共查询到20条相似文献,搜索用时 15 毫秒
1.
In this paper we propose a semi-meshless discretization method for the approximation of viscosity solutions to a first order Hamilton–Jacobi–Bellman (HJB) equation governing a class of nonlinear optimal feedback control problems. In this method, the spatial discretization is based on a collocation scheme using the global radial basis functions (RBFs) and the time variable is discretized by a standard two-level time-stepping scheme with a splitting parameter θ. A stability analysis is performed, showing that even for the explicit scheme that θ=0, the method is stable in time. Since the time discretization is consistent, the method is also convergent in time. Numerical results, performed to verify the usefulness of the method, demonstrate that the method gives accurate approximations to both of the control and state variables. 相似文献
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Seiffertt J. Sanyal S. Wunsch D.C. 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2008,38(4):918-923
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Expensive to compute wall distances are used in key turbulence models and also for the modeling of peripheral physics. A potentially economical, robust, readily parallel processed, accuracy improving, differential equation based distance algorithm is described. It is hybrid, partly utilising an approximate Poisson equation. This also allows auxiliary front propagation direction/velocity information to be estimated, effectively giving wall normals. The Poisson normal can be used fully, in an approximate solution of the eikonal equation (the exact differential equation for wall distance). Alternatively, a weighted fraction of this Poisson front direction (effectively, front velocity, in terms of the eikonal equation input) information and that implied by the eikonal equation can be used. Either results in a hybrid Poisson–eikonal wall distance algorithm. To improve compatibility of wall distance functions with turbulence physics a Laplacian is added to the eikonal equation. This gives what is termed a Hamilton–Jacobi equation. This hybrid Poisson–Hamilton–Jacobi approach is found to be robust on poor quality grids. The robustness largely results from the elliptic background presence of the Poisson equation. This elliptic component prevents fronts propagated from solid surfaces, by the hyperbolic eikonal equation element, reflecting off zones of rapidly changing grid density. Where this reflection (due to poor grid quality) is extreme, the transition of front velocity information from the Poisson to Hamilton–Jacobi equation can be done more gradually. Consistent with turbulence modeling physics, under user control, the hybrid equation can overestimate the distance function strongly around convex surfaces and underestimate it around concave. If the former trait is not desired the current approach is amenable to zonalisation. With this, the Poisson element is automatically removed around convex geometry zones. 相似文献
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Neural-Network-Based Near-Optimal Control for a Class of Discrete-Time Affine Nonlinear Systems With Control Constraints 总被引:6,自引:0,他引:6
Huaguang Zhang Yanhong Luo Derong Liu 《Neural Networks, IEEE Transactions on》2009,20(9):1490-1503
In this paper, the near-optimal control problem for a class of nonlinear discrete-time systems with control constraints is solved by iterative adaptive dynamic programming algorithm. First, a novel nonquadratic performance functional is introduced to overcome the control constraints, and then an iterative adaptive dynamic programming algorithm is developed to solve the optimal feedback control problem of the original constrained system with convergence analysis. In the present control scheme, there are three neural networks used as parametric structures for facilitating the implementation of the iterative algorithm. Two examples are given to demonstrate the convergence and feasibility of the proposed optimal control scheme. 相似文献
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本文研究了不确定离散系统的神经网络自适应控制器的设计。因为它不需要假设系统状态是可测的,一个观测器用来估计不可测状态。与现有离散系统的结果相比,该控制器具有较少的直接自适应参数。因此,可以很方便地实现工程算法。利用Lyapunov分析方法,所有的闭环系统的信号是保证最终有界(UUB),并且能够实现系统输出跟踪参考信号到有界紧集。一个仿真例子,验证了该方法的有效性。 相似文献
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Feedback-Linearization-Based Neural Adaptive Control for Unknown Nonaffine Nonlinear Discrete-Time Systems 总被引:3,自引:0,他引:3
《Neural Networks, IEEE Transactions on》2008,19(9):1615-1625
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基于ANN的非线性系统GPC算法及仿真研究 总被引:2,自引:0,他引:2
将神经网络(ANN)技术应用于常规GPC算法,设计了基于ANN的非线性系统GPC结构方案,并对其控制原理和控制算法进行研究,基于ANN高度非线性映射等特性,运用数字仿真方法,对所设计的控制结构方案进行仿真研究,仿真结果显示,基于ANN的非线性系统GPC结构方案合理可行,并取得了满意的控制效果. 相似文献
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Control of Nonaffine Nonlinear Discrete-Time Systems Using Reinforcement-Learning-Based Linearly Parameterized Neural Networks 总被引:1,自引:0,他引:1
Qinmin Yang Vance J.B. Jagannathan S. 《IEEE transactions on systems, man, and cybernetics. Part B, Cybernetics》2008,38(4):994-1001
A nonaffine discrete-time system represented by the nonlinear autoregressive moving average with eXogenous input (NARMAX) representation with unknown nonlinear system dynamics is considered. An equivalent affinelike representation in terms of the tracking error dynamics is first obtained from the original nonaffine nonlinear discrete-time system so that reinforcement-learning-based near-optimal neural network (NN) controller can be developed. The control scheme consists of two linearly parameterized NNs. One NN is designated as the critic NN, which approximates a predefined long-term cost function, and an action NN is employed to derive a near-optimal control signal for the system to track a desired trajectory while minimizing the cost function simultaneously. The NN weights are tuned online. By using the standard Lyapunov approach, the stability of the closed-loop system is shown. The net result is a supervised actor-critic NN controller scheme which can be applied to a general nonaffine nonlinear discrete-time system without needing the affinelike representation. Simulation results demonstrate satisfactory performance of the controller. 相似文献
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仿射非线性奇异系统的可逆性 总被引:2,自引:0,他引:2
仿射非线性奇异系统的可逆性@王晶@刘晓平¥东北大学自控系仿射非线性系统,奇异系统,可逆性仿射非线性奇异系统的可逆性王晶刘晓平(东北大学自控系沈阳110006)关键词仿射非线性系统,奇异系统,可逆性1)国家自然科学基金、霍英东基金、国家教委跨世纪人才基金资助... 相似文献
11.
