参数未知非线性混沌系统的自适应反演滑模控制

针对一类含有非匹配未知参数和非参数不确定性的混沌系统,基于自适应反演和滑模控制方法,研究自适应反演滑模控制策略,实现了不确定混沌系统的调节问题。与现有自适应控制相比,允许系统存在非匹配未知参数和非参数化的不确定性,增强了控制系统的鲁棒性。仿真算例证明了理论研究成果的正确性和鲁棒性。     

一类非线性系统的自适应模糊滑模定位控制

针对一类由多子系统组成的,具有建模误差和未知不确定性的多变量非线性系统,提出了一种自适应鲁棒定位控制方案。分别在系统数学模型已知或未知的情形下,通过对不确定性的未知范数界描述,基于Lyapunov理论和Barbalat引理,给出了滑模鲁棒控制器的综合设计方法及其自适应控制律,保证整个闭环误差系统的稳定。该方法减少了对系统模型精确度的依赖,避免了传统方法对不确定性的人为预估行为。最后,通过船舶动力定位系统的控制仿真表明了本方法的有效性。     

一类不确定离散网络系统的全局滑模控制

针对动态传输控制协议(transmission control protocol, TCP)网络的拥塞问题,基于离散全局滑模控制理论设计了一种主动队列管理(active queue management, AQM)算法,该算法消除了滑模控制的到达阶段,保证网络系统在整个控制过程中的鲁棒性。为了减弱离散指数趋近律的抖振现象,给出了一种改进的滑模趋近律,使得复杂的网络系统具有良好的性能。仿真结果验证了所设计的控制器对活动的TCP连接数、链路带宽及往返时延的不确定性具有很强的稳定性和鲁棒性。     

基于观测器的不确定非线性系统L1自适应控制

针对一类严格块反馈型不确定非线性系统,采用反步设计方法提出了一种新的基于滑模状态观测器的L1自适应控制方案。由于系统状态不完全可测,首先设计滑模观测器对系统状态进行估计,并分析观测器的收敛性,在此基础上,通过反步法得到系统的理想控制律。为了消除反步控制中的“项数膨胀”,引入非线性跟踪微分器来提取理想控制律的微分信号。为提高系统响应的瞬态性能,消除控制输入的高频振荡,引入L1自适应控制对控制律进行改进,通过理论推导证明了闭环系统的稳定性。最后通过数值仿真算例验证了所设计控制方案具有快速的收敛性、良好的跟踪性等期望性能。     

Global robust optimal sliding mode control for uncertain affine nonlinear systems

The problem of robustifying linear quadratic regulators (LQRs) for a class of uncertain affine nonlinear systems is considered. First, the exact linearization technique is used to transform an uncertain nonlinear system into a linear one and an optimal LQR is designed for the corresponding nominal system. Then, based on the integral sliding mode, a design approach to robustifying the optimal regulator is studied. As a result, the system exhibits global robustness to uncertainties and the ideal sliding mode dynamics is the same as that of the optimal LQR for the nominal system. A global robust optimal sliding mode control (GROSMC) is realized. Finally, a numerical simulation is demonstrated to show the effectiveness and superiority of the proposed algorithm compared with the conventional optimal LQR.     

塔式起重机的神经网络滑模防摆控制

针对塔式起重机存在的负载摆动, 分析塔式起重机的动力学模型, 提出了一种基于遗传算法的塔式起重机神经网络滑模防摆控制新方法. 利用RBF神经网络输出逼近系统的不确定项, 并运用遗传算法优化滑模控制器的参数, 使得参数的收敛速度加快. 该方法削弱了滑模控制系统的高频抖振, 提高了系统的控制性能, 改善了系统的控制品质. 仿真结果表明方法的有效性和可行性.     

