No abstract

Robotics and its applications have become increasingly important in the field of manufacturing industry. Robot manipulators are effectively used for this purpose. Control of manipulators is very important, however, highly nonlinear and multi-input multi-output (MIMO) complex structure of manipulators make the control difficult. This paper presents two-degree of freedom PID controller scheme for a six-degree of freedom rigid robotic manipulator. Traditional PID controllers are widely used due to their simple control structure and ease of implementation in industry. However load disturbances and parametric variation affect the robustness of the controller. The performance of proposed two-degree of freedom PID controller is compared with the traditional PID controllers. Matlab-Simulink program is used for real-time implementation of the proposed method. Experimental results show that two-degree of freedom PID control is better than the traditional PID for manipulator control in real time.

Vision based robot applications have taken a great deal of attention, with the development of electronic and computer technology. The visual feedback loop is very effective for improving the dexterity and flexibility. In this study, application of real time visual servoing approach is presented that enables a robot to robustly execute arm grasping and manipulation tasks. This task is decomposed on four stages a) finding object b) determining object's pose c) moving the robotic arm from an initial position towards the object d) grasping the object. The robot used in this work consists of an arm and head parts. The robotic arm has six degree of freedom, five degree of freedom are located at the arm while one degree of freedom is assigned to the gripper. Head has two degree of fredom which is pan-tilt platform. The image-based control strategy is designed using Fuzzy-PID controller. In this way, position error between target object and griper is minimized and the gripper can grasp the target object precisely. Real-time implementation of the proposed method is carried out using Matlab-Simulink. Experimental results show that, the developed design is valid to detect and grasp objects in real time.

This paper presents a new method of modeling the nonlinear parameters of a coating systems base on neural Networks with artificial neural network neurons. Artificial neural networks (ANNs) are a new type of information processing system based on modeling the neural system of human brain. The wire coating thickness and quality depend on the wire speed, polymer viscosity, polymer melt temperature and the gap between the wire and exit end of the die. In this paper, results of experimental investigation are presented by comparing the coating quality on galvanized mild steel wire using EP 58 PVC molten is used as the coating material in a wire coating extrusıon unit at different extruder temperatures and extruder speeds.The coating thickness and quality are also discussed for different wire speeds of up to 15 m/s. A three layer back propogation artificial neutral network (ANN) model was used for the description of wire coating thickness.On comparing the experimental data, the predictions the ANN model predictions, it is found that the ANN model is capable of predicting the coating thickness. The neural network model shows how the significant parameters influencing thickness can be found. Inthis studies, a back propagation neural network model is developed to map the complex non-linear wire coating thickness between process conditions .

In this study, active suspension control of the interaction between the bridge can be modeled according to the Euler-Bernoulli beam theory, and the quarter car model with three degrees of freedom is studied. The active suspension system consists of a spring, damper, and linear actuator. The active suspension control is designed using classical PID and self-tuning fuzzy PID (STFPID) to reduce the vehicle body's disruptive effects. To determine the performance of the designed controllers, two different road profiles with the bridge oscillations caused by the bridge flexibility were considered as the disruptive effect of the vehicle. When the simulation results were examined in terms of passenger seat displacement and acceleration, the proposed STFPID method significantly increased road holding and ride comfort.