An off-line NMPC strategy for continuous-time nonlinear systems using an extended modal series method

This paper presents a new off-line nonlinear model predictive control (NMPC) approach for continuous-time affine-input nonlinear systems. In this approach, the NMPC-related nonlinear two-point boundary value problem derived from the Pontryagin’s maximum principle is solved by the extended modal series method. The resulting suboptimal control law explicitly depends on the initial conditions and is updated by replacing the initial conditions with the new state measurements in future sampling instants. Therefore, there is no need to repeat the recursive online optimization process in each sampling instant. Since the applicability of NMPC is generally restricted by computational burden of the online optimization, we propose an NMPC scheme, which not only reduces the online computational burden significantly, but also can be applied to fast dynamic systems with short prediction horizons. An efficient algorithm is presented which approximates the order of the modal series such that feasibility of the optimization problem is guaranteed. Closed-loop stability of the proposed NMPC approach is shown using the off-line terminal region calculations suggested in quasi-infinite horizon NMPC scheme. The applicability and effectiveness of the proposed approach are illustrated by two numerical examples.     

Construction of optimal laws of variation of the angular momentum vector of a rigid body

We consider the problem of constructing optimal preset laws of variation of the angular momentum vector of a rigid body taking the body from an arbitrary initial angular position to the required terminal angular position in a given time. We minimize an integral quadratic performance functional whose integrand is a weighted sum of squared projections of the angular momentum vector of the rigid body. We use the Pontryagin maximum principle to derive necessary optimality conditions. In the case of a spherically symmetric rigid body, the problem has a well-known analytic solution. In the case where the body has a dynamic symmetry axis, the obtained boundary value optimization problem is reduced to a system of two nonlinear algebraic equations. For a rigid body with an arbitrarymass distribution, optimal control laws are obtained in the form of elliptic functions. We discuss the laws of controlled motion and applications of the constructed preset laws in systems of attitude control by external control torques or rotating flywheels.     

STUDY ON THE PREDICTION METHOD OF LOW_DIMENSION TIME SERIES THAT ARISE FROM THE INTRINSIC NONLINEAR DYNAMIC

ＩｎｔｒｏｄｕｃｔｉｏｎＬｏｔｓｏｆｔｉｍｅｓｅｒｉｅｓｆｒｏｍｐｒａｃｔｉｃａｌｐｒｏｂｌｅｍｓｂｅｌｏｎｇｔｏｎｏｎｌｉｎｅａｒｃｈａｏｔｉｃｔｉｍｅｓｅｒｉｅｓ.Ｉｔｈａｓｂｅｅｎｐｒｏｖｅｄｉｎｐｒａｃｔｉｃｅｔｈａｔｔｈｅｌｉｎｅａｒｍｏｄｅｌｓｏｆｅｉｔｈｅｒｌｏｗｏｒｄｅｒｓｏｒｈｉｇｈｏｒｄｅｒｓｃａｎｎｏｔｂｅｕｓｅｄｔｏｄｅｓｃｒｉｂｅｎｏｎｌｉｎｅａｒｃｈａｏｔｉｃｔｉｍｅｓｅｒｉｅｓ.Ｈｅｎｃｅｉｔｉｓｖｅｒｙｉｍｐｏｒｔａｎｔｔｏｉｎｖｅｓｔｉｇａｔｅｃｈａｏｔｉｃｔｉｍ…     

High order multiplication perturbation method for singular perturbation problems

This paper presents a high order multiplication perturbation method for sin- gularly perturbed two-point boundary value problems with the boundary layer at one end. By the theory of singular perturbations, the singularly perturbed two-point boundary value problems are first transformed into the singularly perturbed initial value problems. With the variable coefficient dimensional expanding, the non-homogeneous ordinary dif- ferential equations （ODEs） are transformed into the homogeneous ODEs, which are then solved by the high order multiplication perturbation method. Some linear and nonlinear numerical examples show that the proposed method has high precision.     

