含执行器故障的切换非线性系统的事件触发自适应模糊容错控制

本文研究在平均驻留时间约束下,一类含有执行器故障的切换非线性系统输出反馈自适应模糊事件触发容错控制问题.首先,建立了一个模态依赖的状态观测器估计不可测量状态.利用模糊逻辑系统来逼近未知项.其次,构建自适应模糊容错事件触发控制方案能够节省网络资源和数据传输.然后,通过构造多Lyapunov函数和平均驻留时间法,证明闭环系统所有状态半全局一致最终有界的同时排除了Zeno现象.最后,通过数值仿真验证该方法的有效性.     

含执行器故障的切换非线性系统的事件触发自适应模糊容错控制

本文研究在平均驻留时间约束下,一类含有执行器故障的切换非线性系统输出反馈自适应模糊事件触发容错控制问题.首先,建立了一个模态依赖的状态观测器估计不可测量状态.利用模糊逻辑系统来逼近未知项.其次,构建自适应模糊容错事件触发控制方案能够节省网络资源和数据传输.然后,通过构造多Lyapunov函数和平均驻留时间法,证明闭环系统所有状态半全局一致最终有界的同时排除了Zeno现象.最后,通过数值仿真验证该方法的有效性.     

切换非线性系统的输出反馈周期事件触发控制

本文针对一类在任意切换信号作用下的切换非线性系统, 研究了其输出反馈周期事件触发控制问题. 所考 虑的非线性系统采用非严格反馈形式且含有未知时变控制系数. 在本文中, 仅利用采样时刻的系统输出. 为了估计 系统的不可量测的状态, 基于采样的系统输出构造了降维状态观测器. 为了减少通信资源的利用, 提出了一种新的 输出反馈周期事件触发策略, 该策略包含仅利用事件触发时刻的信息构造的输出反馈事件触发控制器以及仅在采 样时刻间歇性监测的离散事件触发机制. 通过选取可容许的采样周期及合适的公共Lyapunov函数, 证明了闭环系统 在任意切换下全局渐近稳定. 最后, 通过将本文中所给出的控制方案应用到数值算例中验证了其有效性.     

不确定非线性系统自适应动态事件触发输出反馈镇定

本文研究了一类不确定非线性系统的动态事件触发输出反馈镇定问题. 显著不同的是系统具有依赖于不可测状态的增长且增长率为输出的未知多项式. 尽管已有一些连续自适应控制器, 但需要巧妙融合非线性状态观测器、系统未知性的动态补偿以及非线性的抵御, 因此这些控制器具有一定的脆弱性, 不能平凡地拓展到不连续情形 (采样误差导致). 为此, 首先通过引入动态高增益和基于高增益的观测器来分别抵御未知增长率和重构系统不可测状态. 进而, 意识到静态事件触发机制的无效性, 通过引入动态事件触发机制, 成功设计出了事件触发输出反馈控制器, 确保了系统状态的全局有界性和收敛性. 数值仿真验证了所设计控制器的有效性.     

基于二维系统的迭代学习事件触发鲁棒控制

本文研究了二维系统框架下,带有事件触发机制的不确定离散系统迭代学习鲁棒控制问题.首先为了减少迭代过程中控制信号的更新次数,构建了一种沿迭代轴的事件触发机制,并提出了基于事件触发机制的迭代学习控制算法.基于二维系统理论,将迭代学习过程转化为等价二维Roesser系统.构造李雅普诺夫函数,结合线性矩阵不等式(LMI)技术,给出了系统渐近稳定的充分条件,进一步得到了控制器增益的求取方法.最后仿真结果验证了提出的事件触发机制的有效性.     

事件触发双模分布式预测控制

本文针对有界扰动作用下的线性离散大系统,提出了事件触发双模分布式预测控制设计方法.利用输入状态稳定性(input-to-state stability,ISS)理论建立了仅与子系统自身信息相关的事件触发条件.只有子系统满足相应的事件触发条件,才进行状态信息的传输和分布式预测控制优化问题的求解,并与邻域子系统交互最优解作用下的关联信息.当子系统进入不变集时,采用状态反馈控制律进行镇定,并与进入不变集的邻域子系统不再交互信息.分析了算法的递推可行性和系统的闭环稳定性,给出了扰动的上界.最后,通过车辆控制系统对算法进行仿真验证,结果表明,本文提出的方法能够有效降低优化问题的求解次数和关联信息的交互次数,节约计算资源和通信资源.     

