Recent developments in semi-active control technologies enhance the possibility of an effective response reduction during a wide range of dynamic loading conditions. Most semi-active control schemes employ magnetorheological dampers (MRDs) as actuators. These devices exhibit nonlinear and hysterical behavior that complicates reactive force estimation to compensate for disturbances. In this paper, we present a novel robust schema to estimate MRD forces using a complex value convolutional neural network (CV-CNN) to overcome these problems. CV-CNN utilizes random complex value convolutional filters as parameters to reduce the measured noise by combining the training stage and the max-by-magnitude operation. Furthermore, CV-CNN is a hysteresis-model-free strategy that overcomes the parameterization in nonlinear systems. The proposed CV-CNN only requires displacement and voltage measurements for force estimation. Different metrics are used to compare results between the CV-CNN, genetic algorithm (GA), particle swarm optimization (PSO), and shallow neural network (SNN). Experimental results show the potential of the proposed CV- CNN for practical applications due to its simplicity and robustness. The CV-CNN computational time is less than that of GA and PSO. In the training stage, the CV-CNN uses 0.7% of GA's time and 1.4% of PSO. Although SNN uses 5.5% of the time consumed by the CV-CNN, the latter performs the force estimation for MRD better; its mean square error is 78.3% lower than the GA's and PSO's, and 71.4% lower than SNN's.

The article deals with the development of active vibration control of seismically-excited building structures. The control scheme is based on an alternative proportional-derived (PD) controller designed based only on the bandwidth of the system, which is an attractive technique for structural vibration suppression purposes and practical motion control solutions. The tuning method is analyzed employing Kharitonov’s theorem and Routh-Hurwitz criteria, which give necessary and sufficient conditions for choosing the two PD range of gains. Based on modal analysis, the system is transformed into a set of decoupled ordinary differential equations to simplify the PD design. An important advantage concerning a classical PD controller is the proposed PD design only uses the natural frequencies, which are relatively easy to estimates around an experimental test. Moreover, the proposed approach does not need frequently tune the gains parameters, so the design procedure is greatly simplified and, the proposed scheme does not need the system parameters, which generally are unknown. This method allows generalizing the controller design for multi-story buildings without modifying the controller structure, by choosing a scalar parameter. The effectiveness of the proposed PD schemes is demonstrated through simulation and experimental results of a reduced scale two-story building prototype.

This work provides an alternative for the semi-active vibration control problem via state feedback plus an active disturbance rejection control (K-ADRC) for a multistory building structure equipped with one magnetorheological damper (MRD) located between the ground and the first story. This control law introduces a disturbance observer (DOB) to estimate the total disturbance in real time, produced by the earthquake perturbation, unmodeled dynamics, and parametric uncertainties, and then cancel their effect using a part of the control signal. Moreover, using a diffeomorphism, the K-ADRC provides a proportional derivative (PD) controller designed to improve internal stability. A prominent feature of the active disturbance rejection control (ADRC) algorithm is that the value of the DOB gain is selected according to the system bandwidth. The stability of the system is verified via Lyapunov analysis. Moreover, the building structure model and the MRD are accomplished by incorporating a nonlinearity state that represents the hysteresis effect to perform real behavior in addition to assuming that acceleration is the only available measurement of building structures. Thus, integral filters are provided based on the appropriate cut-off frequency to estimate displacement and velocity, avoiding bias, offset, and phase shifts. Experimental results from a reduced-scale two-story building prototype demonstrate that the proposed K-ADRC is promising for real applications, and it has better performance than the state feedback (K) and the linear quadratic regulator (LQR) control techniques.

This paper presents an identification system for model parameters of a magnetorheological damper (MRD) through a Genetic Algorithm (GA). First, a novel model of the hysteretic phenomenon is introduced through the trigonometric function tanh, which represents an alternative simple model for MRD. Moreover, the representative curve of the dampers is demonstrated by means of simulation. Finally, model validation is carried out through an experimental non-linear parameters identification based on GA. An advantage of this method is that does not require filtered data, and model parameterization is not required. The employed Genetic Algorithm in identification system was programming by means of the Evolver tool, which is an additional Excel nackage.

This study proposes a new approach to identify structural parameters under seismic excitation using classical and quantum-inspired algorithms. Traditional methods often struggle with complex effects, noise, and computing limits. A five-story building model with mass–spring–damper system was tested to find properties during earthquakes. The study used optimization methods including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and five quantum-inspired versions: Quantum Genetic Algorithm (QGA), Quantum Particle Swarm Optimization (QPSO), Quantum Non-Dominated Sorting Genetic Algorithm II (QNSGA-II), Quantum Differential Evolution (QDE), and Quantum Simulated Annealing (QSA). Additionally, statistical analysis used Shapiro–Wilk for normality, Levene and Bartlett for variance, ANOVA with Tukey–Bonferroni comparisons, Bootstrap model ranking, and Borda count. The results show that the quantum-inspired methods perform better than classical ones. QSA reduced mean squared error (MSE) by 15.3% compared to GA, and QNSGA-II reduced MSE by 8.6% and root mean squared error (RMSE) by 3.5%, with less variation and tighter rankings. The framework addresses computing cost and response time; quantum methods need significant computing power and their accuracy suits offline earthquake assessments and model updates. This balance helps monitor building health when real-time speed is less critical but accuracy matters. The method provides a scalable tool for checking civil structures and could enable digital twins.