---

This paper addresses adaptive observer design problem for joint estimation of the states and unknown parameters for a class of nonlinear systems which satisfying one-sided Lipschitz and quadratic inner bounded conditions. It’s shown that the stability of the proposed observer is related to finding solutions to a quadratic inequality consists of state and parameter errors. A coordinate transformation is used to reformulate this inequality as a linear matrix inequality (LMI). Sufficient conditions that ensure the existence of adaptive observer are expressed in forms of LMIs, which are easily tractable via standard software algorithms. If the proposed conditions are satisfied, then the state estimation errors are guaranteed to converge to zero asymptotically while, the convergence of the parameters is guaranteed when a persistence of excitation condition is held. The effectiveness of the proposed method is shown by simulation for the joint estimation of states and parameters of a numerical system.

---

Due to the high efficiency and low switching losses of resonant power convertors in comparison with switching convertors, nowadays there is a growing trend towards these convertors. However, because of the high frequency of switching in such convertors, it is quite difficult to present an efficient control method. In this paper, based on the state feedback method and using pole placement technique a control strategy is developed which controlling delay time in the converter’s switching to minimize losses in the induction and hardening furnaces. In the propose method, the input voltage is isolated from output load. Moreover, fewer number of elements are employed in the control circuit which will be caused lower costs and small dimensions for control system. As a result, the proposed controller is more economical in comparison with the conventional ones. To show the effectiveness of propose controller, simulation circuits using PSIM software and an experimental setup are provided and the results are reported 

---

The presence of environmental and measurement noises and ignoring the input effects are the main sources of error in system identification using ambient vibration test results. Therefore, reducing uncertainty or noise levels from the records has always been one of the main goals of the new techniques in the field of ambient vibration. Among the modal analysis techniques, stochastic subspace identification is considered as a powerful technique. In this study, the modal analysis method based on canonical correlation analysis in stochastic subspace is presented that identifies dynamic properties in optimized space instead of data space by extracting ortho-normal vector of data space. The advantage of this method, due to the nature of canonical correlation analysis, is lower noise which results in greater accuracy in estimating modal properties. Moreover, the presented process is faster due to the smaller space of identification compared to the previous methods. To validate the proposed method, an analytical model of two-dimensional frame excited under Elcentro earthquake acceleration and also the results of ambient vibration tests carried out on the Alamosa Canyon Bridge are used. The results indicate that this method eliminates more noise than other subspace methods and moreover it is faster in solving practical problems. The computation of dynamic properties, natural frequencies and mode shapes, of Alamosa Canyon Bridge with 30 sampling sensors, space matrix size of 750 and 50 excited modes are carried out in less than 150 seconds with a quad-core 2.30 GHz processor.

---

The dominant excitation forces are generally measurable during the forced vibration tests of structures unlike the ambient vibration tests. Not considering of input forces in the system identification is one of the main sources for error generation in the Operational Modal Analysis (OMA). Therefore, some non-structural dynamic characteristics obtained due to the excitations effects can be eliminated by considering the input forces. In this paper, a special modal analysis is presented in the subspace method that removes the excitation effect of the measured input forces from the test data using orthogonal decomposition and identifies the system with an optimal subspace method based on canonical correlation analysis (SSI-CCA). To evaluate the proposed method, the seismic response of the Pacoima dam and forced vibration test results of the Alamosa Canyon Bridge are used. Non-structural and noisy pole removal, and increased accuracy of the extracted modal properties, specially damping ratios, can be mentioned as one of the important results of this study. Four non-structural modes are identified using the SSI-Data method while the first two modes without any noises, the same as previous results, are extracted using the proposed method. In addition, the damping ratios of the Alamosa Bridge are obtained by Hammer test, which are not obtained in the previous investigations.

---

The purpose of this article is dynamic modeling of a quadrotor and control of its Roll and Pitch angles based on the experimentally measured sensors data. So, after driving nonlinear model of quadrotor equations, the control of the quadrotor’s angular situation was simulated using PID and feedback linearization algorithms. Due to the widespread application of MEMS sensors in measuring the status of various systems and to have a more realistic simulation, sensors data was measured and used in simulation of controllers. Due to errors of MEMS sensors, vibration of motors and airframe, being noise on outputs, Kalman filter was used for estimation of angular situation. As one of the purposes of this paper was the use of its results in actual control of a quadrotor, motor model was used to determine PWM control signals. The results obtained from simulation in Simulink showed good performance of both controllers in controlling roll and pitch angles.