基于事件触发的离散 MIMO 系统自适应评判容错控制

本文针对具有执行器故障的一类离散非线性多输入多输出(Multi-input multi-output, MIMO)系统, 提出了一种基于事件触发的自适应评判容错控制方案. 该控制方案包括评价和执行网络. 在评价网络里, 为了缓解现有的非光滑二值效用函数可能引起的执行网络跳变问题, 利用高斯函数构建了一个光滑的效用函数, 并采用评价网络近似最优性能指标函数. 在执行网络里, 通过变量替换将系统状态的将来信息转化成关于系统当前状态的函数, 并结合事件触发机制设计了最优跟踪控制器. 该控制器引入了动态补偿项, 不仅能够抑制执行器故障对系统性能的影响, 而且能够改善系统的控制性能. 稳定性分析表明所有信号最终一致有界且跟踪误差收敛于原点的有界小邻域内. 数值系统和实际系统的仿真结果验证了该方案的有效性.     

基于T-S模糊双线性模型的事件触发滑模控制

针对模糊双线性系统,研究一类事件触发滑模控制;求出使控制器分母为零的奇异区域,选定事件触发阈值,确定事件触发需满足的条件区域,使奇异区域包含于条件区域;构造积分滑模面,得出常规滑模控制律,并在此基础上,结合事件触发机制,对控制律进行增广,设计辅助控制律,使得系统状态临近奇异区域时,得以平稳过渡,保证系统稳定性,同时保证状态正常到达滑模面;根据设计的事件触发滑模控制律,考虑系统状态趋近奇异区域的不同情况,结合李雅普诺夫稳定性条件,分别对系统状态的可达性和稳定性进行证明;最后,以数值仿真验证所提方法的有效性.     

一类线性切换系统的事件触发输出调节问题

通过设计事件触发状态反馈控制器,研究一类线性切换系统的输出调节问题.相比于时间触发控制,事件触发控制可显著地降低控制任务执行的次数.由于事件触发时刻与切换时刻的相互混合,导致切换系统控制器的设计十分困难.本文通过给出事件触发机制、状态反馈控制器和满足平均驻留时间的切换信号联合设计的方案,得到系统输出调节问题可解的充分条件.非共同坐标变换突破原有各子系统的调节器方程组具有共同解的限制.此外,为了避免Zeno现象,给出相邻事件触发间隔时间存在一个正下界.最后,应用切换RLC电路系统进行仿真验证结论的有效性.     

输入死区和全状态约束下不确定非线性系统的快速稳定事件触发控制

在实际工业系统中普遍存在输入死区、全状态约束等不可忽视的问题,其对系统的性能造成较大的影响,甚至可能会导致系统不稳定.为了克服上述问题,针对一类不确定非线性系统,提出一种快速收敛的自适应神经网络事件触发控制方法.首先,将障碍Lyapunov函数引入到反步控制框架中,采用径向基函数神经网络逼近未知非线性函数,同时设计自适应事件触发机制对输入死区进行动态补偿,通过减少控制信号的更新频率来减轻系统的通信负担,并保证系统所有状态不违反预定义的约束区间.在此基础上,引入快速有限时间稳定理论,在有限时间内能够保证闭环系统所有信号的有界性以及跟踪误差快速收敛到有界的紧集内.最后,通过两个仿真算例验证所提出控制方法的有效性.     

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.     

Event-triggered Control of Positive Systems With State Saturation Using Linear Programming

This paper proposes the event-triggered control of positive systems with state saturation both in discrete-time and continuous-time cases. A 1-norm based event-triggered mechanism is established for positive systems. Under the event-triggered mechanism, the error term between actual and sample states is transformed into interval uncertain form. Together with the properties of saturation, the systems with state saturation are transformed into interval uncertain systems and the corresponding lower and upper bounds of the system matrices are obtained, respectively. Using a linear co-positive Lyapunov function, the event-triggered controller and the auxiliary gain matrix of the domain of attraction are designed in terms of linear programming, respectively. Then, the systems with state and input saturation are also described via interval uncertain systems. An event-triggered controller is designed and thus the closed-loop systems are positive and stable under the designed controller. Furthermore, the presented event-triggered control approach is extended to the continuous-time case. Compared to existing control approaches, the event-triggered control can reduce energy consumption and increase the practicability. Finally, several numerical examples are given to illustrate the effectiveness of the proposed design.

     

柔性机械臂动力学建模研究进展

刚性机械臂由于其较高的工作精度和重复性、较强的承载能力,已广泛应用于危险或相对单一、重复性高工作场景.但刚性机械臂的结构及运作方式不够灵活,无法适用于不定型、非标准、狭窄空间等生产场景.最近几年,柔性机械臂因其结构柔性、作业空间大、人机交互安全等优点而受到广泛关注,有希望应用于医疗、服务和智能制造等领域.但柔性机械臂结构柔软,运动比较自由,在作业过程中柔性效应不可忽略,这对其高精度控制提出了重大挑战.柔性机械臂控制的核心科学问题之一是建立包含结构柔性特征和动态特性的高精度动力学模型.为此,本文对柔性机械臂运动学建模和动力学建模研究进行了综述.作为动力学建模的基础,本文首先综述了柔性机械臂的运动学建模方法,主要介绍了曲率法、伪刚体运动学(PRB)方法、基于Cosserat杆的运动学建模方法、结构几何分析方法、Denavit Hartenberg(D H)法及坐标法、数据驱动和机器学习方法等.随后,本文详细综述了柔性机械臂的动力学建模方法,主要包括集中参数系统法、假设模态法、有限元法.最后,本文简述了目前柔性机械臂动力学研究的主要内容,并对未来研究做出展望.     

