Dynamic Event-Triggered Consensus Control for Input Constrained Multi-Agent Systems With a Designable Minimum Inter-Event Time

This paper investigates the consensus control of multi-agent systems(MASs) with constrained input using the dynamic event-triggered mechanism(ETM).Consider the MASs with small-scale networks where a centralized dynamic ETM with global information of the MASs is first designed.Then,a distributed dynamic ETM which only uses local information is developed for the MASs with large-scale networks.It is shown that the semi-global consensus of the MASs can be achieved by the designed bounded control protocol where the Zeno phenomenon is eliminated by a designable minimum inter-event time.In addition,it is easier to find a trade-off between the convergence rate and the minimum inter-event time by an adjustable parameter.Furthermore,the results are extended to regional consensus of the MASs with the bounded control protocol.Numerical simulations show the effectiveness of the proposed approach.

Neural adaptive chaotic control with constrained input using state and output feedback

This paper presents neural adaptive control methods for a class of chaotic nonlinear systems in the presence of constrained input and unknown dynamics. To attenuate the influence of constrained input caused by actuator saturation, an effective auxiliary system is constructed to prevent the stability of closed loop system from being destroyed. Radial basis function neural networks（RBF-NNs） are used in the online learning of the unknown dynamics, which do not require an off-line training phase. Both state and output feedback control laws are developed. In the output feedback case, high-order sliding mode（HOSM） observer is utilized to estimate the unmeasurable system states. Simulation results are presented to verify the effectiveness of proposed schemes.

Constrained sliding mode control of nonlinear fractional order input affine systems

Asymptotic stability of nonlinear fractional order affine systems with bounded inputs is dealt.The main contribution is to design a new bounded fractional order chattering free sliding mode controller in which the system states converge to the sliding surface at a determined finite time.To eliminate the chattering in the sliding mode and make the input controller bounded,hyperbolic tangent is used for designing the proposed fractional order sliding surface.Finally,the stability of the closed loop system using this bounded sliding mode controller is guaranteed by Lyapunov theory.A comparison with the integer order case is then presented and fractional order nonlinear polynomial systems are also studied as the special case.Finally,simulation results are provided to show the effectiveness of the designed controller.

Event-triggered H_(∞) consensus control for input-constrained multi-agent systems via reinforcement learning

This article presents an event-triggered H_(∞) consensus control scheme using reinforcement learning (RL) for nonlinear second-order multi-agent systems (MASs) with control constraints. First, considering control constraints, the constrained H_(∞) consensus problem is transformed into a multi-player zero-sum game with non-quadratic performance functions. Then, an event-triggered control method is presented to conserve communication resources and a new triggering condition is developed for each agent to make the triggering threshold independent of the disturbance attenuation level. To derive the optimal controller that can minimize the cost function in the case of worst disturbance, a constrained Hamilton–Jacobi–Bellman (HJB) equation is defined. Since it is difficult to solve analytically due to its strongly non-linearity, reinforcement learning (RL) is implemented to obtain the optimal controller. In specific, the optimal performance function and the worst-case disturbance are approximated by a time-triggered critic network;meanwhile, the optimal controller is approximated by event-triggered actor network. After that, Lyapunov analysis is utilized to prove the uniformly ultimately bounded (UUB) stability of the system and that the network weight errors are UUB. Finally, a simulation example is utilized to demonstrate the effectiveness of the control strategy provided.

Neural-Network-Based Control for Discrete-Time Nonlinear Systems with Input Saturation Under Stochastic Communication Protocol

In this paper,an adaptive dynamic programming(ADP)strategy is investigated for discrete-time nonlinear systems with unknown nonlinear dynamics subject to input saturation.To save the communication resources between the controller and the actuators,stochastic communication protocols(SCPs)are adopted to schedule the control signal,and therefore the closed-loop system is essentially a protocol-induced switching system.A neural network(NN)-based identifier with a robust term is exploited for approximating the unknown nonlinear system,and a set of switch-based updating rules with an additional tunable parameter of NN weights are developed with the help of the gradient descent.By virtue of a novel Lyapunov function,a sufficient condition is proposed to achieve the stability of both system identification errors and the update dynamics of NN weights.Then,a value iterative ADP algorithm in an offline way is proposed to solve the optimal control of protocol-induced switching systems with saturation constraints,and the convergence is profoundly discussed in light of mathematical induction.Furthermore,an actor-critic NN scheme is developed to approximate the control law and the proposed performance index function in the framework of ADP,and the stability of the closed-loop system is analyzed in view of the Lyapunov theory.Finally,the numerical simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Receding horizon H_∞ control for constrained time-delay systems

A receding horizon Hoo control algorithm is presented for linear discrete time-delay system in the presence of constrained input and disturbances. Disturbance attenuation level is optimized at each time instant, and the receding optimization problem includes several linear matrix inequality constraints. When the convex hull is applied to denote the saturating input, the algorithm has better performance. The numerical example can verify this result.

Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems

In this paper, neural networks are used to approximately solve the finite-horizon constrained input H-infinity state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game. The game value function is approximated by a neural network with time- varying weights. It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded L2 gain. The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line. The effectiveness of the method is shown on the Rotational/Translational Actuator benchmark nonlinear control problem.

输入饱和约束下导弹自动驾驶仪鲁棒最优自适应控制

针对遭受外部扰动和输入饱和约束条件下的导弹自动驾驶仪系统,研究其鲁棒最优控制问题。首先,引入光滑函数处理输入饱和约束,并设计超螺旋干扰观测器实现对外部扰动的实时估计;然后,结合滑模控制理论,设计基于观测器的自适应滑模控制策略;接着,针对等效滑动模态系统,引入自适应动态规划算法构造评价网络求解哈密顿⁃雅可比⁃贝尔曼(HJB)方程,设计新的更新律在线学习最优控制策略使得系统稳定的同时满足预设的性能指标;最后,导弹自动驾驶仪系统的仿真结果表明了算法的有效性。

输入受限无人帆船自适应航向跟踪控制

针对带有输入受限且存在未知控制系数约束情形下,考虑了具有未知模型和风、浪等外界干扰的无人船航向跟踪控制问题,提出一种受限控制输入约束下的跟踪控制方法。该方法运用RBF神经网络对未知模型进行在线逼近,利用Nussbaum自适应增益技术解决未知控制系数问题。根据滑模控制理论,设计具有指数趋近律的鲁棒控制项,保证所得误差闭环系统快速响应且最终趋向0。为弱化传统滑模控制产生的抖振问题,将符号函数替换成饱和函数使控制输入变得平滑。引入一种误差辅助系统,构建了帮助误差闭环系统输入退出饱和机制。通过李雅普诺夫稳定性理论,给出了跟踪控制方法的稳定性数学分析过程,证明误差闭环跟踪控制系统的所有信号最终一致有界性。最后通过仿真结果验证了所得理论的有效性。

单天线功率约束和统计CSI下的MU-MIMO下行链路线性预编码

近年来,多用户多输入多输出(Multiple-User Multiple-Input Multiple-Output,MU-MIMO)下行链路的预编码算法设计吸引了越来越多研究者的兴趣。然而目前并没有对基站端已知信道误差概率分布且约束条件为单天线功率约束(Per-Antenna Power Constraints,PAPCS)的情况下的线性预编码算法的研究。针对上述情况,以遍历和速率(Expected Sum Rate)最大化为优化准则,主要基于约束随机逐次凸近似(Constrained Stochastic Successive Convex Approximation,CSSCA)、二阶对偶法、交替方向乘子法(Alternating Direction Method of Multipliers,ADMM)及高斯随机化(Gaussian Randomization)设计了线性预编码算法。所提算法的适用场景更符合实际情况,而且实验仿真结果证明,算法的性能较好。

持续有界扰动下的非线性H_∞鲁棒预测控制

针对未知但有界的持续扰动,提出了一种约束非线性H_∞鲁棒预测控制策略.首先,引入离散系统的输入状态稳定性概念;其次,采用仿射输入定义预测控制的控制律,并给出相应终端约束集的估计解法.进一步,得到预测控制闭环系统的鲁棒稳定性结论.最后,数值仿真验证了上述策略的有效性.

基于自适应动态规划的矿渣微粉生产过程跟踪控制

矿渣微粉是一种新型绿色环保型建材,可以大大提高水泥混凝土的力学性能.本文以矿渣微粉生产过程为研究对象,针对该过程难以通过机理建模进行辨识和控制的特点,利用数据驱动的思想,建立矿渣微粉生产过程的递归神经网络模型.在此基础上,利用自适应动态规划,设计具有控制约束的跟踪控制器,并将其应用到矿渣微粉生产过程中.仿真分析表明,建立的数据驱动模型能够有效地辨识矿渣微粉生产过程,同时,本文提出的控制方法能够实现输入受限的微粉比表面积及磨内压差的最优跟踪控制.

