A novel multi-objective quantum particle swarm algorithm for suspension optimization

Grotti, Ewerton; Mizushima, Douglas Makoto; Backes, Artur Dieguez; Awruch, Marcos Daniel de Freitas; Gomes, Herbert Martins

doi:10.1007/s40314-020-1131-y

Cited by 16 publications

(16 citation statements)

References 31 publications

(45 reference statements)

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“…To ensure ride comfort, the dynamic load on the suspension should overcome the friction force most of the time. Thus, our discussion focuses on the case of small friction force, of which a general dynamic equation of the 2-DOF system is 24 No 5 a Numerical simulation Yes (stochastic) Wang et al 16 No 1 Numerical simulation Yes Shangguan et al 15 No 2 Numerical simulation No Grotti et al 22 No…”

Section: Seat Model With Frictionmentioning

confidence: 99%

“…It is a fixed-point problem because s v 01 depends on the overall equivalent damping c e . With equations (22) and (25), c e can be determined from given c 1 , k 1 , and f.…”

Section: Gaussian Equivalent Linearizationmentioning

confidence: 99%

“…18,19 Hence, stochastic global optimization techniques, such as genetic algorithms and particle swarm algorithms, have been investigated by many researchers. [20][21][22] Extensive computation is required within stochastic global optimization techniques due to extensive iterations for a globally optimal solution, and the convergence speed depends greatly on the complexity of the optimization problem. 23 The characteristics of seat suspension optimizations performed by previous researchers and this study are presented in Table 1.…”

Section: Introductionmentioning

confidence: 99%

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Multi-objective optimization of vehicle seat suspension with friction under random excitation

Luo

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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A seat suspension contributes greatly to vehicle ride comfort as a result of direct contact with the human body. Friction in a seat suspension produces strong non-smooth nonlinearity in seat dynamics, which makes the simulation-based optimization on the seat suspension’s performance time-consuming. This study tries to address parameter optimization on a vehicle seat suspension with the friction force in an analytical approach. A two degrees of freedom model is firstly established for the human body-seat system with friction and subjected to bandlimited random excitation. The nonlinear model is converted into an equivalent linear model by using Gaussian linearization. The dynamic responses of the linear model have then derived analytically and validated by Monte Carlo simulations. Based on the analytical solution, a multi-objective optimization strategy is proposed for the seat suspension. The acceleration of the human body and the suspension travel are chosen as the objective indexes to evaluate seat performance. Simulation results show that the proposed optimization strategy is efficient, where a global optimum is guaranteed owing to the analytical expression of the objective function. The optimization approach taking advantage of model linearization can be applied to similar mechanical systems with friction.

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Section: Seat Model With Frictionmentioning

confidence: 99%

“…It is a fixed-point problem because s v 01 depends on the overall equivalent damping c e . With equations (22) and (25), c e can be determined from given c 1 , k 1 , and f.…”

Section: Gaussian Equivalent Linearizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi-objective optimization of vehicle seat suspension with friction under random excitation

Luo

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…Therefore, less information regarding the influence of weighting matrices on the closed-loop presentation has inspired scientists to look into the efficiency of swarm intelligence techniques handling the LQR weight selection problem. A novel multiobjective archived based quantum particle optimizer (MOQPSO) is introduced and compared with the long-established COGA-II and NSGA-II algorithm on a half car and a bus suspension system [11].…”

Section: Introductionmentioning

confidence: 99%

Ant Colony Optimization Tuned Closed-Loop Optimal Control Intended for Vehicle Active Suspension System

et al. 2022

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In recent years, the suspension system in modern vehicles has played a key role both as far as driving safety and comfort is concerned. To satisfy these vehicle performance specifications,active suspension is currently studied and implemented in practice in recent decades.In contrast to passive suspensions, by introducing force into system, active suspension can alter the suspension dynamic in realtime. A design of a controller is needed for real-time tuning of the control force in an active suspension system (ASS) to fulfill the challenging control objectives of suspension system comprising road handling, ride convenience, and travel suspension. This research proposed a novel ant colony optimization (ACO) algorithm for solving multi-objective weight optimization problem of the linear quadratic regulator (LQR) for automobiles ASS. The optimization problem of ASS is to design a state-feedback controller (SFC) as a result ACO is used to find optimal LQR weights. Here bothQ and R weight matrix of LQR is tuned. On a quarter-car ASS (QCASS) system, the effectiveness of ACO-tuned LQR is analytically checked with hardware in loop (HIL) analysis for an irregular road surface. Here, forexperiment, ISO road D rough runway, bumpy path, and pulse-type road profile are taken into account. Experimental findings illustrate that the proposed procedure can substantially reduce the acceleration of the Car body due to irregular road profiles compared to classical tuned LQR and model predictive control (MPC). The proposed controller shows the profound impact on the efficiency of the control schemes for three different road profiles.

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“…These models can be categorized into three groups of quarter, 14–16 half 17–20 and full-vehicle models. 21–25 Naturally, if the vehicle model in simulation is more complete, the dynamic response obtained from the model will be more reliable. Fossati et al 26 showed that a full vehicle model under random road excitations with optimized suspension parameters can have a better performance in comparison with non-optimized one.…”

Section: Introductionmentioning

confidence: 99%

Parameters uncertainty in pareto optimization of nonlinear inerter-based suspension system under nonstationary random road excitation

Seifi

Hassannejad

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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Ignoring the possible impacts of uncertainties in vehicle components during the design phase can undermine the safety of passengers and the vehicle performance. The main function of a suspension system is to provide satisfactory ride comfort and road-holding with a sufficiently low probability of rollover. Despite many studies on the design of new suspension systems with inerters, the effect of uncertainties in vehicle weight and tire stiffness on the design of suspension with inerters has not received much attentions. This paper presents a new type of suspension with inerters and asymmetric dampers and investigates the dynamic behavior of a vehicle under variable vehicle speed. Moreover, the effect of uncertainties on the choice of acceptable values of inerters is evaluated. For this investigation, the authors developed a 9-DOF full vehicle model with roll and yaw motions under non-stationary random road excitations in the time and frequency domains and studied its dynamic response with different suspension models. The optimal design was performed using a multi-objective optimization algorithm called MOEA/D. The best model was then used to determine the effect of uncertainties on the choice of inerters. The optimization results show that using the optimized suspension with inerters and nonlinear dampers instead of conventional design improves the ride comfort by 0.16%, the vehicle road-holding by 3.54%, and the rollover probability by 44.73%. In the proposed model, by changing the values of vehicle parameters with uncertainty, the choice of inerters to have an acceptable performance would be variable.

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A novel multi-objective quantum particle swarm algorithm for suspension optimization

Cited by 16 publications

References 31 publications

Multi-objective optimization of vehicle seat suspension with friction under random excitation

Multi-objective optimization of vehicle seat suspension with friction under random excitation

Ant Colony Optimization Tuned Closed-Loop Optimal Control Intended for Vehicle Active Suspension System

Parameters uncertainty in pareto optimization of nonlinear inerter-based suspension system under nonstationary random road excitation

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