共查询到19条相似文献,搜索用时 171 毫秒
1.
2.
3.
含有非线性不确定参数的电液系统滑模自适应控制 总被引:3,自引:1,他引:2
针对含有非线性不确定参数的电液控制系统, 提出了一种滑模自适应控制方法. 该控制方法主要是为了解决由于初始控制容积的不确定性而引起的, 非线性不确定参数自适应律设计的难题. 其主要特点为, 通过定义一个新型的特Lyapunov 函数, 进而构建系统的自适应控制器及参数自适应律, 并结合滑模控制方法及一种简单的鲁棒设计方法, 给出整个电液系统的滑模自适应控制器, 及所有不确定参数的自适应律. 试验结果表明, 采用该控制方法能够取得良好的性能, 尤其可以补偿非线性不确定参数对系统的影响. 相似文献
4.
一类非线性不确定系统的最优自适应控制 总被引:1,自引:1,他引:1
研究了一类含有系统扰动,并且状态项与控制项中同时含有未知参数的非线性系统的反馈稳定问题.在控制器的设计中,将原系统的自适应稳定问题转化为扩展系统的非自适应稳定问题,并利用扩展系统的鲁棒控制Lyapunov函数,设计出使原系统自适应稳定的控制律.进一步,利用逆最优的方法,证明了该控制律同时也是满足某种性能指标的最优控制。 相似文献
5.
针对传统自适应控制系统设计的自适应律参数收敛慢进而影响控制系统瞬态性能的问题,研究一类新的基于参数估计误差修正的鲁棒自适应律设计.首先引入滤波操作给出参数估计误差的提取方法,构建出含参数估计误差修正项的自适应律,进而将该自适应律用于控制器设计和分析中,可同时实现控制误差和参数估计误差指数收敛.对比分析了几类传统自适应律和所提出自适应律的收敛性和鲁棒性,并给出了保证参数收敛所需持续激励条件的一种直观、简便的在线判别方法.数值仿真及基于自制三自由度直升机系统俯仰轴实验结果表明,基于参数误差修正的自适应律及控制器可得到优于传统自适应方法的跟踪控制和参数估计性能. 相似文献
6.
针对带有未知参数的惯性轮摆系统,提出了一种自适应控制律设计方法。首先利用坐标变换将惯性轮摆系统的动力学模型转化为级联系统的形式。然后,针对系统参数未知的问题,在已有的惯性轮摆系统反馈控制律的基础上,利用控制器迭代设计思想,设计了惯性轮摆系统的自适应控制律,并利用李雅普诺夫稳定性理论证明了所得自适应控制律可以使得带有未知参数的惯性轮摆系统保持在摆杆垂直向上的平衡状态。最后以一个实际的惯性轮摆系统为例,采用该系统的物理参数进行仿真,分析了不同自适应参数下惯性轮摆系统各状态的收敛速度及摆起和稳定时间。仿真结果验证了所设计自适应控制器能够使惯性轮摆系统从垂直向下的平衡位置摆起并稳定在垂直向上的平衡位置。 相似文献
7.
非线性离散时间系统的自适应模糊补偿控制 总被引:1,自引:0,他引:1
针对一类非线性离散时间系统,提出一种自适应模糊逻辑补偿控制方案.控制律由跟踪控制律和逼近误差补偿控制律两部分组成,利用模糊逻辑系统对系统参数扰动和外界干扰进行自适应补偿,由模糊滑模控制律实现对模糊逻辑系统逼近误差的进一步补偿.所设计的控制器可保证闭环系统一致最终有界.将该控制器用于月球探测车动态转向系统中,仿真结果表明了该方法的有效性. 相似文献
8.
利用反演法的系统性和结构特点,研究了一类含有非线性参数的不确定非线性互联系统的鲁棒分散自适应控制问题.首先,在较直观、较一般的假定下,根据系统的结构特点利用反演法设计出其控制器和自适应律,并且每个子系统控制器和自适应律的构成只利用了本身系统的状态信息,即所谓的分散控制;其次,利用Lyapunov理论证明了所设计的控制器和自适应律使得被控系统的状态及参数估计误差一致终极有界.最后,算例仿真验证了所设计的控制算法的有效性. 相似文献
9.
10.
基于系统浸入和流形不变自适应方法的静止无功补偿器非线性鲁棒自适应控制方法 总被引:2,自引:0,他引:2
本文提出一种将系统浸入和流形不变(I&I)自适应控制方法与L2-增益抑制鲁棒控制方法相结合的静止无功补偿器(SVC)的非线性鲁棒自适应控制方法.所提方法首先通过参数估计误差和鲁棒控制律的设计,使得所构造的表示参数估计误差函数的流形不变且吸引,从而使参数估计误差在这一流形上收敛于零.然后,通过所设计的可调参数对参数估计误差的收敛性能进行控制,以此来保证参数估计器对不确定参数的自适应估计能力.最后,采用自适应逆推算法推导鲁棒控制律,并通过使不确定外部扰动满足从输入到输出的耗散性来保证系统对不确定扰动的鲁棒性.仿真结果表明,利用所提方法设计的SVC控制器和参数替换律在参数估计、发电机功角动态响应方面优于传统自适应逆推算法,从而提高了输电系统的稳定水平. 相似文献
11.