Study on the Prediction Method of Low-Dimension Time Series that Arise from the Intrinsic Nonlinear Dynamics

The prediction methods and its applications of the nonlinear dynamic systems determined from chaotic time series of low-dimension are discussed mainly. Based on the work of the foreign researchers, the chaotic time series in the phase space adopting one kind of nonlinear chaotic model were reconstructed. At first, the model parameters were estimated by using the improved least square method. Then as the precision was satisfied, the optimization method was used to estimate these parameters. At the end by using the obtained chaotic model, the future data of the chaotic time series in the phase space was predicted. Some representative experimental examples were analyzed to testify the models and the algorithms developed in this paper. The results show that if the algorithms developed here are adopted, the parameters of the corresponding chaotic model will be easily calculated well and true. Predictions of chaotic series in phase space make the traditional methods change from outer iteration to interpolations. And if the optimal model rank is chosen, the prediction precision will increase notably. Long term superior predictability of nonlinear chaotic models is proved to be irrational and unreasonable. Paper from Chen Yu-shu, Member of Editorial of Committee, AMM Foundation item: the National Natural Science Foundation of China (19990510); the National Key Basic Research Special Fund(G1998020316) Biography: Ma Jun-hai(1965-), Professor, Doctor     

基于WOA-BP神经网络的超低温冻土抗压强度预测模型研究

为获得超低温冻土抗压强度预测模型, 探究超低温状态下冻土的物理性质及力学性质的变化, 对含水率19%, 22%, 25%和28%的低液限黏土土样进行?180 °C ~ ?10 °C的单轴压缩强度试验, 并测量?80 °C ~ ?10 °C土样的未冻水含量, 建立基于WOA-BP神经网络和BP神经网络的预测模型, 探究含水率、温度、未冻水含量与超低温冻土抗压强度关系. 预测结果表明: 含水率、温度、未冻水含量与超低温冻土抗压强度存在复杂的非线性关系, 特别是在?180 °C ~ ?80 °C区间内, 现有的线性拟合公式已无法准确预测该区间内冻土抗压强度; 基于WOA-BP神经网络预测模型的整体预测效果较好, 其绝对误差平均值为1.167 MPa, 相对误差平均值为7.62%, BP神经网络预测模型的绝对误差平均值为8.462 MPa, 相对误差平均值为47.99%. 基于鲸鱼优化算法的BP神经网络预测模型预测误差明显小于BP神经网络预测模型及线性拟合值, 更接近实测值. 该预测模型具有较高精确度, 能有效解决超低温冻土抗压强度与其影响因素间复杂的非线性关系, 可为人工冻结技术在地层应急工程中的应用提供参考.      

Algebraic growth in a Blasius boundary layer: Nonlinear optimal disturbances

The three-dimensional, algebraically growing instability of a Blasius boundary layer is studied in the nonlinear regime, employing a nonparallel model based on boundary layer scalings. Adjoint-based optimization is used to determine the “optimal” steady leading-edge excitation that provides the maximum energy growth for a given initial energy. Like in the linear case, the largest transient growth is found for inlet streamwise vortices, that yield streamwise streaks downstream. Two different definitions of growth are employed, providing qualitatively similar results, although the spanwise wavenumbers of optimal growth differ by up to 20% in the two cases. The wavelength of the most amplified optimal disturbance increases with the initial amplitude. For large input amplitudes, significant deformations of the mean velocity field are found; in such cases it is reasonable to expect that nonlinear streaks may break down through a secondary instability.     

The Validity Range of Low Fidelity Structural Membrane Models

Three classes of structural models can be considered for the characterization of thin extensible pressurized membranes. A low-fidelity, linear model assumes that all of the membrane’s resistance to a transverse pressure is provided from geometric stress-stiffening, and that movement along the membrane is purely out-of-plane. A medium-fidelity model can include geometric nonlinearity (finite strains, non-conservative pressure loads); while the highest level of membrane modeling assumes material nonlinearity: specifically, hyperelasticity. Common modeling applications such as performance prediction, failure prevention, and structural optimization all require repeated function evaluations. As computational cost is proportional to model fidelity, what is the validity range of the two lower fidelity models discussed above? The presence of a large pre-tension within the membrane is known to lessen the role of nonlinear elastic effects: the range of linear transverse deformation increases with pre-tension. Conversely, very low pre-tensions require the use of a nonlinear model, as the linear model becomes unbounded. This work studies the Hencky–Campbell problem for model validation purposes: hydrostatic inflation of a thin flat circular membrane, clamped along its boundary, with an arbitrary initial tension. A full-field, non-contact visual image correlation system is used to estimate the material properties of the rubber membrane, measure the state of pre-tension in the circular sheet, and document the displacement and strain fields as a function of applied pressure. The resulting data set is then compared directly with numerical simulations, in order to estimate the location of the surface of data points wherein a particular low fidelity model (either linear or geometrically nonlinear) loses its predictive capability.     

