Optimal Tuning of PID Controller Using Grey Wolf Optimizer Algorithm for Quadruped Robot

Year 2018, Volume: 6 Issue: 1, 29 - 35, 15.02.2018

Muhammed Arif Şen Mete Kalyoncu

https://doi.org/10.17694/bajece.401992

Cited By: 15

Abstract

The research and
development of quadruped robots is grown steadily in during the last two
decades. Quadruped robots present major advantages when compared with tracked
and wheeled robots, because they allow locomotion in terrains inaccessible.
However, the design controller is a major problem in quadruped robots because
of they have complex structure. This paper presents the optimization of two PID
controllers for a quadruped robot to ensure single footstep control in a
desired trajectory using a bio-inspired meta-heuristic soft computing method
which is name the Grey Wolf Optimizer (GWO) algorithm. The main objective of
this paper is the optimization of K_P, K_I and K_D
gains with GWO algorithm in order to obtain more effective PID controllers for
the quadruped robot leg. The importance to this work is that GWO is used first
time as a diversity method for a quadruped robot to tune PID controller.
Moreover, to investigate the performance of GWO, it is compared with widespread
search algorithms. Firstly, the computer aided design (CAD) of the system are
built using SolidWorks and exported to MATLAB/SimMechanics. After that, PID
controllers are designed in MATLAB/Simulink and tuned gains using the newly
introduced GWO technique. Also, to show the efficacy of GWO algorithm
technique, the proposed technique has been compared by Genetic Algorithm (GA)
and Particle Swarm Optimization (PSO) algorithm. The system is simulated in
MATLAB and the simulation results are presented in graphical forms to
investigate the controller’s performance.

Keywords

Quadruped Robot, PID controller, Optimization, Gait definition, Grey Wolf Optimizer, Genetic Algorithm, Particle Swarm Optimization, Trajectory Tracing

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