输入受限系统的多模型自适应控制(英文)

针对时不变或含跳变参数的时变被控对象建立多个模型 ,并构成多模型输入受限控制器 .每个采样时刻基于性能指标 ,选择与被控对象最接近的模型 ,将基于此模型的控制器切换为当前控制器 .多模型控制器可对时不变被控对象或含跳变参数的时变被控对象 ,在保证输入输出稳定的同时迅速改善瞬态响应 ,并且使输出渐近跟踪设定值 .

输入受限广义预测控制算法的可行性

在系统输入受限的情况下,采用一般的广义预测控制或受限时域预测控制有可能会造成控制算法不可行,从而使系统的性能变坏或不稳定.针对这个问题,本文提出一种保证可行性的控制算法,并证明该算法能够使系统渐近稳定且最终使条件设定值跟踪系统的实际设定值.

Markov跳变系统的单步约束预测控制

针对离散Markov跳变系统,研究带输出约束的单步预测控制问题。首先设计基于模态的单步预测控制器,驱动系统状态到达相应的终端不变集内,以此保证约束预测控制系统的稳定性,接着进一步讨论了系统存在峰值有界噪声输入情形。其中终端不变集是通过在预测时域外寻求一个虚拟的带约束的状态反馈控制器以保证系统的随机稳定性来获得,为计算方便,控制器的优化问题转化为SDP问题,并得到以LMI描述的优化问题。最后对Markov跳变系统描述的经济学动力系统的仿真结果说明了本控制器设计方法能够在峰值有界噪声输入下保证系统的稳定性,并且使控制变量满足给定的约束条件。

多变量非线性系统的有约束模糊预测控制

针对多变量非线性系统提出了一种带约束输入的广义预测控制(GPC)算法.首先对多变量非线性系统建立T-S模糊模型,利用模糊聚类算法和正交最小二乘算法对输入变量的模糊划分及后件部分的参数分别进行辨识,然后在每个采样点对系统进行局部动态线性化.根据得到的系统线性化模型设计GPC算法,该算法充分考虑了控制输入及其增量受约束的情况,而且不必求D iophantine方程,大大减小了计算量.仿真结果表明该算法能保证系统输出有效跟踪设定值,而且控制输入和控制增量均在其约束范围之内.

多变量非线性系统的有约束模糊预测解耦控制

针对多变量非线性系统提出了一种带约束输入的广义预测解耦控制算法:首先对多变量非线性系统建立T-S模糊模型,然后在每个采样点对系统进行局部动态线性化,将得到的系统线性化模型进行对角解耦,然后对其设计带输入约束的GPC算法.该算法充分考虑了控制输入及其增量受约束的情况,而且不必求Dio-phantine方程,减小了计算量,且削弱了变量之间的耦合程度.最后的仿真结果说明了该算法对多变量非线性系统的有效性.

仿射非线性系统的广义逐点最小范数控制

在Freeman的逐点最小范数控制器的基础上,提出了一种新的非线性控制器设计框架-广义逐点最小范数控制器,并证明了其连续性.通过一个引导函数,新的控制器可以和其他的控制器设计策略结合,从而大大提高了控制器设计的灵活性.另外,给出了新方法的两个应用:改善局部线性化控制器稳定域较小的缺陷;及和其它控制器设计方法结合,使之能够简单有效地处理具有输入约束的系统.

有界扰动多变量Hammerstein系统输入到状态稳定模型预测控制

考虑具有状态和控制约束的有界未知扰动多变量Hammerstein系统,提出一种具有输入到状态稳定和有限L_2增益性能的鲁棒非线性模型预测控制策略.基于多变量线性子系统H_∞控制律,滚动预测非线性代数方程的解算误差,继而在线优化计算满足系统约束条件的预测控制量.利用输入到状态稳定性概念和L_2增益思想,建立闭环系统关于该扰动信号具有鲁棒稳定性和L_2增益的充分条件,使闭环系统不仅满足系统约束,而且对不确定扰动输入和解算误差具有鲁棒性.最后以工业聚丙烯多牌号切换过程控制为例,仿真验证本文算法的有效性.

一种基于路径的测试数据自动生成算法

提出了一种新的基于路径测试数据自动生成的算法.该算法将路径中的线性谓词函数直接作为线性算术表示来构造谓词函数关于输入变量的线性约束,仅当谓词函数是输入变量的非线性函数时,才计算其线性算术表示.因而不必计算所有谓词函数的线性算术表示,也不必计算谓词片和确定输入依赖集,以及构造谓词函数关于输入变量的增量的线性约束.理论分析和实例证明,该算法具有简单、容易、有效且计算量小等特点.