Recently the synchronization control for chaotic systems with unknown parameters has attracted great attention among the researchers and diverse synchronization schemes have been reported in the literature. In this review article, we carefully revisit several recent articles published from 2010 to the present and find that several reported schemes are problematic. The imperfect synchronization schemes are categorized into five cases according to their defect types. By providing a general theorem for the adaptive synchronization design, we further present modified schemes to correct the defects in these articles. In addition, we have emphasized the significant linear independence condition for ensuring successful identification, as this condition has been neglected in several previous articles. We also summarize three cases when this condition is not valid, and accordingly four approaches are proposed to guarantee the successful parameter estimation for uncertain chaotic systems. 相似文献
Fixed-interval smoothing, as one of the most important types of state estimation, has been concerned in many practical problems especially in the analysis of flight test data. However, the existing sequential filters and smoothers usually cannot deal with nonlinear or high-dimensional systems well. A state-of-the-art technique is employed in this study to explore the fixed-interval smoothing problem of a conceptual two-dimensional airfoil model in incompressible flow from noisy measurement data. Therein, the governing equations of the airfoil model are assumed to be known or only partially known. A single objective optimization problem is constructed with the classical Runge–Kutta scheme, and then estimations of the system states, the measurement noise and even the unknown parameters are obtained simultaneously through minimizing the objective function. Effectiveness and feasibility of the method are examined under several simulated measurement data corrupted by different measurement noises. All the obtained results indicate that the introduced algorithm is applicable for the airfoil model with cubic or free-play structural nonlinearity and leads to accurate state and parameter estimations. Besides, it is highly robust to Gaussian white and even more complex heavy-tailed measurement noises. It should be emphasized that the employed algorithm is still effective to high-dimensional nonlinear aeroelastic systems.
This paper presents an algorithm to compute the aerodynamic forces and moments of an aeroelastic wing undergoing large amplitude heave and pitch limit cycle oscillations. The technique is based on inverting the equations of motion to solve for the lift and moment experienced by the wing. Bayesian inferencing is used to estimate the structural parameters of the system and generate credible intervals on the lift and moment calculations. The inversion technique is applied to study the affect of mass coupling on limit cycle oscillation amplitude. Examining the force, power, and energy of the system, the reasons for amplitude growth with wind speed can be determined. The results demonstrate that the influence of mass coupling on the pitch–heave difference is the driving factor in amplitude variation. The pitch–heave phase difference not only controls how much aerodynamic energy is transferred into the system but also how the aerodynamic energy is distributed between the degrees of freedom. 相似文献