Blast Load Input Estimation of the Medium Girder Bridgeusing Inverse Method

Innovative adaptive weighted input estimation inverse methodology for estimating theunknown time-varying blast loads on the truss structure system is presented. This method isbased on the Kalman filter and the recursive least square estimator (RLSE). The filter models thesystem dynamics in a linear set of state equations. The state equations of the truss structureare constructed using the finite element method. The input blast loads of the truss structuresystem are inverse estimated from the system responses measured at two distinct nodes. Thiswork presents an efficient weighting factor  applied in the RLSE, which is capable of providinga reasonable estimation results. The results obtained from the simulations show that the methodis effective in estimating input blast loads, so has great stability and precision.Defence Science Journal, 2008, 58(1), pp.46-56, DOI:http://dx.doi.org/10.14429/dsj.58.162

Adaptive input estimation method and fuzzy robust controller combined for active cantilever beam structural system vibration control

This paper studies the active vibration control of a cantilever beam structural system by combining the adaptive input estimation method with the fuzzy robust controller. The unknown inputs can be estimated using the measurement dynamic displacement of a beam structural system. That is to say, the adaptive input estimation method can estimate the dynamic inputs of every step time on-line, while the active component applies the same magnitude inverse force into the feedback control. The simulation results show that the proposed synthesis control system has disturbance compensation capability. It can suppress the vibration in a disturbance structural system more effectively and promote controller performance

Determination of Moving Tank and Missile ImpactForces on a Bridge Structure

A method to determine the moving tank and missile impact forces on a bridge is developed. The presentmethod is an online adaptive recursive inverse algorithm, which is composed of the Kalman filter  and therecursive least square estimator (RLSE), to estimate the force inputs on the bridge structure. The state equationsof the bridge structure were constructed by using the model superposition and orthogonal technique. Byadopting this inverse method, the moving tank and missile impact force inputs acting on the bridge structuresystem can be estimated from the measured dynamic responses. Besides, this work presents an efficientweighting factor applied in the RLSE, which is capable of providing a reasonable estimation results. The resultsobtained from the simulations show that the method is effective in determining the moving tank and missileimpact forces, so that the acceptable results can be obtained.Defence Science Journal, 2008, 58(6), pp.752-761, DOI:http://dx.doi.org/10.14429/dsj.58.170

Active vibration control of smart structural system using a novel control approach

This study presents a novel numerical algorithm based on an active control approach to reduce the earthquake responses of a smart building structural system. The proposed control algorithm is a synthesis of the adaptive input estimation method (AIEM) and linear quadratic Gaussian (LQG) controller. An unknown system input can be estimated by the AIEM while the LQG controller offers optimal control forces to suppress seismic vibration. The proposed control technique was evaluated for a smart building structural system under seismic vibration on line optimal control problem. The numerical simulation results show that the proposed method has more robust active vibration control performance than the conventional LQG method

Modern methods for power system harmonics estimation

Harmonics has been present for a long time and its presence shapes the performance of a power system. Therefore, estimation of harmonics is of paramount importance while analyzing a power system network. Following the inception of harmonics, various filters have been devised to achieve an optimal control strategy for harmonic alleviation. This thesis introduces various algorithms to analyze harmonics in the power system. The objective is to estimate the power system voltage magnitude in the presence distortions taking into account the noise by employing different estimation approaches. We have focused our attention towards the study of Least Mean Squares (LMS) based filter, Recursive Least squares (RLS) based filter, Kalman filter (KF) and Extended Kalman (EKF) filter. For a test signal LMS, RLS, KF and EKF based algorithms have been analyzed and results have been compared. The proposed estimation approaches are tested for only static signals

Power System Harmonics Estimation Using Adaptive Filters

Accurate estimation and tracking of power quality disturbances requires efficient adaptive model based techniques which should have elegant structures to be implemented in practical systems. Adaptive filters have been used as a popular estimator to track the time-varying power quality events, but the performance is limited due to higher order nonlinearity exists in system dynamics. Harmonics generated in the generation and distribution system are one of the critical power quality issues to be addressed properly. Least mean square (LMS) and recursive least square (RLS) based adaptive estimation models can be used to track the harmonic amplitudes and phases in practical power system applications. Due to time varying nature of harmonic parameters, modifications have to be incorporated in adaptive filters based modeling during estimation of the harmonic parameters and decaying DC components present in the distorted power signals. Volterra expansions can be combined with the adaptive filtering to improve the estimation accuracy and enhance the convergence rate of the estimation model