فهرست مطالب aylar khooshehmehri

The hemispherical resonator gyro (HRG) is a type of precision inertial sensor that has the advantages of direct angle measurement and unlimited dynamic range. The overall accuracy of the HRG is due to the quality of its resonator shell, and improving the performance of resonators requires a proper understanding of the processes of energy damping in each resonance cycle, which has a significant impact on sensor performance. In this paper, in order to investigate the losses in the hemisphere shell resonator, first, the equations governing the shell are studied, and three-dimensional modeling is performed in COMSOL software. By performing mechanical simulations, the resonance modes and the natural frequency of the shell are investigated, and finally, the second and third resonance modes are selected as the optimal operating mode of the gyroscope. Also, by performing thermal simulations, the dominant energy damping processes, such as thermo-elastic damping and anchor loss were analyzed and simulated, and the effect of shell material on damping was investigated. Then the quality factor of the resonator was evaluated based on its geometry and material. In this way, according to the scope of work of the gyroscope, this process can be used to design the specifications of the shell to achieve a resonator with the desired quality factor.

Vibratory gyroscopes with hemispherical resonators are among the new electromechanical gyroscopes that have advantage of bias stability and insensitive to external environmental perturbations like vibrations, accelerations and shocks.Due to the dynamic model, symmetrical structure and high quality coefficient of the oscillator, one of the challenges of vibrating gyroscopes with symmetrical oscillator is the limitation of the dynamic opening of the input frequency. In this paper, by limiting the design of the filter, the dynamic opening limitation of the input frequency is removed by an innovative method. Then, by processing the output signal of the filter, a model for measuring the rotational speed is designed. This design shows the effect of the bottom receiving filter on the size line. By designing the filter, the dynamic range of the input frequency in the gyroscope has been increased from 3 to 100 Hz. With the proper design of the lower filter, the accuracy of the rotation speed measurement line increases to 0.05%.

The Josephson junction is a device consisting of two superconducting electrodes connected by a weak junction such as a thin insulation coating. The Josephson junction has chaotic behavior parameters not desirable in high-frequency applications. In this paper, a model predictive control approach based on an ant colony optimization algorithm is proposed to synchronize two Josephson junction models with different parameters. Here, the Josephson junction is described with a nonlinear model, and the synchronization is obtained using the slave–master technique. For this purpose, an appropriate objective function is defined to assess the particles within the state space. This objective function minimizes simultaneously the tracking error, control effort, and control smoothness. The dynamic optimization problem is solved using an ant colony optimization algorithm. Numerical simulations are conducted to assess the efficiency of the proposed algorithm. Also, a Monte Carlo evaluation is achieved to compute the statistic performance of the suggested controller. In addition, sensitivity analysis to changes in the number of ants and the number of iteration of the inner loop of the algorithm was performed. The results show that the controller is significantly sensitive to reducing the number of iteration of the inner loop.

The use of autonomous underwater vehicles (AUV) for the exploration and oceanology science has been a field of interest of several research centers around the world in the last decade. The inertial navigation system (INS) has commonly been used as the principal means of localization for autonomous underwater vehicles. The main drawback of using only the inertia navigation system is the escalating error in the estimated position and attitude due to the error in the output of the inertia measurement unit and its integration. Traditionally external sensors such as: GPS, acoustic transponders, Doppler velocity logs (DVL), or cameras have been used to aid the solution of the inertial navigator by constraining the errors in the estimated positions. These external sensors have several practical disadvantages, basically related to their reliance on external information. One source of information that can assist in the localization of the vehicle, without the need for extra additional external sensing, is the vehicle’s dynamic model. The model of the vehicle is capable of representing the attitude of the system according to the control inputs and the external forces acting on it. In this work, we follow a model-aided inertial navigation system (MA-INS) for Remus 100 AUV based on a 6-DOF non-linear dynamic model. The proposed navigation system utilizes the knowledge of the device dynamics through an experimentally validated mathematical model. The results show that the proposed navigation system improves underwater navigation capabilities for the systems that lack conventional aiding equipment.

In this paper, two particle filters are proposed to estimate the state and control the attitude of a projectile with a moving mass actuator. Since the dynamic equations of a flying object with a moving mass actuator are nonlinear, a nonlinear estimator and controller are used. The first filter takes the angle measurement and mass displacement along with noise as observations and uses them to estimate the angle, the rate of angle, the mass displacement, and the velocity of the mass. In this research, the nonlinear predictive control problem becomes a dynamic optimization problem and then, at each time step, the best control signal is calculated online using the second particle filter in the finite horizon and applied to the system. The cost function used for the first filter is the error of the values measured and calculated by each particle. The cost function in the predictive control problem for each particle is the error of the angle tracking and the control effort. The simulation results for the performed scenarios show that the algorithm proposed for solving the nonlinear problem of controlling the attitude of a moving mass actuated projectile has a good performance in the presence of Gaussian and non-Gaussian noises. Also, due to the random nature of the problem, statistical analysis of the results is performed using Monte Carlo simulation.

In this paper, a model predictive control approach based on a generic particle filter is proposed to synchronize two Josephson junction models with different parameters. For this purpose, an appropriate objective function is defined to assess the particles within the state space. This objective function minimizes simultaneously the tracking error, control effort, and control smoothness. The dynamic optimization problem is solved using a generic particle filter. Here, Josephson junction is described with Resistive Capacitive Inductive Shunted Josephson model, and the synchronization is obtained using the slave–master technique. Moreover, to verify the implementation capability of the proposed algorithm, a processor in loop experiment is performed. The results show that the open-loop system, without the controller, has a chaotic behavior. Numerical simulations are conducted to assess the performance of the proposed algorithm. The results show that the proposed approach can be implemented in a real-time application. Also, the performance of the suggested controller is compared with the proportional integral derivative controller and sliding mode controller.

In this paper, a new predictive guidance algorithm based on the whale optimization is proposed. Here, nonlinear kinematics of the engagement between a pursuer and a target is used. The proposed heuristic guidance algorithm uses the whale optimization dynamic algorithm for calculating the guidance command. The proposed objective function minimizes simultaneously the line-of-sight rate error, the guidance command, and its fluctuations. The dynamic model of the pursuer autopilot is considered as a first-order lag. The performance of the proposed guidance algorithm for maneuvering and non-maneuvering targets is evaluated using numerical simulations. In addition, it is evaluated for the cases when the pursuer has a high initial heading error.

he Josephson Fluxonic Diode is a long Josephson Junction that uses oppositely directed magnetic flux quanta, associated with current votrices and antivortices, as its basis operation. the movement of the vortices creates a changing phase. this in turn creates a voltage drop. in this paper, discrete JJ is proposed instead of continues JJ in JFD. Also, by numerical calculation of the discrete sine- Gordon equation, the manner of the carriers is discussed in discrete JJ. Numerically study of the dynamics of JFD based on discrete JJ shows a rectification. Because of it is easy to adjust the value of the junction parammeters, and it is possible to have single flux quantum stuffed in a small area by adjusting the discreetness parameter, it is expected that the useful and practical Josephson fluxonic devices can be made by discrete JJs.