Multiobjective optimization of parallel kinematic mechanisms by the genetic algorithms

Robotica ◽  
2011 ◽  
Vol 30 (5) ◽  
pp. 783-797 ◽  
Author(s):  
Ridha Kelaiaia ◽  
Olivier Company ◽  
Abdelouahab Zaatri
Keyword(s):  
Genetic Algorithms ◽  
Multiobjective Optimization ◽  
High Speed ◽  
Optimization Problem ◽  
Multiobjective Optimization Problem ◽  
Dimensional Synthesis ◽  
Nsga Ii ◽  
Parallel Kinematic ◽  
Parallel Kinematic Mechanisms ◽  
Kinematic Mechanisms

SUMMARYIt is well known that Parallel Kinematic Mechanisms (PKMs) have an intrinsic dynamic potential (very high speed and acceleration) with high precision and high stiffness. Nevertheless, the choice of optimal dimensions that provide the best performances remains a difficult task, since performances strongly depend on dimensions. On the other hand, there are many criteria of performance that must be taken into account for dimensional synthesis, and which are sometimes antagonist. This paper presents an approach of multiobjective optimization for PKMs that takes into account several criteria of performance simultaneously that have a direct impact on the dimensional synthesis of PKMs. We first present some criteria of performance such as the workspace, transmission speeds, stiffness, dexterity, precision, as well as dynamic dexterity. Secondly, we present the problem of dimensional synthesis, which will be defined as a multiobjective optimization problem. The method of genetic algorithms is used to solve this type of multiobjective optimization problem by means of NSGA-II and SPEA-II algorithms. Finally, based on a linear Delta architecture, we present an illustrative application of this methodology to a 3-axis machine tool in the context of manufacturing of automotive parts.

Defining the Space of Feasible Transmission Designs for Retrofitting a Rear-Wheel-Drive Automobile With a Power Split CVT

2019 ◽  
Author(s):  
Sean D. Vermillion
Keyword(s):  
Multiobjective Optimization ◽  
Fuel Economy ◽  
Optimization Problem ◽  
Rear Wheel ◽  
Gear Ratio ◽  
Multiobjective Optimization Problem ◽  
Nsga Ii ◽  
Feasible Set ◽  
Wheel Drive ◽  
Power Split

Abstract In this paper, we describe a strategy for modeling the feasible set of power-split continuously variable transmission (CVT) system designs for retrofitting rear-wheel-drive consumer automobiles. A design is considered feasible if it produces a higher fuel economy than the stock vehicle’s fuel economy rating. Towards modeling the feasible set of designs, we first model a vehicle with a power-split CVT system taking into account the system’s mass and CVT efficiency. In this model, the effective design variables are the mass of the transmission system, the CVT functional efficiency, and effective gear ratio defining the allowable power split through and around the CVT. We formulate the set of feasible design solutions utilizing a multiobjective optimization problem to define the boundaries of the maximum allowable system mass, minimum allowable efficiency, and minimum allowable effective gear ratio. We solve this multiobjective optimization problem using NSGA-II and fit a quadratic model to the NSGA-II results to define a surrogate model of the feasible design set. We show that this surrogate modeling approach is sufficient for predicting the feasibility of a candidate transmission design.

scholarly journals MultiObjective Genetic Modified Algorithm (MOGMA)

2012 ◽  
Vol 12 (2) ◽  
pp. 23-33
Author(s):  
Elica Vandeva
Keyword(s):  
Genetic Algorithms ◽  
Multiobjective Optimization ◽  
Pareto Front ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Multiobjective Optimization Problem ◽  
Multiobjective Optimization Problems ◽  
Modified Algorithm

Abstract Multiobjective optimization based on genetic algorithms and Pareto based approaches in solving multiobjective optimization problems is discussed in the paper. A Pareto based fitness assignment is used − non-dominated ranking and movement of a population towards the Pareto front in a multiobjective optimization problem. A MultiObjective Genetic Modified Algorithm (MOGMA) is proposed, which is an improvement of the existing algorithm.

Metaheuristic Tuning and Practical Implementation of a PID Controller Employing Genetic Algorithms

2019 ◽  
Author(s):  
Luis Angel ◽  
Jairo Viola ◽  
Mauro Vega
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Multiobjective Optimization ◽  
Pid Controller ◽  
Optimization Problem ◽  
Settling Time ◽  
Practical Implementation ◽  
Multiobjective Optimization Problem ◽  
External Disturbances ◽  
Generator System

Abstract PID controllers tuning is a complex task from the optimization perspective because it is a multiobjective optimization problem, which must ensure the accomplishment of a set of desired operating conditions of the closed-loop system as the overshoot, the settling time, and the steady state error. Employing metaheuristic optimization techniques is possible to find optimal solutions for the PID tuning multiobjective optimization problem with less computational cost. This paper presents the using of genetic algorithms as metaheuristic optimization technique for the tuning of a PID controller employed for the speed control of a motor-generator system. The genetic algorithm is designed to find the PID controller proportional, integral, and derivate terms that ensure the desired overshoot and settling time of the motor-generator system. The practical implementation of the PID controller is performed with a data acquisition card and the Matlab Stateflow toolbox. The proposed controller is contrasted with a PID controller tuned by the Internal Model Control technique. A robustness analysis is performed to evaluate the system response in the presence of the external disturbances. Obtained results shown that the PID controller tuned by genetic algorithm has a better response in the presence of external disturbances.

Integrated optimal design of a high-speed planar parallel manipulator

2018 ◽  
Vol 233 (9) ◽  
pp. 2976-2990 ◽  
Author(s):  
Zhengsheng Chen ◽  
Minxiu Kong
Keyword(s):  
Optimal Design ◽  
Parallel Manipulator ◽  
High Speed ◽  
Design Method ◽  
Tracking Error ◽  
Dimensional Synthesis ◽  
Tracking Accuracy ◽  
Nsga Ii ◽  
Parameters Tuning ◽  
Planar Parallel Manipulator

To obtain excellent comprehensive performances of the planar parallel manipulator for the high-speed application, an integrated optimal design method, which integrated dimensional synthesis, motors/reducers selection, and control parameters tuning, is proposed, and the 3RRR parallel manipulator was taken as the example. The kinematic and dynamic performances of condition number, velocity index, acceleration capability, and low-order frequency are taken into accounts for the dimensional synthesis. Then, to match motors/reducers parameters and keep an economical cost, the constraint equations and the parameters library are built, and the cost is chosen as one of the optimization objectives. Also, to get high tracking accuracy, the dynamic forward plus proportional–derivative control scheme is introduced, and the tracking error is chosen as one of the optimization objectives. Hence, the optimization model including dimensional synthesis, motors/reducers selection and controller parameters tuning is established, which is solved by the genetic algorithm II (NSGA-II). The result shows that comprehensive performances can be effectively promoted through the proposed integrated optimal design, and the prototype was constructed according to the Pareto-optimal front.

Sign in / Sign up

Export Citation Format

Share Document