基于干扰观测器的时滞非线性切换系统滑模控制方法

为实现更加精准的时滞非线性切换系统滑模控制,应用干扰观测器设计一种新的系统滑模控制方法。构建时滞非线性切换系统模型,针对系统在发生结构变化时会产生复合干扰变化的情况,设计了一种非线性切换干扰观测器,实施系统不连续干扰的估计。通过 Backstepping 方法结合干扰观测器,设计一种切换滑模控制器,依据标量非线性特性打造一个滑模面,通过滑模控制器算法使时滞非线性切换系统能够满足滑模面的实际可达性条件,完成切换滑模控制器设计,实现系统的滑模控制。对设计的滑模控制方法进行测试,实验中选择的时滞非线性切换系统为一种变后掠翼 NSV 。实验结果表明,该设计方法能够实现较为准确地切入信号跟踪,表现出了很好的切换复合干扰估计性能。     

一类非线性时滞系统的迭代学习控制设计

为了研究时滞对非线性系统的迭代学习控制收敛性的影响,采用了λ范数和一系列不等式技术,通过建立精确的数学模型,分析了在PD学习律下的Hopfield非线性神经网络系统。在全局Lipschi-tz连续条件下,研究了确保系统跟踪误差收敛的充分条件。理论推导证明时滞对这类非线性系统的迭代学习控制系统的收敛性没有显著的影响,仿真结果表明,迭代学习控制可以实现对非线性时滞系统的精确轨迹跟踪。     

Hybrid discretization method for time-delay nonlinear systems

A hybrid discretization scheme that combines the virtues of the Taylor series and Matrix exponential integration methods is proposed. In the algorithm, each sampling time interval is divided into two subintervals to be considered according to the time delay and sampling period. The algorithm is not too expensive computationally and lends itself to be easily inserted into large simulation packages. The mathematical structure of the new discretization scheme is explored and described in detail. The performance of the proposed discretization procedure is evaluated by employing case studies. Various input signals, sampling rates, and time-delay values are considered to test the proposed method. The results demonstrate that the proposed discretization scheme is better than previous Taylor series method for nonlinear time-delay systems, especially when a large sampling period is inevitable.     

Model algorithm control for path tracking of wheeled mobile robots

This paper proposes a model algorithm control (MAC) method for the path tracking control of differentially steered wheeled mobile robots (WMRs) subject to nonholonomic constraints. The continuous dynamic model of the wheeled mobile robot is presented and used as the model to be controlled. The MAC controller is designed based on the sampled-data representation of the system. In this paper the case that there exists time delay in the control input is also considered. A time discretization method using the Taylor series and the zero-order-hold (ZOH) assumption is proposed to discretize the continuous dynamic model of the WMR. This time discretization method is especially useful in the case of the time delayed system. It can provide finite dimensional and more accurate discretized form model of the mobile robot with input time delay and convert it into a general nonlinear time discretized form to which the MAC controller can be applied. Simulations are conducted to show the performance and feasibility of the proposed control strategy. In these simulations the WMR is controlled to track two difference reference paths such as the “8” shape path and the circular path. The bounded inertial parameters uncertainties and some disturbance are also considered in the model of the control system.     

Image noise variance estimation using the mixed Lagrange time-delay autoregressive model

The mixed Lagrange time-delay estimation autoregressive (MLTDEAR) model is proposed as a solution to estimate image noise variance. The only information available to the proposed estimator is a corrupted image and the nature of additive white noise. The image autocorrelation function is calculated and used to obtain the MLTDEAR model coefficients; the relationship between the MLTDEAR and linear prediction models is utilized to estimate the model coefficients. The forward-backward prediction is then used to obtain the predictor coefficients; the MLTDEAR model coefficients and prior samples of zero-offset autocorrelation values are next used to predict the power of the noise-free image. Furthermore, the fundamental performance limit of the signal and noise estimation, as derived from the Cramer-Rao inequality, is presented.     

