Iterative refinement algorithm for efficient velocities and accelerations solutions in closed-loop multibody dynamics

Pan, Yongjun; Dai, Wei; Huang, Liubin; Li, Zhixiong; Mikkola, Aki

doi:10.1016/j.ymssp.2020.107463

Cited by 17 publications

(4 citation statements)

References 37 publications

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“…Computational errors refer to errors related to the computer in the implementation and simulation output process. This includes the discretization methods within the modeling tools, algorithm selection, errors in the compilation process, errors in the numerical integration and limitations in the accuracy of the computer itself [22,23]. In the process of simulation modeling, both model description and model structure have usually undergone multiple validations by researchers worldwide.…”

Section: Error Analysis In Construction Machinery Simulation Modelingmentioning

confidence: 99%

Study of Error Flow for Hydraulic System Simulation Models for Construction Machinery Based on the State-Space Approach

Su,

Rao,

Wang

et al. 2023

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This study presents an error flow research method for simulation models of hydraulic systems in construction machinery based on the state-space approach, aiming to ensure the reliable application of digital twin models. Initially, a comprehensive analysis of errors in the simulation modeling of hydraulic systems in construction machinery was conducted, highlighting simulation model parameters as the primary error sources. Subsequently, a set of metrics for assessing the accuracy of simulation models was developed. Following this, an error flow analysis method for simulation models of hydraulic systems in construction machinery was explored based on the state space approach, delving into the sources, transmission, and accumulation of errors in the simulation modeling of valve-controlled cylinder systems. The research results unequivocally indicate that the spring stiffness, viscous damping coefficient, and hydraulic cylinder external leakage coefficient are critical parameters affecting the accuracy of valve-controlled cylinder system simulation models. Furthermore, it was observed that the simulation model of the control valve has a significantly greater impact on the errors in the valve-controlled cylinder system simulation model than the hydraulic cylinder model. In conclusion, the reliability of the error flow model was confirmed through simulation experiments, revealing a maximum relative error of only 3.73% between the error flow model and the results of the simulation experiments.

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Section: Error Analysis In Construction Machinery Simulation Modelingmentioning

confidence: 99%

Study of Error Flow for Hydraulic System Simulation Models for Construction Machinery Based on the State-Space Approach

Su,

Rao,

Wang

et al. 2023

Actuators

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“…It is capable of describing the relative coordinates using a minimal set of independent relative coordinates. The equations of motion of closed-loop multibody systems, in turn, can be formulated as [15,[39][40][41]]…”

Section: Multibody Model Of the Vehiclementioning

confidence: 99%

“…The semi-recursive multibody model is typically more efficient than the multibody models used by the majority of commercial software applications. Pan developed a vehicle model using semi-recursive dynamics equations and validated it against commercial software solutions [14,15]. In this study, an efficient semi-recursive multibody formulation is introduced to model the vehicle dynamics [16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Improved Vehicle Vibration Control through Optimization of Suspension Parameters Using the Response Surface Method and a Non-Linear Programming with a Quadratic Lagrangian Algorithm

Dai

Pan

et al. 2023

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Vibration-control techniques generally fall into two categories: passive methods that optimize the structure of the suspension to absorb any impact from the ground, and active methods that directly control the vertical force of the suspension by hydraulic or electric actuators when the vehicle traverses a bumpy road. In this study, a vibration-control method is described that employs both an optimal controller and suspension parameter optimization. Continuous speed bumps are implemented to simulate more complex and realistic driving conditions. First, a vehicle system is modeled using a semi-recursive multibody formulation, which allows for a more precise description of the longitudinal–vertical dynamics. Then, an optimal control method for vehicle vibration control is introduced. Second, the Latin hypercube design is utilized to analyze the response surface methodology (RSM) model. For suspension optimization, the RSM model and the non-linear programming with a quadratic Lagrangian (NLPQL) algorithm are employed. Thirdly, both passive suspension optimization and active motion control are employed for vibration control. The results indicate that the presented method can effectively control vehicle vibration, decreasing the average vibration by 30.8%. The results suggest that the novel approach can also enhance the ride comfort in autonomous vehicles traversing, e.g., a series of speed bumps.

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“…However, this expression yields a small set of independent relative accelerations zi , which in turn results in a higher computational efficiency. Therefore, the equations of motion (Equation ( 1)) can be used for real-time simulation in low-cost hardware [32][33][34]. On the other hand, to mitigate the issues related to unavailability of direct estimation of vehicle characteristics and high computational costs linked to commercial software based vehicle dynamics simulations, this efficient multibody formulation is introduced for vehicle simulation and data acquisition.…”

Section: Vehicle Dynamics Data Acquisitionmentioning

confidence: 99%

An Improved Deep Neural Network Model of Intelligent Vehicle Dynamics via Linear Decreasing Weight Particle Swarm and Invasive Weed Optimization Algorithms

Nie

Min

Pan

et al. 2022

Sensors

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We propose an improved DNN modeling method based on two optimization algorithms, namely the linear decreasing weight particle swarm optimization (LDWPSO) algorithm and invasive weed optimization (IWO) algorithm, for predicting vehicle’s longitudinal-lateral responses. The proposed improved method can restrain the solutions of weight matrices and bias matrices from falling into a local optimum while training the DNN model. First, dynamic simulations for a vehicle are performed based on an efficient semirecursive multibody model for real-time data acquisition. Next, the vehicle data are processed and used to train and test the improved DNN model. The vehicle responses, which are obtained from the LDWPSO-DNN and IWO-DNN models, are compared with the DNN and multibody results. The comparative results show that the LDWPSO-DNN and IWO-DNN models predict accurate longitudinal-lateral responses in real-time without falling into a local optimum. The improved DNN model based on optimization algorithms can be employed for real-time simulation and preview control in intelligent vehicles.

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Iterative refinement algorithm for efficient velocities and accelerations solutions in closed-loop multibody dynamics

Cited by 17 publications

References 37 publications

Study of Error Flow for Hydraulic System Simulation Models for Construction Machinery Based on the State-Space Approach

Study of Error Flow for Hydraulic System Simulation Models for Construction Machinery Based on the State-Space Approach

Improved Vehicle Vibration Control through Optimization of Suspension Parameters Using the Response Surface Method and a Non-Linear Programming with a Quadratic Lagrangian Algorithm

An Improved Deep Neural Network Model of Intelligent Vehicle Dynamics via Linear Decreasing Weight Particle Swarm and Invasive Weed Optimization Algorithms

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