具有动态不确定性的自适应动态面控制

针对一类具有未建模动态的纯反馈非线性系统,提出一种自适应动态面控制方法。利用神经网络逼近未知连续函数,通过引入一种动态信号克服未建模动态。与现有结果相比,提出的设计方案简化了对未建模动态的处理过程,取消了神经网络逼近误差有界的假设。理论分析证明了该自适应控制方法能够保证闭环系统是半全局一致终结有界的,仿真结果验证了该方案的有效性。     

基于S函数调节的非线性自适应动态面控制

针对一类控制增益未知的多输入多输出（MIMO）非线性系统,提出了一种基于神经网络的鲁棒自适应动态面控制方法．利用动态面控制解决反推法的计算膨胀问题;同时在参数自适应律中引入S（Sigmoid）函数,动态调节神经网络的收敛速度,解决了自适应初始阶段的抖振现象．利用李亚普诺夫稳定性定理,证明了闭环系统所有信号最终有界,系统的跟踪误差最终收敛到有界紧集内．仿真结果表明了该方法的有效性．     

采样非线性系统的动态神经网络稳定自适应控制

研究了一类采样数据非线性系统的动态神经网络稳定自适应控制方法.不同于静态 神经网络自适应控制,动态神经网络自适应控制中神经网络用于逼近整个采样数据非线性系 统,而不是动态系统中的非线性分量.系统的控制律由神经网络系统的动态逆、自适应补偿项 和神经变结构鲁棒控制项组成.神经变结构控制用于保证系统的全局稳定性,并加速动态神 经网络系统的适近速度.证明了动态神经网络自适应控制系统的稳定性,并得到了动态神经 网络系统的学习算法.仿真研究表明,基于动态神经网络的非线性系统稳定自适应控制方法 较基于静态神经网络的自适应方法具有更好的性能.     

面向控制性能的一类非线性关联系统输出反馈动态面控制

针对一类含有完全未知关联项的多输入/多输出非线性系统,提出了输出反馈动态面自适应控制方案,克服了反推控制中的微分爆炸问题;利用神经网络逼近系统中的未知关联项,对于每个子系统只需对一个参数设计自适应律;引入性能函数和输出误差变换,跟踪误差信号的收敛速率、最大超调量和稳态误差等控制性能指标均可得到保证.理论证明了闭环系统的所有信号半全局一致有界,仿真结果验证了所提方案的有效性.     

改进的非线性鲁棒自适应动态面控制

针对不确定多输入多输出严格反馈块控非线性系统,提出一种鲁棒自适应动态面控制方法.该方法在反推自适应神经网络控制中引入动态面控制简化控制律,同时对自适应律进行改进以改善系统的过渡过程动态品质,保证了系统在简化的控制律下仍具有良好的动态特性.通过Lyapunov方法证明了闭环系统所有信号均有界,系统的跟踪误差指数收敛到有界紧集内.最后给出的某新型战斗机六自由度仿真结果表明了该方法的有效性.     

具有未建模动态的自适应神经网络动态面控制

对一类具有未建模动态的严格反馈非线性系统,提出一种自适应神经网络动态面控制方案.该方案将动态面控制方法扩展到具有未建模动态的严格反馈非线性系统的控制器设计中,拓展了动态面控制方法的应用范围.利用动态面控制方法引入的紧集来处理未建模动态对于系统的影响.利用Young's不等式,提出两种自适应参数调节方案.与现有研究结果相比,有效地减少了可调参数的数目,放宽了动态不确定性的假设,无需虚拟控制增益系数导数的信息.通过理论分析,证明了闭环控制系统是半全局一致终结有界的,且跟踪误差收敛到原点的一个小邻域内.     

一类非线性多变量系统的多模型自适应控制

针对一类不确定的非线性多变量离散时间动态系统,提出了一种基于切换的多模型自适应控制方法.该控制方法的特点在于以下两个方面:首先,引入一个高阶差分算子使得非线性系统的非线性项的限制条件不再要求全局有界;其次,提出的控制方法由线性自适应控制器、神经网络非线性自适应控制器以及切换机构组成:线性控制器用来保证闭环系统的输入输出信号有界,神经网络非线性控制器用来改善闭环系统的性能,基于性能指标的切换机构在每一时刻选择性能指标较好的控制器对系统进行控制.理论分析和仿真实验说明了提出的多模型自适应控制方法的有效性.     

