Least squares based iterative identification algorithms for input nonlinear controlled autoregressive systems based on the auxiliary model

For the difficulty that the information vector in the identification model contains the unknown variables, we substitute these unknown variables with the outputs of the auxiliary model and then develop an auxiliary model based recursive least squares algorithm, an auxiliary model based least squares iterative (AM-LSI) algorithm, and derive an equivalent matrix decomposition based AM-LSI algorithm for input nonlinear controlled autoregressive systems based on the auxiliary model. The simulation results show that the proposed algorithms can estimate the parameters of a class of input nonlinear systems.     

Optimal input design for hydrodynamic derivatives estimation of nonlinear dynamic model of AUV

Input design has a dominant role in developing the dynamic model of an autonomous underwater vehicle (AUV) through system identification. Optimal input design is the process of generating informative inputs that can be used to provide a good-quality dynamic model of AUV. In this paper, amplitude-modulated pseudo-random binary signal (APRBS) inputs are optimally designed in order to estimate the hydrodynamic derivatives of an AUV’s nonlinear dynamic model. The input controls are designed so as to minimize uncertainty in estimating hydrodynamic derivatives. The employed approach can design multiple inputs and apply constraints on an AUV system’s inputs and outputs. The genetic algorithm is utilized to solve the constraint optimization problem. The presented algorithm is used for designing the input signals of Hydrolab300 AUV, and the estimation obtained by these inputs is compared with that of zigzag maneuver. According to the results, the designed APRBS inputs improve the uncertainties that exist in estimating hydrodynamic derivatives better than zigzag inputs.     

A dynamic model for a Timoshenko beam in an elastic-plastic state

Summary In this paper, equations which describe the propagation of elastic-plastic waves of combined stress resultants in a Timoshenko beam under symmetrical bending and tension or compression are derived. It is assumed that the beam material exhibits a weak work-hardening and strain-rate independent behaviour. The normality rule is postulate for the plastic strain rates of an infinitesimal volume element. The wave propagation is described by a hyperbolic system of differential equations, the state variables of which are the stress resultants and the strain rates of an infinitesimal beam element. It is shown that the actual distribution of shear stress in the plastic range need not be taken into account by means of a plastic shear correction factor.

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Ein dynamisches Modell für einen Timoshenko-Balken im elastisch-plastischen Zustand

Übersicht Diese Arbeit stellt ein Modell vor, das die Ausbreitung von elastischen und plastischen Wellen kombinierter Schnittgrößen in dynamisch hochbeanspruchten Balken unter gerader Biegung und Normalkraft beschreibt. Das Werkstoff-verhalten wird als schwach verfestigend und zeitunabhängig angenommen. Die Normalitätsregel wird für die plastischen Verzerrungsgeschwindigkeiten eines Volumenelements postuliert. Die Ausbreitung der Wellen wird durch ein hyperbolisches System von Differentialgleichungen beschrieben, dessen Zustandsvariablen die Schnittgrößen und die Formänderungs-geschwindigkeiten eines Balkenelements sind. Schließlich wird gezeigt, daß die Einführung eines plastischen Schubkorrekturfaktors nicht erforderlich ist.

     

Recursive least squares parameter estimation algorithm for dual-rate sampled-data nonlinear systems

This paper focuses on the identification problem of Hammerstein systems with dual-rate sampling. Using the key-term separation principle, we derive a regression identification model with different input updating and output sampling rates. To solve the identification problem of the dual-rate Hammerstein systems with the unmeasurable variables in the information vector, an auxiliary model-based recursive least squares algorithm is presented by replacing the unmeasurable variables with their corresponding recursive estimates. Convergence properties of the algorithm are analyzed. Simulation results show that the proposed algorithm can estimate the parameters of a class of nonlinear systems.     

Particle filtering for nonlinear dynamic state systems with unknown noise statistics

In this paper, we provide a tutorial for the applications of cost-reference particle filter (CRPF) to problems in signal processing disciplines. CRPF works in particle filtering (PF) framework although it may not be viewed as a Bayesian approach because the estimation is not based on the expected posterior function. CRPF has an interesting feature, i.e., the information of the noise statistics is not needed in its applications as opposed to the cases of the Kalman filter and standard PF approaches that work in dynamic state systems. Therefore, it is highly effective when the noise information is not available; nevertheless, it may not show optimal performance in general. In this paper, we introduce and disseminate this useful approach that is not known to many researchers even in related fields, and show how to effectively apply to problems which we provide as examples.     

