Drive System Sizing of a 6-DOF Parallel Robotic Platform

2014 ◽  
Author(s):  
Hermes Giberti ◽  
Davide Ferrari
Keyword(s):  
Optimal Solution ◽  
Parallel Robot ◽  
Drive System ◽  
Pareto Optimal ◽  
Hardware In The Loop ◽  
Motion Simulation ◽  
Inverse Dynamic ◽  
Kinematic Chains ◽  
Fixed Base ◽  
Parallel Kinematic

In this work, it is considered a 6-DoF robotic device intended to be applied for hardware-in-the-loop (HIL) motion simulation with wind tunnel models. The requirements have led to a 6-PUS parallel robot whose linkages consist of six closed-loop kinematic chains, connecting the fixed base to the mobile platform with the same sequence of joints: actuated Prism (P), Universal (U), and Spherical (S). As is common for parallel kinematic manipulators (PKMs), the actual performances of the robot depend greatly on its dimensions. Therefore, a kinematic synthesis has been performed and several Pareto-optimal solutions have been obtained through a multi-objective optimization of the machine geometric parameters, using a genetic algorithm. In this paper, the inverse dynamic analysis of the robot is presented. Then, the results are used for the mechanical sizing of the drive system, comparing belt- to screw-driven units and selecting the motor-reducer groups. Finally, the best compromise Pareto-optimal solution is definitely chosen.

Kinematic Modelling and VR Simulation of a 3DOF Medical Parallel Robot with One Decoupled Motion

2013 ◽  
Vol 837 ◽  
pp. 567-572
Author(s):  
Nadia Cretescu ◽  
Mircea Neagoe ◽  
Radu Saulescu
Keyword(s):  
Kinematic Model ◽  
Kinematic Chain ◽  
Parallel Robot ◽  
Parallel Structure ◽  
Level Control ◽  
Kinematic Chains ◽  
Fixed Base ◽  
Decoupled Motion ◽  
Kinematic Modelling ◽  
High Level

The robot studied in the paper has a 3DOF parallel structure of type 1PRRR+2PRPaR, with two coupled motions and one decoupled motion, composed by a mobile platform connected to the fixed base by three kinematic chains (one open kinematic chain of Prismatic Revolute Revolute Revolute type and two kinematic chains of Prismatic Revolute Parallelogram Revolute type). An analytical kinematic modelling of the parallel robot of type 1PRRR+2PRPaR is firstly presented in this paper, followed by a numerical simulation of the closed-form kinematic model and by a Virtual Reality (VR) application with control aspects. An innovative user interface for high-level control of the parallel 1PRRR+2PRPaR type robot is developed in MATLAB - Simulink and SimMechanics environment.

Dynamic Modeling and Simulation of Parallel Kinematic Machines

2004 ◽  
Author(s):  
S. J. Du ◽  
M. Kalveram ◽  
K. Weinert
Keyword(s):  
Modeling And Simulation ◽  
Dynamic Modeling ◽  
Inverse Dynamics ◽  
Virtual Work ◽  
Simulation Method ◽  
Euler Method ◽  
Inverse Dynamic ◽  
Kinematic Chains ◽  
Parallel Kinematic ◽  
Five Axis

This paper presents an effective method for inverse dynamic modeling of a five-axis milling machine with parallel kinematic chains (PKM). For solving the inverse dynamics, the methodology of using the principle of virtual work is introduced, which corrects a theoretic error in formulating the dynamic equations of motions sound in previous literatures. A corresponding computational algorithm for solving the inverse dynamics of the parallel kinematic machine is given and two cases of motion trajectories are calculated to check the proposed method. The corrected dynamic modeling is robust and features higher computational efficiency than other dynamic modeling methods such as recursive Newton-Euler method or Lagrangian formulations. Using this dynamic modeling and simulation method, we can anticipate the dynamic behavior of the five-axis machine and develop a suitable algorithm for motion control and dynamic optimization.

