状态时延和全状态约束下的多智能体系统自适应事件触发控制

研究有向通信图下非线性多智能体系统的一致控制问题.首先,通过引入性能函数,使输出误差满足预定性能;其次,采用障碍Lyapunov函数,保证所有状态满足约束条件,结合李雅普诺夫-克拉索夫斯基(Lyapunov-Krasovskii,LK)泛函和杨氏不等式消除状态时延的影响,利用径向基函数神经网络(radial basis...     

具有指定性能和全状态约束的多智能体系统事件触发控制

针对一类非严格反馈的非线性多智能体系统一致性跟踪问题,在考虑全状态约束和指定性能的基础上提出了一种事件触发自适应控制算法.首先,通过设计性能函数,使跟踪误差在规定时间内收敛于指定范围.然后,在反步法中引入Barrier Lyapunov函数使所有状态满足约束条件,结合动态面技术解决传统反步法产生的"计算爆炸"问题,并利用径向基函数神经网络（Radial basis function neural networks,RBF NNs）处理系统中的未知非线性函数.最后基于Lyapunov稳定性理论证明系统中所有信号都是半全局一致最终有界的,跟踪误差收敛于原点的有界邻域内且满足指定性能.仿真结果验证了该控制算法的有效性.     

一类状态/输入受限的不确定非仿射非线性系统鲁棒自适应backstepping控制

针对一类状态/输入受限的不确定严格反馈非仿射非线性系统跟踪控制问题,提出一种鲁棒自适应backstepping控制策略.在保证系统精度的前提下,对状态/输入受限的非仿射系统进行Taylor级数在线展开,得到其仿射形式;为保证系统复合扰动在线准确逼近,提出基于投影算子的递归扰动模糊神经网络干扰观测器(RPFNNDO);在考虑不确定系统存在状态受限和输入饱和等因素下,结合障碍Lyapunov函数、 tanh函数及Nussbaum函数,利用backstepping方法设计控制器,并采用Lyapunov稳定理论分析闭环系统稳定性.应用于无人机航迹控制的仿真结果验证了所提方法的有效性.     

带有不稳定子系统的切换非线性系统的积分输入状态稳定

本文针对带有不稳定子系统的切换非线性系统研究了系统的积分输入状态稳定性问题. 应用导数不定的 类Lyapunov函数得出切换非线性系统的积分输入状态稳定. 导数不定的类Lyapunov函数方法比传统的导数正定 的Lyapunov函数的方法更具有一般性. 文中包含两种情况: 当所有子系统为积分输入状态稳定时, 切换非线性系统 是积分输入状态稳定的; 当部分子系统为非积分输入状态稳定时, 本文证明了切换非线性系统的积分输入状态稳 定. 最后应用一个仿真例子描述了所提结果的有效性.     

具有输入量化和全状态约束的非严格反馈随机非线性系统的有限时间动态面控制

研究具有输入量化和全状态约束的非严格反馈随机非线性系统的有限时间自适应跟踪控制.首先,利用双曲正切函数进行非线性映射,消除全状态约束的限制,将系统变换为无约束系统;其次,引入滞回量化器克服量化信号中的抖动和量化误差.为实现有限时间控制,提出概率意义下半全局有限时间稳定控制方法,加快系统的收敛速度,并在此基础上采用径向基函数神经网络逼近未知非线性函数;接着,基于动态面控制技术和高斯函数的性质,对变换后的非严格反馈随机系统进行自适应控制设计,所设计的控制器能够保证闭环系统中的所有信号在概率意义下有限时间稳定;最后通过仿真实验表明所设计控制方案的有效性.     

输入饱和下非线性系统的事件触发控制器设计

本文研究了具有输入饱和的非线性系统事件触发控制策略设计问题.首先,针对输入饱和下非线性系统,建立混杂系统模型.其次,当非线性函数满足Lipschitz条件下,给出闭环混杂系统局部一致渐近稳定性的稳定判据,并设计了事件触发饱和控制器.然后,当非线性函数满足扇区条件时,给出闭环混杂系统框架下满足局部一致渐近稳定性的LMI条件,并设计了事件触发饱和控制器.进一步地,在事件触发饱和控制器作用下,分析了非线性系统的半全局鲁棒镇定性.最后,结合两个仿真实例说明了所提出事件触发控制策略的有效性.     

