Multi-objective genetic algorithm control

Multi-objective genetic algorithm control

For access to this article, please select a purchase option:

Buy chapter PDF
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Flexible Robot Manipulators: Modelling, Simulation and Control — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

This chapter presents the adoption of multi-objective evolutionary algorithms for control of flexible manipulator systems. Fundamentals of multi-objective optimisation and formulation of multi-objective optimisation algorithms are presented. The process of developing a multi-objective genetic algorithm (MOGA) is presented and is used in design of controllers for set-point tracking and end-point vibration control of a single link flexible manipulator. The potential of MOGA is utilised to develop an approach for automatic design of multi-modal command shapers. Two detailed case studies involving design of multi-modal command shapers for an open-loop case and for a closed-loop case in combination with a proportional derivative (PD) control are presented and their performances in set-point tracking and vibration reduction are assessed and discussed.

Chapter Contents:

  • 12.1 Introduction
  • 12.2 Multi-objective optimisation
  • 12.3 Evolutionary multi-objective algorithms
  • 12.3.1 Non-dominated sorting genetic algorithm
  • 12.3.2 Niched-Pareto genetic algorithm
  • 12.3.3 Multi-objective genetic algorithm
  • 12.3.4 Strength Pareto evolutionary algorithm
  • 12.3.5 Strength Pareto evolutionary algorithm 2
  • 12.3.6 Pareto archived evolution strategy
  • 12.3.7 Non-dominated sorting genetic algorithm II
  • 12.4 Multi-objective genetic algorithm
  • 12.4.1 Evaluation and ranking
  • 12.4.2 Fitness assignment
  • 12.4.3 Fitness sharing
  • 12.5 Vibration control of single-link flexible manipulator
  • 12.6 Controller design using MOGA
  • 12.7 Case study 12.1: multi-modal command shaping for open-loop control
  • 12.7.1 Design problem and MOGA-based command shaping technique
  • 12.7.2 Implementation
  • Parameter encoding
  • Design of impulses and shaped command
  • Objective functions
  • 12.7.3 Non-dominated solution set and results
  • 12.8 Case study 12.2: multi-modal command shaping for closed-loop control
  • 12.8.1 Design objectives
  • 12.8.2 Implementation
  • Objective functions
  • Non-dominated solution sets
  • Goal values and constraint handling
  • Validation of the approach
  • 12.9 Summary

Inspec keywords: vibration control; control system synthesis; manipulator dynamics; flexible manipulators; PD control; genetic algorithms; position control

Other keywords: multimodal command shaper automatic design; proportional derivative control; multiobjective genetic algorithm control; controller design; PD control; end-point vibration control; set-point tracking control; MOGA; multiobjective optimisation algorithms; single link flexible manipulator system; open-loop case; vibration reduction; closed-loop case; multiobjective evolutionary algorithms

Subjects: Control system analysis and synthesis methods; Spatial variables control; Vibrations and shock waves (mechanical engineering); Mechanical variables control; Optimisation; Manipulators; Optimisation techniques; Robot and manipulator mechanics

Preview this chapter:
Zoom in

Multi-objective genetic algorithm control, Page 1 of 2

| /docserver/preview/fulltext/books/ce/pbce086e/PBCE086E_ch12-1.gif /docserver/preview/fulltext/books/ce/pbce086e/PBCE086E_ch12-2.gif

Related content

This is a required field
Please enter a valid email address