Abstract
In this paper, speed and direction angle control of four-wheel drive skid-steered mobile robot (4WD SSMR) is realized by Fractional-Order Proportional Integral (FOPI) controller. Speed and direction angle of the mobile robot are calculated by using angular velocity of each motors. FOPI controller produces the torques of each motor of mobile robot for trajectory tracking and stabilization in the desired position. A well-tuned conventional PI controller is also applied to mobile robot for comparison with the FOPI. Experimental results prove that the FOPI shows better trajectory tracking performance than PI controller in terms of trajectory tracking accuracy and error levels.DOI: http://dx.doi.org/10.5755/j01.eie.22.5.16337
Highlights
In the past few years, researchers have focused on the automated guided vehicle (AGV) trajectory tracking problems and different approaches have been discussed
It is necessary to determine the optimum parameters. It is presented the electromagnetism and evolutionary optimization algorithms [15], fractional-order controller design using genetic algorithms [16], particle swarm optimization algorithm [17], an iterative optimization method according to nonlinear function minimization [18], an auto-tuning method for the fractional order PIλDμ controller using the relay test [19]
The optimization of the tree parameters K p, Ki and makes designing of Fractional-Order Proportional Integral (FOPI) controller more challenging than integer order PI controller
Summary
In the past few years, researchers have focused on the automated guided vehicle (AGV) trajectory tracking problems and different approaches have been discussed. Normey-Rico et al [1] have proposed a path tracking controller based on a robust PID algorithm Their method uses a simple linearized model of the mobile robot composed of an integrator and a delay. Huang et al [4] suggested a high-gain observer based adaptive output feedback tracking control design scheme for nonholonomic mobile robots They used observers to estimate the unknown linear and angular velocities respectively. It is necessary to determine the optimum parameters It is presented the electromagnetism and evolutionary optimization algorithms [15], fractional-order controller design using genetic algorithms [16], particle swarm optimization algorithm [17], an iterative optimization method according to nonlinear function minimization [18], an auto-tuning method for the fractional order PIλDμ controller using the relay test [19]. The actual position of the 4WD SSMR is represented by generalized coordinates, Pc (xc , yc , )
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