共查询到19条相似文献,搜索用时 78 毫秒
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针对一类具有死区输入非线性系统,提出一种实现有限作业区间轨迹跟踪控制的神经网络迭代学习算法.基于Lyapunov-like方法设计学习控制器,回避了常规迭代学习控制中受控系统非线性特性需满足全局Lipschitz连续条件的要求.为处理输入死区,利用神经网络逼近这种强非线性特性;同时,通过对神经网络逼近误差界的估计并在控制器中设置补偿作用以消除其影响,从而提高系统的跟踪性能.
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针对一类单输入单输出不确定非线性重复跟踪系统,提出一种基于完全未知高频反馈增益的自适应迭代学习控制,与普通迭代学习控制需要复习增益稳定性前提条不同,自适应迭代学习控制通过不断修改Nussbaum形式的高频学习增益达到收敛,经证明当迭代次数i→∞时,重复跟踪误差可一致收敛到任意小界δ。仿真结果表明了该控制方法的有效性。 相似文献
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针对一类具有死区非线性输入的非线性系统,同时考虑系统中存在未建模不确定项,设计了自适应控制器及未知参数的自适应估计率.该控制器使得闭环系统全局稳定且实现了输出信号对参考信号的精确跟踪.仿真结果进一步证实了该控制器能对未知死区及未建模动态进行有效的补偿。 相似文献
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齿隙非线性输入系统的迭代学习控制 总被引:3,自引:1,他引:2
针对一类具有输入齿隙特性的非线性系统, 提出一种实现有限作业区间轨迹跟踪的迭代学习控制方法. 在系统不确定项可参数化的情形下, 基于类Lyapunov方法设计迭代学习控制器, 回避了常规迭代学习控制中受控系统非线性特性需满足全局Lipschitz连续条件的要求. 对未知时变参数进行泰勒级数展开, 参数估计采用微分学习律, 并在控制器设计中, 采用双曲函数处理级数展开后的余项以及齿隙特性里的有界误差项, 以保证控制器可导, 且可抑制颤振. 引入一级数收敛序列确保系统输出完全跟踪期望轨迹, 且闭环系统所有信号有界. 相似文献
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具有未知非线性死区的自适应模糊控制 总被引:2,自引:0,他引:2
基于滑模控制原理,利用模糊系统的逼近能力,提出一种自适应模糊控制方法.该方法提出一种简化非线性死区输入模型,取消了非线性死区输入模型的倾斜度相等以及死区边界对称的条件,还取消了非线性死区输入模型各种参数已知的条件.该方法通过引入逼近误差的自适应补偿项来消除建模误差和参数估计误差的影响.理论分析证明了闭环系统是半全局一致终结有界,跟踪误差收敛到零.仿真结果表明了该方案的有效性. 相似文献
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连续非线性系统的迭代学习控制方法* 总被引:7,自引:1,他引:7
本文根据误差收敛准则,提出了连续非线性系统的迭代学习控制算法,给出了PID型学习控制算法的收效条件,实际应用表明,该方法可以逼近预定的任意轨线。 相似文献
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非线性系统闭环P型迭代学习控制的收敛性 总被引:15,自引:3,他引:15
本文得到并证明了当被控系统的状态方程为一类非线性方程时,采用闭环P型学习律迭代学习控制的收敛的充分条件和必要条件,最后,我们给出了典型的仿真结果。 相似文献
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Adaptive output control of uncertain nonlinear systems with non-symmetric dead-zone input 总被引:1,自引:0,他引:1
Hong-Jun Ma Author Vitae Author Vitae 《Automatica》2010,46(2):413-420
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. 相似文献
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This paper focuses on the adaptive control of a class of nonlinear systems with unknown deadzone using neural networks. By
constructing a deadzone pre-compensator, a neural adaptive control scheme is developed using backstepping design techniques.
Transient performance is guaranteed and semi-globally uniformly ultimately bounded stability is obtained. Another feature
of this scheme is that the neural networks reconstruction error bound is assumed to be unknown and can be estimated online.
