Biased compensation recursive least squares-based threshold algorithm for time-delay rational models via redundant rule

This paper develops a biased compensation recursive least squares-based threshold algorithm for a time-delay rational model. The time-delay rational model is transformed into an augmented model by using the redundant rule, and then, a recursive least squares algorithm is proposed to estimate the parameters of the augmented model. Since the output of the augmented model is correlated with the noise, a biased compensation method is derived to eliminate the bias of the parameter estimates. Furthermore, based on the structures of the augmented model parameter vector and the rational model parameter vector, the unknown time delay can be computed by using a threshold given in prior. A simulated example is used to illustrate the efficiency of the proposed algorithm.     

Identification for disturbed MIMO Wiener systems

The identification of Multi-input Multi-output (MIMO) Wiener systems is concerned in this paper. The system presented is comprised of a multi-dimensional linear subsystem and a memory-less nonlinear block which is made of discontinuous asymmetric piece-wise linear functions. A recursive algorithm is proposed to estimate all the unknown parameters of the system with interference noises. It is shown that the recursive algorithm for the disturbed MIMO Wiener system is convergent. Finally, some simulation results illustrate the identification accuracy and the convergence rate.     

柔性关节-柔性臂空间机器人的神经网络自适应反演控制及双重柔性振动抑制

空间机器人系统的柔性主要体现在空间机器人的臂杆和连接各臂杆之间的铰关节。由于空间机器人系统结构的复杂性,以往研究人员对同时具有柔性关节和柔性臂的系统关注不够。为此探讨了参数未知柔性关节-柔性臂空间机器人系统的动力学模拟、轨迹跟踪控制算法设计和关节、臂杆双重柔性振动的主动抑制问题。首先,采用多体动力学建模方法并结合漂浮基空间机器人固有的线动量和角动量守恒动力学特性,推导了系统的动力学方程。以此为基础,考虑到空间机器人实际应用中各关节铰具有较强柔性的情况,引入一种关节柔性补偿控制器解决了传统奇异摄动法应用受关节柔性限制问题,导出了适用于控制系统算法设计的数学模型。然后,利用该模型,基于反演思想在慢时标子系统中设计神经网络自适应控制算法来补偿系统参数未知和柔性关节引起的转动误差,实现系统运动轨迹跟踪性能;针对快时标子系统,设计了鲁棒最优控制算法抑制因柔性关节及柔性臂引起的系统双重弹性振动,保证系统的稳定性。最后,通过仿真对比实验验证了所设计控制算法的有效性。     

Filtering based recursive least squares algorithm for Hammerstein FIR-MA systems

We consider the parameter estimation problem for Hammerstein finite impulse response (FIR) systems. An estimated noise transfer function is used to filter the input–output data of the Hammerstein system. By combining the key-term separation principle and the filtering theory, a recursive least squares algorithm and a filtering-based recursive least squares algorithm are presented. The proposed filtering-based recursive least squares algorithm can estimate the noise and system models. The given examples confirm that the proposed algorithm can generate more accurate parameter estimates and has a higher computational efficiency than the recursive least squares algorithm.     

Estimation of the relaxation spectrum from dynamic experiments using Bayesian analysis and a new regularization constraint

The relaxation spectrum is estimated from dynamic experiments using Bayesian analysis and a new regularization constraint. In the Bayesian framework, a probability can be calculated for each estimate of the spectrum. This offers several advantages; (1) an optimal estimate of the relaxation spectrum may be calculated as the mean of a large number of estimates, and (2) reliable errors for the optimal estimate can be provided using the deviation of all estimates from the mean. Furthermore, the Bayesian approach (3) gives an estimate of the overall noise level of the experiment, which is usually an important but unknown parameter for the calculation of relaxation spectra from dynamic experiments by indirect methods (determining the regularization parameter), and finally, (4) the information content in a given set of experimental data can be quantified. The validity of the Bayesian approach is demonstrated using simulated data.     

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。     

基于新息自适应卡尔曼滤波算法的多类型结构响应重构

为改善传统卡尔曼滤波KF(Kalman filter)算法在过程噪声方差和测量噪声方差未知的情况下响应重构精度降低甚至发散的问题，提出了一种基于新息自适应卡尔曼滤波IAKF(innovation-based adaptive Kalman filter)算法的多类型响应重构方法。首先根据新息统计特性对卡尔曼滤波增益和状态估计误差协方差矩阵进行实时自适应调整；然后利用有限测点的加速度传感器的测量数据，结合模态法对结构各个位置的加速度、速度、位移以及应变进行响应重构；最后对起重机桁架和简支梁分别进行数值模拟和试验分析。结果表明，该方法能够有效地调整过程噪声方差并估计测量噪声方差，未测点的重构响应时程曲线与计算响应或测量响应时程曲线吻合良好。     

Robust adaptive dynamic surface control design for a flexible air-breathing hypersonic vehicle with input constraints and uncertainty

