Abstract: In this paper, autonomous performance of a small
manufactured unmanned helicopter is tried to be increased. For this
purpose, a small unmanned helicopter is manufactured in Erciyes
University, Faculty of Aeronautics and Astronautics. It is called as
ZANKA-Heli-I. For performance maximization, autopilot parameters
are determined via minimizing a cost function consisting of flight
performance parameters such as settling time, rise time, overshoot
during trajectory tracking. For this purpose, a stochastic optimization
method named as simultaneous perturbation stochastic approximation
is benefited. Using this approach, considerable autonomous
performance increase (around %23) is obtained.
Abstract: This paper describes a sliding mode controller for
autonomous underwater vehicles (AUVs). The dynamic of AUV
model is highly nonlinear because of many factors, such as
hydrodynamic drag, damping, and lift forces, Coriolis and centripetal
forces, gravity and buoyancy forces, as well as forces from thruster.
To address these difficulties, a nonlinear sliding mode controller is
designed to approximate the nonlinear dynamics of AUV and
improve trajectory tracking. Moreover, the proposed controller can
profoundly attenuate the effects of uncertainties and external
disturbances in the closed-loop system. Using the Lyapunov theory
the boundedness of AUV tracking errors and the stability of the
proposed control system are also guaranteed. Numerical simulation
studies of an AUV are included to illustrate the effectiveness of the
presented approach.
Abstract: The paper presents a method for a simple and
immediate motion planning of a SCARA robot, whose end-effector
has to move along a given trajectory; the calculation procedure
requires the user to define in analytical form or by points the
trajectory to be followed and to assign the curvilinear abscissa as
function of the time. On the basis of the geometrical characteristics
of the robot, a specifically developed program determines the motion
laws of the actuators that enable the robot to generate the required
movement; this software can be used in all industrial applications for
which a SCARA robot has to be frequently reprogrammed, in order
to generate various types of trajectories with different motion times.
Abstract: The problem of manipulator control is a highly
complex problem of controlling a system which is multi-input, multioutput,
non-linear and time variant. In this paper some adaptive
fuzzy, and a new hybrid fuzzy control algorithm have been
comparatively evaluated through simulations, for manipulator
control. The adaptive fuzzy controllers consist of self-organizing,
self-tuning, and coarse/fine adaptive fuzzy schemes. These
controllers are tested for different trajectories and for varying
manipulator parameters through simulations. Various performance
indices like the RMS error, steady state error and maximum error are
used for comparison. It is observed that the self-organizing fuzzy
controller gives the best performance. The proposed hybrid fuzzy
plus integral error controller also performs remarkably well, given its
simple structure.
Abstract: This paper compares the heuristic Global Search
Techniques; Genetic Algorithm, Particle Swarm Optimization,
Simulated Annealing, Generalized Pattern Search, genetic algorithm
hybridized with Nelder–Mead and Generalized pattern search
technique for tuning of fuzzy PID controller for Puma 560. Since the
actual control is in joint space ,inverse kinematics is used to generate
various joint angles correspoding to desired cartesian space
trajectory. Efficient dynamics and kinematics are modeled on Matlab
which takes very less simulation time. Performances of all the tuning
methods with and without disturbance are compared in terms of ITSE
in joint space and ISE in cartesian space for spiral trajectory tracking.
Genetic Algorithm hybridized with Generalized Pattern Search is
showing best performance.