Formation Tracking for Nonlinear Multi-agent Systems with Input and Output Quantization via Adaptive Output Feedback Control

Signal quantization can reduce communication burden in multi-agent systems, whereas it brings control challenge to multi-agent formation tracking. This paper studies the output feedback control problem for formation tracking of multi-agent systems with both quantized input and output.The agents are described by a nonlinear dynamic model with unknown parameters and immeasurable states. To estimate immeasurable states and solve the uncertainties, state observers are developed by using dynamic high-gain tools. Through proper parameter designs, an output feedback quantized controller is established based on quantized output signals, and the quantization effect on the control system is eliminated. Stability analysis proves that, with the proposed control scheme, multi-agent systems can track the reference trajectory while forming and maintaining the desired formation shape.In addition, all the signals in the closed-loop systems are bounded. Finally, the numerical simulation and practical experiment are provided to verify the theoretical analysis.     

一类非线性MIMO系统的自适应模糊输出反馈控制

针对一类多输入多输出非线性系统设计了自适应模糊观测器和控制器。该方法不需要系统状态完全可测的条件，而是通过自适应模糊观测器估计系统的状态。采用基于状态估计的模糊基函数模型的模糊逻辑系统逼近非线性函数，并对模糊建模误差和外扰的存在采用了鲁棒补偿控制项以保证良好的观测与跟踪性能，该控制器保证了跟踪误差和观测误差的一致最终有界性。仿真结果表明所提出的方法具有良好的观测效果与跟踪效果。     

Adaptive Event-Triggering Consensus for Multi-Agent Systems with Linear Time-Varying Dynamics

In this paper, the authors study the fully distributed event-triggering consensus problem for multi-agent systems with linear time-varying dynamics, where each agent is described by a linear time-varying system. An adaptive event-triggering protocol is proposed for time-varying multi-agent systems under directed graph. Based on the Gramian matrix of linear time-varying systems, the design of control gain is done and sufficient conditions ensuring the consensus of linear time-varying multi-agent systems are obtained. It is shown that the coupling strength is closely related to the triggering condition. When it comes to undirected graph, it is shown that the coupling strength is independent on the triggering condition and thus the design procedure is of more freedom than the directed case. In addition, it is also proved that Zeno behaviours can be excluded in the proposed protocols. A numerical example is presented to demonstrate the effectiveness of the theoretical results.

     

基于滑模观测器的偏置动量卫星姿态跟踪控制

针对在偏航姿态测量信息未知时偏置动量卫星姿态跟踪控制,提出了一种新的滑模观测器及其对应的自适应滑模控制器设计方法。基于滚动轴和俯仰轴信息,设计了一种经过平滑的滑模观测器,抑制高频抖震的同时提高状态估计的鲁棒性;设计的比例积分滑模面,能实现积分滑模控制,抑制稳态误差,优化滑模的全程鲁棒性,并采用自适应方法,对不确定参数进行在线更新,补偿不确定参数的影响。数值仿真结果表明,相对于龙伯格观测器,该方法能提高偏航姿态信息估计精度,在保证系统鲁棒性的同时,滚动和偏航轴姿态跟踪精度分别提高约50%。     

具有弱通讯的多智能体分布式自适应协同跟踪控制

针对具有弱通讯的高阶严反馈非线性多智能体系统,提出一种新颖的分布式自适应反演控制方法,并研究了该系统的协同跟踪控制问题。首先,以5个智能体作为被控对象,其中一个智能体以分布式结构组成“领航者-跟随者”编队模式,并且将领航者的运动速度作为整个编队系统的前行速度,其余智能体作为跟随者跟随领航者编队运动。其次,采用自适应反演方法,对系统中由于弱通讯导致的不确定参数进行自适应估计,并对系统设计补偿追踪控制律,使系统中的智能体能够实现自主跟踪时变的参考轨迹,最终以最优的轨迹避开障碍物,并保持期望队形运动。接着,根据Lyapunov稳定性理论,证明了所提方法的有效性。最后,通过仿真对比表明,所提方法能够使多智能体系统的横向、纵向跟踪误差以及参考轨迹的相对误差均实现快速收敛,并在跟踪过程中保持该系统渐近稳定。     

Output consensus for heterogeneous nonlinear multi-agent systems based on T-S fuzzy model

In this paper, the output consensus problem of general heterogeneous nonlinear multi-agent systems subject to different disturbances is considered. A kind of Takagi-Sukeno fuzzy modeling method is used to describe the nonlinear agents’ dynamics. Based on the model, a distributed fuzzy observer and controller are designed based on parallel distributed compensation scheme and internal reference models such that the heterogeneous nonlinear multi-agent systems can achieve output consensus. Then a necessary and sufficient condition is presented for the output consensus problem. And it is shown that the consensus trajectory of the global fuzzy model is determined by the network topology and the initial states of the internal reference models. Finally, some simulations are given to illustrate and verify the effectiveness of the proposed scheme.     

