Adaptive neural output feedback tracking control for a class of nonlinear systems

In this paper, an adaptive neural output feedback control scheme based on backstepping technique and dynamic surface control (DSC) approach is developed to solve the tracking control problem for a class of nonlinear systems with unmeasurable states. Firstly, a nonlinear state observer is designed to estimate the unmeasurable states. Secondly, in the controller design process, radial basis function neural networks (RBFNNs) are utilised to approximate the unknown nonlinear functions, and then a novel adaptive neural output feedback tracking control scheme is developed via backstepping technique and DSC approach. It is shown that the proposed controller ensures that all signals of the closed-loop system remain bounded and the tracking error converges to a small neighbourhood around the origin. Finally, two numerical examples and one realistic example are given to illustrate the effectiveness of the proposed design approach.     

增长率为输出的未知多项式非线性系统的全局输出反馈跟踪

本文研究了一类增长线性地依赖于不可测状态非线性系统的输出反馈自适应实用跟踪问题.很不同的是,本文所研究系统的增长率是输出的未知多项式(系数未知、幂次已知),且关于被跟踪参考信号的假设相当弱(仅本身和其导数为已知的),为解决该问题,通过灵活采用通用控制和死区的思想和方法,引入了带有新型动态增益的观测器来重构不可测的系统状态,进而构造了自适应输出反馈跟踪控制器.可以证明,当控制器中的设计参数适当选取时,闭环系统所有状态有界,并且跟踪误差趋于事先给定的充分小的区域.数值仿真说明了所提方法的有效性.     

基于观测器的输出反馈电子节气门控制器设计

针对电子节气门系统的状态变量不完全可测量, 设计了一个基于观测器的输出反馈电子节气门控制系统. 该系统由一个估计不可测量状态的降阶观测器和一个非线性状态反馈控制器组成. 同时在控制器中引入了跟踪误差的积分项以抑制跟踪静差. 将建模误差和观测器误差等不确定性看作外部扰动, 在输入到状态稳定性(Input to state stability, ISS)理论框架下分析了跟踪误差系统的鲁棒性, 并据此给出了选择控制器参数的指导性原则.仿真及实验结果表明, 基于观测器的输出反馈控制器能够很好地实现电子节气门的跟踪控制.     

Decentralized adaptive neural fixed-time tracking control of constrained interconnected nonlinear systems with partially unmeasurable states

This article devises a new adaptive fixed-time tracking control strategy for interconnected nonlinear systems containing partially unmeasurable states and time-varying output constraints. Radial basis function neural networks, as function approximators, are utilized to model the unknown functions, and the partially unmeasurable states of the systems are estimated by a reduced-order observer. By constructing a transferred function, system outputs are directly constrained in a time-varying constraint bound. Meanwhile, the first-order sliding mode differentiators are utilized to reduce the computational burden caused by the repeated differentiations of virtual controllers. Under the Lyapunov function and the fixed-time theory, the decentralized adaptive fixed-time controllers are constructed. It is proved that the closed-loop systems are fixed-time stable and the output signals are restricted in the bounded compact set. Finally, two simulation examples demonstrate the validity of the proposed control scheme.     

含未建模动态的非线性系统输出反馈DSC设计

针对一类含有未建模动态和未知控制增益符号的非线性系统,提出了一种输出反馈自适应跟踪控制方案.首先利用Kreisselmeier观测器实现了不可测状态的估计,在此基础上以回归设计方式设计了输出反馈动态面控制系统,通过引入Nussbaum函数解决了控制方向未知问题.该方案解克服了传统反推控制方法中的微分爆炸现象,并且所有未...     

Cooperative Adaptive Fuzzy Output Feedback Control for Synchronization of Nonlinear Multi‐Agent Systems in the Presence of Input Saturation

This paper considers the leader‐following synchronization problem of nonlinear multi‐agent systems with unmeasurable states in the presence of input saturation. Each follower is governed by a class of strict‐feedback systems with unknown nonlinearities and the information of the leader can be accessed by only a small fraction of followers. An auxiliary system is introduced and its states are used to design the cooperative controllers for counteracting the effect of input saturation. By using fuzzy logic systems to approximate the unknown nonlinearities, local adaptive fuzzy observers are designed to estimate the unmeasurable states. Dynamic surface control (DSC) is employed to design distributed adaptive fuzzy output feedback controllers. The developed controllers guarantee that the outputs of all followers synchronize to that of the leader under directed communication graphs. Based on Lyapunov stability theory, it is proved that all signals in the closed‐loop systems are semiglobally uniformly ultimately bounded (SGUUB), and the tracking error converges to a small neighborhood of the origin. An example is provided to show the effectiveness of the proposed control approach.     

Cooperative learning neural network output feedback control of uncertain nonlinear multi-agent systems under directed topologies

Without assuming that the communication topologies among the neural network (NN) weights are to be undirected and the states of each agent are measurable, the cooperative learning NN output feedback control is addressed for uncertain nonlinear multi-agent systems with identical structures in strict-feedback form. By establishing directed communication topologies among NN weights to share their learned knowledge, NNs with cooperative learning laws are employed to identify the uncertainties. By designing NN-based κ-filter observers to estimate the unmeasurable states, a new cooperative learning output feedback control scheme is proposed to guarantee that the system outputs can track nonidentical reference signals with bounded tracking errors. A simulation example is given to demonstrate the effectiveness of the theoretical results.     

Global robust output regulation control for cascaded nonlinear systems using the internal model principle

This article considers the global robust output regulation problem via output feedback for a class of cascaded nonlinear systems with input-to-state stable inverse dynamics. The system uncertainties depend not only on the measured output but also all the unmeasurable states. By introducing an internal model, the output regulation problem is converted into a stabilisation problem for an appropriately augmented system. The designed dynamic controller could achieve the global asymptotic tracking control for a class of time-varying reference signals for the system output while keeping all other closed-loop signals bounded. It is of interest to note that the developed control approach can be applied to the speed tracking control of the fan speed control system. The simulation results demonstrate its effectiveness.     

Bounded output feedback tracking control of fully actuated Euler-Lagrange systems

In this short note we deal with the problem of trajectory tracking control of fully actuated Euler-Lagrange systems with unmeasurable velocities and bounded control inputs. In order to avoid the use of velocity measurements we introduce a nonlinear dynamic extension for the plant which renders the closed-loop system semi-globally asymptotically stable, hence, we prove that by increasing some of the dynamic extension parameters it is possible to enlarge the domain of attraction and to maintain the control inputs bounded.     

Data rate for output feedback consensus of uncertain nonlinear multiagent systems

This article considers the data rate problem for output feedback consensus of uncertain nonlinear multiagent systems. Each agent is modeled by an nth order integrator with unknown nonlinear dynamics and unmeasurable states. An (n+2)th order extended state observer (ESO) is first designed to estimate the unmeasurable agent states and the unknown nonlinear dynamics. Based on the output of the ESO and a dynamic encoding and decoding scheme, a distributed consensus protocol is proposed. It is shown that, for a connected undirected network with nth order uncertain nonlinear agents, consensus can be guaranteed with merely one bit information exchange between each pair of adjacent agents at each time step.