A finite-time fuzzy adaptive output-feedback fault-tolerant control for underactuated wheeled mobile robots systems

This paper investigates the adaptive fuzzy finite-time output-feedback fault-tolerant control (FTC) problemfor a class of nonlinear underactuated wheeled mobile robots (UWMRs) system with intermittent actuatorfaults. The UWMR system includes unknown nonlinear dynamics and immeasurable states. Fuzzy logic systems(FLSs) are utilized to work out immeasurable functions. Furthermore, with the support of the backsteppingcontrol technique and adaptive fuzzy state observer, a fuzzy adaptive finite-time output-feedback FTC scheme isdeveloped under the intermittent actuator faults. It is testifying the scheme can ensure the controlled nonlinearUWMRs is stable and the estimation errors are convergent. Finally, the comparison results and simulationvalidate the effectiveness of the proposed fuzzy adaptive finite-time FTC approach.

Adaptive Output-feedback Regulation for Nonlinear Delayed Systems Using Neural Network

A novel adaptive neural network （NN） output-feedback regulation algorithm for a class of nonlinear time-varying timedelay systems is proposed. Both the designed observer and controller are independent of time delay. Different from the existing results, where the upper bounding functions of time-delay terms are assumed to be known, we only use an NN to compensate for all unknown upper bounding functions without that assumption. The proposed design method is proved to be able to guarantee semi-global uniform ultimate boundedness of all the signals in the closed system, and the system output is proved to converge to a small neighborhood of the origin. The simulation results verify the effectiveness of the control scheme.

Output-feedback Dynamic Surface Control for a Class of Nonlinear Non-minimum Phase Systems

In this paper, an output-feedback tracking controller is proposed for a class of nonlinear non-minimum phase systems.To keep the unstable internal dynamics bounded, the method of output redefinition is applied to let the stability of the internal dynamics depend on that of redefined output, thus we only need to consider the new external dynamics rather than internal dynamics in the process of designing control law. To overcome the explosion of complexity problem in traditional backstepping design, the dynamic surface control(DSC) method is firstly used to deal with the problem of tracking control for the nonlinear non-minimum phase systems. The proposed outputfeedback DSC controller not only forces the system output to asymptotically track the desired trajectory, but also drives the unstable internal dynamics to follow its corresponding bounded and causal ideal internal dynamics, which is solved via stable system center method. Simulation results illustrate the validity of the proposed output-feedback DSC controller.

A new nonlinear output tracking controller via output-feedback

In this paper, the output tracking control is investigated for a class of nonlinear systems when only output is available for feedback. Based on the multivariable analog of circle criterion, an observer is first introduced. Then, the observer-based output tracking controller is constructively designed by using the integral backstepping approach together with completing square. It is shown that, under relatively mild conditions, all the closed-loop signals are uniformly bounded. Meanwhile the system output asymptotically tracks the desired output. A simulation example is given to illustrate the effectiveness of the theoretical results.

Adaptive output-feedback control for MIMO nonlinear systems with time-varying delays using neural networks

An adaptive neural network output-feedback regulation approach is proposed for a class of multi-input-multi-output nonlinear time-varying delayed systems.Both the designed observer and controller are free from time delays.Different from the existing results,this paper need not the assumption that the upper bounding functions of time-delay terms are known,and only a neural network is employed to compensate for all the upper bounding functions of time-delay terms,so the designed controller procedure is more simplified.In addition,the resulting closed-loop system is proved to be semi-globally ultimately uniformly bounded,and the output regulation error converges to a small residual set around the origin.Two simulation examples are provided to verify the effectiveness of control scheme.

Output-feedback adaptive stochastic nonlinear stabilization using neural networks

For the first time, an adaptive backstepping neural network control approach is extended to a class of stochastic non- linear output-feedback systems. Different from the existing results, the nonlinear terms are assumed to be completely unknown and only a neural network is employed to compensate for all unknown nonlinear functions so that the controller design is more simplified. Based on stochastic LaSalle theorem, the resulted closed-loop system is proved to be globally asymptotically stable in probability. The simulation results further verify the effectiveness of the control scheme.

