Hybrid fertilized particle swarm optimization for engineering design with application to vibration control

In this paper, vibration isolation and control for two types of representative engineering equipment are considered, i.e. sensitive equipment and machinery equipment. Isolation designs for the two equipments are carried out, respectively, for the purpose of chasing an optimal strategy and in which latest swarm intelligence—particle swarm optimization (PSO)—technique is adopted. In the investigation of single-stage system for sensitive equipment, transmissibility of displacement of equipment and relative displacement between equipment and foundation show that this design cannot obtain a desired isolation effect, but an implementation using multi-objective PSO (MOPSO) technique can overcome this when both the objectives are balanced. Then a two-stage system is investigated, and the transmissibility indicates that the disadvantages can be effectively eliminated, and the obtained gbest solutions using MOPSO can prove this. Based on the two-stage passive isolation, active control is proposed for better vibration attenuation, which is focused on $$H_{\infty }$$ criterion, and the control outputs are consisted of multi-objective fitness functions, and the latter strategy can seize the control effect of the two objectives well compared with the single-objective PSO-based $$H_{\infty }$$ control. Following the strategies for the sensitive equipment, similar strategies for machinery equipment are performed promptly. In the single-stage system, transmissibility derivations of transmitted force to the foundation and inertial force of equipment are same with the ones of displacement and relative displacement of single-stage design of sensitive equipment; in addition, chunk foundation is often utilized for machinery equipment, and the vibration of which should be taken into consideration. In view of these, a two-stage system is proposed, and the transmissibility indicates a drift for entering into the desired isolation region is feasible and the MOPSO-based validation is presented. After this, active vibration control is also investigated, and a multi-objective control using $$H_{\infty }$$ method is also performed using MOPSO, and the validations further confirm the importance and necessity of a multi-objective control. Single-objective and multi-objective vibration controls should be taken into account for sensitive and machinery equipment, by which a balanced and optimized control can be performed.

Vibration control of beams with piezoelectric sensors and actuators using particle swarm optimization

Introduction: In order to solve the problems of time-consuming and poor effects of traditional mechanical vibration control methods for the relay, the mechanical characteristics and vibration control of railway signal relays are studied in this paper. Based on the analysis of the mechanical characteristics of railway signal relays, the mechanical characteristic parameters of the relay, such as contact force, initial pressure, contact clearance, and overtravel are explored. On this basis, mechanical vibration control is completed based on particle swarm optimization. Methods: First, sensors are used to collect the data on the railway signal relay, and the mechanical vibration control model of the railway signal relay is built. Then, the structure of the PID vibration controller and LQR vibration controller in the model is analyzed. Finally, the controller parameters are adjusted through particle swarm optimization to improve the mechanical vibration control effect of the relay. Results: The simulation results show that the average signal-to-noise ratio of the method is 67dB, the collected data has low noise, and the control time is short, which is 1.4 s. Conclusion: The displacement of the railway signal relay controlled by the method is always less than 0.15 mm, and the control effect is good, which can be widely used in practice.

Vehicle suspension system has a wide range of applications in the automotive industry. This paper presents a simple and robust adaptive neuro-fuzzy inference system (ANFIS) for vibration control of a vehicle active suspension system (VASS). Performance requirements such as ride comfort, suspension stroke and good road holding are considered simultaneously in the vibration control issue. In order to attain desired training data for ANFIS, the optimal PID controller is designed in which particle swarm optimization (PSO) method is employed to adjust the PID parameters. Simulation results demonstrate the effectiveness and robustness of the proposed ANFIS-based vibration control strategy.

In response to the varying dynamic control requirements of different vibration systems under external loads, displacement, velocity, and acceleration are selected as the control objectives. Intelligent optimization algorithms and BP neural network methods are employed to determine the optimal design parameters for series viscous mass dampers (SVMD), tuned inerter mass dampers (TID), tuned viscous mass dampers (TVMD), and tuned mass dampers (TMD). The effectiveness and robustness of these vibration control systems are subsequently compared. The results indicate that the optimal parameters obtained through intelligent optimization algorithms are more accurate than those derived from the analytical solution based on fixed-point theory. Among the four optimization algorithms — particle swarm optimization (PSO), wild horse optimization (WHO), snow ablation optimization (SAO), and differentiated creative search (DCS), the WHO algorithm demonstrate superior performance in terms of solution accuracy, stability, and efficiency. Furthermore, the use of BP neural networks for optimal parameter prediction offers high predictive accuracy, minimal error, and robust generalization capability, significantly improving prediction efficiency. Finally, under the same mass ratio, TVMD demonstrates superior vibration control effectiveness and robustness across separate optimization objectives, including displacement, velocity, and acceleration.