A. Delgado 《Neural computing & applications》2000,9(2):113-123
Two applications of Self Organising Map (SOM) networks in the context of nonlinear control are introduced, one in approximate
feedback linearisation and the second in optimal control. It is shown that a modified SOM can be used to approximately Input/Output
(I/O) linearise and to control nonlinear systems using a combination of the SOM learning algorithm, and a biologically inspired
optimisation algorithm known as chemotaxis. A proof to guarantee the stability of the closed loop during the training of the
network and the operation of the whole system is included. The results are illustrated with simulations of a single link manipulator. 相似文献
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基于神经元网和带死区的最小二乘算法的非线性离散时间系统的自适应控制 总被引:1,自引:0,他引:1
针对非线性离散时间系统,提出了一种用带死区的最小二乘算法去调节神经网参数的算法,同其他算法相比,这种算法具有非常高的收敛速度.对于这种自适应控制算法,证明了闭环系统的所有信号是有界的,跟踪误差收敛到以零为原点的球中. 相似文献
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Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks 总被引:4,自引:0,他引:4
This paper deals with adaptive tracking for discrete-time multiple-input-multiple-output (MIMO) nonlinear systems in presence of bounded disturbances. In this paper, a high-order neural network (HONN) structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the whole controlled system, including the extended Kalman filter (EKF)-based NN learning algorithm. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor. 相似文献
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Data-based Fault Tolerant Control for Affine Nonlinear Systems Through Particle Swarm Optimized Neural Networks 下载免费PDF全文
Haowei Lin Bo Zhao Derong Liu Cesare Alippi 《IEEE/CAA Journal of Automatica Sinica》2020,7(4):954-964
In this paper, a data-based fault tolerant control (FTC) scheme is investigated for unknown continuous-time (CT) affine nonlinear systems with actuator faults. First, a neural network (NN) identifier based on particle swarm optimization (PSO) is constructed to model the unknown system dynamics. By utilizing the estimated system states, the particle swarm optimized critic neural network (PSOCNN) is employed to solve the Hamilton-Jacobi-Bellman equation (HJBE) more efficiently. Then, a data-based FTC scheme, which consists of the NN identifier and the fault compensator, is proposed to achieve actuator fault tolerance. The stability of the closed-loop system under actuator faults is guaranteed by the Lyapunov stability theorem. Finally, simulations are provided to demonstrate the effectiveness of the developed method. 相似文献
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针对一类具有特殊模型的非线性系统本文提出了一种新型神经网络预测控制算法。该算法利用线性系统预测控制技术和神经网络的非线性映射及并行处理能力来求实际控制量,避免了解非线性方程和非线性预测控制所需的在线数值寻优计算,减少了计算量和计算时间。仿真结果表明了该算法的何效性。 相似文献
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非线性时滞大系统自适应神经网络分散控制 总被引:7,自引:3,他引:4
针对一类未知非线性时滞关联大系统,提出一种自适应神经网络分散跟踪控制方案.采用神经网络逼近各子系统内部的非线性函数和关联项中的时滞非线性函数;利用占有方法处理时滞项,采用Backstepping技术设计分散控制律和参数自适应律.基于Lyapunov-Krasoviskii泛函证明了闭环大系统所有信号半全局一致最终有界.通过调节设计参数和增加神经元个数,可以实现任意输出跟踪精度.实例仿真说明了该方案的可行性。 相似文献
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In this paper, an iterative learning control method is proposed for a class of nonlinear discrete-time systems with well-defined relative degree, which uses the output data from several previous operation cycles to enhance tracking performance. A new analysis approach is developed, by which the iterative learning control is shown to guarantee the convergence of the output trajectory to the desired one within bound and the bound is proportional to the bound on resetting errors. It is further proved effective to overcome initial shifts and the resultant output trajectory can be assessed as iteration increases. Numerical simulation is carried out to verify the theoretical results and exhibits that the proposed updating law possesses good transient behavior of learning process so that the convergence speed is improved. 相似文献