مجله کنترل
سال ششم شماره 4 (زمستان 1391)

Nowadays almost in the all branches of control even intelligent control strictly positive real transfer functions are used to stabilize closed loop control systems. These transfer functions in a closed loop control system can help to generate a lyapunov function for guarantee the stability of closed loop system. Nevertheless, the necessary and sufficient conditions for strictly positive real transfer function matrices have been different in the literature for many years. Recently, it is shown that there are inconsistencies between definition and theorems of strict positive realness. In this paper, we present and prove an important difference between positive real and strictly positive real transfer function matrices. Moreover, we introduce more complete and simpler necessary and sufficient conditions for strictly positive real transfer function matrices. An important advantage of present work is usage of Markov parameters of multivariable systems. We know these parameters are easily calculated in both frequency and time domains, so using these parameters can make a similar framework in both frequency and time domains. Presenting some examples, we show the advantages of our new results in comparison to before works.

Measurement and control of TDS (Total Dissolved Solids) of water has significant importance for industry, agriculture, livestock and health. The system designed and manufactured in this paper measures, displays and controls TDS of water by installing on domestic and industrial purification systems. Technology of manufacturing TDS measurement system is such that it can compensate for the adverse effect of temperature on measurement. Meanwhile, calibration of the measurement system will be explained by discussing several examinations conducted in Research Institute for Food Science and Technology. In order to control TDS, mathematical model for domestic water purification devices of reverse osmosis (RO) is obtained by running practical experiments. Then, system controller is designed by Ziegler-Nichols oscillation method with the most appropriate controller being implemented. Operation of controlling TDS is done through real time and continuous controlling of a mix valve. This valve is closed parallel with a membrane filter or RO, so one can control TDS by adding or reducing inlet water into outlet water of this filter. The system developed by this research is produced in pilot by receiving scientific approvals from relevant authorities and has found numerous applications in industry.

In this paper dynamic analysis and robust PID control of fully-constrained parallel cable driven manipulators are studied in detail. Since in this class of manipulators, cables should remain in tension for all maneuvers in their workspace, feedback control of such robots becomes more challenging than that of conventional parallel robots and research on this topic especially on robust stability analysis is limited. In this paper, structured and unstructured uncertainties are considered in the dynamic model and Jacobian of therobot, and a robust PID controller is proposed for the cable driven parallel robot. To ensure that all the cables are in tension, a corrective term is used in the proposed PID control based on the internal force concept. Then, robust stability of the closed-loop system is analysed through Lyapunov’s second method and it is shown that by suitable selection of the controller gains, the closed-loop system would be robustly stable. Finally, the effectiveness of the proposed PID algorithm is examined through experiments on a planar cable driven robot and it is shown that the proposed control structure is able to provide suitable performance in practice.

In this paper, a new method for determining the position and orientation of the object in respect to the camera coordinate using the image is presented. True picture of the camera is obtained by a nonlinear mapping between the three-dimensional world and two-dimensional image. Some researchers assumed linear mapping to make approximation of position and orientation easier, but restrictions were imposed on the object size. Also the use of nonlinear mapping and geometric relationships between object and its image is so challenging and difficult task, but increase the accuracy and do not raises the limit on the size of the object. The case in which an object is placed in front of the camera can become different according to the orientation type and its distance from the main axis of the camera. In this paper, to determine 6DOF position and orientation of an object using its image, a system of nonlinear equations with ten equations and 5 unknowns is extracted. In solving nonlinear equations, it is important to choose an appropriate algorithm to minimize the error and needs little time. With regard to the non-exact values in practical test, the kind of optimization algorithm is proposed and. Then a way for selecting initial guess is proposed to always have a solution and reduce the run time. Accuracy of extracted equations is shown by determining 6 DOF position and orientation of one theodolite.

In this paper, we perform adaptive radius of the kernel by an edge detection method with tracking algorithm based on kernel density and provides a robust tracking algorithm, in combination with the resampling particle filter algorithm. In the first frame, by suitable kernel density estimation, is obtained the weighted histogram of the target model and by adding random noise variance at this place, are predicted the position of candidate particles in the next step. The weighted histogram of this candidate particles are compared with the same density kernel by the target model and are weighted the candidate particles by Bhattacharyya distance. The resampling algorithm, estimates the target position in the next frame. Finally, radius of the kernel is consistent with the target model changes. If needed, the target model is updated according to the best model of particle similar to the target model, adaptively.

In this paper, using the information obtained in the star tracker, strap down inertial navigation system positioning error in suborbital flight is reduced. In this regard, the position error equations of different channel in inertial navigation are modeled and error share of each sensor in position and attitude determination drifts are investigated. In continue celestial navigation and utilizing star tracker in attitude determination and positioning on different frames are considered. Then new equation of inertial navigation aiding by attitude determined with star tracker are introduced. According to the newequations, attitude and position in navigation and inertial frame were calculated again and comparing with the parameters obtained by inertial navigation, considerable reducing position error, especially in the height channel, is shown.