不确定分数阶多涡卷混沌系统自适应重复学习同步控制

研究了不确定分数阶多涡卷混沌系统的自适应重复学习同步控制问题.通过利用滞环函数,设计了一类参数可调的分数阶多涡卷混沌系统.针对这类分数阶多涡卷混沌系统,在考虑非参数化不确定性、周期时变参数化不确定性、常参数化不确定性和外部扰动情况下,提出了一种重复学习同步控制方案.利用自适应神经网络技术补偿了系统中的函数型不确定性,通过自适应重复学习控制技术处理了周期时变参数化不确定性,并利用自适应鲁棒学习项处理了神经网络逼近误差和干扰的影响,实现了主系统和从系统的完全同步.综合利用分数阶频率分布模型和类Lyapunov复合能量函数方法证明了同步误差的学习收敛性.数值仿真验证了所提方法的有效性.     

Neural network approximation for periodically disturbed functions and applications to control design

This paper addresses the approximation problem of functions affected by unknown periodically time-varying disturbances. By combining Fourier series expansion into multilayer neural network or radial basis function neural network, we successfully construct two kinds of novel approximators, and prove that over a compact set, the new approximators can approximate a continuously and periodically disturbed function to arbitrary accuracy. Then, we apply the proposed approximators to disturbance rejection in the first-order nonlinear control systems with periodically time-varying disturbances, but it is straightforward to extend the proposed design methods to higher-order systems by using adaptive backstepping technique. A simulation example is provided to illustrate the effectiveness of control schemes designed in this paper.     

不确定参数下离散混沌系统的开闭环控制

在很多实际环境中,系统模型参数经常是不确定的,使离散混沌系统的控制策略可能不再有效。当参数不能直接观测时,开闭环控制方案将由于不精确的系统模型而失效。针对这个问题,提出了参数自适应开闲环控制策略来控制不确定参数的离散混沌系统。对开闲环控制在不确定参数条件下,参数变化满足控制需要的条件进行了讨论。同时,提出了参数自适应开闲环控制,将原有的开闭环控制推广到更大的范围。从Henon系统的仿真中,参数自适应开闲环控制策略显示了良好的抗扰动特性。     

基于S类函数的严格反馈非线性周期系统的自适应控制

针对一类严格反馈非线性周期系统, 在周期非线性可时变参数化的条件下设计自适应控制器. 通过将周期时变参数展开成傅里叶级数, 并采用微分自适应律估计未知系数, 进行控制器反推设计. 引入S类函数, 并在控制器设计中应用S类函数处理截断误差项对系统跟踪性能的影响, 同时, S类函数能确保虚拟控制的可微. 给出几种不同的S类函数设计, 分析比较将其应用于控制器设计时产生的不同效果. 理论分析与仿真结果表明, 提出的控制方法能够实现系统输出跟踪期望轨迹, 且闭环系统所有信号有界.     

一类非线性参数化系统自适应重复学习控制

针对一类高阶非线性参数化系统, 利用分段积分机制, 提出了一种新的自适应重复学习控制方法. 该方法结合反馈线性化, 可以处理参数在一个未知紧集内周期性快时变的非线性系统, 通过引进微分-差分混合型参数自适应律, 设计了一种自适应控制策略, 使广义跟踪误差在误差平方范数意义下渐近收敛于零, 通过构造Lyapunov泛函, 给出闭环系统收敛的一个充分条件. 实例仿真结果说明了该方法的可行性.     

A new robust adaptive control algorithm for linear time-varying plants

Robustness is an important property of a control system. Robust adaptive control has been an active research area for more than a decade. Since it was shown that un-modelled dynamics or even a small bounded disturbance can cause most adaptive control algorithms to go unstable, various modifications of the adaptive control algorithms have been developed to counteract instability and improve robustness with respect to unmodelled dynamics and bounded disturbances. However, we know that most of the modified approaches to achieve robustness require knowledge of either the parameter of bounding function on the unmodelled dynamics, or the upper bound on the disturbances, or the bound on the norm of unknown matching controller/plant parameters, and such knowledge can hardly be obtained in practice. A new indirect adaptive control algorithm for linear time-varying plants is proposed to achieve robustness to a class of unmodelled dynamics, bounded disturbances and plant parameter time variations. A modified relative dead zone technique is used, so that knowledge of the parameters of the upper bounding function on the unmodelled dynamics and the disturbances is not required. The stability analysis and the robust performance of adoptively controlled time-varying systems are provided for the new scheme. A simulation example is given to show the effectiveness of the proposed algorithm.     

