Smart base-isolated buildings rest on flexible pads known as base isolators that minimize the effect of external disturbances along with active/semi-active actuators. The strategies used to control these active components are typically based on system models that are known a priori. Although these models describe some of the most important dynamics of the elements involved in the system, the high degree of uncertainty in the behavior of a structure under external disturbances is very difficult to characterize using a fixed model. In this work, we propose a strategy that deals with this issue: the input that controls the actuator in the base isolation system results from the compound action of a controller that relies on a model of the system that is known a priori, and a control policy that is designed based on online data-driven inferences on the behavior of the system. In this way, the control design process incorporates both the prior information about the system and the unknowns of the system, such as non-modeled parameters and nonlinear behaviors in the building. We show through simulations the performance of the proposed method in an eight-story building subjected to seismic loading.

Smart materials, such as magnetorheological (MR) fluid, have received considerable research attention in recent years due to their unique capabilities. MR fluid, which possesses a magnetic field controllable viscosity, has been extensively studied for vehicular applications with the aim of synthesizing optimal MR fluids, designing optimal MR dampers, and developing control strategies. However, a comprehensive study that primarily focuses on developing a cost-effective semi-active suspension system for a commercial vehicle in a developing nation is still lacking. This study addresses this gap by synthesizing an in-house MR fluid and studying its rheological properties. Subsequently, a novel single-sensor-based controller is developed and closed-loop simulations are conducted on a quarter-car semi-active model. Finally, the overall semi-active quarter-car suspension system is experimentally tested using a suspension test rig. The performance of the proposed system in terms of ride comfort and road holding is evaluated and is compared with simple control strategies. The dynamic range of the developed semi-active MR damper is found to be around 2.3, indicating a significant MR effect. The results suggest an intermediate response using the proposed acceleration-driven controller (ADV) at lower frequencies and similar performance to that of the skyhook controller at higher frequencies. The cost-effective methodology proposed in this study is effective and can be adapted for other semi-active engineering applications.

The standard l₁-norm regularization is recently introduced for impact force identification, but generally underestimates the peak force. Compared to l₁-norm regularization, l_p-norm (0 ≤ p < 1) regularization, with a nonconvex penalty function, has some promising properties such as enforcing sparsity. In the framework of sparse regularization, if the desired solution is sparse in the time domain or other domains, the under-determined problem with fewer measurements than candidate excitations may obtain the unique solution, i.e., the sparsest solution. Considering the joint sparse structure of impact force in temporal and spatial domains, we propose a general l_p-norm (0 ≤ p < 1) regularization methodology for simultaneous identification of the impact location and force time-history from highly incomplete measurements. Firstly, a nonconvex optimization model based on l_p-norm penalty is developed for regularizing the highly under-determined problem of impact force identification. Secondly, an iteratively reweighed l₁-norm algorithm is introduced to solve such an under-determined and unconditioned regularization model through transforming it into a series of l₁-norm regularization problems. Finally, numerical simulation and experimental validation including single-source and two-source cases of impact force identification are conducted on plate structures to evaluate the performance of l_p-norm (0 ≤ p < 1) regularization. Both numerical and experimental results demonstrate that the proposed l_p-norm regularization method, merely using a single accelerometer, can locate the actual impacts from nine fixed candidate sources and simultaneously reconstruct the impact force time-history; compared to the state-of-the-art l₁-norm regularization, l_p-norm (0 ≤ p < 1) regularization procures sufficiently sparse and more accurate estimates; although the peak relative error of the identified impact force using l_p-norm regularization has a decreasing tendency as p is approaching 0, the results of l_p-norm regularization with 0 ≤ p ≤ 1/2 have no significant differences.

A novel approach that the smart control of robotics can be realized by a fuzzy controller and an appropriate Runge-Kutta scheme in this paper. A recently proposed integral inequality is selected based on the free weight matrix, and the less conservative stability criterion is given in the form of linear matrix inequalities (LMIs). We demonstrate that this target information obtained through image processing is subjected to smart control with computer-vision robotic to Arduino, and the infrared beacon was utilized for the operation of practical illustrations. A fuzzy controller derived with a fuzzy Runge-Kutta type functions is injected into the system and then the system is stabilized asymptotically. In this study, a fuzzy controller and a fuzzy observer are proposed via the parallel distributed compensation technique to stabilize the system. This paper achieves the goal of real-time following of three vehicles and there are many areas where improvements were made. Finally, each information is transmitted to Arduino via I2C to follow the self-propelled vehicle. The proposed calculation is approved in reproductions and ongoing smart control tests.

In recent years, with the vigorous development of visual algorithms, a large amount of research has been conducted on blade surface defect detection methods represented by deep learning. Detection methods based on deep learning models must rely on a large and rich dataset. However, the geographical location and working environment of wind turbines makes it difficult to effectively capture images of blade surface defects, which inevitably hinders visual detection. In response to the challenge of collecting a dataset for surface defects that are difficult to obtain, a multi-class blade surface defect generation method based on the StyleGAN3 (Style Generative Adversarial Networks 3) deep learning model and PBGMs (Physics-Based Graphics Models) method has been proposed. Firstly, a small number of real blade surface defect datasets are trained using the adversarial neural network of the StyleGAN3 deep learning model to generate a large number of high-resolution blade surface defect images. Secondly, the generated images are processed through Matting and Resize operations to create defect foreground images. The blade background images produced using PBGM technology are randomly fused, resulting in a diverse and high-resolution blade surface defect dataset with multiple types of backgrounds. Finally, experimental validation has proven that the adoption of this method can generate images with defect characteristics and high resolution, achieving a proportion of over 98.5%. Additionally, utilizing the EISeg annotation method significantly reduces the annotation time to just 1/7 of the time required for traditional methods. These generated images and annotated data of blade surface defects provide robust support for the detection of blade surface defects.