具有输入时滞的随机前馈非线性系统的状态反馈镇定

针对一类具有输入时滞的随机前馈非线性系统, 首次研究了它的状态反馈镇定问题. 首先引入一个变量变换, 将其与齐次占优方法巧妙结合, 通过构造合适的 Lyapunov-Krasovskii 泛函, 设计了一个状态反馈控制器, 使得闭环系统的平衡点依概率全局渐近稳定.     

带有死区输入的非线性系统的自适应控制

针对一类具有死区非线性输入的非线性系统,同时考虑系统中存在未建模不确定项,设计了自适应控制器及未知参数的自适应估计率.该控制器使得闭环系统全局稳定且实现了输出信号对参考信号的精确跟踪.仿真结果进一步证实了该控制器能对未知死区及未建模动态进行有效的补偿。     

一类非线性时滞系统自适应控制研究

针对一类非线性时滞系统，本文提出一种自适应控制器的设计方案，采用backstepping和domination方法构建了一个无记忆自适应控制器。放松了对非线性时滞函数的要求(例如全局Lipschitz条件)，实现了对给定目标轨线的全局渐近跟踪，保证了闭环系统所有信号全局一致有界：基于Lyapunov—Krasoviskii泛函方法证明了闭环系统的稳定性。仿真结果说明了这种控制方法的可行性和优点。     

一类高阶非线性不确定系统自适应稳定控制

研究了一类高阶非线性不确定性系统的自适应稳定控制设计问题.因该系统的非线性程度高,其控制系数不等同、符号已知、但数值未知,故在此之前其稳定控制设计问题没有得到解决.本文应用自适应技术,结合设计参数的适当选取,从而得到了设计该类非线性系统状态反馈稳定控制器的新方法,并基于反推技术,给出了稳定控制器的设计步骤.所设计的状态反馈控制器使得闭环系统的状态全局渐近收敛于零,其余闭环信号一致有界.最后通过一个仿真例子说明了控制设计方法的有效性.     

一类非线性时滞系统的自适应动态面控制

本文对于一类含不确定输入时滞和干扰的非线性系统的跟踪控制问题提出了一种自适应动态面控制方案. 利用动态面控制方法避免了传统的后推设计中存在的复杂度爆炸问题. 分别构造了一个滤波器和一个虚拟观测器来产生辅助信号. 采用神经网络来逼近未知的连续函数. 跟踪误差被证明最终收敛到一个足够小的紧集. 给出了一个数字仿真示例验证了理论结果.     

不确定非线性时滞系统的自适应滑模控制

针对一类不确定非线性时滞系统的鲁棒滑模控制器设计问题,提出了一种自适应滑模控制方法,该方法能够有效地削弱系统的输入抖动.基于自适应滑模控制技术和Lyapunov稳定方法,克服了系统不确定性和时滞特性影响,不但保证系统状态可以在有限的时间内到达滑模面,而且还保证了系统的渐近稳定特性.最后给出的仿真结果验证了该控制方案的有效性.     

输入时滞非线性系统的新型动态面Funnel控制

本文针对一类带有输入时滞的不确定非线性系统, 提出了新型动态面Funnel控制方案. 首先设计补偿动态 变量将输入时滞系统转换成无时滞的系统, 仅需在递归控制的最后一步补偿, 从而优化了控制器设计过程. 其次, 构造Funnel函数, 使系统的瞬态和稳态跟踪误差被限制在给定边界内. 最后, 提出新型非线性动态面控制方法, 不仅 避免了自适应反推控制中的“微分爆炸”问题, 而且消除了边界层误差, 使得系统的跟踪误差最终渐近收敛到零. 理 论分析表明该闭环系统的所有信号一致最终有界, 仿真结果验证了该控制方案的有效性.     

非线性时滞系统的稳定自适应控制

针对模型不确定性的连续时间时滞系统，提出了一种新的神经网络自适应控制。系统的辨识模型是由神经网络和系统的已知信息组合构成，在此基础上，建立时滞系统的预测模型。基于神经网络预测模型的自适应控制器能够实现期望轨线的跟踪，理论上证明了闭环系统的稳定性。连续搅拌釜式反应器仿真结果表明了该控制方案的有效性。     

输入饱和非线性系统的周期自适应补偿学习控制

针对一类输入饱和不确定Brunovsky标准型非线性时滞系统,提出一种周期自适应跟踪补偿学习算法. 利用信号置换思想重组系统,基于最小公倍周期函数变换,将时滞时变项和不确定项合并为辅助参数,进而设计周期自适应学习律估计该辅助量,并利用饱和补偿器逼近和补偿超出饱和限的部分,由此构成综合控制器,以保证系统状态对有界期望值的跟踪,解决了饱和输入周期系统的重复迭代学习控制问题. 最后通过构造Lyapunov-Krasovskii复合能量函数的差分,计算证明了系统跟踪误差的收敛性和闭环信号值的有界性. 常见耦合非线性机械臂系统的力矩控制仿真,进一步验证了该算法的有效性.     

