Robotics and Autonomous Systems 84 (2016) 64–75 Contents lists available at ScienceDirect Robotics and Autonomous Systems journal homepage: www.elsevier.com/locate/robot Parameter tuning of PID controller with reactive nature-inspired algorithms Dušan Fister a , Iztok Fister Jr. b,∗ , Iztok Fister b , Riko Šafarič b a Faculty of Mechanical Engineering, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia b Faculty of Electrical Engineering and Computer Science, University of Maribor, Smetanova 17, 2000 Maribor, Slovenia highlights • PSO is the most reactive nature-inspired algorithm among BA, HBA, GA, DE, CS and PSO. • Population based nature-inspired algorithms (e.g., PSO, BA, HBA, DE and CS) can be used for online implementation of PID parameter tuning. • Low population sizes in nature-inspired algorithms are sufficient for PID tuning to obtain reactive response of SCARA robot. article info Article history: Received 25 March 2016 Accepted 19 July 2016 Available online 27 July 2016 Keywords: PID controller Stochastic nature-inspired population-based algorithm Evolutionary algorithms Swarm intelligence-based algorithms abstract A PID controller is an electrical element for reducing the error value between a desired setpoint and an actual measured process variable. The PID controller operates according to its input parameters, which need to be set before its run. The optimal values of these parameters must be found during the so-called tuning process. Today, this process can be automatized using stochastic, nature-inspired, population- based algorithms, such as evolutionary and swarm intelligence-based algorithms. Unfortunately, these algorithms are too time consuming, and so the reactive, nature-inspired algorithms using a limited number of fitness function evaluations are proposed in this paper. Two reactive evolutionary algorithms (differential evolution and genetic algorithm), and four reactive, swarm intelligence-based algorithms (bat, hybrid bat, particle swarm optimization and cuckoo search), were used to tune the PID controller in our comparative study. Only ten individuals and ten iterations (generations) were used in order to select the most appropriate optimization algorithm for fast tuning of controller parameters. The results were compared using statistical analysis and showed that particle swarm optimization is the best option for such a task. © 2016 Elsevier B.V. All rights reserved. 1. Introduction A PID controller is an electrical element for reducing an error value between a desired setpoint and an actual process variable. The desired setpoint can be set by a function generator, while the actual process variable is measured by a sensor. A set of input parameters is required for proper controller service. Therefore, the optimal input parameters need to be searched for in a so-called tuning process. Only tuned parameters ensure correct behavior of the electrical and mechanical systems, long-term service, and damage prevention. The PID controller can be described as a closed-loop system, i.e., a system in which the actual process ∗ Corresponding author. E-mail addresses: dusan.fister@student.um.si (D. Fister), iztok.fister1@um.si (I. Fister Jr.), iztok.fister@um.si (I. Fister), riko.safaric@um.si (R. Šafarič). variable has to be controlled. There are many examples of closed- loop systems, such as: • robot mechanism control, • temperature control, • level control, • direction control, etc. In this paper, we propose parameter tuning of the PID controller controlling the robot arm mechanism. This arm simulates the movement of a human arm and consists of two joints powered by two motors. This type of robot arm is also referred to as a Selective Compliance Assembly Robot Arm (SCARA) and was designed by Hiroshi Makino in 1980. The structure of the robot arm enables precise positioning in industrial robotics and electronics. Usually, SCARA is accompanied by another motor or hydraulic piston for vertical movement of the robot’s top. The main task of the SCARA is to capture objects, manipulate them in 3-D space, and then put http://dx.doi.org/10.1016/j.robot.2016.07.005 0921-8890/© 2016 Elsevier B.V. All rights reserved.