محمد محجوب

The natural frequencies and mode shapes of bone are widely used in the diagnosis of bone fracture healing, bone remodeling, osteoporosis diagnosis, and implant-bone interaction. These characteristics can be estimated using numerical analysis and finite element modeling and measured and validated by modal testing of laboratory samples. Bone heterogeneity in finite element models affects its natural frequency. In this research, the effect of bone heterogeneity and density-elasticity relationships on the natural frequency of bone and the accuracy of their estimation have been investigated. A cow tibia bone was prepared and subjected to modal test. The experimental results of the modal test were compared with the results of the 3D finite element model created from the bone CT scan images. Inhomogeneity based on the proposed density-elasticity relationships was applied element by element (continuous) and regionally based on a tolerance (discrete) in the model. Comparing the results of heterogeneous models with experimental results shows that there is very little difference between the natural frequencies of discrete and continuous heterogeneous models. So, for the first five frequencies, a discrete heterogeneous model with 20 regions can predict the natural frequencies with an error of less than 2% with less cost and shorter time.

In this study, modeling and fault detection of a novel faulty quadrotor is presented. It is assumed that a quadrotor vehicle has encountered a fault during a flight accident, and as a result, one of the rotors does not operate vertically. Although the rotor's rotational axis has deviated from the vertical direction, the amount of produced thrust remains constant. Detecting this fault along with utilizing a proper controlling approach can reduce the risk of failure in the vehicle. Based on this statement, the procedure of this study has been developed in three main stages. First, the kinematic and dynamic equations governing the faulty system are driven using Newton's second law and Euler's principle. Then, equations governing the faulty system and the Thau observer are employed to calculate the residual value. This parameter is calculated based on the differences between states’ measurement and estimation. Eventually, by comparing the computed residual value with the assumed threshold, thrust deviation in the shortest possible time has been detected.

Today, due to ever-increasing demand for fast and precise movements and changes, along with small-scale actuations in many engineering fields, the use and efficiency of smart materials has increased in importance. Magnetic Shape Memory Alloy (MSMA) is one of the latest smart materials having both shape memory and magnetic properties. As a matter of fact, in normal room temperatures, it has magnetic field-induced strains far more than any other smart materials such as magnetostrictive, piezoelectric or electrostrictive materials and its frequency response is greater than thermal shape memory alloy. However, on the downside, asymmetric hysteresis is a property that constrains its widespread applications. Prandtl-Ishlinskii model is one of the powerful phenomenological models for simulating asymmetric, non-linear hysteresis used to simulate smart material behavior. In the present study, MSMA hysteresis behavior simulation has been investigated through a new approach using generalized Prandtl-Ishlinskii model. After identifying the model parameters, the study compares the predicted output with the experimental results. For validation the model, using different data, model accuracy has been checked and prediction error has been compared. The experimental results have approved the capability of the model in predicting the hysteresis behavior. Thanks to invertible and simplicity potential of the generalized Prandtl-Ishlinskii model, the inverse of model can be applied as a feedforward controller for compensating the hysteresis behavior. It should also be noted that all the experimental results have been yielded through using experimental set-up.

In this paper, modeling and feedback linearization controller for trajectory tracking of a novel six-rotor UAV (Unmanned Aerial Vehicle) is developed. Because of the very simple structure and high maneuverability, quadrotors are one of the most preferred types of UAVs but the main problem of them is their small payload. In the proposed novel model, two coaxial propellers are added to the center of vehicle to improve the ability of lifting heavier payloads, and to excel anti-crosswind capability of quadrotor, while the dynamic and steering principle is preserved. The dynamic model is obtained via Newton Euler approach. Model is under actuated, nonlinear, and has strongly coupled terms. Also, two types of nonlinear controllers are presented. First one is a conventional input-output feedback linearization controller which involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. Second controller is a feedback linearization based on the hierarchical control strategy that yields easier controller. To compensate actuator’s dynamic and moreover, to avoid complexity of controller, a two-stage algorithm is utilized. The obtained simulation results confirm that the performance of hierarchical controller is more convenient in terms of position tracking and disturbance rejection than conventional controller.

In this paper, modeling and tow type of nonlinear controller for trajectory tracking of a novel five-rotor UAV (Unmanned Aerial Vehicle) is developed. Because of the very simple structure and high maneuverability, quadrotors are one of the most preferred types of UAVs but the main problem of them is their small payload. In the proposed novel model, one propeller is added to the center of vehicle to improve the ability of lifting heavier payloads, and to excel anti-crosswind capability of quadrotor. The dynamic model is obtained via Newton Euler approach. The model is under actuated, nonlinear, and has strongly coupled terms. Also, two types of nonlinear controllers are presented. First one is a conventional input-output feedback linearization controller which involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. Second controller is a BackStepping controller based on the hierarchical control strategy that yields easier controller. The obtained simulation results confirm that the performance of BackStepping controller is convenient in terms of stability, position tracking and it is robust in presence of disturbance.

In this paper, modeling and design of a trajectory tracking control system for a novel multi-rotor UAV (Unmanned Aerial Vehicle) is developed. The UAV is similar to a quadrotor with an extra no feedback propeller which is added to center of vehicle. The additional rotor improves the ability of lifting heavier payloads, and anti-crosswind capability for quadrotor. For validation, the dynamic model is obtained via both Newton Euler and Lagrange approaches. The dynamical model is under actuated, nonlinear, and has strongly coupled terms. Therefore, an appropriate control system is necessary to achieve desired performance. The proposed nonlinear controller of this paper is an input-output feedback linearization companioned with an optimal LQR controller for the linearized system. The controller involves high-order derivative terms and turns out to be quite sensitive to un-modeled dynamics. Therefore, precise model of UAV is derived by considering actuator’s dynamics. To compensate the actuator’s dynamic and moreover, to avoid complexity in the controller, a second control loop is utilized. The obtained simulation results confirm that the proposed control system has a promising performance in terms of stabilization and position tracking even in presence of external disturbances.

This paper deals with the design of an unknown input observer (UIO) with the assumption that the well-known observer matching condition is not satisfied. The proposed method can be used for fault detection problems with the use of residual vector. The basis of method is to compensate the unmatched uncertainties with the use of a set of auxiliary outputs. The introduced auxiliary outputs are obtained from successive integration of the system measurements and known inputs. Then, an unknown input observer is proposed which estimates exponentially the outputs. Therefore, the residual vector, generated from the estimated outputs and the actual outputs, will be obtained which insensitive to the unmatched disturbances. At the same time, the sensitivity of the proposed residual vector to the fault in sensors is investigated. The generated residual vector will be more robust against the presence of noise in the measurements. It is shown through numerical simulations that the proposed residual vector is sensitive to the presence of fault in sensors while it is insensitive to the presence of the unknown input. In addition, a comparison with a derivative based method is presented.