高阶不确定非线性系统线性状态反馈自适应控制设计

研究了一类控制系数未知的高阶不确定非线性系统的自适应镇定控制设计. 尽管该问题已经得到解决,但是所设计的控制器是非线性反馈形式,较为复杂. 与现有文献不同,本文通过综合运用增加幂积分技术和切换自适应控制方法,给出了该控制问题的更为简单且易于实现的新型线性反馈控制器,使得系统状态有界且最终趋于零. 值得指出的是,与切换自适应控制文献相比,本文所研究的非线性系统具有更严重的不确定/未知性和更强的非线性,这主要体现在未知的系统控制系数和更高的系统幂次中.     

Adaptive Iterative Learning Control for a Class of Nonlinear Time-varying Systems with Unknown Delays and Input Dead-zone

This paper presents an adaptive iterative learning control (AILC) scheme for a class of nonlinear systems with unknown time-varying delays and unknown input dead-zone. A novel nonlinear form of dead-zone nonlinearity is presented. The assumption of identical initial condition for iterative learning control (ILC) is removed by introducing boundary layer function. The uncertainties with time-varying delays are compensated for by using appropriate Lyapunov-Krasovskii functional and Young0s inequality. Radial basis function neural networks are used to model the time-varying uncertainties. The hyperbolic tangent function is employed to avoid the problem of singularity. According to the property of hyperbolic tangent function, the system output is proved to converge to a small neighborhood of the desired trajectory by constructing Lyapunov-like composite energy function (CEF) in two cases, while keeping all the closedloop signals bounded. Finally, a simulation example is presented to verify the effectiveness of the proposed approach.      

Pseudo-Predictor Feedback Control for Multiagent Systems with Both State and Input Delays

This paper is concerned with the consensus problem for high-order continuous-time multiagent systems with both state and input delays. A novel approach referred to as pseudo-predictor feedback protocol is proposed. Unlike the predictor-based feedback protocol which utilizes the open-loop dynamics to predict the future states, the pseudo-predictor feedback protocol uses the closed-loop dynamics of the multiagent systems to predict the future agent states. Full-order/reduced-order observer-based pseudo-predictor feedback protocols are proposed, and it is shown that the consensus is achieved and the input delay is compensated by the proposed protocols. Necessary and sufficient conditions guaranteeing the stability of the integral delay systems are provided in terms of the stability of the series of retarded-type time-delay systems. Furthermore, compared with the existing predictor-based protocols, the proposed pseudo-predictor feedback protocol is independent of the input signals of the neighboring agents and is easier to implement. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approaches.      

Neural Network Based Adaptive Tracking Control for a Class of Pure Feedback Nonlinear Systems With Input Saturation

In this paper, an adaptive neural networks (NNs) tracking controller is proposed for a class of single-input/singleoutput (SISO) non-affine pure-feedback non-linear systems with input saturation. In the proposed approach, the original input saturated nonlinear system is augmented by a low pass filter. Then, new system states are introduced to implement states transformation of the augmented model. The resulting new model in affine Brunovsky form permits direct and simpler controller design by avoiding back-stepping technique and its complexity growing as done in existing methods in the literature. In controller design of the proposed approach, a state observer, based on the strictly positive real (SPR) theory, is introduced and designed to estimate the new system states, and only two neural networks are used to approximate the uncertain nonlinearities and compensate for the saturation nonlinearity of actuator. The proposed approach can not only provide a simple and effective way for construction of the controller in adaptive neural networks control of non-affine systems with input saturation, but also guarantee the tracking performance and the boundedness of all the signals in the closed-loop system. The stability of the control system is investigated by using the Lyapunov theory. Simulation examples are presented to show the effectiveness of the proposed controller.      

Adaptive Tracking Control for Nonlinear Systems with a Class of Input Nonlinearities

An adaptive tracking control approach is presented for nonlinear systems with a class of input nonlinearities. A generalized model has been developed for a class of non‐smooth nonlinearities that include dead‐zone, backlash and ‘backlash‐like’ hysteresis. By using the developed model and Nussbaum‐gain technique, the problem of input nonlinearity is solved perfectly. The proposed method is available even when the designer is uncertain about the type of input nonlinearities mentioned above, and the knowledge on the bounds of these nonlinearity parameters is not required. Furthermore, it is proved that all closed‐loop signals are bounded and the tracking error converges to a small residual set asymptotically. Two simulation examples are provided to demonstrate the effectiveness of the proposed method.     

