Auto-tuning of PID Controllers for Robotic Manipulators Using PSO and MOPSO

This work proposes two approaches to automatic tuning of PID position controllers based on different global optimization strategies. The chosen optimization algorithms are PSO and MOPSO, i. e. the problem is handled as a single objective problem in the first implementation and as a multiobjective problem in the second one. The auto-tuning is performed without assuming any previous knowledge of the robot dynamics. The objective functions are evaluated depending on real movements of the robot. Therefore, constraints guaranteeing safe and stable robot motion are necessary, namely: a maximum joint torque constraint, a maximum position error constraint and an oscillation constraint. Because of the practical nature of the problem in hand, constraints must be observed online. This requires adaptation of the optimization algorithm for reliable observance of the constraints without affecting the convergence rate of the objective function. Finally, Experimental results of a 3-DOF robot for different trajectories and with different settings show the validity of the two approaches and demonstrate the advantages and disadvantages of every method.

[1]  Peter J. Angeline,et al.  Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences , 1998, Evolutionary Programming.

[2]  John J. Craig,et al.  Introduction to Robotics Mechanics and Control , 1986 .

[3]  Krister Forsman,et al.  A new criterion for detecting oscillations in control loops , 1999, 1999 European Control Conference (ECC).

[4]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[5]  Jens Kotlarski,et al.  A Practical Approach for the Auto-tuning of PD Controllers for Robotic Manipulators using Particle Swarm Optimization , 2017, ICINCO.

[6]  Rafael Kelly,et al.  A stable motion control system for manipulators via fuzzy self-tuning , 2001, Fuzzy Sets Syst..

[7]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[8]  A. Rezaee Jordehi,et al.  Parameter selection in particle swarm optimisation: a survey , 2013, J. Exp. Theor. Artif. Intell..

[9]  Puren R. Ouyang,et al.  Comparative Study of DE, PSO and GA for Position Domain PID Controller Tuning , 2015, Algorithms.

[10]  Kaspar Althoefer,et al.  Fuzzy PID tuning for robot manipulators , 1998, IECON '98. Proceedings of the 24th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.98CH36200).

[11]  Makoto Iwasaki,et al.  GA-Based Practical Auto-Tuning Technique for Industrial Robot Controller with System Identification , 2012 .

[12]  Leandro dos Santos Coelho,et al.  Improved multiobjective particle swarm optimization for designing PID controllers applied to robotic manipulator , 2014, 2014 IEEE Symposium on Computational Intelligence in Control and Automation (CICA).

[13]  Russell C. Eberhart,et al.  Parameter Selection in Particle Swarm Optimization , 1998, Evolutionary Programming.

[14]  Kamran Behdinan,et al.  Particle swarm approach for structural design optimization , 2007 .

[15]  Andrew A. Goldenberg,et al.  Neurofuzzy control of modular and reconfigurable robots , 2003 .

[16]  Leandro dos Santos Coelho,et al.  Tuning of PID controller based on a multiobjective genetic algorithm applied to a robotic manipulator , 2012, Expert Syst. Appl..

[17]  Jyoti Ohri,et al.  Improved PSO tuned Classical Controllers (PID and SMC) for Robotic Manipulator , 2015 .

[18]  Jaroslaw Sobieszczanski-Sobieski,et al.  Particle swarm optimization , 2002 .

[19]  M. Analoui,et al.  PID Gain Tuning Using Genetic Algorithms and Fuzzy Logic for Robot Manipulator Control , 2009, 2009 International Conference on Advanced Computer Control.

[20]  Tore Hägglund,et al.  A Control-Loop Performance Monitor , 1995 .

[21]  Fang Sheng,et al.  Genetic algorithm and simulated annealing for optimal robot arm PID control , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.