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Multi-objective genetic algorithm control

Multi-objective genetic algorithm control

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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
  • 12.7.2.1 Parameter encoding
  • 12.7.2.2 Design of impulses and shaped command
  • 12.7.2.3 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
  • 12.8.2.1 Objective functions
  • 12.8.2.2 Non-dominated solution sets
  • 12.8.2.3 Goal values and constraint handling
  • 12.8.2.4 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

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