Tracking control of nonlinear lumped mechanical continuous-time systems: A model-based iterative learning approach

This paper presents a nonlinear model-based iterative learning control procedure to achieve accurate tracking control for nonlinear lumped mechanical continuous-time systems. The model structure used in this iterative learning control procedure is new and combines a linear state space model and a nonlinear feature space transformation. An intuitive two-step iterative algorithm to identify the model parameters is presented. It alternates between the estimation of the linear and the nonlinear model part. It is assumed that besides the input and output signals also the full state vector of the system is available for identification. A measurement and signal processing procedure to estimate these signals for lumped mechanical systems is presented. The iterative learning control procedure relies on the calculation of the input that generates a given model output, so-called offline model inversion. A new offline nonlinear model inversion method for continuous-time, nonlinear time-invariant, state space models based on Newton's method is presented and applied to the new model structure. This model inversion method is not restricted to minimum phase models. It requires only calculation of the first order derivatives of the state space model and is applicable to multivariable models. For periodic reference signals the method yields a compact implementation in the frequency domain. Moreover it is shown that a bandwidth can be specified up to which learning is allowed when using this inversion method in the iterative learning control procedure. Experimental results for a nonlinear single-input-single-output system corresponding to a quarter car on a hydraulic test rig are presented. It is shown that the new nonlinear approach outperforms the linear iterative learning control approach which is currently used in the automotive industry on durability test rigs.     

H∞ output-feedback fuzzy proportional-integral control of fully delayed input/output systems

This paper presents the output-feedback fuzzy proportional-integral (PI) controller design for uncertain nonlinear systems with both fully delayed input and output. Based on the Takagi–Sugeno (T–S) fuzzy model representation, the output-feedback PI control is realized via parallel distributed PI compensation and novel LMI gain design. Although the T–S fuzzy PI controller is simple, asymptotic output regulation is assured to overcome the effect of uncertainty, state delay, and full input/output delays. When considering disturbance and measurement noise, the control performance is achieved by robust gain design. Furthermore, state observers and bilinear matrix inequality conditions are removed in this paper. Finally, time-delay Chua׳s circuit system and a continuous-time stirred tank reactor are taken as applications to show the expected performance.     

Synthesis of nonlinear discrete control systems via time-delay affine Takagi-Sugeno fuzzy models

The affine Takagi-Sugeno (TS) fuzzy model played a more important role in nonlinear control because it can be used to approximate the nonlinear systems more than the homogeneous TS fuzzy models. Besides, it is known that the time delays exist in physical systems and the previous works did not consider the time delay effects in the analysis of affine TS fuzzy models. Hence a parallel distributed compensation based fuzzy controller design issue for discrete time-delay affine TS fuzzy models is considered in this paper. The time-delay effect is considered in the discrete affine TS fuzzy models and the stabilization issue is developed for the nonlinear time-delay systems. Finally, a numerical simulation for a time-delayed nonlinear truck-trailer system is given to show the applications of the present approach.     

Sensitivity analysis of systems with a cascade compensator embedded in a Smith predictor to dead-time variation

This paper considers the use of a cascade compensator embedded in a Smith predictor in the forward path of a system being designed, which is an effective way to solve two problems: providing the desired process performance in the system and placing the dead-time element outside the closed loop so that it only increases the transient time by the value of the time delay and does not affect the overshoot value. It is shown that the Smith predictor should be used with caution because, being based on an additive signal compensation scheme, it may be not robust and may require accurate knowledge of the time-delay value for robust implementation. The problem of sensitivity analysis of the system to dead-time variation is stated and solved.     

Adaptive fuzzy predictive sliding control of uncertain nonlinear systems with bound-known input delay

In this paper, a new Adaptive Fuzzy Predictive Sliding Mode Control (AFP-SMC) is presented for nonlinear systems with uncertain dynamics and unknown input delay. The control unit consists of a fuzzy inference system to approximate the ideal linearization control, together with a switching strategy to compensate for the estimation errors. Also, an adaptive fuzzy predictor is used to estimate the future values of the system states to compensate for the time delay. The adaptation laws are used to tune the controller and predictor parameters, which guarantee the stability based on a Lyapunov-Krasovskii functional. To evaluate the method effectiveness, the simulation and experiment on an overhead crane system are presented. According to the obtained results, AFP-SMC can effectively control the uncertain nonlinear systems, subject to input delays of known bound.     

Repetitive control approach towards automatic tuning of Smith predictor controllers

This paper proposes a new method for automatic tuning of the Smith predictor controller based on a Repetitive Control (RC) approach. The method requires the input of a periodic reference signal which can be derived from a relay feedback experiment. A modified repetitive control scheme repetitively changes the control signal to achieve tracking error convergence. Once a satisfactory performance is achieved through the learning control, the parameters of the Smith predictor controller can be computed from the signals using a nonlinear least squares algorithm. The same relay feedback experiment can provide an initial parameter vector for an efficient implementation of the parameter estimation. Simulations and experimental results will be furnished to illustrate the effectiveness of the proposed tuning method.     