一类非线性非最小相位系统的直接自适应控制

针对一类不确定的离散时间非线性非最小相位动态系统,提出了一种基于神经网络和多模型的直接自适应控制方法．该控制方法由线性直接自适应控制器,神经网络非线性直接自适应控制器以及切换机构组成．线性控制器用来保证闭环系统输入输出信号有界,非线性控制器用来改善系统性能．切换策略通过对上述两种控制器的切换,保证闭环系统输入输出有界的同时,改善了系统性能．理论分析以及仿真结果表明了所提出的直接自适应控制方法的有效性．     

基于控制性能的MIMO非线性系统动态面控制

针对一类不确定非线性MIMO(multiple-input multiple-output)系统,在动态面控制方法的基础上,提出了自适应跟踪控制方案.通过引入性能函数和输出误差转换,保证输出信号具有指定的跟踪速度、跟踪误差、最大超调量.为了避免控制奇异问题,采用神经网络直接逼近期望控制信号.该方案无需估计神经网络的权值,仅对1个参数进行自适应律设计.理论证明了闭环系统所有信号有界,仿真结果验证了所提方案的有效性.     

非线性增益递归滑模动态面自适应NN控制

针对一类严反馈非线性不确定系统的跟踪控制问题,提出一种非线性增益递归滑模动态面 (Dynamic surface control, DSC)自适应控制方法. 通过设计一个新的非线性增益函数,并构造递归滑模动态面的控制策略和新的Lyapunov函数,同时利用神经网络在线逼近系统不确定项, 该方法有效解决了具有输入饱和约束条件下系统控制精度与动态品质间的矛盾,增强了控制器对其自身参数摄动的非脆弱性. 理论证明了闭环系统所有状态是半全局一致最终有界的,且跟踪误差可收敛至任意小.     

一个基于神经网络模型的鲁棒自适应控制算法

对于具有不确定因素的离散非线性动态系统,通过校正神经网络预报器的输出,运用加权 预报控制性能指标和网络辨识器模型局部线性化的思想,提出了一个间接鲁棒自适应神经网 络控制算法,仿真研究证实了该控制策略的鲁棒性和有效性.     

具有未建模动态和输出约束的耦合系统的分散自适应控制

针对一类具有输入、状态未建模动态和非线性输入的耦合系统,提出一种自适应神经网络控制方案.利用径向基函数神经网络逼近未知非线性连续函数;引入动态信号和正则化信号处理状态及输入未建模动态;通过引入非线性映射,将具有时变输出约束的严格反馈系统化为不含约束的严格反馈系统.最后,通过理论分析验证闭环系统中所有信号是半全局一致最终有界的,仿真结果进一步验证了所提出控制方案的有效性.     

一种鲁棒神经网络自适应控制策略及其应用

针对具有外部干扰等不确定因素的离散未知非线性受控对象,提出了一种鲁棒神经网络自适应控制策略.该策略运用自适应预测及带遗忘因子的递推最小二乘参数估计的思想,对神经网络的预报输出进行修正,利用鲁棒反馈控制器保证系统稳定性,并对控制信号的增量进行限幅以抑制突变大幅值干扰信号对系统的影响.将提出的控制方法应用于实验室级液面系统的仿真中,结果表明了该控制策略的有效性.     

Robust adaptive control of nonlinear systems represented by input-output models

A robust adaptive control scheme is proposed for a class of nonlinear systems represented by input-output models with unmodeled dynamics. The scheme does not require the unknown parameters to satisfy the linear dependence condition and parameter estimation is not needed. With the proposed control scheme, all the variables in the closed-loop system are bounded in the presence of unmodeled dynamics and bounded disturbances. Moreover, the mean-square tracking error can be made arbitrarily small by choosing some design parameters appropriately.     

