基于动态线性逼近的非线性系统预测控制

对于一类常见多重时滞非线性离散时间系统,提出了基于动态线性逼近的增量型简化递推预测模型,广义预测控制律、噪声估计器以及带有参数限定时域长度的参数自适应递推预报算法,实现了对存在较大滞后的时滞非线性系统的广义预测控制,通过仿真表明,该算法对于一类非线性系统实现预测控制是正确和有效的。     

非线性模糊自适应直接广义预测控制

针对广义预测控制(GPC)计算量大的缺陷,提出一种模糊自适应直接广义预测控制(FDGPC)方法.该方法利用中值定理将一类非线性系统等价表示为时变线性系统,通过模糊逻辑系统直接设计预测控制器,利用三次样条基函数多项式逼近广义误差时变系数,基于广义误差估计值对控制器参数θu和广义误差估计值中的未知向量θe'进行自适应调整.理论证明了该方法可使广义误差收敛到原点的一个小邻域内.仿真结果验证了此方法的有效性.     

一类非线性系统的自适应控制研究

首先针对滑模控制中的高频颤动问题，把模糊神经网络（Fuzzy Neural Networks)用于滑模控制，提出了模糊神经网络滑模控制器（Fuzzy Neural Neural Networks Silding Mode Control)设计方法。由于用模糊神经网络的连续输出替代了滑模控制中的控制信号硬切换，因此FNNSMC能有效地消除颤动且其鲁棒比一般的滑模控制器强，但是其动态上升时间比滑模控制器的大。为解决这个问题，利用滑模控制器（Silding Mode Contrller）具有响应快的特点，在FNNSMC的之上，提出了一种自适应控制方案。该方法由SMC和FNNSMC构成，将SMC与FNNSMC有机结合，通过平滑切换实现自适应控制。我们的边界层内用FNNSMC控制。由于在边界层外SMC不会发生高频颤动现象，而在边界层内FNNSMC能消除颤动。因此文中提出的自适应控制方案不仅能消除颤动而且其动态性能良好。理论分析和仿真结果均说明了所提方法的有效性。     

基于神经网络的一类非线性系统自适应滑模控制

针对可以分解为标称系统和不确定系统两部分的SISO非线性系统,提出了一种基于神经网络的自适应滑模控制方案。控制器由标称控制律和补偿控制律组成,标称控制律用来控制标称系统,补偿控制律是基于Lyapunov稳定性理论设计的自适应神经滑模控制律,用来控制不确定系统,而神经网络用来逼近系统的不确定性。理论分析和计算机仿真都证明,本文提出的控制策略不但能解决这类系统的轨迹跟踪控制问题,而且可以保证闭环系统的渐近稳定性。     

一类不确定非线性系统的自适应模糊跟踪控制

针对一类不确定非线性连续系统，用模糊系统对未知函数进行逼近的基础上，利用Lyapunov稳定性理论，提出了一种新的自适应模糊跟踪控制算法，此算法的特点是，无论取多少条模糊系统的规则，自适应学习的参数只有一个，便于实现，而且还能确保闭环系统渐近稳定，并使系统的跟踪误差为零，仿真研究表明，所提出的算法是有效的。     

基于模糊神经网络滑模控制器的一类非线性系统自适应控制

虽然滑模控制具有控制简单和对不确定性与扰动不灵敏等优点，但是控制信号中的颤动是其应用中需解决的主要问题。该文首先针对一类非线性系统提出了一个新型控制器-模糊神经网络滑模控制器。新控制器不仅能消除颤动，而且比一般滑模控制器具有更强的鲁棒性。然而它与一般滑模控制器相比有较大的跟踪误差。为了解决这个问题，提出了结合滑控制器和模糊神经网络滑模控制器的自适应控制方法。这种自适应控制方案可以减小跟踪误差，增强系统的鲁棒性和消除控制信号中的颤动。仿真结果说明了控制方案的有效性。     

非线性系统的一种神经网络自适应控制方法

提出了一种用于控制未知非线性系统的神经网络控制方案,文中用三层前馈神经网络建立未知系统的动态模型,另用一个三层前馈神经网络在线产生控制规则,两个仿真算例验证了比较控制方案的有效性。     

水轮机调速系统的非线性自适应控制

水门开度控制不仅对电力系统暂态稳定性的改善有极其重要的作用,而且对抑制电力系统低频振荡、改善动态品质也有不可低估的作用。国内外水轮机调速器的控制器设计通常是基于具有准确参数的理想水轮机模型。论文考虑了水轮发电机固有的非线性以及水轮机参数的不确定性,基于微分几何理论和自适应控制方法推导了水轮机调速的非线性自适应控制律(NAC)。数字仿真试验表明,所设计的调速器控制律具有较强的参数自适应能力,在改善系统动态特性和提高系统大干扰稳定性方面优于未考虑参数不确定的非线性控制律(CNGC)。     