Adaptive tracking of nonlinear systems with non-symmetric dead-zone input

Quite successfully adaptive control strategies have been applied to uncertain dynamical systems subject to dead-zone nonlinearities. However, adaptive tracking of systems with non-symmetric dead-zone characteristics has not been fully discussed with minimal knowledge of the dead-zone parameters. It is shown that the controlled system preceded by a non-symmetric dead-zone input can be represented as an uncertain nonlinear system subject to a linear input with time-varying input coefficient. To cope with this problem, a new adaptive compensation algorithm is employed without constructing the dead-zone inverse. The proposed adaptive scheme requires only the information of bounds of the dead-zone slopes and treats the time-varying input coefficient as a system uncertainty. The new control scheme ensures bounded-error trajectory tracking and assures the boundedness of all the signals in the adaptive closed loop. By appropriate selections of the controller parameters, we show that the smoothness of the controller does not affect the accuracy of trajectory tracking control. A numerical example is included to show the effectiveness of the theoretical results.     

Concise adaptive neural control of uncertain nonlinear systems with periodically nonlinear time-varying parameterized disturbances

This note makes effort at the problem of robust adaptive control for uncertain nonlinear systems with periodically nonlinear time-varying parameterized disturbances with known common period. A concise adaptive neural control scheme is developed by fusion of the Backstepping method and a novel MLN (minimum learning network) technique. In the control scheme, the intermediate variables, i.e., the virtual controls, do not appear in the finally actual control effort, and only one neural network is introduced to compensate sum of the uncertainties in the whole system. Thus, the outstanding advantage of the corresponding scheme is that the control law with a concise structure is model-independent and easy to implement in the process industries due to less computational burden. Based on the Lyapunov synthesis, it is proven that with the developed concise adaptive controller, all the signals in the closed-loop system converge to a small neighborhood of zero. Finally, three comparison examples demonstrate the effectiveness of the proposed algorithm.     

Robust adaptive tracking for time-varying uncertain nonlinear systems with unknown control coefficients

Presents a robust adaptive control approach for a class of time-varying uncertain nonlinear systems in the strict feedback form with completely unknown time-varying virtual control coefficients, uncertain time-varying parameters and unknown time-varying bounded disturbances. The proposed design method does not require any a priori knowledge of the unknown coefficients except for their bounds. It is proved that the proposed robust adaptive scheme can guarantee the global uniform ultimate boundedness of the closed-loop system signals and disturbance attenuation.     

Adaptive Robust Control for a Class of Uncertain MIMO Non-affine Nonlinear Systems

In this paper, the adaptive robust tracking control scheme is proposed for a class of multi-input and multioutput (MIMO) non-affine systems with uncertain structure and parameters, unknown control direction and unknown external disturbance based on backstepping technique. The MIMO nonaffine system is first transformed into a time-varying system with strict feedback structure using the mean value theorem, and then the bounded time-varying parameters are estimated by adaptive algorithms with projection. To handle the possible "controller singularity" problem caused by unknown control direction, a Nussbaum function is employed, and the dynamic surface control (DSC) method is applied to solve the problem of "explosion of complexity" in backstepping control. It is proved that the proposed control scheme can guarantee that all signals of the closed-loop system are bounded through Lyapunov stability theorem and decoupled backstepping method. Simulation results are presented to illustrate the effectiveness of the proposed control scheme.      

Adaptive iterative learning control for nonlinearly parameterized systems with unknown time-varying delays

First of all, an adaptive iterative learning control strategy is developed for a class of nonlinearly parameterized systems with two unknown time-varying parameters and one unknown time-varying delay. The proposed control law includes a PID-type feedback term in time domain and an adaptive learning term used to estimate the unknown time-varying vector in iteration domain. By constructing a Lyapunov-Krasovskii-like composite energy function, we prove the stability of the closed-loop system and the convergence of the tracking error. Then, the design idea is further extended to a broader class of systems with mixed parameters in which the unknown time-invariant vector is estimated by a PI-type learning law in time domain. The simulation results, for a time-delay chaotic system, confirm the effectiveness of the proposed control scheme.     

Robust adaptive tracking control of uncertain systems with time-varying input delays

In this paper, the problem of robust adaptive tracking control of uncertain systems with time-varying input delays is studied. Under some mild assumptions, a robust adaptive controller is designed by using adaptive backstepping technique such that the system is globally stable and the system output can track a given reference signal. At the same time, a root mean square type of bound is obtained for the tracking error as a function of design parameters and thus can be adjusted. Finally, one numerical example is given to show the effectiveness of the proposed scheme.     

Estimation-based approach for real-time optimisation of uncertain nonlinear systems

In this paper, the minimisation of an unknown but measurable cost function with uncertain dynamics is considered. The drift term of the uncertain dynamical system and the gradient of the objective function are treated as unknown time-varying parameters. A novel estimation scheme based on almost invariant manifolds is proposed to estimate the time-varying parameters. A direct gradient-based adaptive extremum-seeking controller is designed to solve the uncertain optimisation problem. This approach is shown to improve the transient performance of real-time optimisation control systems.     