离散输入受限系统的增益调度事件触发和自触发控制

针对离散输入受限系统,分别设计静态和动态的增益调度事件触发和自触发控制算法.首先设计一种基于离散参量Lyapunov方程的静态增益调度事件触发控制算法,该算法通过事件触发机制更新控制增益,使得在增大闭环系统收敛速率的同时节约通讯资源.为了避免对采样状态和测量误差的连续监测,设计了相应的静态增益调度自触发控制算法;同时,...     

约束非线性系统输入到状态稳定经济型模型预测控制

考虑约束非线性系统经济型最优控制问题,提出一种关于经济性能输入到状态稳定的经济型模型预测控制(EMPC)策略.通过离线计算系统的最优经济稳态点,构建关于该稳态点跟踪的稳定最优控制问题.在此基础上,利用稳定最优控制问题的最优值函数和关于经济性能函数的松弛量构造EMPC优化问题的收缩约束,再结合不变集原理和输入到状态稳定性...     

具有输入和输出约束的高阶随机系统神经网络控制

针对高阶非线性系统,开展自适应神经网络跟踪控制器设计,系统受到随机扰动的影响.首次把输入和输出约束问题引入到高阶系统的跟踪控制中,并假定系统动态是未知.首先借用高斯误差函数表达连续可微的非对称饱和模型以实现输入约束,和障碍Lyapunov函数保证系统输出受限;其次,针对高阶非线性系统,径向基函数(RBF)神经网络用来克服未知系统动态和随机扰动.在每一步的backstepping计算中,仅用到单一的自适应更新参数,从而克服了过参数问题;最后,基于Lyapunov稳定性理论提出自适应神经网络控制策略,并减少了学习参数.最终结果表明设计的控制器能保证所有闭环信号半全局最终一致有界,并能使跟踪误差收敛到零值小的邻域内.仿真研究进一步验证了提出方法的有效性.     

控制方向未知的全状态约束非线性系统的鲁棒自适应跟踪控制

针对一类控制方向未知的含有时变不确定参数和未知时变有界扰动的全状态约束非线性系统,本文提出了一种基于障碍Lyapunov函数的反步自适应控制方法.障碍Lyapunov函数保证了系统状态在运行过程中始终保持在约束区间内;Nussbaum型函数的引入解决了系统控制方向未知的问题;光滑投影算法确保了不确定时变参数的有界性.障碍Lyapunov函数、Nussbaum型函数及光滑投影算法与反步自适应方法的有效结合首次解决了控制方向未知的全状态约束非线性系统的跟踪控制问题.所设计的自适应鲁棒控制器能在满足状态约束的前提下确保闭环系统的所有信号有界.通过恰当地选取设计参数,系统的跟踪误差将收敛于0的任意小的邻域内.仿真结果表明了控制方案的可行性.     

Adaptive event-triggered tracking control for a class of stochastic nonlinear systems with full-state constraints

This paper proposes an adaptive event trigger-based sample-and-hold tracking control scheme for a class of strict-feedback nonlinear stochastic systems with full-state constraints. By introducing a tan-type stochastic Barrier Lyapunov function (SBLF) combined with radial basis function neural networks (RBFNNs), which is used to approximate the nonlinear functions in backstepping procedures, an adaptive event-triggered controller is designed. It is shown with stochastic stability theory that all the states cannot violate their constraints, and Zeno behavior is excluded almost surely. Meanwhile, all the signals of the closed-loop systems are bounded almost surely and the tracking error converges to an arbitrary small compact set in the fourth-moment sense. A simulation example is given to show the effectiveness of the control scheme.     

Adaptive output control of uncertain nonlinear systems with non-symmetric dead-zone input

This paper deals with the adaptive output feedback control problem of a class of uncertain nonlinear systems with an unknown non-symmetric dead-zone nonlinearity. The nonlinear system considered here is dominated by a triangular system without zero dynamics satisfying polynomial growth in the unmeasurable states. An adaptive control scheme is developed without constructing the dead-zone inverse. The proposed adaptive control scheme requires only the information of bounds of the slopes and the breakpoint of dead-zone nonlinearity. The novelty of this paper is that a universal-type adaptive output feedback controller is numerically constructed by using a sum of squares (SOS) optimization algorithm, which ensures the boundedness of all the signals in the adaptive closed-loop without knowing the growth rate of the uncertainties. An example is presented to show the effectiveness of the proposed approach.     