一类不确定非线性系统的自适应模糊滑模控制

针对一类不确定非线性系统自适应模糊控制中,为了保证系统稳定性而附加监督控制问题,根据滑模控制原理并利用模糊系统的逼近能力,提出了一种Ⅰ型间接自适应模糊滑模控制方法。该方法取消了监督控制,用滑模控制器增加了逼近误差的自适应补偿,李雅普诺夫稳定性理论分析证明,控制系统全局稳定且跟踪误差收敛到零。将这种控制器应用到过程控制的典型对象液位控制中,仿真结果表明了该控制器的有效性和可行性。     

非线性系统自适应控制及其在电力系统中的应用

电力系统中存在众多不确定的参数,所面临的运行条件和外界扰动复杂多变,有必要引入自适应控制理论来处理这些不确定因素。非线性系统的自适应控制与线性系统相比,在研究方法等方面都有着很大的不同,目前处在继续发展的阶段。文章首先对近十几年来非线性系统的参数自适应控制、鲁棒自适应以及智能化自适应控制做了简明的归纳,然后介绍了其中一些研究成果在电力系统稳定与控制研究中的应用,最后对全文做了总结和展望。     

Self-triggered adaptive prescribed-time tracking control for stochastic nonlinear systems

This paper proposes a self-triggered (ST) adaptive prescribed-time tracking control method for a class of stochastic nonlinear systems. Different from the existing results, an improved ST mechanism is proposed by adding a judgment condition to reduce the negative effect of excessive design interval on system performance. Based on the one-to-one mapping and backstepping technique, an adaptive prescribed-time tracking control method is proposed, which can make the error converge to the predefined precision set within the predetermined time. Simultaneously, applying the Lyapunov stability method, the boundedness of all signals in the closed-loop system can be ensured. Finally, a detailed simulation example is provided to show the effectiveness of the proposed control strategy.     

随机最优非线性网络控制系统设计

针对网络控制非线性系统中存在的不确定时延,利用Delta算子方法,研究了基于T-S模糊模型的随机最优网络控制问题。采用T-S模型模糊动态逼近非线性系统,将非线性模型模糊化为局部线性模型,设计了本质为非线性的具有时延补偿功能的状态反馈控制器,并进行了稳定性分析,并仿真。结果表明,所提出的建模方法是可行的,实质为非线性的状态反馈的控制器能够有效地补偿时延对系统性能的影响,且补偿效果好。     

Output‐feedback adaptive tracking control of stochastic nonlinear systems using multi‐dimensional Taylor network

In this paper, an adaptive multi‐dimensional Taylor network (MTN) control scheme based on the backstepping and dynamic surface control (DSC) is developed to solve the tracking control problem for the stochastic nonlinear system with immeasurable states. The MTNs are used to approximate the unknown nonlinearities, and then based on the multivariable analog of circle criterion, an observer is first introduced to estimate the immeasurable states. By combining the adaptive backstepping technique and the DSC technique, an adaptive MTN output‐feedback backstepping DSC approach is developed. It is shown that the proposed controller ensures that all signals of the closed‐loop system are remain bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of probability. Finally, the effectiveness of the design approach is illustrated by simulation results.     

Adaptive neural dynamic surface control of MIMO stochastic nonlinear systems with unknown control directions

In this paper, an adaptive neural output‐feedback control approach is considered for a class of uncertain multi‐input and multi‐output (MIMO) stochastic nonlinear systems with unknown control directions. Neural networks (NNs) are applied to approximate unknown nonlinearities, and K‐filter observer is designed to estimate unavailable system's states. Due to utilization of Nussbaum gain function technique in the proposed approach, the singularity problem and requirement to prior knowledge about signs of high‐frequency gains are removed, simultaneously. Razumikhin functional method is employed to deal with unknown state time‐varying delays, so that the offered control approach is free of common assumptions on derivative of time‐varying delays. Also, an adaptive neural dynamic surface control is developed; hence, explosion of complexity in conventional backstepping method is eliminated, effectively. The boundedness of all the resulting closed‐loop signals is guaranteed in probability; meanwhile, convergence of the tracking errors to adjustable compact set in the sense of mean quartic value is also proved. Finally, simulation results are shown to verify and clarify efficiency of the offered approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Direct adaptive neural tracking control for a class of stochastic pure‐feedback nonlinear systems with unknown dead‐zone