Simulation results are given to demonstrate the effectiveness of the proposed controller. 相似文献
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Adaptive neural control of MIMO nonlinear state time-varying delay systems with unknown dead-zones and gain signs 总被引:5,自引:0,他引:5
In this paper, adaptive neural control is proposed for a class of uncertain multi-input multi-output (MIMO) nonlinear state time-varying delay systems in a triangular control structure with unknown nonlinear dead-zones and gain signs. The design is based on the principle of sliding mode control and the use of Nussbaum-type functions in solving the problem of the completely unknown control directions. The unknown time-varying delays are compensated for using appropriate Lyapunov-Krasovskii functionals in the design. The approach removes the assumption of linear functions outside the deadband as an added contribution. By utilizing the integral Lyapunov function and introducing an adaptive compensation term for the upper bound of the residual and optimal approximation error as well as the dead-zone disturbance, the closed-loop control system is proved to be semi-globally uniformly ultimately bounded. Simulation results demonstrate the effectiveness of the approach. 相似文献
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Quite successfully adaptive control strategies have been applied to uncertain dynamical systems subject to dead-zone nonlinearities. However, adaptive tracking of systems with non-symmetric dead-zone characteristics has not been fully discussed with minimal knowledge of the dead-zone parameters. It is shown that the controlled system preceded by a non-symmetric dead-zone input can be represented as an uncertain nonlinear system subject to a linear input with time-varying input coefficient. To cope with this problem, a new adaptive compensation algorithm is employed without constructing the dead-zone inverse. The proposed adaptive scheme requires only the information of bounds of the dead-zone slopes and treats the time-varying input coefficient as a system uncertainty. The new control scheme ensures bounded-error trajectory tracking and assures the boundedness of all the signals in the adaptive closed loop. By appropriate selections of the controller parameters, we show that the smoothness of the controller does not affect the accuracy of trajectory tracking control. A numerical example is included to show the effectiveness of the theoretical results. 相似文献
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Yu-Ping Tian Author Vitae 《Automatica》2003,39(11):1957-1966
This paper addresses the robust learning control problem for a class of nonlinear systems with structured periodic and unstructured aperiodic uncertainties. A recursive technique is proposed which extends the backstepping idea to the robust repetitive learning control systems. A learning evaluation function instead of a Lyapunov function is formulated as a guideline for derivation of the control strategy which guarantees the asymptotic stability of the tracking system. A design example is given. 相似文献
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In this paper, the iterative learning control problem for a class of nonlinear singular impulsive systems is discussed. Then, a D-type (derivative-type) iterative learning control algorithm is presented such that the output tracks the desired output trajectory as accurate as possible. Furthermore, the sufficient condition for the convergence of the proposed algorithm is established in detail. Finally, a numerical example is included to corroborate the theoretical analyses. 相似文献
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Jing Zhou Author Vitae 《Automatica》2008,44(7):1790-1799
In this paper, a decentralized adaptive control scheme is proposed to address output tracking of a class of interconnected time-delay subsystems with the input of each loop preceded by an unknown dead-zone. Each local controller is designed using the backstepping technique and consists of a new robust control law and new updating laws. Unknown time-varying delays are compensated by using appropriate Lyapunov-Krasovskii functionals. Furthermore, by introducing a new smooth dead-zone inverse, the proposed backstepping design is able to eliminate the effects resulting from dead-zone nonlinearities in the input. It is shown that the proposed controller can guarantee not only stability, but also good transient performance. 相似文献
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This paper investigates iterative learning control of nonlinear discrete time non-minimum phase systems in tracking problems. The main objective of this paper is to find an input-to-output mapping in order to stabilize the non-minimum phase systems and to obtain an input update law for handling uncertain systems. In conventional approaches on the tracking of non-minimum phase systems, zero dynamics is stabilized from the system equations and the input is calculated from the state information. For the learning of uncertain systems, conventional approaches depend on the output-to-state and state-to-input mappings. In the proposed method, the inverse system is stabilized using the input-to-output mapping for nonlinear non-minimum phase systems. A new input update law is proposed based on the relative degree and the number of non-minimum phase zeros. This makes the overall proposed learning system have a simple structure as in the classical ILC. 相似文献