Finite-time adaptive neural control for uncertain nonlinear time-delay systems with actuator delay and full-state constraints

This paper investigates finite-time adaptive neural tracking control for a class of nonlinear time-delay systems subject to the actuator delay and full-state constraints. The difficulty is to consider full-state time delays and full-state constraints in finite-time control design. First, finite-time control method is used to achieve fast transient performances, and new Lyapunov–Krasovskii functionals are appropriately constructed to compensate time delays, in which a predictor-like term is utilized to transform input delayed systems into delay-free systems. Second, neural networks are utilized to deal with the unknown functions, the Gaussian error function is used to express the continuously differentiable asymmetric saturation nonlinearity, and barrier Lyapunov functions are employed to guarantee that full-state signals are restricted within certain fixed bounds. At last, based on finite-time stability theory and Lyapunov stability theory, the finite-time tracking control question involved in full-state constraints is solved, and the designed control scheme reduces learning parameters. It is shown that the presented neural controller ensures that all closed-loop signals are bounded and the tracking error converges to a small neighbourhood of the origin in a finite time. The simulation studies are provided to further illustrate the effectiveness of the proposed approach.     

Adaptive feedback control for nonlinear triangular systems subject to uncertain asymmetric dead-zone input

In this paper, an adaptive control strategy is proposed to investigate the issue of uncertain dead-zone input for nonlineartriangular systems with unknown nonlinearities. The considered system has no precise priori knowledge about the dead-zonefeature and growth rate of nonlinearity. Firstly, a dynamic gain is introduced to deal with the unknown growth rate, and thedead-zone characteristic is processed by the adaptive estimation approach without constructing the dead-zone inverse. Then,by virtue of hyperbolic functions and sign functions, a new adaptive state feedback controller is proposed to guarantee theglobal boundedness of all signals in the closed-loop system. Moreover, the uncertain dead-zone input problem for nonlinearupper-triangular systems is solved by the similar control strategy. Finally, two simulation examples are given to verify theeffectiveness of the control scheme.     

Fixed time event-triggered control for high-order nonlinear uncertain systems with time-varying full state constraints

The fixed time event-triggered control for high-order nonlinear uncertain systems with time-varying full state constraints is investigated in this paper. First, the event-triggered control (ETC) mechanism is introduced to reduce the data transmission in the communication channel. In consideration of the physical constraints and engineering requirements, time-varying barrier Lyapunov function (BLF) is deployed to make all the system states confined in the given time-varying constraints. Then, the radial basis function neural networks (RBF NNs) are used to approximate the unknown nonlinear terms. Further, the fixed time stability strategy is deployed to make the system achieve semiglobal practical fixed time stability (SPFTS) and the convergence time is independent of the initial conditions. Finally, the proposed control scheme is verified by two simulation examples.     

Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints

In this paper, adaptive tracking control is proposed for a class of uncertain multi-input and multi-output nonlinear systems with non-symmetric input constraints. The auxiliary design system is introduced to analyze the effect of input constraints, and its states are used to adaptive tracking control design. The spectral radius of the control coefficient matrix is used to relax the nonsingular assumption of the control coefficient matrix. Subsequently, the constrained adaptive control is presented, where command filters are adopted to implement the emulate of actuator physical constraints on the control law and virtual control laws and avoid the tedious analytic computations of time derivatives of virtual control laws in the backstepping procedure. Under the proposed control techniques, the closed-loop semi-global uniformly ultimate bounded stability is achieved via Lyapunov synthesis. Finally, simulation studies are presented to illustrate the effectiveness of the proposed adaptive tracking control.     

不确定非线性离散系统指令滤波事件触发控制

本文提出了考虑输入饱和的一类不确定非线性离散系统的事件触发指令滤波控制方法. 采用指令滤波控制技术解决了传统反步法存在的“因果矛盾”问题, 引入补偿机制提高了系统的控制精度; 利用事件触发机制能够避免自适应律和控制律的频繁更新, 降低了计算负担, 提高了资源利用率; 运用模糊逻辑系统逼近系统中未知的非线性函数; 结合李雅普诺夫稳定性理论, 验证了提出的控制方案能够保证跟踪误差收敛到原点小的邻域内以及闭环系统的所有信号有界. 仿真结果表明, 本文提出的控制方法具有较强的鲁棒性及较好的跟踪性能.     

Adaptive output control of nonlinear systems with uncertain dead-zone nonlinearity

In this note, we present a new scheme to design adaptive controllers for uncertain systems preceded by unknown dead-zone nonlinearity. The control design is achieved by introducing a smooth inverse function of the dead-zone and using it in the controller design with backstepping technique. For the design and implementation of the controller, no knowledge is assumed on the unknown system parameters. It is shown that the proposed controller not only can guarantee stability, but also transient performance.