This paper discusses on designing of vibration control system using evolutionary algorithms based on Particle Swarm Optimization (PSO). First, a new modified PSO strategy by adding a mutation rule (MPSO) is shown. Search efficiencies with Genetic Algorithms, original PSO and proposed MPSO are compared by solving standard benchmark functions. MPSO shows the excellent performance to optimize parameter of these functions, even it has multi-modal or parameters with epistasis. Second, structural system identification problems using MPSO are discussed. Structural system represented with product of biquad transfer functions can be identified by MPSO strategy. Finally, MPSO is applied to design MDOF vibration controller. The control performance by MPSO controller is described in both simulation and experiment to reduce floor vibration using an active mass damper.

In recent years, magnetorheological dampers (MRD) have played a significant role in vibration control in various fields such as vehicles, military, building structures, etc. However, the mechanical model of MRD is very complex due to its hysteresis, which makes it difficult to identify the model’s parameters. In addition to experimental data, under the condition of no other prior knowledge, eight unknown parameters of the Bouc-Wen model are identified in this study based on the particle swarm optimization (PSO) algorithm. According to the trend of parameter variation with current, fitting research is conducted, and the variation law of parameters with current is summarized. Based on the identified parameters, the MRD’s output force is predicted under any current other than the experimental current. Subsequently, the application of MRD in semi-active control of power equipment is performed out, and proposed Simulink calculation programs for single-stage and two-stage vibration control systems are built. Then, a comparative study with passive and active control is conducted. In this paper, linear quadratic regulator (LQR) control is adopted for the active control, and the controller’s parameters are optimized based on the PSO algorithm. The results show that the adopted semi-active control can significantly reduce the transmitted force from power equipment to the foundation and can effectively mitigate the disturbance of power equipment to the environment. This study offers important guidance for the vibration control of MRD in industrial engineering.

This study presents a systematic investigation into the vibration control and optimal design of a dynamic vibration absorber (DVA) equipped with an inerter and grounded negative stiffness. A combined approach integrating classical optimization theory and intelligent algorithms is adopted. Based on fixed-point theory (FPT), optimal analytical expressions for key parameters are derived, providing a solid theoretical foundation for subsequent multi-parameter optimization via intelligent algorithms. Furthermore, the particle swarm optimization (PSO) algorithm is employed to achieve approximately equal peak amplitudes at the two resonant frequencies in the amplitude-frequency response curve. Comparative simulations with several conventional DVA models demonstrate that, under harmonic excitation of the primary system, the proposed model significantly reduces resonance peaks, enhances system stability, and improves vibration attenuation performance. This work offers both theoretical insights and algorithmic tools to support parameter optimization and practical engineering applications of DVAs.

Flexible manipulator real-time vibration control methods are effective, but finding the right control gain is difficult. The reason for this is that traditional approaches are not permitted by up-to-date and novel architectures. Current population-based meta-heuristic optimization approaches, on the other hand, can provide solutions for such challenges, as they are inspired by many natural phenomena. Therefore, in the study, the Coronavirus herd immunity optimization (CHIO) method, inspired by the herd immunity mechanism, which is a COVID-19 control method, was used for the optimization of a flexible manipulator control gains. Gray-wolf-optimizer (GWO), another up-to-date population-based algorithm, and traditional particle swarm optimizer (PSO) were used to compare the success of the method. The findings reveal that when it comes to optimizing the vibration controller gains of flexible manipulators, CHIO can outperform its contemporary and traditional competitors.

In this paper, machinery equipment induced structural vibration was investigated and a composite system for structure and equipment was proposed. Tuned mass damper (TMD) and active tuned mass damper (ATMD) were respectively performed for vibration control, in addition, particle swarm optimization (PSO) was utilized for pursuing an optimal active control. Numerical results confirmed that the presented active control strategy could achieve a better vibration suppression compared to TMD control. The PSO based active control also gave inspiration for improving the traditional vibration control.

Ground vibrations induced by blasting pose significant environmental and structural challenges in opencast mining operations. Accurate prediction of peak particle velocity (PPV) is crucial for mitigating potential structural damage and ensuring operational safety. This study proposes an integrated machine learning framework that combines a random forest (RF) model with three metaheuristic optimization algorithms, whale optimization algorithm (WOA), particle swarm optimization (PSO), and grey wolf optimizer (GWO), to improve PPV prediction accuracy and robustness. Using a comprehensive dataset of 175 blasting events from the Jayant opencast coal mine in India, incorporating 11 key geomechanical, blast design, and monitoring parameters, the study constructs and compares three hybrid RF models optimized by WOA, PSO, and GWO. The performance of these hybrid models is benchmarked against baseline machine learning methods (support vector regression, kernel extreme learning machine, decision tree) and eight empirical formulas using multiple evaluation metrics. Results demonstrate that the WOA-RF model consistently outperforms others, achieving the highest predictive accuracy with strong generalization capabilities. Furthermore, Shapley additive explanation–based sensitivity analysis elucidates the dominant influence of distance on PPV, validating the physical basis of the models. A key innovation of this work lies in the side-by-side comparative assessment of three metaheuristic algorithms within a unified RF framework, providing valuable insights into their optimization efficiency and model robustness. Complementing the modeling advances, an interactive graphical user interface (GUI) was developed to facilitate practical adoption, enabling rapid local data learning, real-time PPV prediction, and dynamic blast design optimization. This GUI enhances engineer autonomy and supports informed decision-making in field applications. The proposed hybrid framework and its user-friendly interface offer a significant contribution to advancing predictive modeling and operational control of blasting-induced ground vibrations in mining engineering.