A method for the selection of sensor and actuator locations

A new and efficient technique for determining optimal locations of sensors and actuators of intelligent structures is presented. The optimization of sensor and actuator locations is based on the 1st order singular value perturbations of observability and controllability. Using this method the optimal placements of sensors and actuators of the intelligent structurer can be selected. Two numerical examples are given to demonstrate the applications of the method. The impulse responses of structures due to different locations of actuators with the same control law are analyzed in detail. The project supported by the National Natural Science Foundation of China and the Mechanical Technique Development Foundation of China     

Two novel nonlinear multivariate grey models with kernel learning for small-sample time series prediction

For many applications, small-sample time series prediction based on grey forecasting models has become indispensable. Many algorithms have been developed recently to make them effective. Each of these methods has a specialized application depending on the properties of the time series that need to be inferred. In order to develop a generalized nonlinear multivariable grey model with higher compatibility and generalization performance, we realize the nonlinearization of traditional GM(1,N), and we call it NGM(1,N). The unidentified nonlinear function that maps the data into a better representational space is present in both the NGM(1,N) and its response function. The original optimization problem with linear equality constraints is established in terms of parameter estimation for the NGM(1,N), and two different approaches are taken to solve it. The former is the Lagrange multiplier method, which converts the optimization problem into a linear system to be solved; and the latter is the standard dualization method utilizing Lagrange multipliers, that uses a flexible estimation equation for the development coefficient. As the size of the training data increases, the estimation results of the potential development coefficient get richer and the final estimation results using the average value are more reliable. The kernel function expresses the dot product of two unidentified nonlinear functions during the solving process, greatly lowering the computational complexity of nonlinear functions. Three numerical examples show that the LDNGM(1,N) outperforms the other multivariate grey models compared in terms of generalization performance. The duality theory and framework with kernel learning are instructive for further research around multivariate grey models to follow.

     

A nonlinear subspace-prediction error method for identification of nonlinear vibrating structures

The industrial structural systems always contain various kinds of nonlinear factors. Recently, a number of new approaches have been proposed to identify those nonlinear structures. One of the promising methods is the nonlinear subspace identification method (NSIM). The NSIM is derived from the principals of the stochastic subspace identification method (SSIM) and the internal feedback formulation. First, the nonlinearities in the system are regarded as internal feedback forces to its underlying linear dynamic system. The linear and nonlinear components of the identified system can be decoupled. Second, the SSIM is employed to identify the nonlinear coefficients and the frequency response functions of the underlying linear system. A typical SSIM always consists of two steps. The first step makes a projection of certain subspaces generated from the data to identify the extended observability matrix. The second one is to estimate the system matrices from the identified observability matrix. Since the calculated process of the NSIM is non-iterative and this method poses no additional problems on the part of parameterization, the NSIM becomes a promising approach to identify nonlinear structural systems. However, the result generated by the NSIM has its deficiency. One of the drawbacks is that the identified results calculated by the NSIM are not the optimal solutions which reduce the identified accuracy. In this study, a new time-domain subspace method, namely the nonlinear subspace-prediction error method (NSPEM), is proposed to improve the identified accuracy of nonlinear systems. In the improved version of the NSIM, the prediction error method (PEM) is used to reestimate those estimated coefficient matrices of the state-space model after the application of NSIM. With the help of the PEM, the identified results obtained by the NSPEM can truly become the optimal solution in the least square sense. Two numerical examples with local nonlinearities are provided to illustrate the effectiveness and accuracy of the proposed algorithm, showing advantages with respect to the NSIM in a noise environment.     