This paper considers the problem of adaptive neural tracking control for a class of nonlinear stochastic pure‐feedback systems with unknown dead zone. Based on the radial basis function neural networks' online approximation capability, a novel adaptive neural controller is presented via backstepping technique. It is shown that the proposed controller guarantees that all the signals of the closed‐loop system are semi‐globally, uniformly bounded in probability, and the tracking error converges to an arbitrarily small neighborhood around the origin in the sense of mean quartic value. Simulation results further illustrate the effectiveness of the suggested control scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

Adaptive control of stochastic nonlinear systems with Markovian switching

This paper is concerned with the problem of adaptive control for a class of stochastic nonlinear systems with Markovian switching, where the upper bounds of nonlinearities of stochastic Markovian jump systems are assumed to be unknown. Firstly, an adaptation law is developed to estimate these unknown parameters. Then, a class of adaptive state feedback controller is proposed such that not only the estimated errors are bounded almost surely but also, the states of the resulting closed‐loop system are asymptotically stable almost surely. Finally, a numerical example is given to show the validity of the results.Copyright © 2012 John Wiley & Sons, Ltd.     

基于灰色预测的空冷型PEMFC发电系统实时最优温度无模型自适应控制

空冷型质子交换膜燃料电池(PEMFC)发电系统的输出性能受工作温度、气体流速、尾气排放间隔等操作参数的影响,其中工作温度是影响输出性能的关键因素。针对空冷型PEMFC发电系统温度控制所具有的非线性、时滞、慢时变等复杂特性,提出基于灰色预测的无模型自适应控制方法实现实时最优温度控制。该方法将灰色预测的结果代替发电系统当前工作温度测量值。实验结果表明:所提方法能够在不同负载条件下实现对发电系统最优温度进行实时跟踪。与增量式PID控制相比,所提方法有效减小了系统的超调,使发电系统输出功率更平稳,有利于发电系统的长期稳定运行,延长电堆的使用寿命。且所提方法仅根据PEMFC输入输出数据在线对控制器进行调整,对PEMFC参数不敏感,可应用于类似空冷型PEMFC发电系统。     

Observer‐based nonlinear feedback decentralized neural adaptive dynamic surface control for large‐scale nonlinear systems

This paper presents a nonlinear gain feedback technique for observer‐based decentralized neural adaptive dynamic surface control of a class of large‐scale nonlinear systems with immeasurable states and uncertain interconnections among subsystems. Neural networks are used in the observer design to estimate the immeasurable states and thus facilitate the control design. Besides avoiding the complexity problem in traditional backstepping, the new nonlinear feedback gain method endows an automatic regulation ability into the pioneering dynamic surface control design and improvement in dynamic performance. Novel Lyapunov function is designed and rigorous stability analysis is given to show that all the closed‐loop signals are kept semiglobally uniformly ultimately bounded, and the output tracking errors can be guaranteed to converge to sufficient area around zero, with the bound values characterized by design parameters in an explicit manner. Simulation and comparative results are shown to verify effectiveness.     

基于预估模型切换的过热汽温无辨识自适应控制

针对电厂锅炉的过热汽温系统具有大惯性、大时滞和多模型等特性.研究一种基于预估模型切换的无辨识自适应预估控制方法.该方法需设计一个无辨识自适应预估控制器和基于主蒸汽流量变化率的切换律,给出若干个一阶预估模型.在系统运行过程中,这些预估模型在切换律控制下根据运行工况按优化切换时间自适应地切换,而其中的优化切换时间是用切换系统优化理论计算得到的.仿真试验结果表明,该方法适用于过热汽温系统,具有良好的控制品质、较强的抗扰和自适应能力,且对预估模型的精度要求不高,控制参数容易整定,易于工程实现.     

Adaptive neural network control for stochastic constrained block structure nonlinear systems with dynamical uncertainties

Stochastic adaptive dynamic surface control is presented for a class of uncertain multiple‐input–multiple‐output (MIMO) nonlinear systems with unmodeled dynamics and full state constraints in this paper. The controller is constructed by combining the dynamic surface control with radial basis function neural networks for the MIMO stochastic nonlinear systems. The nonlinear mapping is applied to guarantee the state constraints being not violated. The unmodeled dynamics is disposed through introducing an available dynamic signal. It is proved that all signals in the closed‐loop system are bounded in probability and the error signals are semiglobally uniformly ultimately bounded in mean square or the sense of four‐moment and the state constraints are confirmed in probability. Simulation results are offered to further illustrate the effectiveness of the control scheme.