Distributed parameter modeling of single-mast stacker crane structures

This paper presents distributed parameter dynamical modeling capabilities of single-mast stacker crane structures. In the frame structure of stacker cranes due to external excitation or inertial forces undesirable structural vibrations may arise. These vibrations reduce the stability and positioning accuracy of stacker crane and causes increasing cycle time of storage/retrieval operation. Thus it is necessary to investigate of these vibrations. In this paper the dynamical behavior of single-mast stacker cranes is approximated by means of distributed parameter models. The first model is a cantilever beam model with uniform material and cross-sectional properties. This model is used to demonstrate fundamental properties of Euler-Bernoulli beam models. The second model is cantilever beam model with variable cross-sectional properties and lumped masses. The eigenfrequencies and mode shapes of this mast-model are determined by means transfer matrix method. In the third model the whole structure of single-mast stacker crane is modeled

Multi-body modelling of single-mast stacker cranes

LBK

Vehicle Modelling and Simulation in Simulink

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Parameter Estimation of Drag Coefficient and Rolling Resistance of Vehicles Based on GPS Speed Data

In this paper, a parameter estimation method of the model-based design approach is applied to estimate the drag coefficient and the rolling resistance coefficient of a vehicle. In fact, a constant-force parameter (c_const) and a velocity-square-force parameter (c_square) are in the vehicle model, and these result in the sum force applied along the translational DOF that models the vehicle. It is only an assumption that the constant force is the rolling resistance and the force proportional to the square of the velocity is the drag force of the air. Only GPS speed data is used for the estimation process. The conclusion is that parameter estimation is a good alternative when expensive measurement devices are not available to measure the force losses separately and directly

Accuracy Analysis of Two Parallel Manipulators

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Longitudinal Dynamic Modeling and Driving Cycle Tracking Control of an Electric-Driven Vehicle by Means of MATLAB/Simulink/Simscape

MATLAB, Simulink, and Simscape are market-leading products in Model-Based Design (MBD). Applying acausal and causal modeling methods to model the physical plant and control algorithms of mechatronic systems results in high-fidelity virtual prototype models that can be used for Model-in-Loop (MiL) development. In this paper, an electric-driven vehicle is modeled, which can execute various driving cycle inputs. PID controllers are used in order to get the appropriate plant inputs. Idealized anti-lock braking system (ABS) and traction control system (TCS) algorithms provide robustness when the driving cycle input is near-infeasible. The control algorithm is validated in the cases of feasible and infeasible driving cycle inputs

Vehicle Dynamic Simulation Possibilities Using AVL Cruise M

In most cases, when creating vehicle dynamics simulations, we need software that can speed up model creation and simulation. There are many programs on the market for this purpose, but they have different knowledge and user interfaces. We present in this article briefly introduces the use of one of the market's leading vehicle simulation software, the AVL Cruise M

Survey of the Application Fields and Modeling Methods of Automotive Vehicle Dynamics Models

In this paper, a review is presented on automotive vehicle dynamics modeling. Applied vehicle dynamics models from various application fields are analyzed and classified in the first section. Vehicle dynamics models may be simplified because of different reasons: several control/estimation/analysis methods are suitable only for simplified models (e.g. using control-oriented models), or because of the computational cost. Detailed/truth models of vehicle dynamics represent another field of vehicle dynamics modeling, these models play an important role in the virtual prototyping of vehicles. In the second section, the main modeling considerations of vehicle dynamics are presented in longitudinal, lateral and vertical directions. Various physical effects must be considered in the case of vehicle dynamics modeling, a lot of these effects are significant only in a specific direction of the vehicle body, which is the main potential of model simplification. The section presents vehicle modeling considerations in all of the three translational directions of the vehicle body