求解非线性方程组的混沌粒子群算法及应用

针对非线性方程组的求解在工程上具有广泛的实际意义,经典的数值算法如牛顿法存在其收敛性依赖于初值而实际计算中初值难确定的问题,提出以混沌粒子群算法求解非线性方程。它通过将混沌搜索机制有机地引入粒子群算法,使每个粒子从混沌搜索机制与粒子群算法搜索机制中获得适当的搜索方向,以混沌变量的遍历性增强粒子的搜索性能与更全面地应用目标函数的信息,并反映到逐代更新的个体极值和群体极值中,可更有效地调整粒子的移向并最终获得最优解。测试结果表明这一尝试的有效性。最后将所提的方法用于建立复合材料结构的疲劳寿命与应力、温度、湿度的关系模型。     

The brachistochronic motion of a wheeled vehicle

The paper considers the brachistochronic motion of a wheeled vehicle on a horizontal plane surface. The objective is to transfer the vehicle from the specified initial position with given initial kinetic energy to the specified terminal position in minimum time with conserved total mechanical energy of the vehicle. The problem is solved by applying Pontryagin’s maximum principle and singular optimal control theory. The projection of the reaction force of the horizontal plane applied on the front vehicle wheels onto the axis of the front vehicle axle is taken for a control variable. The cases of unbounded and bounded value of this projection are considered. The shooting method is used to solve the two-point boundary value problem arising from Pontryagin’s maximum principle and singular optimal control theory.     

Nonlinear dynamical analysis of noisy time series

Empirical time series in the life sciences are often nonstationary and have small signal-to-noise ratios, making it difficult to accurately detect and characterize dynamical structure. The usual response to high noise is averaging, but time domain averaging is inappropriate, especially when the dynamics are nonlinear. We review alternative delay-space averaging methods based on the topology and short-term predictability of nonlinear dynamics and illustrate their application using the TISEAN software (Hegger, Kantz & Schreiber, 1999). The methods were applied to a Lorenz series, which resembles the dynamics found by Kelly, Heathcote, Heath and Longstaff (2001) in series of decision times. The Lorenz series was corrupted with up to 80% additive Gaussian noise, a lower signal-to-noise ratio than has been used in any previous test of these methods, but consistent with Kelly et al.'s data. Prediction methods performed the best for detecting nonstationarity and nonlinear dynamics, and optimal predictability provided an objective criterion for setting the parameters required by the analyses. Local linear filtering methods performed best for characterization, producing informative plots that revealed the nature of the underlying dynamics. These results suggest that a methodology based on delay-space averaging and prediction could be useful with noisy empirical data series.     

Adaptive nonlinear model predictive control design of a flexible-link manipulator with uncertain parameters

This paper presents a novel adaptive nonlinear model predictive control design for trajectory tracking of flexible-link manipulators consisting of feedback lineariza-tion, linear model predictive control, and unscented Kalman filtering. Reducing the nonlinear system to a linear system by feedback linearization simplifies the optimization prob-lem of the model predictive controller significantly, which, however, is no longer linear in the presence of parame-ter uncertainties and can potentially lead to an undesired dynamical behaviour. An unscented Kalman filter is used to approximate the dynamics of the prediction model by an online parameter estimation, which leads to an adaptation of the optimization problem in each time step and thus to a better prediction and an improved input action. Finally, a detailed fuzzy-arithmetic analysis is performed in order to quantify the effect of the uncertainties on the control structure and to derive robustness assessments. The control structure is applied to a serial manipulator with two flexible links containing uncertain model parameters and acting in three-dimensional space.     

New way to construct high order Hamiltonian variational integrators

This paper develops a new approach to construct variational integrators. A simplified unconventional Hamilton’s variational principle corresponding to initial value problems is proposed, which is convenient for applications. The displacement and momentum are approximated with the same Lagrange interpolation. After the numerical integration and variational operation, the original problems are expressed as algebraic equations with the displacement and momentum at the interpolation points as unknown variables. Some particular variational integrators are derived. An optimal scheme of choosing initial values for the Newton-Raphson method is presented for the nonlinear dynamic system. In addition, specific examples show that the proposed integrators are symplectic when the interpolation point coincides with the numerical integration point, and both are Gaussian quadrature points. Meanwhile, compared with the same order symplectic Runge-Kutta methods, although the accuracy of the two methods is almost the same, the proposed integrators are much simpler and less computationally expensive.     