Robust output-feedback stabilization of a class of nonlinear systems with the nonlinearities of varied form

The back-stepping designs based on confine functions are suggested for the robust output-feedback global stabilization of a class of nonlinear continuous systems; the proposed stabilizer is efficient for the nonlinear continuous systems confined by a bound function, the nonlinearities of the systems may be of varied forms or uncertain; the designed stabilizer is robust means that a class of nonlinear continuous systems can be stabilized by the same output feedback stabilization schemes; numerical simulation examples are given.

Output-feedback Adaptive Control for a Class of Nonlinear Systems with Unknown Control Directions

在这份报纸，产量反馈适应稳定与未知控制方向为非线性的系统的一个班被调查。首先通过线性州的转变，未知控制系数在一起是 lumped，原来的系统被转变到控制设计为变得可行的一个新系统。在为状态和参数估计的一个观察员和一个评估者的介绍以后，分别地，然后，一个建设性的设计过程为产量反馈被给使用综合者 backstepping 并且调节功能技术的适应稳定控制器。而所有另外的靠近环的系统状态被围住，设计的控制器保证状态集成到起源的原来的系统，这被显示出而所有另外的靠近环的系统状态被围住。模拟结果被说明显示出建议途径的有效性。

Observer-based Output-feedback Stabilization Control Design for a Class of Nonlinear Uncertain Systems

The output-feedback stabilization control problem is investigated for a class of nonlinear uncertain systems. Based on the multivariable analog of circle criterion, an observer is designed to estimate the system states and hence the dynamical equations that the estimation error satisfies are derived first. Then, by using integral backstepping approach together with completing square technique, the output-feedback stabilization control is constructively designed such that the closed-loop system is asymptotically stable. Finally, an example is given to illustrate the main results of this paper.

Semiglobal Stabilization via Output-feedback for a Class of Uncertain Nonlinear Systems

这份报纸为不明确的非线性的系统的一个班经由产量反馈认为 semiglobal 是稳定。与存在结果不同，在调查下面的系统拥有更严肃的非线性和实质地增加产量反馈的困难的未知控制系数控制器设计。联合 backstepping 方法和产量反馈支配途径，稳定控制器的 semiglobal 明确地被给，它能保证靠近环的系统在设计参数的适当选择下面完成 semiglobal asymptotic 稳定性。一个模拟例子验证理论结果和建议途径。

Ultimately Bounded Output Feedback Control for Networked Nonlinear Systems With Unreliable Communication Channel: A Buffer-Aided Strategy

This paper concerns ultimately bounded output-feedback control problems for networked systems with unknown nonlinear dynamics. Sensor-to-observer signal transmission is facilitated over networks that has communication constraints.These transmissions are carried out over an unreliable communication channel. In order to enhance the utilization rate of measurement data, a buffer-aided strategy is novelly employed to store historical measurements when communication networks are inaccessible. Using the neural network technique, a novel observer-based controller is introduced to address effects of signal transmission behaviors and unknown nonlinear dynamics.Through the application of stochastic analysis and Lyapunov stability, a joint framework is constructed for analyzing resultant system performance under the introduced controller. Subsequently, existence conditions for the desired output-feedback controller are delineated. The required parameters for the observerbased controller are then determined by resolving some specific matrix inequalities. Finally, a simulation example is showcased to confirm method efficacy.

考虑输入时滞的舵机伺服系统自适应指令滤波输出反馈控制

针对舵机伺服系统运行过程中存在复杂非线性以及信号输入时滞、使系统的精度降低,严重时甚至造成系统失稳的问题,提出了一种考虑输入时滞的自适应指令滤波输出反馈控制策略。首先,将系统中存在的输入时滞考虑为控制器信号输入时滞,给出舵机伺服系统状态方程,选用梯度下降法设计时滞估计律对未知时滞进行估计;其次,将系统中存在的复杂非线性统一为扰动,选用扩展状态观测器(ESO)加以估计,将观测的系统状态值用于设计控制量实现输出反馈控制;然后,选用指令滤波将高阶求导过程转化为求积分,实现信号滤波以抑制微分噪声;最后,基于Lyapunov-Krasovskii泛函稳定性定理证明控制器实现系统有界稳定。仿真及实验表明,所提的控制方法与传统反步控制方法、自抗扰控制和比例微分积分(PID)控制方法相比,响应时间分别提升了92%、88%和51%,跟踪精度分别提升了90%、84%和26%。

