مجله کنترل
سال یازدهم شماره 4 (زمستان 1396)

In this paper a robust autopilot is designed using fuzzy gain scheduling. At first the flight envelop is divided to sub regions using v-gap metric and a suitable operating point is selected in each region and robust static H∞ loop shaping controller is designed for the linearized model. Finally, fuzzy gain scheduling is used to interpolate the local controllers. Simulation results show the generality and effectiveness of the proposed control strategy in terms of the performance and robustness of the system.

This paper proposes a framework of Supervised Deep Learning (SDL) for wheeled robot navigation in soft terrains with a focus on wall following and obstacle avoidance tasks. Here, it is supposed the robot is only equipped with a vision system (Kinect camera). The main challenge while using depth images is high dimensionality of images and extracting proper features of them with a purpose of reducing input dimensionality of controller. To do this, the deep learning is utilized in this paper and the appropriate features which are the representation of depth images are acquired. Four architectures are created using this features and the history of steering commands. These architectures are compared in WEBOT simulator. The experiments show that the proposed architecture with four groups of features including: the represented features of depth data, previous represented features, the position of trajectory in color image, and the history of previous steering commands can control the robot in soft terrain with a variety of obstacles as well.

In this paper, the synchronization of unified chaotic systems in the presence of practical constraints is considered. Constraints that this paper is considered are: dead-zone nonlinearity input, model uncertainty, external disturbance and the unavailability of state variables. First, the dynamical model of the unified chaotic systems in the presence of aforesaid constraints is introduced. Then, an observer-based adaptive controller is designed which guarantees robust synchronization between two unified chaotic systems (i.e. master and slave systems). For this purpose, a theorem is given and according to a Lyapunov stabilization approach, it is guaranteed that the proposed controller leads to robust synchronization objective despite the dead-zone nonlinearity input and also uncertainty terms. Finally, the computer simulations show proper performance of the proposed law in robust synchronization of the unified chaotic systems

In this paper, the object is estimation and torque control of the claw pole eddy current dynamometer. The eddy current dynamometer is included DC excitation coil, poles and rotational cylinder that coupled to the electrical motor. Load testing of electrical motor in different operating point is done through torque control of dynamometer. The close loop system of eddy current dynamometer has two inner and outer control loops. In outer control loop, the torque should estimate. The torque estimation is presented as a function of DC excitation current and rotational speed. After that, as regards to comparison of reference torque and estimation torque, the reference current is determined. Finally, the control of DC current source is done to generate the reference current. Both controllers of loops are PI controller. The experimental and simulation results for 22.5 KW eddy current load is shown the efficiency of proposed estimation and control method. The implementing system is done by using the ARM-LPC-1788 micro controller.

In this paper, a new image processing technique has been proposed to measure the contact angle of symmetric and asymmetric drops. In our proposed method, the contact angle is calculated by analyzing the side view image of the drop without needing any liquid parameters. The procedure of the technique is according to the secant technique coupled with a Gaussian weighted function, as follows: at the beginning the exact location of the contact points is obtained by Harris corner detector function, then a series of points are selected on the drop boundary near the contact points. In the third step, a set of contact angles are estimated by passing secant lines through the contact point and each of the boundary points. In the final step, a gaussian weighted average function is applied to the calculated angles to estimate the main contact angle. The contact angle measurement algorithm is verified by comparing its results with the obtained measurements of a recent standard study. High accuracy, usability for all ranges of contact angles, applicability to both symmetric and asymmetric drops and being fully automatic are other advantages of the presented method.

In this paper, a novel scheme based on sliding mode control method for impedance control of a single link flexible robot arm when it comes into contact with unknown environment, is presented. The proposed control strategy is robust against the changes of the environment parameters (such as stiffness and damping coefficient), the unknown Coulomb friction disturbances, payload and viscous friction variations. The proposed scheme is also valid for both constrained and unconstrained motions. In our new approach, the controller automatically switches from a free to a constrained motion mode therefore it does not need an algorithm to detect collision between the link and the environment. In this regard, impedance control is proposed with the inner loop position. This means that in the free motion, the applied force to the environment is zero and the reference trajectory for the inner loop position is the desired trajectory. In the constrained motion, the reference trajectory for the inner loop is determined by the desired impedance dynamic. Feasibility and effectiveness of the proposed control scheme are demonstrated via numerical simulations.