Prescribed performance adaptive neural output feedback dynamic surface control for a class of strict‐feedback uncertain nonlinear systems with full state constraints and unmodeled dynamics

This paper studies the output feedback tracking control problem for a class of strict‐feedback uncertain nonlinear systems with full state constraints and unmodeled dynamics using a prescribed performance adaptive neural dynamic surface control design approach. A nonlinear mapping technique is employed to address the state constraints. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions. The unmodeled dynamics is addressed by introducing an available dynamic signal. Subsequently, we construct the controller and parameter adaptive laws using a backstepping technique. Based on Lyapunov stability theory, it is shown that all signals in the closed‐loop system are semiglobally uniformly ultimately bounded and that the tracking error always remains within the prescribed performance bound. Simulation results are presented to demonstrate the effectiveness of the proposed control scheme.     

Adaptive tracking control of uncertain MIMO nonlinear systems with time-varying delays and unmodeled dynamics

In this paper, adaptive neural tracking control is proposed based on radial basis function neural networks (RBFNNs) for a class of multi-input multi-output (MIMO) nonlinear systems with completely unknown control directions, unknown dynamic disturbances, unmodeled dynamics, and uncertainties with time-varying delay. Using the Nussbaum function properties, the unknown control directions are dealt with. By constructing appropriate Lyapunov-Krasovskii functionals, the unknown upper bound functions of the time-varying delay uncertainties are compensated. The proposed control scheme does not need to calculate the integral of the delayed state functions. Using Young s inequality and RBFNNs, the assumption of unmodeled dynamics is relaxed. By theoretical analysis, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded.     

Robust adaptive observer for nonlinear systems with unmodeled dynamics

Unmodeled dynamics exist in almost all applications of observers due to the impossibility of using exact and detailed models. It is highly desired that the observers can dominate the effects of unmodeled dynamics independently to prevent the state estimations from diverging and to get the precise estimations. Based on adaptive nonlinear damping, this paper presents a robust adaptive observer for multiple-input multiple-output nonlinear systems with unknown parameters, uncertain nonlinearities, disturbances and unmodeled dynamics. The observer only has one adaptive parameter no matter how high the order of the system is and how many unknown parameters there are. With the proposed observer, neither estimating the unknown parameters or solving linear matrix inequalities is needed. It is shown that the state estimation error is uniformly bounded and can be made arbitrarily small.     

Decentralized robust adaptive control of nonlinear systems withunmodeled dynamics

The authors present a decentralized robust adaptive output feedback control scheme for a class of large-scale nonlinear systems of the output feedback canonical form with unmodeled dynamics. A modified dynamic signal is introduced for each subsystem to dominate the unmodeled dynamics and an adaptive nonlinear damping is used to counter the effects of the interconnections. It is shown that under certain assumptions, the proposed decentralized adaptive control scheme guarantees that all the signals in the closed-loop system are bounded in the presence of unmodeled dynamics, high-order interconnections and bounded disturbances. Furthermore, by choosing the design constants appropriately, the tracking error can be made arbitrarily small regardless of the interconnections, disturbances, and unmodeled dynamics in the system. An illustration example demonstrates the effectiveness of the proposed scheme     

Adaptive neural optimal control via command filter for nonlinear multi-agent systems including time-varying output constraints

In this article, an optimal command-filtered backstepping control approach is proposed for uncertain strict-feedback nonlinear multi-agent systems (MASs) including output constraints and unmodeled dynamics. One-to-one nonlinear mapping (NM) is utilized to recast constrained systems as corresponding unrestricted systems. A dynamical signal is applied to cope with unmodeled dynamics. Based on dynamic surface control (DSC), the feedforward controller is designed by introducing error compensating signals. The optimal feedback controller is produced applying adaptive dynamic programming (ADP) and integral reinforcement learning (IRL) techniques in which neural networks are utilized to approximate the relevant cost functions online with established weight updating laws. Therefore, the entire controller, including feedforward and feedback controllers, not only ensures that all signals in the closed-loop systems are cooperative semi-globally uniformly ultimately bounded (SGUUB) and the outputs maintain in the provided time-varying constraints, but also makes sure that the cost functions achieve minimization. A simulation example is presented to illustrate the feasibility of the proposed control algorithm.