---

Finite element model is the conventional method used for static and dynamic analysis of widely used structures such as dams and bridges, since it is cheap and requires no special tools. Nevertheless, these models are not able to describe the accurate behavior of structures against dynamic loads because of simplifying assumptions used in numerical modeling process, including loading, boundary conditions and flexibility. Nowadays, modal testing is used to solve these problems. The dynamic tests used to identify civil structures’ system usually include forced, free and environment vibration tests. Considering either unknown nature of inputs or failure to measure them, some methods have been developed to analyze the results of dynamic tests which are based on measuring only output data and are known as operational modal analysis. Some of such methods are Peak Picking (PP), Frequency Domain Decomposition (FDD) and stochastic subspace methods. However, unknown nature of applied forces, the presence of environmental noise and measurement errors contribute to some uncertainties within the results of these tests. In this article, a modal analysis is presented within a stochastic subspace which is among the most robust and accurate system identification techniques. In contrast to the previous methodologies, this analysis identifies dynamic properties in optimized space instead of data space by extracting ortho-normal vector of data space. Given the optimum nature of the proposed method, more accuracy in detection and removal of unstable poles as well as high-speed analysis can be served as its advantages. In order to evaluate the proposed method in terms of civil systems detection, seismic data (being among the most real and strong environmental vibrations) and steady-state sinusoidal excitation (which is among the most precise forced vibration tests) were used. In the first step, 2001 San Fernando earthquake data were analyzed using SSI-CCA and SSI-data methods, the results of which are presented in the following. Data processing rate in the SSI-CCA method is almost twice that in SSI-data method which is because of processing in an optimum space while lowering the use of least squares method to compute system vector. Furthermore, there is one unstable pole in the results of the proposed method while 4 noisy characteristics were recognized in the results of SSI-Data method. Estimated damping ratios comprised the major difference observed in this analysis using above-mentioned two methods. Modal damping ratios estimated by the proposed method were 60% closer to the previous results when compared to those of the previous subspace method. Mode shapes of both subspace methods with MAC value of 92% and 75% for the first and the second modes, respectively, are well correlated with each other. Due to lack of access to the mode shape vectors of Alves’s method, it was not feasible to calculate the corresponding MAC value. In the following, forced vibration test results of Rajai Dam conducted by steady sine excitation in 2000 and analyzed by a method known as four spectral, are re-processed Using the SSI-CCA method. As results indicate, using the proposed method the first three modes are obtained that were not on the preliminary results. In addition, other modes are of great fit with the values of the finite element.

---

In this paper, an optimal robust nonlinear model predictive controller based on harmony search algorithm is designed for a type of 3-DOF translational parallel robot. Dynamic model of the mechanism is derived using Lagrange method and the model predictive controller augmented by uncertainty estimator is designed and stability is proved by Lyapanov theorem. Performance of the designed controller is evaluated in different conditions such as presence of disturbance and parameter variation. Furthermore, an optimal trajectory consisting four circular obstacles is designed as the reference trajectory of the robot. In order to obtain the optimum control parameters, a cost function combining control signal rate and error is considered and minimized by harmony search algorithm. In order to compare the performance of the designed controller with other nonlinear controllers, two controllers, an optimal sliding mode and a feedback linearization controller are also designed and their results are compared. Simulation results depict the desirable performance of the three controllers in spite of disturbance and model uncertainty, however, error criteria indicate priority of the robust nonlinear model predictive controller over the two other controllers.

---

---

Infrastructures such as bridges, buildings, pipelines, marine structures, etc., play an important role in human life. Since major disasters in these structures, such as the collapse of bridges or buildings, often result in many casualties, damages, and social and economic problems, most industrialized countries allocate significant funds to monitor their health. Failure detection strategies and continuous monitoring of the structure's condition, especially after natural and manufactured disasters, make necessary measures to be taken in the early stages of failure and can reduce the cost of maintenance and the possibility of collapse. Structural health monitoring methods often provide an opportunity to reduce maintenance, repair, and retrofit costs during the structure's life cycle. Most of the structural health monitoring methods proposed and implemented to identify possible damages depend on the structure's dynamic characteristics. One of the most practical methods, which uses the results of time domain system identification to detect failure, is the damage locating vector (DLV) method. The DLV method aims to identify load combinations that result in zero strain fields for damaged members in both healthy and damaged structures. To accomplish this, we find a vector in the null space of the difference between the plasticity matrices of the two structures. The singular value analysis method is used on the plasticity difference matrix to calculate this space. The method involves applying the space vectors to the healthy structure and recording the internal stresses of the members, which are then converted into weighted normal stress (WSI) using statistical tools. The member with a lower WSI is more likely to be damaged. Since truss structures are usually used in bridges, long-span structures, as well, as a wide range of steel buildings with simple and braced frames, this research uses the covariance-based random subspace optimal method in identifying the modal characteristics, which is very efficient in low excitations, has been taken into consideration to check and monitor health during operation. To investigate the capability of the DLV method in the damage detection of these structures, a 5-story residential building with a simple steel frame was subjected to the Centro earthquake. According to the desired damage scenario, the second and fifth floors were introduced as the damaged floors in this earthquake by applying a 30 and 50% reduction in the cross-section. To account for uncertainty in the data collection, we included the mean root square of the second sensor's data in the results for sensors 3 and 5. As a result of this uncertainty, the damping error between 5 and 10% has been shown in the damaged and healthy structure. Using the method (SSI_ORT), it was observed that two DLV vectors were extracted. Further, with the increasing uncertainty of the random vibration test results, it was observed that the extraction DLVs could extract the possible damaged elements with high accuracy. Next, the effect of input and output noises on the results obtained from the DLV method was investigated. This study found that by increasing the SNR of the outputs by 15% while increasing the error of the extracted modal characteristics, the extracted DlVs also lose sufficient accuracy in diagnosing structural damage.