The flight control problem of a flexible air-breathing hypersonic vehicle is presented in the presence of input constraint and aerodynamic uncertainty. A control-oriented model, where aerodynamic uncertainty and the strong couplings between the engine and flight dynamics are included, is derived to reduce the complexity of controller design. The flexible dynamics are viewed as perturbations of the model. They are not taken into consideration at the level of control design, the influence of which is evaluated through simulation. The control-oriented model is decomposed into velocity subsystem and altitude subsystem, which are controlled separately. Then robust adaptive controller is developed for the velocity subsystem, while the controller which combines dynamic surface control and radial basis function neural network is designed for the altitude subsystem. The unknown nonlinear function is approximated by the radial basis function neural network. Minimal-learning parameter technique is utilized to estimate the maximum norm of ideal weight vectors instead of their elements to reduce the computational burden. To handle input constraints, additional systems are constructed to analyze their impact, and the states of the additional systems are employed at the level of control design and stability analysis. Besides, “explosion of terms” problem in the traditional backstepping control is circumvented using a first-order filter at each step. By means of Lyapunov stability theory, it is proved theoretically that the designed control law can assure that tracking error converges to an arbitrarily small neighborhood around zero. Simulations are performed to demonstrate the effectiveness of the presented control scheme in coping with input constraint and aerodynamic uncertainty.     

未知输入条件下的结构物理参数识别研究

研究在输入信息未知条件下识别结构物理参数的问题,根据建筑结构风荷载的作用特点,提出一类时域识别算法,用于高层建筑结构的结构物理参数识别     

Adaptive fuzzy decentralized control for a class of pure-feedback large-scale nonlinear systems

In this paper, the problem of adaptive fuzzy decentralized control is investigated for a class of pure-feedback nonlinear interconnected large-scale systems. During the controller design, fuzzy logical systems are used to model packaged unknown nonlinearities and backstepping technique is used to construct adaptive fuzzy decentralized controller. It is shown that the proposed control scheme can guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. The main advantage of this study lies in that only one adaptive parameter needs to be estimated online for each subsystem. Simulation results further illustrate the effectiveness of the suggested approach.     

空间机器人执行器自适应分散容错控制算法设计

针对执行器发生部分失效故障的漂浮基空间机器人系统，提出了一种自适应H_∞分散容错控制算法。利用拉格朗日第二类方程建立了系统的动力学模型。根据分散原理将系统分解为以基座或臂杆为单元的多个子系统，并将表示执行器控制能力的有效因子融入到每个子系统，使得单个子系统的执行器故障不会影响相邻执行器的正常运行。通过对每个故障子系统设计形式一致的自适应容错算法实现对整个系统的容错控制。仿真结果表明，与现有某非奇异终端滑模容错算法相比，本文算法具有更快的跟踪速度和更高的跟踪精度。     

未知集中荷载和风荷载共同作用下的结构识别问题研究

对同时作用有未知集中荷载和未知风荷载的剪切型结构的参数识别问题进行了研究。首先提出了直接消去法以解决顶部作用有集中未知荷载时程的剪切型结构的识别问题。对于仅作用未知风荷载的情况，使用简洁的直接代入方法识别结构参数。继而对同时作用有以上两种未知荷载的情况进行了研究。当相邻层的风载时程在时域上完全相关的假定成立时，结合直接消去法和直接代入法在识别出所有结构参数蹬同时反演出未知的集中荷载时程和风荷载时程。文后以文章的最后用仿真算例验证了所提算法的正确性，并对测量噪声对识别结果的影响进行了讨论。     

Highly computationally efficient parameter estimation algorithms for a class of nonlinear multivariable systems by utilizing the state estimates

This paper investigates the parameter estimation issue for an input nonlinear multivariable state-space system. First, the canonical form of the input nonlinear multivariable state-space system is obtained through the linear transformation and the over-parameterization identification model of the considered system is derived. Second, by cutting down the redundant parameter estimates and extracting the unique parameter estimates from the parameter estimation vector in the least-squares identification method, we present an over-parameterization-based partially coupled average recursive extended least-squares parameter estimation algorithm to estimate the parameters. As for the unknown states in the parameter estimation algorithm, a new state estimator is designed to generate the state estimates. Third, in order to improve the computational efficiency of the parameter estimation algorithm, an over-parameterization-based multi-stage partially coupled average recursive extended least-squares algorithm is proposed. Finally, the computational efficiency analysis and the simulation examples are given to verify the effectiveness of the proposed algorithms.

     

A novel alleviating computation algorithm for a class of large-scale nonlinear systems with unknown dead-zones

In this paper, a novel alleviating computation decentralized adaptive fuzzy tracking control approach is presented for a class of uncertain nonlinear large-scale systems which consist of some subsystems with both completely unknown functions and unknown dead-zones. Different from the existing results that are based on the traditional back-stepping scheme as well as approximation technique of fuzzy logic systems (FLSs), this new approach assumes that the norm of optimal approximation parameter vector of FLSs and the approximation error are bounded by unknown parameters. At each design step of this new approach for every subsystem, fewer (only two) bounded adaptive parameters need to be adjusted. Thus, this new approach can alleviate the online computation burden and improve the robust control performance. Meanwhile, under Lyapunov theorem analysis, this approach can not only guarantee that all the signals in the closed-loop system are uniformly ultimately bounded but also guarantee that the outputs can track the reference signals to a small neighborhood of zero. The good performance of this approach is well demonstrated in a simulation example.     