Adaptive critic learning for event-triggered safe control of nonlinear safety-critical systems

In this paper, an event-triggered safe control method based on adaptive critic learning (ACL) is proposed for a class of nonlinear safety-critical systems. First, a safe cost function is constructed by adding a control barrier function (CBF) to the traditional quadratic cost function; the optimization problem with safety constraints that is difficult to deal with by classical ACL methods is solved. Subsequently, the event-triggered scheme is introduced to reduce the amount of computation. Further, combining the properties of CBF with the ACL-based event-triggering mechanism, the event-triggered safe Hamilton–Jacobi–Bellman (HJB) equation is derived, and a single critic neural network (NN) framework is constructed to approximate the solution of the event-triggered safe HJB equation. In addition, the concurrent learning method is applied to the NN learning process, so that the persistence of excitation (PE) condition is not required. The weight approximation error of the NN and the states of the system are proven to be uniformly ultimately bounded (UUB) in the safe set with the Lyapunov theory. Finally, the availability of the presented method can be validated through the simulation.     

Adaptive fuzzy dynamic surface control for uncertain nonlinear systems

In this paper, a robust adaptive fuzzy dynamic surface control for a class of uncertain nonlinear systems is proposed. A novel adaptive fuzzy dynamic surface model is built to approximate the uncertain nonlinear functions by only one fuzzy logic system. The approximation capability of this model is proved and the model is implemented to solve the problem that too many approximators are used in the controller design of uncertain nonlinear systems. The shortage of ＂explosion of complexity＂ in backstepping design procedure is overcome by using the proposed dynamic surface control method. It is proved by constructing appropriate Lyapunov candidates that all signals of closed-loop systems are semi-globally uniformly ultimate bounded. Also, this novel controller stabilizes the states of uncertain nonlinear systems faster than the adaptive sliding mode controller （SMC）. Two simulation examples are provided to illustrate the effectiveness of the control approach proposed in this paper.     

一类非线性离散系统模糊控制器的分析和设计

针对一类非线性离散不确定系统,在系统状态不可测的情况下,以T-S模型描述不同状态空间的局部动态区域,并通过中心平均反模糊化、乘积推理、单点模糊化方法得到全局模糊系统模型.基于李亚普诺夫理论和线性矩阵不等式,设计了一种基于观测器的鲁棒控制器,并对离散状态下的此类系统进行了稳定分析.最后通过M ATLAB仿真,证明了该方法的有效性.     

H∞ output tracking control of uncertain and disturbed nonlinear systems based on neural network model

The work presented in this paper seeks to address the tracking problem for uncertain continuous nonlinear systems with external disturbances. The objective is to obtain a model that uses a reference-based output feedback tracking control law. The control scheme is based on neural networks and a linear difference inclusion (LDI) model, and a PDC structure and H_∞ performance criterion are used to attenuate external disturbances. The stability of the whole closed-loop model is investigated using the well-known quadratic Lyapunov function. The key principles of the proposed approach are as follows: neural networks are first used to approximate nonlinearities, to enable a nonlinear system to then be represented as a linearised LDI model. An LMI (linear matrix inequality) formula is obtained for uncertain and disturbed linear systems. This formula enables a solution to be obtained through an interior point optimisation method for some nonlinear output tracking control problems. Finally, simulations and comparisons are provided on two practical examples to illustrate the validity and effectiveness of the proposed method.     

On Maximum Stability Margin Design of Nonlinear Uncertain Systems: Fuzzy Control Approach

This paper studies the maximum stability margin design for nonlinear uncertain systems using fuzzy control. First, the Takagi and Sugeno fuzzy model is employed to approximate a nonlinear uncertain system. Next, based on the fuzzy model, the maximum stability margin for a nonlinear uncertain system is studied to achieve as much tolerance of plant uncertainties as possible using a fuzzy control method. In the proposed fuzzy control method, the maximum stability margin design problem is parameterized in terms of a corresponding generalized eigenvalue problem (GEVP). For the case where state variables are unavailable, a fuzzy observer‐based control scheme is also proposed to deal with the maximum stability margin for nonlinear uncertain systems. Using a suboptimal approach, we characterize the maximum stability margin via fuzzy observer‐based control in terms of a linear matrix inequality problem (LMIP). The GEVP and LMIP can be solved very efficiently via convex optimization techniques. Simulation examples are given to illustrate the design procedure of the proposed method.