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智能电网是新一代电网建设的目标,也是国际电力工业界的共同选择. 本文研究在储能设备接入电网情况下,建立一套基于自适应动态规划(Adaptive dynamic programming,ADP)的智能电网电能自适应优化控制的理论与方法,实现电网发电端以及用户端的智能交互,开辟对智能电网供需优化匹配与调控方法的新途径. 论文首先给出动态规划的最优性原理以及带有储能设备智能电网的运行方式并提出优化目标;然后,设计新型迭代自适应动态规划方法实现对储能 设备的最优控制,并证明自适应动态规划方法的收敛性,在理论上保证了对智能电网电能的优化;最后,给出仿真例子显示出所提出控制方法的有效性. 相似文献
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应用一种新的自适应动态最优化方法(ADP),在线实现对非线性连续系统的最优控制。首先应用汉密尔顿函数(Hamilton-Jacobi-Bellman, HJB)求解系统的最优控制,并应用神经网络BP算法对汉密尔顿函数中的性能指标进行估计,进而得到非线性连续系统的最优控制。同时引进一种新的自适应算法,基于参数误差,在线实现对系统进行动态最优求解,而且通过李亚普诺夫方法对参数收敛情况也进行详细的分析。最后,用仿真结果来验证所提出的方法的可行性。 相似文献
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针对一类非线性时滞系统,本文提出一种自适应控制器的设计方案,采用backstepping和domination方法构建了一个无记忆自适应控制器。放松了对非线性时滞函数的要求(例如全局Lipschitz条件),实现了对给定目标轨线的全局渐近跟踪,保证了闭环系统所有信号全局一致有界:基于Lyapunov—Krasoviskii泛函方法证明了闭环系统的稳定性。仿真结果说明了这种控制方法的可行性和优点。 相似文献
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对一类未知的非线性的多变量系统,提出了用动态神经网络实现直接自适应控制的策略,基于Lyapunov理论,获得一个稳定并且连续的学习律,避免了递归训练过程,闭环系统被证明是鲁棒稳定的,跟踪误差收敛到一个小的残集,这种方法的特点是即不需要离线学习阶段也不要求初始的参数误差足够小,仿真结果验证了提出的动态网络的自适应控制算法的有效性。 相似文献
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一类非线性时滞系统的自适应模糊动态面控制 总被引:1,自引:0,他引:1
针对一类具有未知方向增益函数的严格反馈非线性时滞系统, 提出了一种自适应模糊动态面控制(Dynamic surface control, DSC)算法. 通过利用DSC设计技术和Lyapunov-Krasovskii函数, 该算法不仅克服了计算膨胀的问题, 而且补偿了未知的时滞. 采用Nussbaum函数解决了虚拟控制增益的符号问题, 并且避免了控制器的奇异性. 所设计的控制器保证了闭环系统所有的状态和信号是半全局有界的, 并且通过选择合适的设计参数可使跟踪误差为任意小. 仿真结果表明了所提出控制器的有效性. 相似文献
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This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear systems. Unlike existing optimal state feedback control, the control input of the optimal parallel control is introduced into the feedback system. However, due to the introduction of control input into the feedback system, the optimal state feedback control methods can not be applied directly. To address this problem, an augmented system and an augmented performance index function are proposed firstly. Thus, the general nonlinear system is transformed into an affine nonlinear system. The difference between the optimal parallel control and the optimal state feedback control is analyzed theoretically. It is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index function. Moreover, an adaptive dynamic programming (ADP) technique is utilized to implement the optimal parallel tracking control using a critic neural network (NN) to approximate the value function online. The stability analysis of the closed-loop system is performed using the Lyapunov theory, and the tracking error and NN weights errors are uniformly ultimately bounded (UUB). Also, the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference signals. Finally, the effectiveness of the developed optimal parallel control method is verified in two cases. 相似文献
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自适应动态规划综述 总被引:10,自引:14,他引:10
自适应动态规划(Adaptive dynamic programming, ADP)是最优控制领域新兴起的一种近似最优方法, 是当前国际最优化领域的研究热点. ADP方法 利用函数近似结构来近似哈密顿--雅可比--贝尔曼(Hamilton-Jacobi-Bellman, HJB)方程的解, 采用离线迭代或者在线更新的方法, 来获得系统的近似最优控制策略, 从而能够有效地解决非线性系统的优化控制问题. 本文按照ADP的结构变化、算法的发展和应用三个方面介绍ADP方法. 对目前ADP方法的研究成果加以总结, 并对这 一研究领域仍需解决的问题和未来的发展方向作了进一步的展望. 相似文献
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Optimal Control for a Class of Complex Singular System Based on Adaptive Dynamic Programming 下载免费PDF全文
This paper presents a new design approach to achieve decentralized optimal control of high-dimension complex singular systems with dynamic uncertainties. Based on robust adaptive dynamic programming (robust ADP) method, controllers for solving the singular systems optimal control problem are designed. The proposed algorithm can work well when the system model is not exactly known but the input and output data can be measured. The policy iteration of each controller only uses their own states and input information for learning, and do not need to know the whole system dynamics. Simulation results on the New England 10-machine 39-bus test system show the effectiveness of the designed controller. 相似文献
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针对一类带有执行器饱和的未知动态离散时间非线性系统, 提出了一种新的最优跟踪控制方案. 该方案基于迭代自适应动态规划算法, 为了实现最优控制, 首先建立了未知系统动态的数据辨识器. 通过引入M网络, 获得了稳态控制的精确表达式. 为了消除执行器饱和的影响, 提出了一个非二次的性能指标函数. 然后提出了一种迭代自适应动态规划算法获得最优跟踪控制的解, 并给出了收敛性分析. 为了实现最优控制方案, 神经网络被用来构建数据辨识器、计算性能指标函数、近似最优控制策略和求解稳态控制. 仿真结果验证了本文所提出的最优跟踪控制方法的有效性. 相似文献
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Based on adaptive dynamic programming (ADP), the fixed-point tracking control problem is solved by a value iteration (Ⅵ) algorithm. First, a class of discrete-time (DT) nonlinear system with disturbance is considered. Second, the convergence of a Ⅵ algorithm is given. It is proven that the iterative cost function precisely converges to the optimal value, and the control input and disturbance input also converges to the optimal values. Third, a novel analysis pertaining to the range of the discount factor is presented, where the cost function serves as a Lyapunov function. Finally, neural networks (NNs) are employed to approximate the cost function, the control law, and the disturbance law. Simulation examples are given to illustrate the effective performance of the proposed method. 相似文献
