Abstract: In this paper, we present optimal control for
movement and trajectory planning for four degrees-of-freedom robot
using Fuzzy Logic (FL) and Genetic Algorithms (GAs). We have
evaluated using Fuzzy Logic (FL) and Genetic Algorithms (GAs)
for four degree-of-freedom (4 DOF) robotics arm, Uncertainties like;
Movement, Friction and Settling Time in robotic arm movement
have been compensated using Fuzzy logic and Genetic Algorithms.
The development of a fuzzy genetic optimization algorithm is
presented and discussed. The result are compared only GA and
Fuzzy GA. This paper describes genetic algorithms, which is
designed to optimize robot movement and trajectory. Though the
model represents is a general model for redundant structures and
could represent any n-link structures. The result is a complete
trajectory planning with Fuzzy logic and Genetic algorithms
demonstrating the flexibility of this technique of artificial
intelligence.
Abstract: This paper presents a design method of self-tuning
Quantitative Feedback Theory (QFT) by using improved deadbeat
control algorithm. QFT is a technique to achieve robust control with
pre-defined specifications whereas deadbeat is an algorithm that
could bring the output to steady state with minimum step size.
Nevertheless, usually there are large peaks in the deadbeat response.
By integrating QFT specifications into deadbeat algorithm, the large
peaks could be tolerated. On the other hand, emerging QFT with
adaptive element will produce a robust controller with wider
coverage of uncertainty. By combining QFT-based deadbeat
algorithm and adaptive element, superior controller that is called selftuning
QFT-based deadbeat controller could be achieved. The output
response that is fast, robust and adaptive is expected. Using a grain
dryer plant model as a pilot case-study, the performance of the
proposed method has been evaluated and analyzed. Grain drying
process is very complex with highly nonlinear behaviour, long delay,
affected by environmental changes and affected by disturbances.
Performance comparisons have been performed between the
proposed self-tuning QFT-based deadbeat, standard QFT and
standard dead-beat controllers. The efficiency of the self-tuning QFTbased
dead-beat controller has been proven from the tests results in
terms of controller’s parameters are updated online, less percentage
of overshoot and settling time especially when there are variations in
the plant.
Abstract: In this research paper we have presented control
architecture for robotic arm movement and trajectory planning using
Fuzzy Logic (FL) and Genetic Algorithms (GAs). This architecture is
used to compensate the uncertainties like; movement, friction and
settling time in robotic arm movement. The genetic algorithms and
fuzzy logic is used to meet the objective of optimal control
movement of robotic arm. This proposed technique represents a
general model for redundant structures and may extend to other
structures. Results show optimal angular movement of joints as result
of evolutionary process. This technique has edge over the other
techniques as minimum mathematics complexity used.
Abstract: This paper is concerned with the application of small
rating Capacitive Energy Storage units for the improvement of
Automatic Generation Control of a multiunit multiarea power
system. Generation Rate Constraints are also considered in the
investigations. Integral Squared Error technique is used to obtain the
optimal integral gain settings by minimizing a quadratic performance
index. Simulation studies reveal that with CES units, the deviations
in area frequencies and inter-area tie-power are considerably
improved in terms of peak deviations and settling time as compared
to that obtained without CES units.