Dynamic simulation of the CAD model in SimMechanics with multiple uses
Dynamic simulation of the CAD model in SimMechanics with multiple uses
When designing a mechatronic system, several steps are taken into account. One of the main steps is the design of a CAD model representing the physical part of the system, and another major point is the development of the mathematical model necessary for the respective controller design. This paper combines both design steps and shows the advantages of using this approach. First, a CAD model is created considering the kinematic and dynamic behavior of the system as well as respective material properties. This CAD model is, in parallel, used for both purposes: as the main basis for developing a mathematical model that will be used for definition of control laws and appropriate system controllers, and also to generate a physical model as result of exporting to MATLAB/Simulink (Simscape/SimMechanics library) in order to simulate the system behavior. This translation does not consider only the standard CAD model export to the SimMechanics library when forces and torques between links are clearly defined, but also the correct way to add corresponding limiting forces/torques. When comparing the behavior of the physical model and the mathematical model, it is important to obtain similar results, especially when it is necessary to perform some simplifications of a mathematical model, as happens in the context of nonlinear systems control. All these issues are discussed in this paper and the obtained simulation results for both models are similar, which confirms the proposed approach.
___
- Gouasmi M, Ouali M, Fernini B, Meghatria M. Kinematic modelling and simulation of a 2-R robot using SolidWorks
and verification by MATLAB/Simulink. Int J Adv Robot Syst 2012; 9: 245.
- Makkonen T, Nevala K, Heikkil¨a R. A 3D model based control of an excavator. Automat Constr 2006; 15: 571-577.
- Udai AD, Rajeevlochana CG, Saha SK. Dynamic simulation of a KUKA KR5 industrial robot using MATLAB SimMechanics. In: 15th National Conference on Machines and Mechanisms; 30 November–2 December 2011; Chennai, India. pp. 1-8.
- Mostyn V, Skarupa J. Improving mechanical model accuracy for simulation purposes. Mechatronics 2004; 14: 777-787.
- Barth M, Fay A. Automated generation of simulation models for control code tests. Control Eng Pract 2013; 21:218-230.
- Rocca GL, Tooren MJLV. Enabling distributed multi-disciplinary design of complex products: a knowledge based engineering approach. Journal of Design Research 2007; 5: 333.
- Tian F, Voskuijl M. Automated generation of multiphysics simulation models to support multidisciplinary design optimization. Adv Eng Inform 2015; 29: 1110-1125.
- Ferretti G, Magnani G, Rocco P. Virtual prototyping of mechatronic systems. Annu Rev Control 2004; 28: 193-206.
- Mattsson SE, Elmqvist H. An overview of the modeling language Modelica. In: Eurosim’98 Simulation Congress;14–15 April 1998; Helsinki, Finland. pp. 1-5.
-
Jonaitis A, Miliune R, Deveikis T. Dynamic model of wind power balancing in hybrid power system. Turk J Elec Eng & Comp Sci 2017; 25: 222-234.
- Solmaz S, Co¸skun T. An automotive vehicle dynamics prototyping platform based on a remote control model car. Turk J Elec Eng & Comp Sci 2013; 21: 439-451.
- Kocaarslan İ, Ak¸cay MT, Ulusoy SE, Bal E, Tiryaki H. Creation of a dynamic model of the electrification and traction power system of a 25 kV AC feed railway line together with analysis of different operation scenarios using MATLAB/Simulink. Turk J Elec Eng & Comp Sci 2017; 25: 4254-4267.
- Amadori K, Tarkian M, Olvander J, Krus P. Flexible and robust CAD models for design automation. Adv Eng Inform. 2012; 26: 180-195.
- Jagatheesa Perumal SK, Natarajan SK. Investigation of adaptive control of robot manipulators with uncertain features for trajectory tracking employing HIL simulation technique. Turk J Elec Eng & Comp Sci 2017; 25: 2513- 2521.
- Adam SAA, Ji-Pin Z, Yi-hua Z. Modeling and simulation of 5DOF robot manipulator and trajectory using MATLAB and CATIA. In: 2017 3rd International Conference on Control, Automation and Robotics; 24–26 April 2017; Nagoya, Japan. New York, NY, USA: IEEE. pp. 36-40.
- Ganesan E, Dash SS, Samanta C. Modeling, control, and power management for a grid-integrated photo voltaic, fuel cell, and wind hybrid system. Turk J Elec Eng & Comp Sci 2016; 24: 4804-4823.
-
Urrea C, Cort´es J, Pascal J. Design, construction and control of a SCARA manipulator with 6 degrees of freedom. J Appl Res Technol 2016; 14: 396-404.
- Kudryavtsev AV, Laurent GJ, Clevy C, Tamadazte B, Lutz P. Characterization of model-based visual tracking techniques for MOEMS using a new block set for MATLAB/Simulink. In: IEEE International Symposium on Optomechatronic Technologies; 5–7 November 2014; Seattle, WA, USA. New York, NY, USA: IEEE. pp. 163-168.
- Güle¸c M¸ Yola¸can E¸ Demir Y, Ocak O, Aydın M. Modeling based on 3D finite element analysis and experimental
study of a 24-slot 8-pole axial-flux permanent-magnet synchronous motor for no cogging torque and sinusoidal backEMF.
Turk J Elec Eng & Comp Sci 2016 24: 262-275.
-
Ibrahim BSKK, Zargoun AMA. Modelling and control of SCARA manipulator. Procedia Computer Science 2014;42: 106-113.
- Tlale NS, Zhang P. Teaching the design of parallel manipulators and their controllers implementing MATLAB, Simulink, SimMechanics and CAD. Int J Eng Educ 2005; 21: 838-845.
- S¸afak KK. Dynamics, stability, and actuation methods for powered compass gait walkers. Turk J Elec Eng & Comp Sci 2014; 22: 1611-1624.
- MathWorks. Simscape: Model and Simulate Multidomain Physical Systems. Natick, MA, USA: MathWorks, 2017.