جستجوی مقالات مرتبط با کلیدواژه « مشاهده گر » در نشریات گروه « مکانیک »

During the vehicle motion trajectory tracking process, there are uncertainty considerations in vehicle modeling, including parameter changes, modeling error, and external disturbance that have a significant effect on the tracking performance. Therefore, in this research, a control strategy for motion trajectory tracking of the in-wheel motor electric vehicle, which has a high capability, is proposed. At first, a dynamic model with two degrees of freedom is used to create a trajectory tracking error model, and then it becomes the problem of tracking yaw. Therefore, the amount of the steering angle as the input of the controller is obtained by controlling yaw tracking. In order to estimate and compensate the uncertainties related to the system, in this research, the design the nonlinear mode observer is applied. And also, the controller algorithm is designed to detect yaw tracking. In the next step, in order to stabilize the in-wheel electric vehicle, a sliding mode control algorithm is designed to achieve the desired yaw torque. Then, by designing the optimal torque distributor, the optimal force of the tires is allocated. Finally, simulation is done using Simulink Matlab/Carsim software. The results of the performed simulations show the performance and high capabilities of the proposed control algorithm in emergency situations and despite external disturbances.

Any defect in the flight control system may cause an irreparable problem. Typically, a highly reliable system with human decision-making power is used to prevent or correct such errors in a flying vehicle. A fault tolerant control system is designed to deal with various types of errors that may occur in the system. Fault-tolerant control systems are divided into two main parts. The first part is the error detection and isolation phase and the second part is the control system design phase to overcome the error effects in the system, depending on the type of error and the location of the error, whether the sensor, actuator, or components, the control system must be able to eliminate error effects. In this paper, a neural-adaptive observer is used in the error detection stage, and in the second stage, a control system is designed based on the back-stepping algorithm. Nonlinear six-degree-of-freedom simulation results for an F-18 aircraft model indicate its suitable efficiency in the detection and compensation of fault effects.

Sliding mode control is one of the most common types robust control that can compensate the model structure and parametric uncertainties, but the main disadvantage of this method is chattering phenomenon. Although a boundary layer around the sliding surface can eliminate chattering effect, it reduces tracking performance and robustness in control. The second generation of sliding mode control called Second Order Sliding Modes (SOSM) is a solution to this problem. Super-Twisting Sliding Mode (STSM) is a modified SOSM control that reduces chattering effect naturally and without a defined boundary layer, while maintaining the robustness of the Conventional Sliding Mode (CSM) control. In this paper, the problem trajectory planning is solved in an environment with fixed obstacles by using firefly optimization algorithm and polynomial trajectories, then STSM control is designed for quadrotor in the presence of uncertainties to tracking path trajectory and the performance of this controller is compared against Feedback Linearization (FL) and CSM control. Also, derivative of some of the states calculates by using super-twisting observer in the closed loop control and stabilization while there is no direct access to them through the sensors.

This paper presents the problem of controlling multiple tasks in a prioritized scheme during accidental external physical interaction with redundant robot. This issue arises when robots are employed in social, unknown, dynamic environments for complex and critical missions. Exploiting robot redundancy to ensure safety and compliance during performing hierarchical tasks is considered in this work. A general approach to control the main task (position/orientation of the end-effector) with allocated priorities beside compliance behavior in the null space of the tasks is proposed. External interactions on the robot body are estimated with an appropriate observer without using any force/torque sensors which is further used to bring compliance in the redundant space. A novel task allocation method is proposed and the convergence of the task space error, interaction estimation error as well as null space velocities are guaranteed. Finally, the performance of the method is investigated through computer simulation and real experiments on KUKA robot arm.

In recent years, to reduce positioning cost for civil and robotic applications, low-cost inertial sensors especially Micro Electro Mechanical System (MEMS) types have been produced. Positioning Error of an inertial navigation system comprising low-cost inertial sensors increases due to significant uncertainty of noises, bias and drift of MEMS sensors in short times. Therefore, combination with an auxiliary system such as the Global Positioning System (GPS) is proposed in order to reduce the errors trough integration estimator algorithms. This paper aims developing a new estimation algorithm for integrated attitude and heading reference system (AHRS) with GPS. Kalman Filter is commonly used for linear systems and its extended version has been used for nonlinear system. Generally, the Kalman type estimators fall in trouble when the system exhibits nonlinear behavior and to overcome these issues, the predictive estimator is considered in the paper. Design process of Model Predictive Observer (MPO) is proposed based on the duality between the problems of control and estimation in linear systems. To assess the performance of the proposed method compared with the extended Kalnman filter, practical tests of AHRS/GPS have been done on car and flight vehicles. The test results of the designed MPO during all tests show the significant superiority in comparison to the extended Kalman filter.