基于模糊观测器的离散混沌系统的自适应同步

针对不确定性离散混沌系统,进行了T-S模糊建模和模糊观测器设计,实现了混沌系统的同步。运用Mamdani型模糊逻辑系统对驱动系统的不确定项进行建模,进而得到不确定性参数的自适应估计。根据状态观测器的设计思想,进行了模糊观测器的设计,在此基础上,结合Lyapunov稳定性定理进行稳定性分析,得到了控制器的表达式,同时保证了同步误差系统的渐近稳定。仿真结果验证了方法的有效性。     

考虑时延的多Euler-Lagrange系统自适应神经网络协调跟踪控制

考虑存在通讯时延,在有向通讯拓扑结构下研究多Euler-Lagrange系统的协调跟踪控制问题。仅有部分跟随者可以获得静态领航者信息。对每一个跟随者设计了一种分布式观测器,以获得领航者的状态量。针对系统模型具有非线性不确定性和外部扰动情况,基于神经网络方法提出了两种分布式自适应协调控制律,分别使每一个跟随者对领航者的跟踪误差最终有界和渐近收敛到零。运用Lyapunov稳定性理论对两种控制律的稳定性进行了证明。数值仿真验证了本文提出的控制律的有效性。     

飞行器抗饱和鲁棒自适应非线性模型预测控制

针对巡航飞行器同时存在较大外部干扰和模型参数不确定性时自适应预测控制性能下降的问题,设计了带有模糊干扰观测器(fuzzy disturbance observer, FDO)补偿的鲁棒自适应非线性模型预测控制(robust adaptive nonlinear model predictive control, RANMPC)方法(简记为FDO-RANMPC方法)。首先,利用具有未知参数限制条件的递推最小二乘方法在线辨识模型参数;其次,利用模糊干扰观测器输出值抵消幅值较大的复合干扰,再利用Tube鲁棒预测控制策略设计了飞行器底层姿态系统具有稳定保障的FDO-RANMPC控制器;最后,在考虑复合不确定性情况下对指令姿态角跟踪的仿真中验证了控制器的有效性及其渐进稳定性。     

一类严格反馈时滞系统的自适应输出反馈控制

针对一类单输入单输出的严格反馈时滞系统研究跟踪控制问题。该控制系统包含不确定项、输出约束、未知死区特性和未知时间延迟。首先,设计一个状态观测器来估计无法测量的系统状态;其次,利用径向基函数(radial basis function,RBF)神经网络去逼近未知的系统内部动态;同时,利用障碍李雅普诺夫(Lyapunov)函数确保输出约束及Lyapunov-Krasovskii方法消除时滞项对系统的影响;最后,基于Lyapunov稳定性理论,构造一个鲁棒自适应神经网络输出反馈控制器,并且克服了过参数问题。结果显示,设计的神经网络输出反馈控制器可以保证闭环系统中的所有信号都是半全局最终一致有界的,跟踪误差能收敛到零值小的领域内。文中通过两个例子进一步验证了提出方法的有效性。     

非完整移动机器人的自适应滑模轨迹跟踪控制

针对动力学模型描述的非完整移动机器人系统，研究了其在未知参数和不确定性干扰下的轨迹跟踪控制问题，基于反演技术和自适应滑模控制的思想设计了具有全局渐近稳定的轨迹跟踪控制律。谊控制律将系统分解为低阶子系统来处理，利用中间虚拟控制量和部分Lyapunov函数简化了控制器设计，并具有直观的稳定性分析．满足了移动机器人的轨迹跟踪要求，且具有设计方法简单、鲁棒性强的特点。仿真结果验证了所设计控制器的有效性和正确性。     

多变量非线性系统的直接自适应模糊预测控制

对一类多变量非线性系统提出了直接自适应模糊预测控制方法,此方法首先对被控对象提出了线性时变子模型加非线性子模型的预测模型,然后直接利用模糊系统设计预测控制器,并基于时变增益自适应律对控制器中的未知向量和逼近误差估计值进行自适应调整。证明了此方法可使跟踪误差收敛到原点的一个邻域内,仿真结果验证了此方法的有效性。     

具有混合执行器故障的多智能体分布式有限时间自适应协同容错控制

针对多智能体编队系统执行器发生故障时,所引起的参数不确定以及系统瞬态不稳定问题,本文采用径向基函数神经网络(radial basis function neural networks, RBFNNs)对不确定参数(未知函数)进行估计。同时,基于反推技术设计出合理的自适应容错控制器,并通过有限时间理论保证系统实现瞬态稳定。首先,本文采用10个智能体作为被控对象,基于有向通讯拓扑结构理论,构建了非线性多智能体系统模型。其次,基于RBFNNs逼近特性,采用反推技术与动态面技术相结合,设计出合理的容错控制器,补偿多智能体中出现的未知非线性执行器故障,并采用有限时间理论解决系统瞬态不稳定问题。接着,基于Lyapunov稳定性理论分析了控制器的稳定性和快速收敛性。最后,通过两种算例对比,验证了所设计的控制器性能优于传统的反推技术,为工程实践提供了一种有效的研究思路。     

