS 1893(1) is more appropriate than other building codes. Building codes of Peru or Turkey need to revise the upper limit. Otherwise, irregular buildings become regular, whereas New Zealand provisions define all buildings as irregular. But, without using equivalent strut, India or Peru describes even regular buildings as irregular; Turkey defines all buildings as regular, but New Zealand provisions are more appropriate. Therefore, it is also desirable to determine stiffness irregularity based on lateral drift (e.g., Peru or Turkey) with revised upper limits; otherwise, designer engineers must wisely use the check given in different code provisions by considering the effect of infill walls.

Ghanemi N.E., Abdeddaim M., Ounis A.

This study investigates the application of Artificial Neural Networks (ANNs) for controlling Active Tuned Mass Dampers (ATMDs) in seismic response reduction. The objective is to develop an AI-based ANN controller that effectively reduces structural vibrations during earthquakes. This approach offers a key advantage: achieving good response reduction with fewer sensors compared to traditional methods like Linear Quadratic Regulators (LQR), leading to increased practicality and cost-effectiveness. A supervised learning approach with the Levenberg–Marquardt backpropagation algorithm trains the ANN controller. The performance of the ANN-controlled ATMD is compared with that of an LQR-controlled system. Additionally, the ANN controller's robustness under signal time delay and noise contamination is evaluated. The ATMD with both controllers is implemented on a 10-story benchmark building subjected to near-field and far-field seismic records. The obtained results indicate significant reductions in peak displacement, acceleration, velocity, inter-story drift, maximum drift, base shear, and structural energy. Notably, the ANN controller achieves this with a reduced sensor requirement compared to the LQR method. Further, the ANN showed good robustness against signal time delay and noise contamination. ANNs demonstrated a high potential for controlling ATMDs for seismic response reduction due to their effectiveness and reduced sensor requirements, making them a conceivably more practical and cost-effective solution.

Peláez-Rodríguez C., Magdaleno A., Iglesias-Pordomingo Á., Pérez-Aracil J.

This work is devoted to design, implement and validate an active mass damper (AMD) for vibration mitigation in slender structures. The control law, defined by means of genetic algorithm optimization, is deployed on a low-cost processor (NI myRIO-1900), and experimentally validated on a 13.5-m lively timber footbridge. As is known, problems arising from human-induced vibrations in slender, lightweight and low-damped structures usually require the installation of mechanical devices, such as an AMD, in order to be mitigated. This kind of device tends to reduce the movement of the structure, which can be potentially large when it is subjected to dynamic loads whose main components match its natural frequencies. In those conditions, the AMD is sought to improve the comfort and fulfil the serviceability conditions for the pedestrian use according to some design guides. After the dynamic identification of the actuator, the procedure consisted of the experimental characterization and identification of the modal properties of the structure (natural frequencies and damping ratios). Once the equivalent state space system of the structure is obtained, the design of the control law is developed, based on state feedback, which was deployed in the low-cost controller. Finally, experimental adjustments (filters, gains, etc.) were implemented and the validation test was carried out. The system performance has been evaluated using different metrics, both in the frequency and time domain, and under different loads scenarios, including pedestrian transits to demonstrate the feasibility, robustness and good performance of the proposed system. The strengths of the presented work reside in: (1) the use of genetic evolutionary algorithms to optimize both the state estimator gain and the feedback gain that commands the actuator, whose performance is further tested and analyzed using different fitness functions related to both time and frequency domains and (2) the implementation of the active control system in a low-cost processor, which represents a significant advantage when it comes to implement this system in a real structure.

Lai Y., Luo W., Huang S., Yang C., Chang C.

Miyamoto K., She J., Sato D., Yasuo N.

This paper presents a method for the automatic selection of weighting matrices for a linear-quadratic regulator (LQR) in order to design an optimal active structural control system. The weighting matrices of a control performance index, which are used to design optimal state-feedback gains, are usually determined by rule of thumb or exhaustive search approaches. To explore an easy way to select optimal parameters, this paper presents a method based on Bayesian optimization (BO). A 10-degree-of-freedom (DOF) shear building model that has passive-base isolation (PBI) under the building is used as an example to explain the method. A control performance index that contains the absolute acceleration, along with the inter-story drift and velocity of each story, is chosen for the design of the controller. An objective function that contains the maximum absolute acceleration of the building is chosen for BO to produce optimal weighting matrices. In the numerical example, a restriction on the displacement of the PBI is used as a constraint for the selection of weighting matrices. First, the BO method is compared to the exhaustive search method using two parameters in the weighting matrices to illustrate the validity of the BO method. Then, thirty-three parameters (which are automatically optimized by the BO method) in the weighting matrices are used to elaborately tune the controller. The control results are compared to those for the exhaustive search method and conventional optimal control, in terms of the control performance of the relative displacement, absolute acceleration, inter-story-drift angle, and the story-shear coefficient of each story. The damping ratio for each mode, and the control energy and power are also compared. The comparison demonstrates the validity of the method.