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

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 KP, KI and KD
 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.