Adaptive neural network tracking control for uncertain nonlinear systems with input delay and saturation

In this article, the adaptive tracking control problem is considered for a class of uncertain nonlinear systems with input delay and saturation. To compensate for the effect of the input delay and saturation, a compensation system is designed. Radial basis function neural networks are directly utilized to approximate the unknown nonlinear functions. With the aid of the backstepping method, novel adaptive neural network tracking controllers are developed, which can guarantee all the signals in the closed‐loop system are semiglobally uniformly ultimately bounded, and the system output can track the desired signal with a small tracking error. In the end, a simulation example is given to illustrate the effectiveness of the proposed methods.     

Adaptive output feedback quantised tracking control for stochastic nonstrict-feedback nonlinear systems with input saturation

This paper is concerned with the problem of adaptive output feedback quantised tracking control for a class of stochastic nonstrict-feedback nonlinear systems with asymmetric input saturation. Especially, both input and output signals are quantised by two sector-bounded quantisers. In order to solve the technical difficulties originating from asymmetric saturation nonlinearities and sector-bounded quantisation errors, some special technique, approximation-based methods and Gaussian error function-based continuous differentiable model are exploited. Meanwhile, an observer including the quantised input and output signals is designed to estimate the states. Then, a novel output feedback adaptive quantised control scheme is proposed to ensure that all signals in the closed-loop system are 4-moment (2-moment) semi-globally uniformly ultimately bounded while the output signal follows a desired reference signal. Finally, the effectiveness and applicability of the design methodology is illustrated with two simulation examples.     

High‐order sliding‐mode observer–based input‐output linearization

The problem of output control in multiple‐input–multiple‐output nonlinear systems is addressed. A high‐order sliding‐mode observer is used to estimate the states of the system and identify the discrepancy between the nominal model and the real plant. The exact and finite‐time estimation may be tackled as long as the system presents the algebraic strong observability property. Thus, a continuous robust input‐output linearization strategy can be obtained with respect to a prescribed output. As a consequence, the closed‐loop dynamics performs robustly to uncertainties/perturbations. To illustrate the advantages of the proposed method, we introduce a study case that demands a robust linear system behavior: the self‐oscillations induced in an underactuated mechanical system through a two‐relay controller. Experiments with an inertial wheel pendulum illustrate the feasibility of the proposed approach.     

Discrete model reference adaptive control with an augmented error signal

A method is developed for designing discrete model reference adaptive control systems when one has access to only the plant's input and output signals. Controllers for single-input, single-output, nonlinear, nonautonomous plants are developed via Lyapunov's second method. The augmented error signal method is employed to ensure that the normally used true error signal approaches zero asymptotically without requiring anticipative values of the plant output signal. Such anticipative signals are replaced by others easily obtained from low pass digital filters operating on the plant's input and output signals.     

Robust adaptive DSC of directly input‐coupled nonlinear systems with input unmodeled dynamics and time‐varying output constraints

In this paper, an adaptive robust dynamic surface control is proposed for a class of uncertain nonlinear interconnected systems with time‐varying output constraints and dynamic input and output coupling. The directly coupled inputs and control inputs are both of nonlinear input unmodeled dynamics. To counteract the instable impact of the nonlinear input unmodeled dynamics, normalization signals are designed on the basis of the convergence rates of their Lyapunov functions. With new state variables and control variables being defined, the real control inputs are obtained through solving the equations of intermediate control laws. The time‐varying constraints on output signals are implemented by introducing asymmetric barrier Lyapunov functions. In addition, dynamic signals and decentralized K‐filters are used to deal with the state unmodeled dynamics and to estimate the unmeasurable states, respectively. By the theoretical analysis, the signals in the closed‐loop system are proved to be semi‐globally uniformly ultimately bounded, and the output constraints are guaranteed simultaneously. A numerical example is provided to show the effectiveness of the proposed approach. Copyright © 2017 John Wiley & Sons, Ltd.     

Adaptive output feedback control for nonlinear time-delay systems using neural network

This paper extends the adaptive neural network （NN） control approaches to a class of unknown output feedback nonlinear time-delay systems. An adaptive output feedback NN tracking controller is designed by backstepping technique. NNs are used to approximate unknown functions dependent on time delay, Delay-dependent filters are introduced for state estimation. The domination method is used to deal with the smooth time-delay basis functions. The adaptive bounding technique is employed to estimate the upper bound of the NN approximation errors. Based on Lyapunov- Krasovskii functional, the semi-global uniform ultimate boundedness of all the signals in the closed-loop system is proved, The feasibility is investigated by two illustrative simulation examples.     

