مجله کنترل
سال چهارم شماره 1 (بهار 1389)

This paper presents a method for line-of-sight stabilization in submarine periscopes with respect to disturbances in the sea surface movements. These disturbances can cause unstable images taken by the periscope and give altered information from the targets that are located on the see surface, on the land, or in the air. To overcome this problem, an image stabilization method is required to generate reference signals for servomotors to remove the unwanted motions incurred on the image sequences. To drive the stabilization equations, basic rotation and homogeneous transformation matrices will be used. The stabilization equations will be derived for two different cases: 1) the range of target is known and 2) the range of target is unknown. In fact, these equations map the input space to the three-dimensional output space. The input space consists of the deviation angles of the periscope platform, the optional target range, which can be measured or given by the operator (optional data), and some desired operator information. The output space consists of three reference signals for three servomotors inside the persicope. The proposed method is implemented on an experimental periscope, mounted on a moving platform, which simulates the sea surface movements. The experimental results show good performance of the proposed method against sea surface movements.

In this paper, a new method for robust H controller design for systems with ellipsoidal parametric uncertainty in terms of solutions to a set of Linear Matrix Inequalities (LMIs) is proposed. It is well-known that the resulted closed-loop robust performance depends as much on the controller as it depends on the shape of the uncertainty set. Subsequently, joint robust H controller design/ uncertainty set shaping is investigated. Therefore, using input design in system identification, uncertainty set would be shaped for robust control design. This way, desired closed-loop performance and controller structure would be translated into the requirements on the input signal spectrum. The Simulation results show the effectiveness of our proposed method.

Infection of the correct data by various kinds of noise in transmission or reception of communication signals is an undeniable fact. Data errors may also exist in statistical socio-economic measurements. Annihilation or reduction of noise is a field that attracts many researchers. On the other hand, fuzzy theory has appeared as a powerful tool in this field. Fuzzy filters are often strong in smoothing of corrupted signals, whereas these filters have simple structure. This paper presents a novel fuzzy logic based smoothing filter that has shown both accuracy and simplicity in recovering noisy images by sharpness reduction. Indeed, the proposed procedure is generalization of a previously introduced Fuzzy Smoothing Filter (FSF) for onedimensional signals. In this method, the sharpness of each point is first determined assigning an index term. Then the procedure looks for “very sharp” points and then smoothes by sharing the values of the eight (or more) neighboring points to correct their values. The lower is the average sharpness of the signal the lower is the signal noise level. This way, the method smoothes out the sharpness and hence reduces the noise of the mix data. To obtain experimental results of the proposed procedure, it is applied to enhance color images corrupted by various levels of noise. The results are compared with some other known methods like AWFM, PWLFIRE and DSFIRE. This comparison exhibits superiority of the proposed method in numerical measures and visual inspection. The method has been designed for annihilation of all kinds of impulse noise in any twodimensional signals, especially socio-economic statistical data.

This paper presents real-data testing results of a real-time freeway traffic state estimator. The general approach to real-time adaptive of freeway traffic state estimation is based on nonlinear macroscopic traffic flow modeling and extended Kalman fliter algorithm. Macroscopic traffic flow model contains three important and unknown parameters (free speed, critical density and exponenet), which should be estimated with off-line or on-line methods. One innovative aspects of the estimator is the real-time joint estimation of traffic flow variables (traffic flow, mean speed and traffic density) and model parameters, that leads to some significant features such as: avoidance of prior model calibration, automatic adaption to changing external conditions (e.g. weather conditions, traffic composition,…). The purpose of the reported real-data testing is, first, to demonstrate some obstacles in previous methods, second, to propose two methods based on dual filtering and fosion of the model parameter estimates for improving the previous methods.

In this paper, parameter and state estimation problem in time delay uncertain systems in the presence of observer gain perturbations is discussed which are linear in unknown parameters and nonlinear in states. A nonlinear non-fragile proportional-integral filtered-error adaptive observer is designed, and its stability conditions based on Lyapunov technique are derived. The optimal gain with maximum disturbance attenuation level among a solution set that satisfies the observer stability conditions is derived using linear matrix inequality approach. A numerical example is provided to demonstrate the effectiveness of the proposed method and the simulation results are provided.

The hardware in the loop simulation is the main method for evaluation of the missile guidance and control system befor flight test. Implementation of this tester in IR homing missile requires accurate and expensive instruments for generate IR scene and rotate the seeker in three dimensions. Signal injection method obviates these equipments by simulation and generation the output signal of IR detector. This paper explains implementation hardware in the loop of autopilot circuits of an IR homing missile. First, evaluation and validation of six degree of freedom simulation was done by using the practical test results. It was done by simulating the laboratory testers and analyzing the command factor. By using this method the gain of missile guidance and control loops in simulation adjusted by real values from laboratory tests. Implemnting the autopilot circuits in the simulation and realization the signal injection method was done by making real time simulation and designing the suitable interface circuit. The compatibility of the results from hardware in the loop and software simulation shows precision of the implementation. Small variance between results has analyzed by the effect of linear signal phase.

This paper presents a novel method for path planning of redundant manipulators. The idea is to use load distribution as an objective function for resolving kinematic redundancy. The method is based on imposing a set of convenient relation among joint torques as dynamic constraints to system. Two algorithms for path planning of redundant manipulators subject to dynamic constraints are proposed. The first algorithm takes advantage of all necessary dynamic constraint equations which can be approximately solved using a pseudo-inverse technique, whereas the second algorithm is based on finding a good starting point with fair load distribution and generate the path by imposing as many dynamic constraint equations as the number of degrees of redundancy. The results for path planning of a 3-DOF planar manipulator using this method are presenteded.