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One characteristic of the Eigensystem Realization Algorithm method for system identification concerns about the difficulty of finding more appropriate parameters to run the algorithm. One of this work's purposes is to tackle the cumbersome task of achieving the ideal algorithm settings, providing additional knowledge about algorithm parameters' influence, and searching to improve results with quicker settings of the algorithm's parameters, especially for Multiple Input Multiple Output (MIMO) systems. The application of a Fit Rate indicator to evaluate the identified system arises as a novelty in the Eigensystem Realization Algorithm applications, aiming to assess the system identification performance and drive the algorithm to better adjustments. Another objective of this article regards the application of a Pseudo Random Binary Sequence as excitation signals, which has not been used until now with the Eigensystem Realization Algorithm, despite being successfully applied in the system identification process. The proposed approach is verified and analyzed with numerical simulations for a mass–spring–damper model of 5 degrees of freedom. The results reported in time response analysis and frequency response analysis allow us to realize the effect of settings accordingly for the system identification improvement. The results analysis was extended to simulate and compare the Pseudo Random Binary Sequence with Gaussian white noise excitation, and the system was also submitted to the presence of measurement noise. [ABSTRACT FROM AUTHOR]

In this article, a control method based on artificial neural networks applied to the vibration control of flexible structures is presented. The direct inverse control method is used. This method consists in the identification of the inverse dynamics of the plant using an artificial neural network to be used as the controller. An application example is proposed, and two problem variations are treated. The application problem is based on a cantilever plate model. The plate model is obtained using the finite element method. For the first problem, the controller is designed using the full-order plant model. In the second example, a model reduction is made to evaluate the performance of this technique applied to control problems with dynamic uncertainties. The results were evaluated according to the time response and frequency response of the closedloop system. To compare the results obtained using the control method based on artificial neural networks, the previous examples were also solved using the H∞ control method. The obtained results show that the control method based on the inverse model using neural networks is effective in solving this kind of problem. [ABSTRACT FROM AUTHOR]

Research about actuator and sensor positioning is important to obtain smart structures that can achieve better performance, and studies concerning controller design techniques are also important. In some studies on smart structures, the positioning of sensors and actuators are defined by some physical criteria and, thereafter, the controller is designed to satisfy some requirements of the controlled system. However, the optimal number and placement of sensors and actuators can also be obtained through the solution of an optimization problem, taking into account, for example, the possible positions to allocate the active elements and the available number of these. This paper presents a discrete heuristic optimization technique in order to determine the discrete positions of the active elements in active control systems. Furthermore, a technique that involves the determination of the number of active elements and the positioning is shown. These techniques have been implemented based on the genetic algorithms. Depending on the desired number of the sensors and actuators, and the number of candidate positions, it is impractical to use a combinatorial algorithm, as this is very expensive in terms of computational time due to the number of possible combinations. Thus, the techniques developed here have the aim to obtain good solutions analyzing fewer combinations than the combinatorial method and in reduced computational time. In this paper, the controllers are designed based on the H∞ control theory. The objective function used to solve the positioning problem of active elements is the H∞ norm of the closed-loop system. [ABSTRACT FROM AUTHOR]

The design of vibration controllers for flexible structures requires special attention due to the size of structural models, generally with a high number of degrees of freedom. The implementation of full order controllers for structures with high numbers of degrees of freedom often requires a high computational processing effort and advanced hardware. To avoid this, it is desirable to use reduced order controllers. The design of reduced order H∞ controllers characterizes a nonconvex optimization problem. In this context, this work presents a direct minimization method to design reduced order H∞ controllers in the controllable canonical form. An optimization problem is formulated to minimize the H∞ norm with an additional constraint to consider stability of the closed-loop system. The solution of the optimization problem is obtained using genetic algorithms, exploiting the advantage of this point of view in the solution of nonconvex problems. This formulation is verified in the active vibration control of a cantilever beam. A comparison of the proposed formulation with the formulation that uses linear matrix inequalities and the Augmented Lagrangian method is presented in this work and some numerical aspects of the problem are discussed. [ABSTRACT FROM AUTHOR]

Mechanical structures are modelled in general with a high number of degrees of freedom, in order to properly describe their behavior. H∞ techniques are commonly used to design vibration controllers, but for the usual techniques the controller order is the same of the plant model, rendering solutions difficult to implement and often infeasibility. One obvious way to deal with this problem is to design a fixed (reduced) order controller. But, due to the rank restriction, the reduced order controller design is a non-convex problem. A methodology to design a fixed order H∞ controller is presented here, and experimental results applied to a cantilever beam are reported. To overcome the difficulties, the Augmented Lagrangian method allows the problem to become a convex one, and linear matrix inequalities are used to solve it. Based on the Finite Element Method, a cantilever beam truncated model is used to design the controllers. It is known to be a very difficult task to assure stability and performance to vibration controllers, due to the several sources of modelling uncertainties. Adequately designed weighing filters are used to attain the necessary robustness of the controller, preventing the excitation of the uncertain modes and so avoiding the spillover phenomenon (Balas, 1990). Using the methodology, two types of H∞ controllers were designed, of complete and fixed order, and implemented over an experimental testbed. Simulated and experimental results are presented and compared. These results permit to conclude that the method achieves the objectives of reducing the vibration level, maintaining robust stability and performance, even for a first order controller. [ABSTRACT FROM AUTHOR]

Many practical problems in structural dynamics are modeled with a high number of degrees of freedom in order to properly describe the structure. A formulation to design robust controllers is the H8 technique where the controller has the same order of the mathematical model and this becomes unpractical and infeasible for most practical problems where the number of degrees of freedom is not small. One way to overcome this difficulty is to employ a model reduction technique, and design a reduced order controller based on this reduced model. In this case, it is required that the reduced controller ensures a good performance also for the nominal model (reduced) and for the real model (non-reduced) of the structure. Since the reduced controller is designed based on a truncated dynamic model, the non-modeled vibration modes can be excited causing the spillover phenomena, which is a severe undesirable effect. This work investigates the behavior of a reduced order controller obtained based on a reduced model through the Guyan reduction. The H8 robust control and Linear Matrix Inequalities (LMI) formulations are employed to the problem of controlling a flexible structure subjected to an external disturbance. Some simulations are performed using a cantilever beam modeled by the finite element method. The results show that the Guyan reduced order model can be used to design a controller to the non-reduced model with success. [ABSTRACT FROM AUTHOR]

Many practical problems in structural dynamics are modeled with a high number of degrees of freedom in order to properly describe the structure. A formulation to design robust controllers is the H_{&infty;} technique where the controller has the same order of the mathematical model and this becomes unpractical and infeasible for most practical problems where the number of degrees of freedom is not small. One way to overcome this difficulty is to employ a model reduction technique, and design a reduced order controller based on this reduced model. In this case, it is required that the reduced controller ensures a good performance also for the nominal model (reduced) and for the real model (non-reduced) of the structure. Since the reduced controller is designed based on a truncated dynamic model, the non-modeled vibration modes can be excited causing the spillover phenomena, which is a severe undesirable effect. This work investigates the behavior of a reduced order controller obtained based on a reduced model through the Guyan reduction. The H_{&infty;} robust control and Linear Matrix Inequalities (LMI) formulations are employed to the problem of controlling a flexible structure subjected to an external disturbance. Some simulations are performed using a cantilever beam modeled by the finite element method. The results show that the Guyan reduced order model can be used to design a controller to the non-reduced model with success. [ABSTRACT FROM AUTHOR]