具有未知参数的混沌系统的有限时间滑模同步控制

针对带有未知参数和非线性输入的两个不同的混沌系统之间的同步问题进行研究. 提出一个相比于传统滑模面具有更快收敛速度的终端滑模面, 并结合自适应控制理论和滑模控制理论, 设计一个自适应滑模控制律, 使同步误差在有限时间内收敛到滑模面, 并沿滑模面在有限时间内收敛到零点, 最终实现两个不同的混沌系统之间的同步. 最后, 以带有不确定性和外部扰动的Lorenz 系统和Liu 系统为例进行数值仿真, 仿真结果表明, 同步误差在有限时间内收敛到零点, 从而验证了所设计控制律的有效性和可行性.     

Novel adaptive learning control of linear systems with completely unknown time delays

A novel output-feedback adaptive learning control approach is developed for a class of linear time-delay systems. Three kinds of uncertainties： time delays, number of time delays, and system parameters are all assumed to be completely unknown, which is dfferent from the previous work. The design procedure includes two steps. First, according to the given periodic desired reference output and the allowed bound of tracking error, a suitable finite Fourier series expansion （FSE） is chosen as a practical reference output to be tracked. Second, by expressing the delayed practical reference output as a known time-varying vector multiplied by an unknown constant vector, we combine three kinds of uncertainties into an unknown constant vector and then estimate the vector by designing an adaptive law. By constructing a Lyapunov-Krasovskii functional, it is proved that the system output can asymptotically track the practical reference signal. An example is provided to illustrate the effectiveness of the control scheme developed in this paper.     

Adaptive iterative learning control for nonlinear time-delay systems with periodic disturbances using FSE-neural network

An adaptive iterative learning control scheme is presented for a class of strict-feedback nonlinear time-delay systems, with unknown nonlinearly parameterised and time-varying disturbed functions of known periods. Radial basis function neural network and Fourier series expansion (FSE) are combined into a new function approximator to model each suitable disturbed function in systems. The requirement of the traditional iterative learning control algorithm on the nonlinear functions (such as global Lipschitz c...     

参数不确定的广义Lorenz系统的同步研究

针对参数不确定的广义Lorenz混沌系统,提出一种新的自适应反馈同步方法。利用Lyapunov稳定性理论,设计了参数不确定的广义Lorenz混沌系统的自适应反馈同步控制器,并给出了参数自适应律的解析式。理论上证明了所设计控制器的正确性,并通过Matlab进行仿真实验,成功地实现了系统状态同步和系统不确定参数的辨识。数值仿真结果验证了所提出方法的可行性和有效性。该方法同步建立时间短,同步精度高,对系统初值没有特殊要求。     

基于参数自适应方法的统一混沌系统的同步控制

采用参数自适应控制方法，根据Lyapunov稳定性原理，通过构造适当的控制函数和设计参数的自适应控制律，分别实现了系统在不同确定参数和相同不确定参数两种情况下的统一混沌系统的同步导出在这两种情况下的统一混沌系统能实现同步的充分条件.相同不确定参数情况下的同步系统控制器结构更简单、同步性能更优．数值仿真证明了该方法的有效性．     

周期时变时滞非线性参数化系统的自适应学习控制

针对一阶未知非线性参数化周期时变时滞系统, 设计了一种自适应学习控制方案. 假设未知时变参数, 时变时滞和参考信号的共同周期是已知的, 通过重构系统方程, 将包含时变时滞在内的所有未知时变项合并成为一个周期时变向量, 采用周期自适应律估计该向量. 通过构造一个Lyapunov-Krasovskii型复合能量函数证明了所有信号有界并且跟踪误差收敛. 结果被推广到一类含有混合参数的高阶非线性系统. 通过两个仿真例子说明本文所提出的控制算法的有效性.     

Global output-feedback stabilization for a class of uncertain time-varying nonlinear systems

This paper investigates the global output-feedback stabilization for a class of uncertain time-varying nonlinear systems. The remarkable structure of the systems is the presence of uncertain control coefficients and unmeasured states dependent growth whose rate is inherently time-varying and of unknown polynomial-of-output, and consequently the systems have heavy nonlinearities, serious uncertainties/unknowns and serious time-variations. This forces us to explore a time-varying plus adaptive methodology to realize the task of output-feedback stabilization, rather than a purely adaptive one. Detailedly, based on a time-varying observer and transformation, an output-feedback controller is designed by skillfully combining adaptive technique, time-varying technique and well-known backstepping method. It is shown that, with the appropriate choice of the design parameters/functions, all the signals of the closed-loop system are bounded, and furthermore, the original system states globally converge to zero. It is worth mentioning that, the heavy nonlinearities are compensated by an updating law, while the serious unknowns and time-variations are compensated by a time-varying function. The designed controller is still valid when the system has an additive input disturbance which, essentially different from those studied previously, may not be periodic or bounded by any known constant.