Acceleration amplitude-phase regulation for electro-hydraulic servo shaking table based on LMS adaptive filtering algorithm

Electro-hydraulic servo shaking table usually requires good control performance for acceleration replication. The poles of the electro-hydraulic servo shaking table are placed by three-variable control method using pole placement theory. The system frequency band is thus extended and the system stability is also enhanced. The phase delay and amplitude attenuation phenomenon occurs in electro-hydraulic servo shaking table corresponding to an acceleration sinusoidal input. The method for phase delay and amplitude attenuation elimination based on LMS adaptive filtering algorithm is proposed here. The task is accomplished by adjusting the weights using LMS adaptive filtering algorithm when there exits phase delay and amplitude attenuation between the input and its corresponding acceleration response. The reference input is weighted in such a way that it makes the system output track the input efficiently. The weighted input signal is inputted to the control system such that the output phase delay and amplitude attenuation are all cancelled. The above concept is used as a basis for the development of amplitude-phase regulation (APR) algorithm. The method does not need to estimate the system model and has good real-time performance. Experimental results demonstrate the efficiency and validity of the proposed APR control scheme.     

异或门低功耗优化展开方法

异或门实际输出信号具有空间相关性，为了便于进行低功耗优化的研究，现有方法将异或门输出信号假设为随机信号，并以异或门输入信号的置1概率为依据进行低功耗优化。文中不仅从概率的角度指出现有方法的局限性，而且推导出直接用输入信号的跳变密度计算2输入端异或门输出信号跳变密度的计算公式，进而提出用输入信号跳变密度对异或门进行低功耗优化展开的新方法。实验结果表明：文中方法的功耗降幅为现有方法的3倍多；同时，文中方法优化展开后与异或门功耗的实际模拟结果相比，其理论计算值的误差比较小（平均仅为0．97％），从实验的角度证明了文中关于异或门低功耗优化展开所做的理论分析的正确性。     

基于MATLAB计步器算法研究

计步器可以帮助人们实时掌握锻炼情况,它通过检测人体行走步数和步幅可计算出行走的路程。为了提高计步器的准确性,借助MATLAB仿真工具,充分利用加速度传感器(ADXL330)输出的三轴加速度信号,经分别处理后,利用基于信号能量自适应门限来检测加速度信号的峰值个数,从而准确地计算出人体行走的步数。最后,对年轻人与老年人行走数据进行采集,通过文中方法与传统方法处理后进行对比。实验结果表明,相对于传统方法,基于信号能量自适应门限检测方法具有更好的性能,能有效地提高计步器的准确度。     

ROBUST ADAPTIVE CONTROL WITH MULTIPLE ESTIMATION MODELS FOR STABILIZATION OF A CLASS OF NON‐INVERSELY STABLE TIME‐VARYING PLANTS

This paper presents an indirect adaptive control scheme for nominally stabilizable non‐necessarily inversely stable continuous‐time systems with unmodelled dynamics. The control objective is the adaptive stabilization of the closed‐loop system with the achievement of a bounded tracking‐error between the system output and a reference signal given by a stable filter. The adaptive control scheme includes several estimation algorithms and a supervisor which selects the appropriate estimator at every certain time and keeping it operating for at least a minimum period of residence time. This selection is based on a performance criterion related to a measure of the estimation errors obtained with each estimator. In this way, the performance of the output signal is improved with regard to the performance achieved with a unique estimation algorithm. All the estimators are either of the least‐squares type or gradient type. However, any well‐posed estimation algorithm is potentially valid for application. These estimators include relative dead‐zones for robustness purposes and parameter ‘a posteriori’ modifications to ensure the controllability of the estimated models of the plant, which is crucial for proving the stabilizability of the plant via adaptive pole‐placement designs.     

一种相干信号DOA估计的新方法研究

针对自适应阵列信号处理中的关键问题之一波达方向(DOA)估计,首先介绍了阵列信号处理中DOA估计的一般处理方法,如先对信号进行解相干处理后用经典MUSIC算法进行估计,然后给出了一种用于相干信号DOA估计的基于扩展卡尔曼滤波理论的新方法,并通过详细理论分析与一般的相干信号DOA估计的处理方法比较表明,不依赖于输入信号的协方差矩阵的特征值属性,能够利用较少的阵元对相干信号DOA和功率同时进行准确估计,仿真效果良好,证明有实用性,对自适应阵列信号处理中对相干信号干扰的研究有一定意义.     

