فهرست مطالب bahram tarvirdizadeh

The use of Unmanned Aerial Vehicles (UAVs) with different features and for a variety of applications has grown significantly. Tracking generic targets, especially human, using the UAV's camera is one of the most active and demanding fields in this area. In this paper we implement two vision-based tracking algorithms to track a human by using a 2D gimbal which can be mounted on UAVs. To ensure smooth movements and reduce the effect of common jumps on the trackers output, the gimbal motion control system is equipped with a Kalman filter followed by a proportional-derivative (PD) controller. Various experimental tests have been designed and implemented to track a human. The evaluation results show success in tracking the high speed movements with one of the algorithms and high accuracy in tracking the challenging movements in the other algorithm. Also in both methods, the tracking computation time is short enough and suitable for real-time implementation. The favorable performance of both algorithms indicate the ability of designed system to be implemented on the UAVs for practical applications.

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.

Human ankle-foot gait is the result of a complex interaction between nerves and muscles. A significant number of prosthetic ankle-foots (passive, semi-active, active) have been designed to restore an identical function of a real limb. Excluding passive and semi-active prosthesis who couldn’t generate any positive work, one of the biggest challenges in creating these prostheses is providing the needed power and energy during movement. Supplying this power and energy, requires a high-torque and high-power actuator having high weight, thereby causing a dramatic increase in the weight and size of that prostheses. In this paper, a combination of an active actuator (an electrical motor) and an passive stimulus (a spring) is utilized, which decrease the needed power and energy, and in addition to walking mode can also support running mode up to 2.5m/s. Accordingly, The first stage of this article includes mechanical modeling of the ankle and evaluation of efficiency and power consumption in all presented models. Then the structure of Series Elastic Actuator differed with the previous structures is selected as the best combination.  In this opted structure, power and energy consumption is dramatically declined up to 58% and 26% in walking mode and 64% and 57% in running mode. Consequently, a lighter motor and battery can supply the required power, so the prosthesis chr(chr('39')39chr('39'))s weight is subtracted.

Due to various advantages of Wheeled Mobile Robots (WMRs), many researchers have focused to solve their challenges. The automatic motion control of such robots is an attractive problem and is one of the issues which should carefully be examined. In the current paper, the trajectory tracking problem of WMRs which are actuated by two independent electrical motors is deliberated. To this end, and also, computer simulation of the system, first the system model is derived at the level of kinematics. The system model is nonholonomic. Then a simple non-mode-based controller based on fuzzy logic will be proposed. The control input resulted from fuzzy logic will then be corrected to fulfill the actuation saturation limits and non-slipping condition. To prove the efficiency of the suggested controller, its response, in terms of the required computational time burden and tracking error, will be compared with a previously suggested method. The obtained simulation results support the superiority of fuzzy based method over a previous study in terms of the considered measures.

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.

This research focuses on proposing an optimal trajectory planning and control method of two link rigid-flexible manipulators (TLRFM) for Dynamic Object Manipulation (DOM) missions. For the first time, achievement of DOM task using a rotating one flexible link robot was taken into account in [20]. The authors do not aim to contribute on either trajectory tracking or vibration control of the End-Effector (EE) of the manipulator; On the contrary, utilizing the powerful tool optimal control accomplishing a point-to-point task for TLRFM is the purpose of the current research. Towards this goal, the pseudospectral method will be developed to meet the optimality conditions subject to system dynamics and boundary conditions. The complicated optimal trajectory planning is formulated as a nonlinear programming problem and solved by SNOPT nonlinear solver. To make robust the response of optimal control against external disturbances as well as model parameter uncertainties, the control partitioning concept is employed. The controlled input is composed of an optimal control-based feedforward part and a PID-based feedback component. The obtained simulation results reveal the usefulness and robustness of the developed composite scheme, in DOM missions.

The problem of observer design for nonlinear systems has got great attention in the recent literature. The nonlinear observer has been a topic of interest in control theory. In this research, a modified robust sliding-mode observer (SMO) is designed to accurately estimate the state variables of nonlinear systems in the presence of disturbances and model uncertainties. The observer has a simple structure but is capable of efficient observation in the state estimation of dynamic systems. Stability of the developed observer and its convergence is proven. It is shown that the estimated states converge to the actual states in a finite time. The performance of the nonlinear observer is investigated by examining its capability in estimation of the motion of a two link rigid-flexible manipulator. The observation process of this system is complicated because of the high frequency vibration of the flexible link. Simulation results demonstrate the ability of the observer in accurately estimating the state variables of the system in the presence of structured uncertainties along with different initial conditions between the observer and the plant.

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.