Networked Learning Predictive Control of Nonlinear Cyber-Physical Systems

Cyber-physical systems integrate computing, network and physical environments to make the systems more efficient and cooperative, and have important and extensive application prospects, such as the Internet of things. This paper studies the control problem of nonlinear cyber-physical systems with unknown dynamics and communication delays. A networked learning predictive control scheme is proposed for unknown nonlinear cyber-physical systems. This scheme recursively learns unknown system dynamics, actively compensates for communication delays and accurately tracks a desired reference. Learning multi-step predictors are presented to predict various step ahead outputs of the unknown nonlinear cyber-physical systems. The optimal design of controllers minimises a performance cost function which measures the tracking error predictions and control input increment predictions. The system analysis leads to the stability criteria of closed-loop nonlinear cyber-physical systems employing the networked learning predictive control scheme. An example illustrates the outcomes of the proposed scheme.

     

不确定非线性系统的自适应输出机动控制

针对不可控不可稳定线性化的非线性系统,研究了鲁棒自适应输出机动控制问题。在未知时变参数的界未知的情况下,利用增加幂积分技术和鲁棒递推设计方法,构造了光滑自适应机动控制器。该控制器不仅能使闭环系统的输出以任意小的跟踪误差跟踪理想的参考路径,而且沿该路径还满足一定的动态任务,即可以使跟踪速度以任意小的误差跟踪预先给定的理想速度。仿真结果表明了该方法的有效性。     

Adaptive neural network tracking control for a class of unknown nonlinear time-delay systems

1 .INTRODUCTIONNeural network (NN) control has made great pro-gress in past decades[1 ~4]. In Ref .[1] , adaptivebounding design technique was applied to adaptiveneural control for a class of strict-feedback nonlin-ear systems . The requirement of a known boundon the network reconstruction error was removed.By introducing an integral Lyapunov function,anadaptive NN control approach was proposed forunknown strict-feedback nonlinear systems[2],where the controller singularity problem was …     

Adaptive tracking controller using BP neural networks for a class of nonlinear systems

An BP neural-network-based adaptive control (NNAC) design method is described whose aim is to control a class of partially unknown nonlinear systems. Making use of the online identification of BP neural networks, the results of the identification could be used into the parameters of the controller. Not only the strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero by Lyapunov theory in the process of this design method.And a simulation example is also presented to evaluate the effectiveness of the design.     

Chaos and Nonlinear Feedback Control of the Arch Micro-Electro-Mechanical System

This paper addresses a nonlinear feedback control problem for the chaotic arch microelectro- mechanical system with unknown parameters, immeasurable states and partial state-constraint subjected to the distributed electrostatic actuation. To reflect inherent properties and design controller, the phase diagrams, bifurcation diagram and Poincare section are presented to investigate the nonlinear dynamics. The authors employ a symmetric barrier Lyapunov function to prevent violation of constraint when the arch micro-electro-mechanical system faces some limits. An RBF neural network system integrating with an update law is adopted to estimate unknown function with arbitrarily small error. To eliminate chaotic oscillation, a neuro-adaptive backstepping control scheme fused with an extended state tracking differentiator and an observer is constructed to lower requirements on measured states and precise system model. Besides, introducing an extended state tracking differentiator avoids repeated derivative for the virtual control signal associated with conventional backstepping. Finally, simulation results are presented to illustrate feasibility of the proposed scheme.     

参数未知的不同结构混沌系统的自适应同步

针对一类混沌系统，研究了参数未知的不同结构混沌系统的自适应同步问题。基于Lyaponuv稳定理论，给出了自适应同步控制器的系统设计过程，以及同步控制器和参数自适应律的解析表达式。对于两个参与同步的不同结构的混沌系统，只要其维数相等且状态变量可测，就可以利用所提出的控制策略达到全局渐近同步。方法简单，无需试凑。以Lorenz系统和Lu系统的自适应同步控制为例，说明了该方法的有效性。     

近空间飞行器鲁棒自适应Backstepping控制

针对变体近空间飞行器(near space vehiele, NSV)大包络、多模态的特性,研究其姿态的鲁棒自适应跟踪控制问题。首先,提出标称变体NSV切换非线性系统的单一且光滑主控制器设计方法,以解决因飞行模态切换引起的主控制舵面跳变。其次,针对切换瞬间复合干扰存在不连续的问题,给出一组同步切换的改进干扰观测器设计方法。将改进干扰观测器的输出与光滑的主控制律相结合组成不确定变体NSV切换非线性系统的复合控制器。采用平均驻留时间分析法证明所提出的控制方法可以保证闭环不确定变体NSV切换非线性系统的稳定性。最后,仿真结果验证了所提方法的有效性。