Adaptive Consensus Quantized Control for a Class of High-Order Nonlinear Multi-Agent Systems With Input Hysteresis and Full State Constraints

For a class of high-order nonlinear multi-agent systems with input hysteresis, an adaptive consensus output-feedback quantized control scheme with full state constraints is investigated. The major properties of the proposed control scheme are: 1) According to the different hysteresis input characteristics of each agent in the multi-agent system, a hysteresis quantization inverse compensator is designed to eliminate the influence of hysteresis characteristics on the system while ensuring that the quantized signal maintains the desired value. 2) A barrier Lyapunov function is introduced for the first time in the hysteretic multi-agent system. By constructing state constraint control strategy for the hysteretic multi-agent system, it ensures that all the states of the system are always maintained within a predetermined range. 3) The designed adaptive consensus output-feedback quantization control scheme allows the hysteretic system to have unknown parameters and unknown disturbance, and ensures that the input signal transmitted between agents is the quantization value, and the introduced quantizer is implemented under the condition that only its sector bound property is required. The stability analysis has proved that all signals of the closed-loop are semi-globally uniformly bounded. The StarSim hardware-in-the-loop simulation certificates the effectiveness of the proposed adaptive quantized control scheme.      

Exponential Consensus for Nonlinear Multi‐Agent Systems with Communication and Input Delays via Hybrid Control

This paper aims to investigate the exponential leader‐following consensus for nonlinear multi‐agent systems with time‐varying communication and input delays by using hybrid control. Based on the Lyapunov functional method, impulsive differential equation theory and matrix analysis, we show that all the followers can achieve leader‐following consensus with the virtual leader exponentially even if only a fraction of followers can obtain the leader's information. Two classes of exponential consensus criteria as well as the convergence rates for the controlled multi‐agent systems are presented under very relaxed interaction topology conditions, i.e., the directed interaction topology among the followers is only required to have p(p>1) disjoint strong components. Finally, two numerical examples are given to validate the proposed theoretical results.     

Observer-based Adaptive Optimal Control for Unknown Singularly Perturbed Nonlinear Systems With Input Constraints

This paper introduces an observer-based adaptive optimal control method for unknown singularly perturbed nonlinear systems with input constraints. First, a multi-time scales dynamic neural network (MTSDNN) observer with a novel updating law derived from a properly designed Lyapunov function is proposed to estimate the system states. Then, an adaptive learning rule driven by the critic NN weight error is presented for the critic NN, which is used to approximate the optimal cost function. Finally, the optimal control action is calculated by online solving the Hamilton-Jacobi-Bellman (HJB) equation associated with the MTSDNN observer and critic NN. The stability of the overall closed-loop system consisting of the MTSDNN observer, the critic NN and the optimal control action is proved. The proposed observer-based optimal control approach has an essential advantage that the system dynamics are not needed for implementation, and only the measured input/output data is needed. Moreover, the proposed optimal control design takes the input constraints into consideration and thus can overcome the restriction of actuator saturation. Simulation results are presented to confirm the validity of the investigated approach.      

Adaptive Neural Network-Based Control for a Class of Nonlinear Pure-Feedback Systems With Time-Varying Full State Constraints

In this paper, an adaptive neural network (NN) control approach is proposed for nonlinear pure-feedback systems with time-varying full state constraints. The pure-feedback systems of this paper are assumed to possess nonlinear function uncertainties. By using the mean value theorem, pure-feedback systems can be transformed into strict feedback forms. For the newly generated systems, NNs are employed to approximate unknown items. Based on the adaptive control scheme and backstepping algorithm, an intelligent controller is designed. At the same time, time-varying Barrier Lyapunov functions (BLFs) with error variables are adopted to avoid violating full state constraints in every step of the backstepping design. All closedloop signals are uniformly ultimately bounded and the output tracking error converges to the neighborhood of zero, which can be verified by using the Lyapunov stability theorem. Two simulation examples reveal the performance of the adaptive NN control approach.      

非线性系统的一种加权自适应控制方法

非线性系统的一种加权自适应控制方法１）许向阳祝和云（浙江大学工业控制研究所杭州３１００２７）关键词自适应控制,非线性系统,间隙非线性．１）国家级工业控制技术重点实验室资助课题．收稿日期１９９５－０３－０７１引言由于非线性系统的多样性,不能用统一的模型...