The torsional oscillation is the dominant vibration form for the impression cylinder of printing machine (printing cylinder for short), directly restricting the printing speed up and reducing the quality of the prints. In order to reduce torsional vibration, the active control method for the printing cylinder is obtained. Taking the excitation force and moment from the cylinder gap and gripper teeth open & closing cam mechanism as variable parameters, authors establish the dynamic mathematical model of torsional vibration for the printing cylinder. The torsional active control method is based on Particle Swarm Optimization(PSO) algorithm to optimize input parameters for the serve motor. Furthermore, the input torque of the printing cylinder is optimized, and then compared with the numerical simulation results. The conclusions are that torsional vibration active control based on PSO is an availability method to the torsional vibration of printing cylinder.

<div class="section abstract"><div class="htmlview paragraph">Proportional integral derivative (PID) control technique is the most common control algorithm applied in various engineering applications. Also, particle swarm optimization (PSO) is extensively applied in various optimization problems. This paper introduces an investigation into the use of a PSO algorithm to tune the PID controller for a semi-active vehicle suspension system incorporating magnetorheological (MR) damper to improve the ride comfort and vehicle stability. The proposed suspension system consists of a system controller that determine the desired damping force using a PID controller tuned using PSO, and a continuous state damper controller that estimate the command voltage that is required to track the desired damping force. The PSO technique is applied to solve the nonlinear optimization problem to find the PID controller gains by identifying the optimal problem solution through cooperation and competition among the individuals of a swarm. A mathematical model of a two degree-of-freedom MR-damped vehicle suspension system is derived and simulated using Matlab/Simulink software. The proposed PSO PID controlled suspension is compared to both the conventional PID controller and the passive suspension systems. System performance criteria are evaluated in both time and frequency domains, in order to quantify the success of the proposed suspension system. The simulated results reflect that the proposed PSO PID controller of the MR-damped vehicle suspension offers a significant improvement in ride comfort and vehicle stability.</div></div>

From the perspective of human vibration perception, reducing vibration stimuli transmitted to occupants is essential for improving ride comfort and reducing fatigue. Intelligent dampers, as key actuators in semi-active suspension systems, provide adjustable damping capabilities for vibration control. This article combines them with biomimetic control principles to study the vibration control of semi-active suspension. The effects of damper forward and inverse models, damping force ranges, and time delays on suspension performance were analyzed. The results show that a function prediction-based damper model, a damping force range below 0.2 times and above 1.4 times the passive curve, and a 10 ms delay could balance vibration reduction and economy. Particle swarm optimization is used to optimize LQR control parameters for different road grades and typical speeds. Inspired by the adaptive behavior of chameleons, graded weights are assigned according to road characteristics, with greater emphasis on comfort on Grade A and B roads and driving stability on Grade C and D roads. The results show that proper matching of damper models and parameter constraints can fully exploit the adjustable damping capability of smart dampers. These findings provide a theoretical basis for designing and optimizing semi-active suspension control strategies.

In this paper, a formulation of a sandwich plate integrating an elastic central layer (isotropic or composite) between two piezoelectric sub-layers (actuators and/or sensors), which can be taken as a smart (intelligent) structure and allowing active control vibrations is presented. A 9-node finite element quadratic plate element with 5 degrees of freedom per node is used which takes into account the effect of transverse shear with an additional degree of freedom for each node of the piezoelectric sub-layer. At First, the static control of the deflection by taking the two piezoelectric sub-layers as actuators with two configurations of the total and partial recovery of the surface is undertaken. Thus, the influence of patches position, for the second configuration, on the attenuation of vibrations is analyzed. In a Second step, the active vibration control using two types of LQR and PID controllers with different control parameters is tested and compared for the two recovery configurations (total and partial) of the piezoelectric elements. It is demonstrated throughout the present results that the performances of the partial recovery are almost as good as those of the total recovery despite a ratio of the surfaces piezoelectric patches which is 1/3. It is also noticed that the PID controller is more efficient than the LQR controller. But, if using the PSO (Particle Swarm Optimization) algorithm, the LQR's parameters are optimized and give almost the same performances as those of the PID controller.DOI: http://dx.doi.org/10.5755/j01.mech.24.5.20645