Abstract
The robotic manipulator using intelligent controller, have become a research hotspot for most robot manufacturers all over the world. Robotic manipulator systems have disadvantages, such as high nonlinearity , slow response and uncertainty ;Type-2 FLC can be a solution to these drawbacks. A fuzzy controller for 5250 Robotic Manipulator (RM) Lab-Volt (LV) is designed to track the joints position with new axis. The RM joint is processed as single case. The Particle Swarm Optimization and Genetic Algorithm are used. New design was implemented of type-2 fuzzy logic based PSO as four controllers ( (T1FLC,T2FLC,PSOT1FLC, PSOT2FLC). The most important parameters of Type-2 Fuzzy are gains (GAINS, input /output) and foot of print uncertainty (FOU) . Four cases are implemented for each joint (fixed FOU fixed Gain, fixed FOU variable Gain, variable FOU fixed Gain, and variable FOU variable Gain). The experimental results for PSOT2FLC gave a good transient response with minimum integral absolute error IAE, and minimum over shoot .
Highlights
Robotic Manipulator (RM) the common and familiar type of robot
RESPONSE PARAMETERS OF ROBOT BASED FOUR PSOT2FLC, 7) The overall time response of T2FLC with PSO for Lab-Volt (5250): According to Table X the second column are robotic manipulator with P-X = 262.0, P-Y = 262.0, P-Z = 986.0, Ө-P = 45°, Ө-r = 90°, third column represents inverse kinematics with mat lab simulation (Proposed Method), and the fourth column represents the experimental numerical values, and the percentage error is in the fifth column
2) PSOT2FLC performance is superior to T2FLC based genetic algorithm (GA), from a side of the point of seeing the integral absolute error, settling time, which is more rising time as stated by Table VI
Summary
RM the common and familiar type of robot. Robotic systems have these days represent a very important sector of many industrial processes such as in manufacturing, military, and civilian [1,2]. The optimization starts with input the number of the iteration (80) and population (200), selecting the number of parameters (thirty two) that need optimization for MFs' and I/O Gains, initializing PSO using the position swarm, limitation for each parameters (24) and velocity update, T1, or, T2FLC, select the fitness equation and, plot the time response of the system [15,16].
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