基于事件触发的网络化系统输出反馈耗散控制

针对事件触发机制下网络化系统的输出反馈耗散控制问题进行研究。首先,引入事件触发机制来减少有限带宽的影响,节约系统资源;其次,考虑网络化系统中存在的外部干扰,利用输入时滞法建立了基于事件触发机制的时滞系统模型;然后,构造一种时滞相关的Lyapunov-Krasovskii泛函,利用改进的积分处理方法推导出了保证闭环系统严格耗散性能的充分条件,并采用锥补线性化算法求出输出反馈控制器增益和事件触发参数矩阵;最后,通过数值仿真验证了所提方法的有效性与优越性。

虚假数据注入式攻击下无人水面船舶自适应神经输出反馈轨迹跟踪控制

本文主要研究网络环境下无人水面船舶(Unmanned surface vessels,USVs)遭受虚假数据注入式(False-data-injection,FDI)攻击的跟踪控制问题.其中,内部和外部不确定以及输入饱和约束等实际因素均考虑在设计中.在控制设计过程中,为避免将船舶速度的攻击信号引入闭环系统,采用分类重构思想,构造一种新的神经网络(Neural network,NN)状态观测器,同时重构船舶速度和攻击信号.进一步,在backstepping设计框架下,利用重构的攻击信号补偿USVs运动学通道因虚假数据注入式攻击引起的非匹配不确定项.在动力学设计通道中,利用自适应神经技术和单参数学习法,重构由内部和外部不确定组成的复合不确定部分,进而提出自适应神经输出反馈控制方案.理论分析表明,即便在FDI攻击、内外不确定以及执行器饱和约束的情况下,所提控制方案仍能迫使USVs跟踪给定的参考轨迹.同时,仿真和比较结果证实了所提控制方案的有效性和优越性.

时空时滞混沌系统反同步控制及应用研究

针对一类新的时空时滞惯性神经网络,研究其反同步控制及在图像保密通信上的应用。采用变量代换准则,避免了原二阶微分系统固有的复杂特性;设计一个有效地输出反馈控制器,并结合詹森(Jensen)不等式和互凸组合(RCC)方法,得到了一个较小保守性的时空时滞惯性神经网络反同步定理;通过仿真实现了主要结果在图像保密通信上的应用并证明了所提加密方法的优越性。

具有扰动的不确定非线性多智能体系统的鲁棒H_(∞)二分一致性

研究了符号网络上具有参数不确定性和外部扰动的一类非线性时变多智能体系统(multi-agent system,MAS)的鲁棒H_(∞)二分一致性问题。采用鲁棒控制中处理不确定性的思想,将所考虑的系统转化为具有范数有界参数不确定性的时不变动力学模型。将鲁棒一致性转化为降阶H_(∞)控制问题。基于李雅普诺夫稳定性理论,得到了具有期望H_(∞)性能的多智能体系统鲁棒二分一致性的线性矩阵不等式(linear matrix inequality,LMI)充分条件。此外,给出了分布式静态输出反馈控制器的设计过程。进一步以二自由度(two-degree-of-freedom,2-DOF)的平面移动机器人为例,验证了所提出的控制器的有效性。

Event-triggered dynamic output-feedback control for a class of Lipschitz nonlinear systems

This paper investigates the problem of dynamic output-feedback control for a class of Lipschitz nonlinear systems.First,a continuous-time controller is constructed and sufficient conditions for stability of the nonlinear systems are presented.Then,a novel event-triggered mechanism is proposed for the Lipschitz nonlinear systems in which new event-triggered conditions are introduced.Consequently,a closed-loop hybrid system is obtained using the event-triggered control strategy.Sufficient conditions for stability of the closed-loop system are established in the framework of hybrid systems.In addition,an upper bound of a minimum inter-event interval is provided to avoid the Zeno phenomenon.Finally,numerical examples of a neural network system and a genetic regulatory network system are provided to verify the theoretical results and to show the superiority of the proposed method.