CHAOS-REGULARIZATION HYBRID ALGORITHM FOR NONLINEAR TWO-DIMENSIONAL INVERSE HEAT CONDUCTION PROBLEM

ＩｎｔｒｏｄｕｃｔｉｏｎＡｓａｋｉｎｄｏｆｉｍｐｏｒｔａｎｔｔｈｅｒｍａｌｃｈａｒａｃｔｅｒｉｓｔｉｃｓｏｆｔｈｅｍａｔｅｒｉａｌ,ｔｈｅｒｍａｌｃｏｎｄｕｃｔｉｖｉｔｙｍｕｓｔｂｅｄｅｔｅｒｍｉｎｅｄｔｏｍａｋｅｑｕａｎｔｉｆｉｃａｔｉｏｎａｌａｎａｌｙｓｉｓｏｆｔｅｍｐｅｒａｔｕｒｅｆｉｅｌｄ .Ｉｔｈａｓｂｅｅｎｔａｋｅｎｄｕｅａｔｔｅｎｔｉｏｎｔｏｅｓｔｉｍａｔｅｔｈｅｔｈｅｒｍａｌｃｏｎｄｕｃｔｉｖｉｔｙｆｒｏｍｉｎｎｅｒａｎｄ/ｏｒｂｏｕｎｄａｒｙｔｅｍｐｅｒａｔｕｒｅｍｅａｓｕｒｅｍｅ…     

A novel numerical algorithm based on Galerkin–Petrov time-discretization method for solving chaotic nonlinear dynamical systems

Nonlinear chaotic systems yield many interesting features related to different physical phenomena and practical applications. These systems are very sensitive to initial conditions at each time-iteration level in a numerical algorithm. In this article, we study the behavior of some nonlinear chaotic systems by a new numerical approach based on the concept of Galerkin–Petrov time-discretization formulation. Computational algorithms are derived to calculate dynamical behavior of nonlinear chaotic systems. Dynamical systems representing weather prediction model and finance model are chosen as test cases for simulation using the derived algorithms. The obtained results are compared with classical RK-4 and RK-5 methods, and an excellent agreement is achieved. The accuracy and convergence of the method are shown by comparing numerically computed results with the exact solution for two test problems derived from another nonlinear dynamical system in two-dimensional space. It is shown that the derived numerical algorithms have a great potential in dealing with the solution of nonlinear chaotic systems and thus can be utilized to delineate different features and characteristics of their solutions.     

Discrete formulation of mixed finite element methods for vapor deposition chemical reaction equations

The vapor deposition chemical reaction processes, which are of extremely extensive applications, can be classified as a mathematical model by the following governing nonlinear partial differential equations containing velocity vector, temperature field, pressure field, and gas mass field. The mixed finite element (MFE) method is employed to study the system of equations for the vapor deposition chemical reaction processes. The semidiscrete and fully discrete MFE formulations are derived. And the existence and convergence (error estimate) of the semidiscrete and fully discrete MFE solutions are demonstrated. By employing MFE method to treat the system of equations for the vapor deposition chemical reaction processes, the numerical solutions of the velocity vector, the temperature field, the pressure field, and the gas mass field can be found out simultaneously. Thus, these researches are not only of important theoretical means, but also of extremely extensive applied vistas.     

Epsilon-continuation approach for truss topology optimization

In the present paper, a so-called epsilon-continuation approach is proposed for the solution of singular optimum in truss topology optimization problems. This approach is an improved version of the epsilon-relaxed approach developed by the authors previously. In the proposed approach, we start the optimization process from a relaxation parameter with a relatively large value and obtain a solution by applying the epsilon-relaxed approach. Then we decrease the value of the relaxation parameter by a small amount and choose the optimal solution found from the previous optimization process as the initial design for the next optimization. This continuation process is continued until a small termination value of the relaxation parameter is reached. Convergence analysis of the proposed approach is also presented. Numerical examples show that this approach can alleviate the dependence of the final solution on the initial choice of the design variable and enhance the probability of finding the singular optimum from rather arbitrary initial designs. The project supported by the National Natural Science Foundation of China (10102003, 10032010 and 10032030)