归一化循环相关超声回波时延估计

传统超声回波时延估计算法是在高斯噪声背景下展开研究的,而实际工况中超声回波不仅含有高斯噪声,还含有脉冲冲击噪声(α稳定分布噪声)等,这导致传统算法失效。为了解决上述问题,本文提出了一种针对混合噪声特别是包含噪声背景下的超声回波时延估计算法:归一化循环相关时延估计算法。首先,对归一化循环相关算法理论进行了简要的介绍。接着,对归一化循环相关时延估计算法进行了理论推导分析。然后,结合仿真分析,在相同α混合噪声情况下对传统循环相关和归一化循环相关时延估计进行比较。最后,在不同信噪比下,对归一化循环相关时延估计算法的估计性能进行了分析。通过对比实验发现,在噪声特征指数趋于1时,循环相关算法已不能估计出时延,而归一化循环相关算法的误差仍能保持在0.4μs;且在-10dB信噪比下,归一化循环相关算法时延估计也能保持在10μs误差范围内。本文所提归一化循环相关算法在混合噪声特别是包含α噪声情况下能够对超声回波时延进行精确估计,具有传统算法所不能比拟的优势。     

考虑控制电信号中间量的机床运动控制误差数据驱动建模方法

现有数据驱动的机床运动控制误差建模方法通常使用端到端的模型,即通过机器学习算法直接构建参考轨迹信息(速度、加速度等)与伺服误差之间的模型,以降低建模复杂度。然而,该方法忽视了控制电信号对运动控制系统非线性扰动的反映,而导致建立的模型精度受限。为解决此问题,提出了一种使用控制电信号作为中间量的数据驱动运动控制误差建模方法。该方法采集参考轨迹信息(速度、加速度、急动度等)、控制电信号、跟踪误差以及构造的换向特征,构建并训练基于参考轨迹信息的控制电信号预测网络,以及基于电信号和参考轨迹信息的运动控制误差预测网络,利用控制电信号这一中间量有效反应系统所受非线性扰动的特点,实现了高精度的运动控制误差数据驱动建模。在实际验证测试时,将参考轨迹信息输入电信号预测网络,而后将得到的预测控制电信号和参考轨迹信息输入跟踪误差预测网络,即可实现运动控制误差的预测。通过实验对所提出的建模方法进行了验证,所提出方法相对于传统的端到端建模方法,运动控制误差的预测精度在X轴和Y轴分别提升16.33%和20.42%,误差补偿后运动控制轮廓精度相较于未补偿提升85.59%,验证了所提出方法的可行性。     

Finite-time decentralized adaptive neural constrained control for interconnected nonlinear time-delay systems with dynamics couplings among subsystems

The problem of finite-time decentralized neural adaptive constrained control is studied for large-scale nonlinear time-delay systems in the non-affine form. The main features of the considered system are that 1) unknown unmatched time-delay interactions are considered, 2) the couplings among the nested subsystems are involved in uncertain nonlinear systems, 3) based on finite-time stability approach, asymmetric saturation actuators and output constraints are studied in large-scale systems. First, the smooth asymmetric saturation nonlinearity and barrier Lyapunov functions are used to achieve the input and output constraints. Second, the appropriately designed Lyapunov-Krasovskii functional and the property of hyperbolic tangent functions are used to deal with the unknown unmatched time-delay interactions, and the neural networks are employed to approximate the unknown nonlinearities. Note that, due to unknown time-delay interactions and the couplings among subsystems, the controller design is more meaningful and challenging. At last, based on finite-time stability theory and Lyapunov stability theory, a decentralized adaptive controller is proposed, which decreases the number of learning parameters. It is shown that the designed controller can ensure that all closed-loop signals are bounded and the tracking error converges to a small neighborhood of the origin. The simulation studies are presented to show the effectiveness of the proposed method.     

Digital redesign of analog smith predictor for systems with input time delays

This paper presents a new methodology for digitally redesigning an existing analog Smith predictor control system, such that the cascaded analog controller with input delay can be implemented with a digital controller. A traditional analog Smith predictor system is reformulated into an augmented system, which is then digitally redesigned using the predicted intersampling states. The paper extends the prediction-based digital redesign method from a delay free feedback system to an input time-delay cascaded system. A tuning parameter v is optimally determined online such that in any sampling period, the output response error between the original analogously controlled time-delay system and the digitally controlled sampled-data time-delay system is significantly reduced. The proposed method gives very good performance in dealing with systems with delays in excess of several integer sampling periods and shows good robustness to sampling period selection.     

