In this study, a motion control based on fuzzy logic is designed so that mobile robots can make the turns they make when moving in an unknown environment more flexibly and smoothly. Fuzzy logic control is suitable for controlling mobile robots because the results can be obtained under uncertainty. Fuzzy logic control is implemented through a set of rules created using expert knowledge. The fuzzy rules created in this paper are designed to allow mobile robots to escape from obstacles, to avoid contact with walls, and to make soft turns without harming their structure. According to the obtained simulation results, the mobile robot has been shown to have successful results in fuzzy logic based motion control.
In this paper, speed control of a DC Motor with time varying loaded is performed by using sliding mode control (SMC), classical PID control and iterative learning control (ILC) methods. SMC is a robust nonlinear control method which has insensibility against to external disturbing effects and parametric variations of system. On the other hand, a control method of ILC provides an excellent performance on tracking. In the iterative learning PID (IL-PID) controller, the parameters of PID are automatically adjusted by using the algorithm of iterative learning. In this study, firstly, a DC Motor is modeled by using real data. Secondly, controllers which are an iterative learning PID (IL-PID), SMC-based and classical PID are designed and tested. Moreover, performance analysis of these controllers is done for load changes in the time interval. According to obtained results, the output of SMCbased system converges quickly to the reference value and the system gives the fastest response when changing of load occurs. Another result of this study is that the steady state error based on the learning success of ILC is decreased by IL-PID controller. The novel part of this study is that the comparison of these types of controllers is firstly made with this study.
|Prepared by Emre Hasan Dursun, M.Latif Levent, Akif Durdu, Ömer Aydoğdu
This study proposes a new method on “brain image segmentation”. Algorithm is based on the Multistable Cellular Neural Networks (Multistable-CNN). Evaluations are performed on well-known BRAINWEB database. The metrics for performance evaluations of the algorithm were JACCARD, DICE, TPF and TNF. The results are compared with the SPM8, FSL FAST and Brain Suite software packages. The Multistable CNN algorithm used in this study differs from conventional CNN algorithm. The Multistable CNN’s can perform multiple segmentations in a single run. Algorithm is simple and doesn’t require complex calculations. Evaluations show that proposed algorithm’s performance is adequate. Algorithm also does not require any atlas or image registration.
In this study social learning and skill acquisition of a robotic hand via teaching and imitation was aimed. The subject of Human-Robot collaboration, which includes the theme of this paper, is a common field of experiments in our age of technology. Many disabilities can be defeated or many other things, which a human being would not be able to do, can be done with the help of this technology. As an example, a robotic hand can be a light of hope of a person who does not have a hand or wants to hold an object remotely over the internet. So that in our paper it is explained how a robotic hand can learn via imitation. In the experiment a robotic hand, which was printed by a 3D printer, was used and controlled wirelessly by a computer that recognizes human hand gesture via image processing algorithms. The communication between the computer and the robot is provided with a Bluetooth module. First of all, the image processing algorithms such as filtering and background subtraction were applied to the frames of the camera and extracted the features. Secondly, the process of teaching and testing of Artificial Neural Networks (ANNs) was made for the recognition of the hand and the gestures. After that, recognized actions were imitated by the robotic-hand hardware. Eventually, the learning of the robot via imitation was achieved with some small errors and the results are given at the end of the paper.
In this paper, real-time position control of rotary servo system is performed by using fuzzy logic. SRV-02 DC servo system produced by Quanser is equipped with a DC motor. Servo system is loading by using various metallic weights and performance analysis are made according to international performance criteria. When making comparison of performance, it is seen that controller which is designed via fuzzy logic shows better results on position control than PID control which is a conventional control method and commonly used industrial applications.
Today, there are many problems in control applications. The most important one of them is the problem of the construction of a mathematical model for control unit. Complex algorithms without mathematical model are required traditionally to control the systems. The fuzzy logic which is one of the methods of intelligent control allows us to control the system according to the rules which are written by the system specialist. Wireless sensor networks are used in the applications such as target tracking and monitoring. In recent years, sensors are produced as cheap, small and smart. These types of sensors can communicate with each other by using the wireless networks. The design of a wireless system depends on the goals of the application, cost and system constraints. In this study, a distributed control system is modeled and controlled by fuzzy logic in the wireless sensor networks based industrial environments.
Today, submersible deep well pumps are the most commonly used type of pump purpose of irrigation from groundwater sources. For these pumps, one of the major factors affecting the performance is the dynamic water level changing in wells. Changes of input water-speeds of pump are tracked according to water level in the well. Different types of vortex which is based on water movements may occur in entrance of the pump. The resulting vortex and types depend on submergence, well diameter and pump parameters such as flow rate, diameter, suction inlet-section and shape.
In this study, the test unit equipped for research purposes was used in a deep well. According to the dynamic water level of submersible pump, changes in pressure, flow rate and noise were automatically saved. Fuzzy logic controller as a method of intelligent control methods was applied in this study in order to determine the consisting of the vortex.
At the end, it is shown that the vortex in the well is automatically detected by depending on the value of submergence, pressure, flow rate and noise.