Genetic Algorithm of Sliding Mode Control Design for Manipulator Robot
Abstract: The dynamical model
of manipulator robot is represented by equations systems which are nonlinear
and strongly coupled. Furthermore, the inertial parameters of manipulator
depend on the payload which is often unknown and variable. The sliding mode
controller (SMC) provides an effective and robust means of controlling
nonlinear plants. The performance of SMC depends on control parameter selection
of gain switching (k) and sliding surface constant (s). It is very difficult to
obtain the optimal control parameters. In this paper, a control parameter
selection algorithm is proposed by genetic algorithm to select the gain
switching (k) and sliding surface constant parameter (s) so that the controlled
system can achieve a good overall performance in the sliding mode controller
design. Testing is done by giving a reference position for joint 1 and joint 2
of the robot manipulator of 45O (degree) with the controller performance
indicator is settling time <2 seconds, and the tracking error tolerance is
1%. Simulation results demonstrate better performance of the PML with a genetic
algorithm with a small response time by 1.03 seconds to 1.05 seconds joint 1
and 2 as well as for tracking error of the output state by 0.0015 degree for
joint 1 and 0.0004 degree for joint 2.
Keywords: sliding mode
controller (SMC), manipulator robot, nonlinear system, tracking error, genetic algorithm
Author: Ahmad Riyad Firdaus,
Arief Syaichu Rahman
Journal Code: jptkomputergg120094
