Trajectory Tracking Control for Robot Manipulator using Fractional Order-Fuzzy-PID Controller

Abstract

Robotic manipulator is a Multi-Input Multi-Output (MIMO), highly nonlinear and coupled system. Therefore, designing an efficient controller for this system is a challenging task for the control engineers. In this paper, the Fractional Order-Fuzzy-Proportional Integral Derivative (FO-Fuzzy-PID) controller is investigated for the first three joints of robot arm (PUMA 560) for trajectory tracking problem. To study the effectiveness of FO-Fuzzy-PID controller, its performance is compared with other three non model controllers namely Fuzzy-PID, Fractional Order PID (FOPID) and conventional PID. Genetic algorithm (GA) optimization technique was used for tuning parameters of FOPID and conventional PID controllers. Simulation results clearly indicate the superiority of FO-Fuzzy-PID controller over the other controllers for trajectory tracking, better steady state and RMS errors. All controllers were tested by simulation under the same conditions using SIMULINK under MATLAB2013a.

A New Computed Torque Control System with an Uncertain RBF Neural Network Controller for a 7-DOF Robot

“…The CTL control system with the RBFcontroller Table 1 The actuator parameters K p K d 20 5 Table 2 The puncture arm parameters K t K p2 K d2 0.01 3000 500…”

“…Although these control structures are identical to modelfree intelligent proportional integral (IPI) controlling system, their superiorities have been demonstrated through experimental tests [18]. A fuzzy logic controller (FLC) has been employed to construct a trajectory tracking control system for a wheeled mobile robot [20]. In a trajectory tracking control system, the feed forward velocity data was added to the control system as the correcting data from the FLC in accordance with the postures errors.…”

Model‐based robust control design and experimental validation of collaborative industrial robot system with uncertainty

“…In the literature [13, 14], a fractional‐order PID control method is proposed to reduce steady‐state and transient errors of the robot system and improve the tracking performance. In the literature [15], a perturbation observer‐based PD control method is proposed to improve the control performance of the robot system by online compensation of unknown nonlinear friction and perturbation.…”

Seismic control of the structures with active tuned mass damper and variable fractional order fuzzy proportional–integral–derivative controller

“…Das et al (2012) designed fractional order fuzzy PID (FOFPID) controllers for time-delayed nonlinear systems and used a genetic algorithm to optimize their proposed controller parameters. This controller was also used to control robots, and it was shown that the FOFPID controller performs better than the classic PID controllers, fuzzy PID, and FOPID (Ardeshiri et al, 2019;Mohammed et al, 2016;Sharma et al, 2014). Zamani et al (2018) used a FOFPID controller to control a structure with base isolation and semi-active variable friction dampers under earthquake excitation.…”