Ayca Gokhan Ak

Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to process system control.

ROBOT TRAJECTORY TRACKING WITH ADAPTIVE RBFNN-BASED FUZZY SLIDING MODE CONTROL

Dėl skaiciavimų apsunkinimo ir dinamisko neapibrėžtumo klasikinius modeliu pagrįstus valdymo principus sunku taikyti robotų sistemoms. Siame straipsnyje pristatomas nuo modelio nepriklausomas neapibrėžto slankiojo režimo valdymas, pagrįstas neuroniniais tinklais. Kai naudojami klasikiniai slankiojo režimo kontroleriai, norint apskaiciuoti ekvivalentiską valdymą, reikia sistemos dinamikos ir sistemos parametrų. Kai robotas valdomasRBFNN pagrįstu neapibrėžtu slankiuoju režimu, RBFNN kuria-mas taip, kad imituotų ekvivalentiskus valdymo slankiuoju režimu veiksmus (SMC). Naudojant adaptyvų algoritmą RBFNN svoriai pakeiciami taip, kad sistemos būsena atitiktų slankųjį pavirsių ir slystų kartu su juo. Pradiniai RBFNN svoriai prilyginami nuliui, o vėliau koreguojami eigos metu, nereikia jokių prižiūrimų mokymosi procedūrų. Pristatomas metodas įdiegtas pramoni-niame robote (Manutec-r15) ir palygintas su PID kontroleriu. Atlikti eksperimentiniai tyrimai parodė, kad sis metodas puikiai tinka pramoninių robotų trajektorijos sekimo taikomosioms programoms vykdyti. http://dx.doi.org/10.5755/j01.itc.40.2.430

Fuzzy sliding mode controller with neural network for robot manipulators

This paper presents an approach of cooperative control that is based on the concept of combining neural networks and the methodology of fuzzy sliding mode control (SMC). The aim of this study is to overcome some of the difficulties of conventional control methods such as controllers requires system dynamics in detailed. In the proposed control system, a neural network (NN) is developed to mimic the equivalent control law in the SMC. The structure of the NN that estimates the equivalent control is a standard two layer feed-forward NN with the backprobagation algorithm. The weights of the NN are updated such that the corrective control term of the SMC goes to zero.

Three link robot control with fuzzy sliding mode controller based on RBF neural network

The purpose of this paper is to propose adaptive fuzzy sliding mode control (SMC) based on radial basis function neural network (RBFNN) for trajectory tracking problem of three link robot manipulator. A RBFNN is used to compute the equivalent control of sliding mode control. A Lyapunov function is selected for the design of the SMC and an adaptive algorithm is used for weight adaptation of the RBFNN. Simulation results of three link Scara robot manipulator verify the validity of the proposed controller in the presence of uncertainties

Ant Colony Optimization (ACO)

The ant colony optimization algorithm (ACO) is an evolutionary meta-heuristic algorithm based on a graph representation that has been applied successfully to solve various hard combinatorial optimization problems. Initially proposed by Marco Dorigo in 1992 in his PhD thesis [49], the main idea of ACO is to model the problem as the search for a minimum cost path in a graph. Artificial ants walk through this graph, looking for good paths. Each ant has a rather simple behavior so that it will typically only find rather poor-quality paths on its own. Better paths are found as the emergent result of the global cooperation among ants in the colony [13, 15, 50-52].

Adaptive Neural Network Based Fuzzy Sliding Mode Control of Robot Manipulator

A fuzzy sliding mode controller based on radial basis function neural network (RBFNN) is proposed in this paper. In the applications of sliding mode controllers the main problem is that a whole knowledge of the system dynamics and system parameters are required to be able to compute equivalent control. In this paper, an RBFNN is used to compute the equivalent control. The weights of the RBFNN are changed according to adaptive algorithm for the system state to hit the sliding surface and slide along it. The initial weights of the RBFNN set to zero, and then tune online, no supervised learning procedures are needed. Computer simulations of three link robot manipulator for trajectory tracking verify the validity of the proposed adaptive neural network based fuzzy sliding mode controller in the presence of uncertainties

NN approaches on Fuzzy Sliding Mode Controller design for robot trajectory tracking

The main problem of sliding mode controllers is that a whole knowledge system parameters is required to compute the equivalent control. Neural networks are used to compute the equivalent control. Standard two layer feedforward neural network training with the backprobagation algorithm and Radial Basis Function Neural Networks (RBFNN) are the most popular methods that used on robot control. This paper applies these structures to Fuzzy Sliding Mode Control (FSMC). Methods are tested for robot trajectory tracking with computer simulations. Computer simulations of three link robot manipulator show that RBFNN is more efficient on FSMC for trajectory control applications.

Field Programmable Gate Arrays Based Real Time Robot Arm Inverse Kinematic Calculations and Visual Servoing

DOI: 10.26650/electrica.2018.49877 Reliability and precision are very important in space, medical, and industrial robot control applications. Recently, researchers have tried to increase the reliability and precision of the robot control implementations. High precision calculation of inverse kinematic, color based object recognition, and parallel robot control based on field programmable gate arrays (FPGA) are combined in the proposed system. The precision of the inverse kinematic solution is improved using the coordinate rotation digital computer (CORDIC) algorithm based on double precision floating point number format. Red, green, and blue (RGB) color space is converted to hue saturation value (HSV) color space, which is more convenient for recognizing the object in different illuminations. Moreover, to realize a smooth operation of the robot arm, a parallel pulse width modulation (PWM) generator is designed. All applications are simulated, synthesized, and loaded in a single FPGA chip, so that the reliability requirement is met. The proposed method was tested with different objects, and the results prove that the proposed inverse kinematic calculations have high precision and the color based object recognition is quite successful in finding coordinates of the objects.

An Application for Process System Control

The purpose method is implemented to stabilize the pressure of the tank at the desired pressure level adjusting the input air flow despite the continuous exhaust output flowing as a disturbance. Because of compressibility of air and nonlinear characteristic of valves, realized system has nonlinear dynamics. Cubic trajectory function was used as an input reference signal, to prevent the pressure fluctuations and large overshoot in tank which could be harmful in some process [56].

Fuzzy Sliding Mode Controller with RBF Neural Network for Robotic Manipulator Trajectory Tracking

This paper proposes a fuzzy sliding mode controller with radial basis function neural network (RBFNN) for trajectory tracking of robot manipulator. The main problem of sliding mode controllers is that a whole knowledge of the system dynamics and system parameters is required to compute the equivalent control. In this paper, a RBFNN is proposed to compute the equivalent control. Computer simulations of three link robot manipulator for trajectory tracking indicate that the proposed method is a good candidate for trajectory control applications.