面向控制器设计的多变量系统辨识实验信号

给出一种当多交量系统辨识模型用于控制器设计时的开环实验输入信号．假定系统未楚模动态可以用加性不确定性表示，取实际输出和理想输出误差平方均值最小，将其与系统辨识最小二乘法相结合，得到一种最优输入信号的设计方法．因输入信号与控制性能的相关性，可获得比普通随机信号更好的辨识结果．仿真结果证明了该方法的有效性．     

Persistence of excitation in extended least squares

In least-squares parameter estimation schemes, "persistency of excitation" conditions on the plant states are required for consistent estimation. In the case of extended least squares, the persistency conditions are on the state estimates. Here, these "persistency of excitation" conditions are translated into "sufficiently rich" conditions on the plant noise and inputs. In the case of adaptive minimum variance control schemes, the "sufficiently rich" conditions are on the noise and specified output trajectory. With sufficiently rich input signals, guaranteed convergence rates of prediction errors improve, and it is conjectured that the algorithms are consequently more robust.     

A hybrid fuzzy-statistical clustering approach for estimating the time of changes in fixed and variable sampling control charts

Control charts are the most popular Statistical Process Control (SPC) tools used to monitor process changes. When a control chart produces an out-of-control signal, it means that the process has changed. However, control chart signals do not indicate the real time of the process changes, which is essential for identifying and removing assignable causes and ultimately improving the process. Identifying the real time of the process change is known as change-point estimation problem. Most of the traditional change-point methods are based on maximum likelihood estimators (MLE) which need strict statistical assumptions. In this paper, first, we introduce clustering as a potential tool for change-point estimation. Next, we discuss the challenges of employing clustering methods for change-point estimation. Afterwards, based on the concepts of fuzzy clustering and statistical methods, we develop a novel hybrid approach which is able to effectively estimate change-points in processes with either fixed or variable sample size. Using extensive simulation studies, we also show that the proposed approach performs considerably well in all considered conditions in comparison to powerful statistical methods and popular fuzzy clustering techniques. The proposed approach can be employed for processes with either normal or non-normal distributions. It is also applicable to both phase-I and phase-II. Finally, it can estimate the true values of both in- and out-of-control states’ parameters.     

具有有界输入的网络控制系统时延独立稳定性

基于有界时延、无数据包丢失、传感器时钟驱动、执行器事件驱动,文章提出了用一个特定的实值函数来调节动态输出反馈控制器的输出以确保控制对象输入有界的方法,建立了具有分布时延、有界输入、动态输出反馈网络控制系统的非线性数学模型.用李雅普诺夫第二方法和线性矩阵不等式描述分析了系统的渐近稳定性,并推导出与时延无关的系统渐近稳定的充分条件.最后MATLAB仿真算例说明：稳定判据是可行的,有界输入的控制方法是有效的.     

非参数模型控制在液位控制系统中的应用研究

针对工业控制过程中液位系统的时变和明显的滞后特征,研究了非参数模型控制方法在液位控制系统中的设计方案,讨论了控制算法中引入的伪偏导数的在线估计问题,实现了通过液位系统的输入输出信息并利用递归最小二乘法对伪偏导数进行在线估计的过程,仿真实验验证了非参数模型算法对液位控制的鲁棒性、快速性及抗干扰性,通过仿真比较,展示了该算法性能优于PID算法和模糊控制的结果.     

Adaptive receding horizon predictive control for constrained discrete-time linear systems with parameter uncertainties

This paper proposes a discrete-time model predictive control (MPC) scheme combined with an adaptive mechanism. To this end, first, an adaptive parameter estimation algorithm suitable for MPC is proposed, which uses the available input and output signals to estimate the unknown system parameters. It enables the prediction of a monotonically decreasing worst-case estimation error bound over the prediction horizon of MPC. These distinctive features allow for future model improvement to be explicitly considered in MPC. Thus, a less conservative adaptive-type MPC controller can be developed based on the proposed estimation method. Second, we show how the discrete-time adaptive-type state-feedback MPC controller is constructed by combining the on-line parameter estimation scheme with a modified robust MPC method based on the comparison model. The developed MPC controller guarantees feasibility and stability of the closed-loop system theoretically in the presence of input and state constraints. A numerical example is given to demonstrate its effectiveness.     

An algorithm for multirate sampling adaptive control

A stable multirate sampling adaptive control algorithm is presented which enables the fast sampling rate to be applied in closed-loop feedback control. The sampling rate is only restricted by the time needed for the control law computation (as in the nonadaptive control case) plus the estimation error computation. The plant input and output are recorded prior to the currently obtained estimate and used to compute the coming estimate and controller coefficients. Thus, the computation is not dependent upon the inputs and outputs appearing during the updating process. The closed-loop system is shown to be stable     

一种基于信号极点先验知识和隔点抽样的磁共振波谱参数估计方法

提出了一种新的参数估计方法．该方法通过有效地结合隔点抽样技术和信号极点先验知识,实现了波谱的参数估计．文中的理论和实验证明,该方法不论是从计算速度还是从参数估计的精度来比较,都要明显优于经典的HTLS方法和HTLSPK方法．