Decentralized sliding mode quantized feedback control for a class of uncertain large-scale systems with dead-zone input

This paper is concerned with the robust quantized feedback stabilization problem for a class of uncertain nonlinear large-scale systems with dead-zone nonlinearity in actuator devices. It is assumed that state signals of each subsystem are quantized and the quantized state signals are transmitted over a digital channel to the controller side. Combined with a proposed discrete on-line adjustment policy of quantization parameters, a decentralized sliding mode quantized feedback control scheme is developed to tackle parameter uncertainties and dead-zone input nonlinearity simultaneously, and ensure that the system trajectory of each subsystem converges to the corresponding desired sliding manifold. Finally, an example is given to verify the validity of the theoretical result.     

自适应卡尔曼滤波在惯导初始对准中的应用研究

本文研究了自适应卡尔曼滤波技术在惯导系统中的应用。在噪声统计特性未知或近似已 知的情况下,采用常规卡尔曼滤波会导致较大的状态估计误差,甚至使滤波发散;而自适应卡 尔曼滤波在估计状态的同时,利用观测数据带来的信息,可在线估计噪声的统计特性,从而不 断地改进滤波器的设计,由此得到的滤波估计比常规卡尔曼估计精度更高。本文采用Ｓａｇｅ 和 Ｈｕｓａ 自适应滤波算法,结合惯导初始对准,给出了计算机仿真。仿真结果进一步证实在噪声统 计特性不确切知道的情况下,自适应卡尔曼滤波的估计精度高于常规卡尔曼滤波的估计精度。     

由多平衡态快子系统所诱发的簇发振荡及机理

通过引入子电路模块, 并选取适当的参数及非线性电阻特性, 建立了多时间尺度下具有多平衡态的四维广义哈特利(Hartley) 电路模型. 基于快子系统的多平衡态及其稳定性, 给出了参数空间的分岔集, 得到了不同区域中的动力学特性及其相应的分岔模式和临界条件. 针对两种典型具有不同分岔特征的情形, 分别给出了多平衡态参与下的两种不同的周期簇发振荡行为, 结合快子系统的分岔分析, 揭示了沉寂态和激发态之间相互转化的产生机制, 指出多平衡态不仅会导致多种沉寂态和激发态同时参与同一周期簇发振荡, 也会导致簇发振荡模式的多样性.      

部分输入未知时求解动力复合反演问题的补偿算法

将作者提出的全量补偿算法[8] 推广为一类适用于一般多自由度系统的时域补偿识别算法。该方法是基于最小二乘原则的一类迭代计算方法 ,对于结构上的部分作用力已知的情况 ,可用来同时识别结构的参数并反演输入。文中应用不同类型的结构分析实例说明了此方法适用于实际工程动力检测的可能性。     

Local joint information based active fault tolerant control for reconfigurable manipulator

This paper is concerned with the active fault tolerant control problem for reconfigurable manipulator actuator based on local joint information. It is considered that the entire reconfigurable manipulator system consists of a couple of independent joint modules as subsystems, which are controlled using unified radial basis function neural network adaptive algorithm using local joint information when actuators are fault free. For the subsystem in actuator fault situation, fault detection is achieved through comparing the user defined threshold to the residual between actual velocity value and nonlinear velocity observation value. The unknown input state observer is exploited for fault identification. Based on the information aforementioned, a compensation term is added to the proposed control algorithm for switching to realize active fault tolerant control when subsystem in fault. The advantages of the presented scheme are that unlike the complex control structure in centralized control, this scheme possesses simple control structure, as well as could isolate and tolerant the fault in subsystem. Furthermore, it can be easily applied to different configurations without any parameters modification. It means that the local fault could not affect the joint in normal situation. In order to demonstrate the effectiveness of the proposed method, two different 2-DOF reconfigurable manipulators are employed for simulation.     

Set-values filtering for discrete time-delay genetic regulatory networks with time-varying parameters

We propose a new discrete-time model for genetic regulatory networks (GRNs) in this paper. The new model includes time-varying degradation rates, translation rates of mRNA, and protein as well as time-delay and bounded external noise, which better approximates the practical GRNs. The aim is to estimate the concentrations of mRNA and protein in the proposed model. Due to the unknown external noise and time-varying parameters in this model, the set-values filtering is adopted. The so-called set-values filtering problem is to find an upper bound for the quadratic estimation error of the filtering dynamics and minimize this bound at each time step provided that the external noise is bounded. A recursive linear matrix inequality (RLMI) based optimization approach is developed to compute the filter gains. The effectiveness of the design method is illustrated via two numerical examples.