فهرست مطالب بهرام تارویردی زاده

Sports simulation mechanisms practically have a lot of appeal and application to implement virtual reality environments. Cycling simulator has a more special place in sports and urban transportation due to the popularity of cycling activities. In this paper, the design and construction of a new motion generating mechanism for an interactive cycling simulator is presented. First, by using the references, the range of velocity and acceleration that the user should experience while cycling has been extracted, and by analyzing other cycling simulators, the required degrees of freedom and its range have been determined. Then, a new motion generating mechanism with two degrees of freedom is designed. Genetic algorithm has been used for dimensional optimization of the mechanism parameters. Kinematics and dynamic equations of the mechanism have been extracted. According to the dynamic model of the mechanism, a controller has been designed using the feedback linearization method and the performance have been investigated. Finally, the designed motion generating mechanism has been constructed and the position control has been implemented on the platform.

Stress has affected human’s lives in many areas, today. Stress can adversely affect human’s health to such a degree as to either cause death or indicate a major contributor to death. Therefore, in recent years, some researchers have focused to developing systems to detect stress and then presenting viable solutions to manage this issue.
Generally, stress can be identified through three different methods including (1) Psychological Evaluation, (2) Behavioral Responses and finally (3) Physiological Signals. Physiological signals are internal signs of functioning the body, and therefore nowadays are commonly used in various medical and non-medical applications. Since these signals are correlated with the stress, they have been commonly used in detection of the stress in humans. Photoplethysmography (PPG) and Galvanic Skin Response (GSR) are two of the most common signals which have been widely used in many stress related studies. PPG is a noninvasive method to measure the blood volume changes in blood vessels and GSR refers to changes in sweat gland activity that are reflective of the intensity of human emotional state.
Design and fabrication of a real-time handheld system in order to detect and display the stress level is the main aim of this paper. The fabricated stress monitoring device is completely compatible with both wired and wireless sensor devices. The GSR and PPG signals are used in the developed system. The mentioned signals are acquired using appropriate sensors and are displayed to the user after initial signal processing operation. The main processor of the developed system is ARM-cortex A8 and its graphical user interface (GUI) is based on C++ programming language. Artificial Neural Networks such as MLP and Adaptive Neuro-Fuzzy Inference System (ANFIS) are utilized to modeling and estimation of the stress index. The results show that ANFIS model have a good accuracy with a coefficient of determination values of 0.9291 and average relative error of 0.007.

The tractor-trailer system is a wheeled mobile robot (WMR), including one wheeled unit named tractor that is equipped with actuators and one or some wheeled units named trailer that are connected to each other with a passive joint. As a special case, in this paper, the system locomotion is by tractor, and trailer is completely passive. In this paper, we develop the kinematic model of tractor-trailer; so the inputs of system are supposed to be tractor angular velocity and trailer linear velocity. Because of pure rolling condition between wheel and ground surface, we encounter a nonholonomic system. In this paper Model Predictive Control (MPC) is used in control designing of Tractor-Trailer for the first time and the goal is trajectory tracking of trailer position. First, the kinematic equations of tractor-trailer are developed. Then a feasible reference geometrical path is considered. After linearizing the system equations around reference path and using MPC that is an optimal control based method, a tracking controller is designed that minimizes a predefined cost function. Considering actuator saturation in system inputs, we solve a constrained optimization problem using numerical methods. The controller designed in this paper, will be compared against classic controller PID and its better performance is presented. Finally, the effectiveness and robustness of controller against disturbances and parameter uncertainties is proved using MATLAB results.

In this study, the control problem of a knee rehabilitation robot is examined. The main drawback of rehabilitation facilities, such as continuous passive motion, is the lack of feedback from the interaction force between the robot and patient leg. This means that if during the exercises, an unvoluntary motion by patient is generated, the increased interaction force can then damage the patient leg. The interaction force is increased because the robot tries to hold the patient leg along the prescribed reference path. In the current paper, to realize the compliant behavior of the robot, the concept of admittance along with two control methods including adaptive model reference and integral backstepping will be utilized. Adopting admittance control method, the robot will deviate the prescribed path so that the interaction force can be decreased. The obtained simulation results reveal the good performance of the robot even in the presence of noisy sensory data. Additionally, it has been shown that the proposed combined admittance and backstepping controller has better performance, in terms of tracking error and decrease of interaction force, as compared with the model adaptive reference model.

To study the relation between the amount of Exhaust Gas Temperature (EGT) as the output quantity of an experimental gas turbine engine and the parameter of engine rotational speed (RPM), as its input quantity, two different data mining approaches were employed in the present work. Artificial Neural Network (ANN) and Multiple Polynomial Regression (MPR) techniques were used to predict the nonlinear relation between the input and output of the engine. The related experiments were already performed by using an experimental micro gas turbine engine with an engine rotational speed in the range of 0 ~ 108000 RPM. The results show that, in general, both the ANN and MPR approaches have good predicting capability for estimating exhaust gas temperature values. Also the results of using the ANN and MPR approaches show that the degree of agreement between the predicted and measured values of exhaust gas temperature is higher for the case of employing the ANN approach. In other words, the ANN has better predicting capability for estimation of exhaust gas temperature than the MPR method.

In this article, a novel intelligent online tip-over avoidance algorithm is presented considering the interactions between the mobile base and manipulator arm. To this end, the newly suggested dynamic stability margin measure named Moment-Height-Stability (MHS) is adopted. Additionally, a function representing the increment of postural stability margin metric is defined based on MHS. The system dynamic equilibrium is then enhanced using a fuzzy logic approach. The response of the suggested method of this paper is compared with that of a previously Force-Angle based proposed one considering a planar mobile manipulator. First the dynamics of the robot is derived using Newton-Euler method via MAPLE 16 and it is verified through the model provided in SimMechanics toolbox of Simulink. The efficiency of the suggested method is illustrated in comparison to the previous one on a destabilizing robot path. Besides, the performance of proposed method of the present study is investigated in the presence of external disturbances. The obtained simulation results reveal the effectiveness of the performance of the suggested technique for stability improvement of wheeled mobile manipulators once encountering unexpected disturbing situations.