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为连续非线性系统提出了一种有效的最优控制设计方法. 广义模糊双曲模型(Generalized fuzzy hyperbolic model, GFHM)首次作为逼近器用来估计 HJB (Hamilton-Jacobi-Bellman)方程的解 (值函数,即它是状态与代价函数之间的映射), 然后,利用该近似解获得最优控制. 本文方法只需要一个GFHM估计值函数. 首先, 阐述了对于连线非线性系统最优控制的设计过程; 然后,证明了逼近误差是一致最终有界的 (Uniformly ultimately bounded, UUB); 最后, 一个数值例子验证了本文方法的有效性. 另一个例子通过与神经网络自适应动态规划的方法作比较, 演示了本文方法的优点. 相似文献
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Adaptive Dynamic Surface Control with Guaranteed L∞ Tracking Performance for a Class of Uncertain Nonlinear Systems with Unknown Time Delays 下载免费PDF全文
Qichao Zhao 《Asian journal of control》2014,16(2):589-601
This paper is aimed at exploring dynamic surface control (DSC) for a class of uncertain nonlinear systems in strict‐feedback form with time delays. Combining the Finite Covering Lemma (Heine‐Borel Theorem) with neural networks, a novel method is proposed to approximate time delay terms, which leads to the abandonment of traditional Lyapunov‐Krasovskii functionals. Then, a surface error modification and an initialization technique are proposed to guarantee the tracking performance. Moreover, by applying a newly‐developed neural network based adaptive control technique, it is shown that the update law for the proposed DSC scheme is needed only at the last design step with only one parameter being estimated online, which significantly reduces the computational burden, compared with current DSC schemes. Simulation results are presented to illustrate the efficiency of the proposed scheme. 相似文献
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Online Adaptive Approximate Optimal Tracking Control with Simplified Dual Approximation Structure for Continuous-time Unknown Nonlinear Systems 下载免费PDF全文
This paper proposes an online adaptive approximate solution for the infinite-horizon optimal tracking control problem of continuous-time nonlinear systems with unknown dynamics. The requirement of the complete knowledge of system dynamics is avoided by employing an adaptive identifier in conjunction with a novel adaptive law, such that the estimated identifier weights converge to a small neighborhood of their ideal values. An adaptive steady-state controller is developed to maintain the desired tracking performance at the steady-state, and an adaptive optimal controller is designed to stabilize the tracking error dynamics in an optimal manner. For this purpose, a critic neural network (NN) is utilized to approximate the optimal value function of the Hamilton-Jacobi-Bellman (HJB) equation, which is used in the construction of the optimal controller. The learning of two NNs, i.e., the identifier NN and the critic NN, is continuous and simultaneous by means of a novel adaptive law design methodology based on the parameter estimation error. Stability of the whole system consisting of the identifier NN, the critic NN and the optimal tracking control is guaranteed using Lyapunov theory; convergence to a near-optimal control law is proved. Simulation results exemplify the effectiveness of the proposed method. 相似文献
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A Novel Distributed Optimal Adaptive Control Algorithm for Nonlinear Multi-Agent Differential Graphical Games 下载免费PDF全文
Majid Mazouchi Mohammad Bagher Naghibi-Sistani Seyed Kamal Hosseini Sani 《IEEE/CAA Journal of Automatica Sinica》2018,5(1):331-341
In this paper, an online optimal distributed learning algorithm is proposed to solve leader-synchronization problem of nonlinear multi-agent differential graphical games. Each player approximates its optimal control policy using a single-network approximate dynamic programming (ADP) where only one critic neural network (NN) is employed instead of typical actorcritic structure composed of two NNs. The proposed distributed weight tuning laws for critic NNs guarantee stability in the sense of uniform ultimate boundedness (UUB) and convergence of control policies to the Nash equilibrium. In this paper, by introducing novel distributed local operators in weight tuning laws, there is no more requirement for initial stabilizing control policies. Furthermore, the overall closed-loop system stability is guaranteed by Lyapunov stability analysis. Finally, Simulation results show the effectiveness of the proposed algorithm. 相似文献
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一类非线性系统的自适应控制 总被引:1,自引:0,他引:1
针对一类未知的MIMO非线性系统的控制问题,提出了一种基于混合遗传算法的自适应RBF神经网络控制器(HGA-RBFNNC),使系统能跟踪期望输出.采用混合遗传算法,在线确定RBF神经网络的结构和参数,当误差满足一定要求时,该控制器转入按照基于Lyapunov稳定性理论的自适应律进行网络权值的进一步调整,这样既在线建立神经网络又保证了整个系统的全局稳定性和收敛性.仿真实验结果表明,该控制器能够快速跟踪期望输出,而且具有很好的稳定性和收敛性. 相似文献