Time-discretization of nonlinear systems with time delayed output via Taylor series

An output time delay always exists in practical systems. Analysis of the delay phenomenon in a continuous-time domain is sophisticated. It is appropriate to obtain its corresponding discrete-time model for implementation via a digital computer. A new method for the discretization of nonlinear systems using Taylor series expansion and the zero-order hold assumption is proposed in this paper. This method is applied to the sampled-data representation of a nonlinear system with a constant output time-delay. In particular, the effect of the time-discretization method on key properties of nonlinear control systems, such as equilibrium properties and asymptotic stability, is examined. In addition, ‘hybrid’ discretization schemes resulting from a combination of the ‘scaling and squaring’ technique with the Taylor method are also proposed, especially under conditions of very low sampling rates. A performance of the proposed method is evaluated using two nonlinear systems with time-delay output.     

自适应量化嵌入数字水印的音频隐密通信方法

本文提出了一种基于数字水印的音频隐秘通信方法。鉴于隐藏效果的直观性。原始水印信息以二值图像为例,也可为其他类型如语音。该算法首先对有具体含义的原始水印信息进行预处理。使之成为适于嵌入音频的一维数宇水印序列;然后对音频载体分段,根据各段能量大小选定隐藏段。再对各段进行离散余弦变换（DCT）;考虑到不可见性和鲁棒性的折中。本文依据噪声模型确定的噪声敏感度因子在DCT域的中低频系数区选定具体嵌入位置;并采用量化嵌入方法,可实现盲提取。实验结果表明该方法具有良好的不可见性和鲁棒性,能抵抗滤波、重采样、重量化、加噪声等常规信号处理攻击。     

基于主流形识别的非线性时间序列降噪方法及其在故障诊断中的应用

提出了一种新的基于主流形识别的非线性时间序列降噪方法。新的降噪方法将一维时间序列重构到高维相空间,利用非线性降维方法找出动力学系统在相空间中具有全域正交坐标系的低维主流形,然后根据主流形反求一维时间序列,进而达到降噪的目的。对洛伦兹信号进行的数值试验证明,与奇异谱分解等现有非线性分析方法相比,基于主流形识别的降噪方法能更加有效地消除混沌时间序列中的高斯白噪声。将该方法应用于带有断齿故障的齿轮箱振动信号的故障分析中,成功地提取出了淹没在带噪信号中的冲击特征。     

Discretization of nonlinear systems with delayed multi-input via Taylor series and scaling and squaring technique

An input time delay always exists in practical systems. Analysis of the delay phenomenon in a continuous-time domain is sophisticated. It is appropriate to obtain its corresponding discrete-time model for implementation via digital computers. In this paper a new scheme for the discretization of nonlinear systems using Taylor series expansion and the zero-order hold assumption is proposed. The mathematical structure of the new discretization method is analyzed. On the basis of this structure the sampled-data representation of nonlinear systems with time-delayed multi-input is presented. The delayed multi-input general equation has been derived. In particular, the effect of the time-discretization method on key properties of nonlinear control systems, such as equilibrium properties and asymptotic stability, is examined. Additionally, hybrid discretization schemes that result from a combination of the scaling and squaring technique (SST) with the Taylor series expansion are also proposed, especially under conditions of very low sampling rates. Practical issues associated with the selection of the method’s parameters to meet CPU time and accuracy requirements, are examined as well. A performance of the proposed method is evaluated using a nonlinear system with time delay: maneuvering an automobile.     

初态学习下时滞非线性系统的迭代学习控制

针对在有限时间区间上重复运行的一类状态时滞非线性系统,研究了一种带有初态学习的迭代学习控制算法。基于算子谱理论证明了系统在任意初态条件下经过迭代学习后,其输出能够完全跟踪期望轨迹,得到了该算法谱半径形式的收敛条件,比范数形式的收敛条件放宽了。并通过对系统初态的迭代学习,放松了迭代学习控制算法对初始状态函数的要求。仿真结果验证了所提算法的有效性。最后,将该算法应用到含有状态时滞的非线性间歇过程,结果也表明了该算法的有效性和可靠性。