Adaptive neural control for a class of uncertain stochastic nonlinear systems with dead-zone

The problem of adaptive stabilization is addressed for a class of uncertain stochastic nonlinear strict-feedback systems with both unknown dead-zone and unknown gain functions.By using the backstepping method and neural network(NN) parameterization,a novel adaptive neural control scheme which contains fewer learning parameters is developed to solve the stabilization problem of such systems.Meanwhile,stability analysis is presented to guarantee that all the error variables are semi-globally uniformly ultimately bounded with desired probability in a compact set.The effectiveness of the proposed design is illustrated by simulation results.     

Discrete sliding mode prediction control of uncertain switched systems

The robust stabilization problem for a class of uncertain discrete-time switched systems is presented. A predictive sliding mode control strategy is proposed, and a discrete-time reaching law is improved. By applying a predictive sliding surface and a reference trajectory, combining with the state feedback correction and rolling optimization method in the predictive control strategy, a predictive sliding mode controller is synthesized, which guarantees the asymptotic stability for the closed-loop systems. The designed control strategy has stronger robustness and chattering reduction property to conquer with the system uncertainties. In addition, a unique nonswitched sliding surface is designed. The reason is to avoid the repetitive jump of the trajectories of the state components of the closed-loop system between sliding surfaces because it might cause the possible instability. Finally, a numerical example is given to illustrate the effectiveness of the proposed theory.     

基于RBF神经网络的鲁棒滑模观测器设计

针对非线性不确定性系统,提出一种鲁棒滑模观测器。所提出的鲁棒滑模观测器通过滑模与相应的控制策略来实现。设计参数的选取不需要求解大量方程,同时能保证对系统的非线性不确定性具有鲁棒性,系统中不确定性的上界值采用RBF神经网络进行自适应学习。通过设计滑模,可以调整观测器跟踪系统状态的收敛速度,使状态估计达到预期的指标。仿真结果验证了提出方法的有效性。     

Neural adaptive sliding mode speed tracking control of a DC motor

We propose a BPNN based adaptive sliding mode control scheme for speed tracking of a DC motor with unknown system nonlinearities. The input-output linearization technique is used to cancel the nonlinearities, and output of the BPNN is incorporated into the controller in the proposed scheme. It is shown that the rotor speed of a DC motor can follow any arbitrarily selected trajectories under variable load torque. Then the application of the approach is tested via some simulations.     

分散自适应模糊滑模控制器与车辆跟随控制

针对一类不确定非线性大系统提出了一种新的组合型自适应分散模糊滑模控制算法.借助模糊逻辑系统与滑模方法设计的控制器加权组合了间接与直接自适应两种模糊控制器,可以同时融合被控对象知识与控制知识两种模糊信息来提高自适应效果.在一类大系统框架下基于统一的格式提出了间接与直接自适应模糊控制设计方案,并进一步消除了现有各种相关控制算法的缺陷.闭环大系统被证明是一致渐近稳定的.该算法应用于自动公路系统车辆的跟随控制,仿真结果表明提出的组合型自适应模糊控制系统比通常间接或直接自适应模糊控制系统具有更好的跟踪性能.     

Decentralized model reference adaptive sliding mode control based on fuzzy model

1.INTRODUCTIONIn recent years,decentralized control of interconnect-ed systems has become an i mportant and challengingtopic[1~3].However,it is usually difficult to modelthe systemexactly due to the complex real environ-ment.In the past several years,active research hasbeen carried out in controller design based on univer-sal approxi mators,such as fuzzy control and neuralnetwork control[4~6].For a class of SISO nonlinearsystems,adaptive fuzzy control approaches were pro-posed based on …     

不确定多变量线性系统的快速收敛滑模控制

讨论了一类不确定多变量线性系统滑模变结构控制的快速收敛问题,提出一种指数型快速终端滑模变结构控制方案。通过非奇异线性变换将不确定系统化为简约标准型,而后设计了非线性指数型快速终端滑模超曲面,给出了滑模面参数矩阵的选择方法。在此基础上设计了变结构控制律,使系统状态在有限时间内到达滑模面上,随后保持在滑模面上并按指数型终端滑模的规律运动,在有限时间内收敛到平衡点。与普通的快速终端滑模相比,系统状态能以更短的时间到达平衡点。该方法具有全局稳定性和鲁棒性,仿真结果证明其有效性。     

