Abstract
This paper proposes the design and simulation
of Interval Type-2 Fuzzy Logic Control using
MATLAB/Simulink to control the position of the
bucket of the backhoe excavator robot during
digging operations. In order to reach accurate
position responses with minimum overshoot and
minimum steady state error, Ant Colony
Optimization (ACO) algorithm is used to tune the
gains of the position and force parts for the forceposition controllers to obtain the best position
responses. The joints are actuated by the electrohydraulic actuators. The force-position control
incorporating two-Mamdani type-ProportionalDerivative-Interval Type-2 Fuzzy Logic
Controllers for position control and 3-
Proportional-Derivative Controllers for force
control. The nonlinearity and uncertainty in the
model that inherit in the electro hydraulic actuator
system are also studied. The nonlinearity includes
oil leakage and frictions in the joints. The friction
model is represented as a Modified LuGre friction
model in actuators. The excavator robot joints are
subjected to Coulomb, viscous and stribeck
friction. The uncertainty is represented by the
variation of bulk modulus. It can be shown from
the results that the ACO obtain the best gains of
the controllers which enhances the position
responses within the range of (19, 23 %)
compared with the controllers tuned manually.
of Interval Type-2 Fuzzy Logic Control using
MATLAB/Simulink to control the position of the
bucket of the backhoe excavator robot during
digging operations. In order to reach accurate
position responses with minimum overshoot and
minimum steady state error, Ant Colony
Optimization (ACO) algorithm is used to tune the
gains of the position and force parts for the forceposition controllers to obtain the best position
responses. The joints are actuated by the electrohydraulic actuators. The force-position control
incorporating two-Mamdani type-ProportionalDerivative-Interval Type-2 Fuzzy Logic
Controllers for position control and 3-
Proportional-Derivative Controllers for force
control. The nonlinearity and uncertainty in the
model that inherit in the electro hydraulic actuator
system are also studied. The nonlinearity includes
oil leakage and frictions in the joints. The friction
model is represented as a Modified LuGre friction
model in actuators. The excavator robot joints are
subjected to Coulomb, viscous and stribeck
friction. The uncertainty is represented by the
variation of bulk modulus. It can be shown from
the results that the ACO obtain the best gains of
the controllers which enhances the position
responses within the range of (19, 23 %)
compared with the controllers tuned manually.
Keywords
ackhoe excavator robot
Ant Colony Optimization
ForcePosition control
IT2FLC