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 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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