不确定奇异系统的Terminal滑模控制

针对一类不确定奇异系统,研究其Terminal滑模控制问题。通过非奇异线性变换把不确定奇异系统变换成受限等价形式,利用Lyapunov函数方法,首次提出了一种新的Terminal滑模控制策略,给出了特殊的Terminal滑模超曲面,设计了相应的滑模控制器,实现了滑模运动,保证了系统状态变量在有限时间内收敛到平衡点。给出数值算例,说明了方法的有效性。     

基于滑模观测器的非线性系统迭代学习控制

针对相当广泛的一类非线性系统有限时间轨迹跟踪问题,提出了一种基于滑模观测器的迭代学习控制算法。根据系统的非线性特性,利用一种滑模观测器对系统的状态进行估计,根据估计信号设计了一种类D型开环迭代学习控制律。这种控制方法不需要对系统的跟踪误差信号进行微分,从而对系统的量测噪声不敏感。给出了控制算法的收敛性证明,通过仿真实验证明了这种算法的有效性。     

New adaptive quasi-sliding mode control for nonlinear discrete-time systems

A new adaptive quasi-sliding mode control algorithm is developed for a class of nonlinear discrete-time systems, which is especially useful for nonlinear systems with vaguely known dynamics. This design is model-free, and is based directly on pseudo-partial-derivatives derived on-line from the input and output information of the system using an improved recursive projection type of identification algorithm. The theoretical analysis and simulation results show that the adaptive quasi-sliding mode control system is stable and convergent.     

非线性系统执行器死区故障的鲁棒自适应控制

针对一类具有不对称执行器死区故障的不确定非线性系统,基于反推滑模控制原理提出了一种神经网络鲁棒自适应控制方案。通过简化死区故障模型,取消了模型倾斜度相等和边界对称条件,结合动态面控制避免了传统反推设计方法存在的计算复杂性问题。所提控制方案取消了控制方向已知的条件,消除了执行器死区故障的影响,使得系统输出趋于给定参考轨迹的一个小领域。仿真结果验证了该方法的有效性。     

一类基于神经网络非线性随机系统自适应滤波

给出非线性MIMO随机系统可观性定义和条件，将非线性SISO确定性系统局部可观性理论拓展到非线性MIMO随机系统，基于这一理论在系统模型和噪声统计未知情况下，提出一类基于神经网络的非线性离散随机系统自适应滤波器的设计方法，考虑过程方程的动态特性和输出方程的静态特性，设计了动态神经网络作为系统的滤波器，前馈神经网络作为系统的输出预报器，充分利用已知观测信息训练两个神经网络，从而提高了状态估计的精度，该方法克服了扩展Kalman滤波要求模型和统计特性精确已知的不足，仿真例子验证了所提出的估计方法的有效性。     

Decentralized direct adaptive neural network control for a class of interconnected systems

1 .INTRODUCTIONA number of interconnected systems found in theworld, such as electric power systems ,industrymanipulators and computer networks , are oftencomposed of a set of subsystems . A centralizedcontrol strategy for the requirement of a large a-mount of information exchange between the sub-systems . A decentralized control method, devel-oped based only on local measurements ,is oftenpreferable . At present ,there have been many re-search results for adaptive control of interconnec-…     

一类非线性系统的滑模控制稳态精度研究

针对一类非线性系统的线性滑模控制和Terminal滑模控制稳态精度问题,分析使用饱和函数代替滑模控制律中符号函数的线性滑模控制和Terminal滑模控制系统的稳态误差与饱和函数宽度的数值关系,得出两者的数学表达式,为协调既削弱滑模控制的颤振又保证稳态精度的滑模控制系统的饱和函数宽度的选择提供了理论依据。定量地比较线性滑模控制和Terminal滑模控制的稳态精度,得出了Terminal滑模控制比线性滑模控制有更好稳态精度的结论,通过数值仿真算例证明理论分析的正确性。