12.
Stabilization of nonlinear uncertain systems with stochastic actuator failures and time‐varying delay
下载免费PDF全文
![点击此处可从《国际强度与非线性控制杂志<br>》网站下载免费的PDF全文](/ch/ext_images/free.gif)
In this paper, we investigate the adaptive state‐feedback stabilization problem for a class of nonlinear systems subject to parametric uncertainties, time‐varying delay, and Markovian jumping actuator failures. First, some fundamental results, including the infinitesimal generator and conditions for the existence and uniqueness of the solution, are established for nonlinear systems w.r.t. Markovian vector and time‐varying delay. Subsequently, corresponding stability criterion is generalized to the considered systems. By employing the backstepping method and the tuning function technique, a systematic adaptive fault‐tolerant control scheme is proposed, which guarantees the boundedness in probability of all the closed‐loop signals. It is noted that no fault detection and diagnostic block are needed, and the control law can be adapted automatically by taking account of the innovative state information. The efficiency of the designed controller is demonstrated by an illustrative example. Copyright © 2015 John Wiley & Sons, Ltd. 相似文献
13.
In this paper an adaptive guidance law based on the characteristic model is designed to track a reference drag acceleration for reentry vehicles like the Shuttle. The characteristic modeling method of linear constant systems is extended for single-input and single-output (SlSO) linear time-varying systems so that the characteristic model can be established for reentry vehicles. A new nonlinear differential golden-section adaptive control law is presented. When the coefficients belong to a bounded closed convex set and their rate of change meets some constraints, the uniformly asymptotic stability of the nonlinear differential golden-section adaptive control system is proved. The tracking control law, the nonlinear differential golden-section control law, and the revised logical integral control law are integrated to design an adaptive guidance law based on the characteristic model. This guidance law overcomes the disadvantage of the feedback linearization method which needs the precise model. Simulation results show that the proposed method has better performance of tracking the reference drag acceleration than the feedback linearizaUon one. 相似文献
14.
15.
针对非线性马尔科夫跳变多智能体系统在有向固定拓扑下的领导跟随一致性问题,为减少智能体间不必要的通信传输,节约网络资源,保证系统性能,提出一种自适应事件触发控制策略.首先,将每一个智能体均视为马尔科夫跳变系统,且马尔科夫链的转移概率部分未知;通过简单的模型转换建立误差系统,将多智能体系统一致性问题转化为误差系统的稳定性问题;在此基础上,构造合适的Lyapunov-Krasovskii泛函并利用Jensen不等式和线性矩阵不等式等技术给出使多智能体系统达到领导跟随一致性的充分条件及控制器设计方法;通过求解线性矩阵不等式可以得到多智能体系统一致性控制器增益矩阵和事件触发参数矩阵;最后,通过数值仿真验证所提出方法的有效性. 相似文献
16.
17.
18.
Observer-based adaptive fuzzy-neural control for unknown nonlineardynamical systems 总被引:18,自引:0,他引:18
Yih-Guang Leu Tsu-Tian Lee Wei-Yen Wang 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》1999,29(5):583-591
In this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown nonlinear dynamical systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the nonlinear system are not assumed to be available for measurement. Also, the unknown nonlinearities of the nonlinear dynamical systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance. 相似文献
19.
Tong
Ma 《国际强度与非线性控制杂志
》2020,30(12):4565-4583
》2020,30(12):4565-4583
This paper synthesizes a filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties. The multivariable nonlinear systems under consideration have both matched and mismatched uncertainties, which satisfy the semiglobal Lipschitz condition. The nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF)‐based neural network incorporated with a piecewise constant adaptive law, where the adaptive law will generate adaptive parameters by solving the error dynamics between the real system and the state predictor with the neglection of unknowns. The combination of GRBF‐based neural network and piecewise constant adaptive law relaxes hardware limitations (CPU). A filtering control law is designed to handle the nonlinear uncertainties and deliver a good tracking performance with guaranteed robustness. The matched uncertainties are cancelled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. Since the virtual reference system defines the best performance that can be achieved by the closed‐loop system, the uniform performance bounds are derived for the states and control signals via comparison. To validate the theoretical findings, comparisons between the model reference adaptive control method and the proposed filtering adaptive neural network control architecture with the implementation of different sampling time are carried out. 相似文献