Pareto domain: an invaluable source of process information

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Geraldine Cáceres Sepúlveda ◽  
Silvia Ochoa ◽  
Jules Thibault
Keyword(s):  
Case Studies ◽  
Optimal Solution ◽  
Pareto Optimal ◽  
Process Variables ◽  
Optimal Point ◽  
Decision Variables ◽  
Visualization Tools ◽  
Pareto Domain ◽  
And Performance ◽  
Typical Process

AbstractDue to the highly competitive market and increasingly stringent environmental regulations, it is paramount to operate chemical processes at their optimal point. In a typical process, there are usually many process variables (decision variables) that need to be selected in order to achieve a set of optimal objectives for which the process will be considered to operate optimally. Because some of the objectives are often contradictory, Multi-objective optimization (MOO) can be used to find a suitable trade-off among all objectives that will satisfy the decision maker. The first step is to circumscribe a well-defined Pareto domain, corresponding to the portion of the solution domain comprised of a large number of non-dominated solutions. The second step is to rank all Pareto-optimal solutions based on some preferences of an expert of the process, this step being performed using visualization tools and/or a ranking algorithm. The last step is to implement the best solution to operate the process optimally. In this paper, after reviewing the main methods to solve MOO problems and to select the best Pareto-optimal solution, four simple MOO problems will be solved to clearly demonstrate the wealth of information on a given process that can be obtained from the MOO instead of a single aggregate objective. The four optimization case studies are the design of a PI controller, an SO2 to SO3 reactor, a distillation column and an acrolein reactor. Results of these optimization case studies show the benefit of generating and using the Pareto domain to gain a deeper understanding of the underlying relationships between the various process variables and performance objectives.

scholarly journals Mechanical-Level Hardware-In-The-Loop and Simulation in Validation Testing of Prototype Tower Crane Drives

Energies ◽  
2020 ◽  
Vol 13 (21) ◽  
pp. 5727
Author(s):  
Michał Michna ◽  
Filip Kutt ◽  
Łukasz Sienkiewicz ◽  
Roland Ryndzionek ◽  
Grzegorz Kostro ◽  
...  
Keyword(s):  
Laboratory Testing ◽  
Environmental Parameters ◽  
Experimental Tests ◽  
Drive System ◽  
Hardware In The Loop ◽  
Dynamic Simulations ◽  
Tower Crane ◽  
University Of Technology ◽  
New Type ◽  
Drive Systems

In this paper, the static and dynamic simulations, and mechanical-level Hardware-In-the-Loop (MHIL) laboratory testing methodology of prototype drive systems with energy-saving permanent-magnet electric motors, intended for use in modern construction cranes is proposed and described. This research was aimed at designing and constructing a new type of tower crane by Krupiński Cranes Company. The described research stage was necessary for validation of the selection of the drive system elements and confirmation of its compliance with applicable standards. The mechanical construction of the crane was not completed and unavailable at the time of testing. A verification of drive system parameters had to be performed in MHIL laboratory testing, in which it would be possible to simulate torque acting on the motor shaft. It was shown that the HIL simulation for a crane may be accurate and an effective approach in the development phase. The experimental tests of selected operating cycles of prototype crane drives were carried out. Experimental research was performed in the LINTE^2 laboratory of the Gdańsk University of Technology (Poland), where the MHIL simulator was developed. The most important component of the system was the dynamometer and its control system. Specialized software to control the dynamometer and to emulate the load subjected to the crane was developed. A series of tests related to electric motor environmental parameters was carried out.

scholarly journals Integration of Operability Framework and Pareto Optimal Front to Calculate Optimal Condition of Ethane Cracking Process

2020 ◽  
pp. 105-113
Author(s):  
M. Farsi
Keyword(s):  
Pareto Front ◽  
Optimal Solution ◽  
Optimization Procedure ◽  
Operating Conditions ◽  
Base Case ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Pareto Optimal Front ◽  
Multi Objective ◽  
Cracking Process

The main aim of this research is to present an optimization procedure based on the integration of operability framework and multi-objective optimization concepts to find the single optimal solution of processes. In this regard, the Desired Pareto Index is defined as the ratio of desired Pareto front to the Pareto optimal front as a quantitative criterion to analyze the performance of chemical processes. The Desired Pareto Front is defined as a part of the Pareto front that all outputs are improved compared to the conventional operating condition. To prove the efficiency of proposed optimization method, the operating conditions of ethane cracking process is optimized as a base case. The ethylene and methane production rates are selected as the objectives in the formulated multi-objective optimization problem. Based on the simulation results, applying the obtained operating conditions by the proposed optimization procedure on the ethane cracking process improve ethylene production by about 3% compared to the conventional condition.  

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