This paper is concerned with the problem of identifying and controlling flexible structures. The structures used exhibit some of the characteristics found in large flexible space structures (LFSSs). Identifying LFSS are problematic in the sense that the modes are of low frequency, lightly damped, and often closely spaced. The proposed identification algorithm utilizes modal contribution coefficients to monitor the data collection. The algorithm is composed of a two-step process, where the input signal for the second step is recomputed based on knowledge gained about the system to be identified. In addition, two different intelligent robust controllers are proposed. In the first controller, optimization is concerned with performance criteria such as rise time, overshoot, control energy, and a robustness measure among others. Optimization is achieved by using an elitism based genetic algorithm (GA). The second controller uses a nested GA resulting in an intelligent linear quadratic regulator/linear quadratic Gaussian (LQR/LQG) controller design. The GAs in this controller are used to find the minimum distance to uncontrollability of a given system and to maximize that minimum distance by finding the optimal coefficients in the weighting matrices of the LQR/LQG controller. The proposed algorithms and controllers are tested numerically and experimentally on a model structure. The results show the effectiveness of the proposed two-step identification algorithm as well as the utilization of GAs applied to the problem of designing optimal robust controllers.
In this paper, the passivity-based boundary controller for the vibration suppression of the flexible beam is studied. The undamped shear beam model is used as a beam model. The beam is a parameter-distributed system represented by partial differential equations (PDEs). This technique uses the energy principle for control design by exploiting the passivity property of the beam. The storage or energy function of the beam is first introduced and then used to determine the passivity-based controller. The passivity of the system is proven using direct integration. The feedback system is proven in the sense of finite-gain [Formula: see text] stability. The proposed controller consists of the damping and elastic components applied at the beam end so that the domain (body) of the beam is not disturbed. The beam PDEs are treated directly without model reduction or truncation, so the control spillover problem is avoidable. The beam model PDEs are solved numerically by using the finite difference method. Numerical simulation results of the beam under control are presented to verify the performance of the control scheme.
A lightweight amphibious aircraft hybrid composite wing was designed and optimized in this study. The Ansys Composite PrepPost and Ansys Mechanical Module use finite element modeling to simulate and assess the static structural test. It is possible to build a lightweight and cost-effective composite wing by balancing the amount and orientation of carbon fiber and glass fiber ply patterns. The BII2 wing design case (spar/rib/skin : is the best option of 72 case studies, with a total weight of 45.46 kg and a manufacturing cost of 1,288 USD. The optimal design composite wing mock-up was built and tested on a universal test rig. The test demonstrated that the optimal wing design could withstand the maximum load (+6G and -3G) without structural collapse. The experimental structural deformation and elastic strain were consistent with the FEM model, within an acceptable error range.
The results of real world application of system identification (SI) algorithms are always affected by the process and measurement noise associated with the system to be identified and the measurement set-up. A small signal to noise ratio generally results into poor and sometimes unusable dynamical models. Missing system dynamics and characteristics in the inferred system model may cause undesirable and sometimes harmful control laws. Robust control is one avenue to avoid causing undesirable system performance due to the large model uncertainties. In this paper, an alternative approach is presented. In particular, a proof is given that no dynamic controller can reduce the noise influence in linear system identification. Eigensystem Realization Algorithms (ERA) used commonly in the Observer Kalman Filter Identification (OKID) algorithm allows for the discrimination between noise and system modes based on the magnitude of the singular values. Having a large noise content embedded in the input/output data is reflected by large singular values for the noise and system modes. This leaves them indistinguishable from each other. Hence the number and exact selection of the system modes using the traditional Eigensystem realization and the DC realization algorithm is impracticable. A new selection scheme is proposed for the Eigensystem realization portion of the OKID algorithm. The selection is done using a modified Genetic Algorithm (GA). The GA uses a cost function based on the step response, which is addition data to the random data collected from the experiment. The GA proposed adapts the probability density function for the selection scheme of the mating chromosomes based on the approximated cost gradient. Simulation results of the proposed algorithm in comparison with the traditional used method are presented. The results indicate an improved ability to extract system models from very noise data.
This paper presents an efficient implementation of constraint optimum input design for on-line system identification for systems exhibiting actuator saturation. The constraint optimal input is calculated recursively based on the imminent available information content in the inverse correlation matrix of the data. The new input is computed one step ahead of time with a predictive filter so that it will increase the information content in the inverse correlation matrix. The information content is maximized with help of a simple genetic algorithm. A numerical example indicates superiority of the proposed method over the traditional method where white gaussian noise is used as the input.
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