部分输入未知条件下结构动力复合反演的分解算法

在充分利用部分输入已确知而部分输入未知的激励特性的基础上，提出了结构动力复合反演的分解算法，该算法从源头上消除了迭代过程中参数识别与荷载反演的相互影响，降低了问题的计算规模。对于线性参数系统，该算法不经过任何迭代计算即可一次性完成结构参数识别及荷载反演。将其与松弛法结合，可解决非线性参数系统的识别问题，与文献[4]的方法比较，其收敛速度有显著提高。     

求解非线性结构动力方程的预估校正-辛时间子域法

提出了求解非线性结构动力方程的预估校正-辛时间子域法。首先,将结构非线性动力方程转换为状态空间方程,在任一时间子域内利用改进的欧拉法对各离散时刻的状态变量值进行预估和校正。然后,将离散的非线性项用Lagrange插值多项式展开并视为外荷载,结合辛时间子域法即可求解非线性动力系统的响应。这种方法不必对状态矩阵求逆,无需计算高阶导数,计算简单,格式统一,易于编程。算例结果表明,本文方法具有较高的计算精度、效率和稳定性,是一种求解非线性结构动力方程的有效方法。     

An adaptive quenching algorithm for a nonlinear single-degree-of-freedom system

An indirect adaptive quenching algorithm for a nonlinear single-degree-of-freedom system with unknown constant system parameters is presented. The system is subject to external or parametric sinusoidal disturbances and the resulting control signal is also sinusoidal. The quenching algorithm provides a reduction in the control effort required compared to direct disturbance cancellation. The disturbance sinusoid and the unknown parameters are incorporated into the system model and an extended Kalman filter (EKF) with modified update equations is used to estimate the system state and parameters. The estimates are then used to form the quenching signal. The adaptive quenching algorithm is found to work well inside a quenching region defined by the separatrices and suggests the use of a hybrid control law. The algorithm was verified by implementing it on an analog computer.     

A new technique to generate independent periodic attractors in the state space of nonlinear dynamic systems

This paper proposes a method to generate several independent periodic attractors, in continuous-time nonchaotic systems (with an equilibrium point or a limit cycle), based on a switching piecewise-constant controller. We demonstrate here that the state space equidistant repartition of these attractors is on a precise zone of a precise curve that depends on the parameters of the system. We determine the state space domains where the attractors are generated from different initial conditions. A mathematical formula giving their maximal number in function of the controller piecewise-constant values is then deduced. Throughout this study, the proposed methodology is illustrated with several examples.     

Estimation of Lyapunov exponents from a time series for n-dimensional state space using nonlinear mapping

It has been demonstrated that when estimating Lyapunov exponents using a time series, nonlinear mapping used for characterizing the evolution of the neighbors leads to more accurate negative exponents and is more robust to noise in the times series. However, the number of unknown elements of the matrices associated with nonlinear mapping increases significantly with the embedding dimensions of the state space where the dynamics is reconstructed. Such unknown coefficients are solved from a set of linear algebraic equations based on the least square-root fit method. Derivation of such linear equations and computer programming are tedious and error prone especially for the systems with high embedding dimensions. In this work, we develop a general form of the linear algebraic equations and the corresponding computer program in terms of arbitrary embedding dimensions. A stable robotic system with all negative Lyapunov exponents and the Lorenz system with positive, zero, and negative exponents are used to demonstrate the efficacy of the proposed method. The work can contribute significantly to estimating Lyapunov exponents for systems with large embedding dimensions.     

Gradient-based parameter estimation for input nonlinear systems with ARMA noises based on the auxiliary model

This paper presents a gradient-based iterative identification algorithm and an auxiliary-model-based multi-innovation generalized extended stochastic gradient algorithm for input nonlinear systems with autoregressive moving average (ARMA) noises, i.e., the input nonlinear Box–Jenkins (IN–BJ) systems. The estimation errors given by the gradient-based iterative algorithm are smaller than the generalized extended stochastic gradient algorithm under same data lengths. A simulation example is provided.     

Complex dynamic behavior in a viral model with state feedback control strategies

With the consideration of mechanism of prevention and control for the spread of viral diseases, in this paper, we propose two novel virus dynamics models where state feedback control strategies are introduced. The first model incorporates the density of infected cells (or free virus) as control threshold value; we analytically show the existence and orbit stability of positive periodic solution. Theoretical results imply that the density of infected cells (or free virus) can be controlled within an adequate level. The other model determines the control strategies by monitoring the density of uninfected cells when it reaches a risk threshold value. We analytically prove the existence and orbit stability of semi-trivial periodic solution, which show that the viral disease dies out. Numerical simulations are carried out to illustrate the main results.     

非线性动力有限元重叠区域分裂的隐式并行算法

针对大规模结构非线性瞬态动力分析非常耗时,提出了相应的并行算法。该算法采用无条件稳定的Ne-wmark-β方法(平均加速技术)进行时间积分,并结合区域分裂技术进行分析。它不同于已有的采用非重叠区域的并行算法,而是采用重叠区域的并行算法。对给定结构有限元分析的质量、阻尼、刚度矩阵进行分裂可推出重叠区域分裂算法的计算公式。为改善每一步的求解,采用预估和校正子方案。编写了该算法的程序,在工作站机群上实现了数值算例,验证了算法的性能。计算结果表明该算法优于非重叠区域分裂算法。     

Mem-models and state event location algorithm for a prototypical aerospace system

Nonlinear Dynamics - This paper presents a suite of dynamic models enhanced by mem-models. A prototypical pogo stick model might mimic a space hopper or a segmented telescope mirror actuator, while...