2 results listed
Ball and beam system (BBS) is a benchmark hardware for designing control action. The
structure of the system is based on changing the angle of the beam so that the position of the ball
is changed. It is desired to move the ball to a reference position. In this paper Fuzzy Logic
Controller (FLC) is applied for this problem. Instead of conventional FLC, the derivative and
integral terms are integrated to the FLC, which is called as PID-like FLC. This controller has a
constant Fuzzy structure with variable parameters. The performance of the controller is based on
these parameters. Therefore, in this study, the parameters of PID-like FLC are optimized by using
three optimization algorithms; Genetic Algorithm, Particle Swarm Optimization, and Differential
Evolution. The performance of the controller is demonstrated on both simulation and hardware
environment. The performance of the optimization algorithm with respect to the obtained
performances are compared in this paper.
International Data Science & Engineering Symposium
IDSES
O. Tolga ALTINÖZ
A. Egemen YILMAZ
Economical dispatch (ED) problem is defined to
obtain an equilibrium point between power generator and cost of
each generator. As a conventional definition, the ED problem is
defined as the sum of cost from each generator under
constraints. Even total cost is defined as the main objective of the
problem, the loss at the transmission lines is included as that
total power generation must be equal to the loss at the
transmission lines and demand at the load. However loss as the
transmission line is an important topic for efficiency. Therefore,
in this paper transmission loss is defined as a new objective
without changing the constraints. This multi-objective ED
problem is solved by using multiobjective optimization
algorithms. For this purpose three MOEA are applied to the
problem and compared with each other. These algorithms are
Multi-Objective Particle Swarm Optimization (MOPSO),
Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Nondominated Sorting Genetic Algorithm II (NSGA-II). The
performance of these algorithms is aimed to improve with chaosbased random number generator. In total, 6 set of results from
multiobjective optimization (both conventional and chaos
improved) results are compared and discussed with each other.
1.st International Conference Energy Systems Engineering
ıcese'17
O. Tolga ALTINÖZ