WO2024108331A1 - 一种具有隐私保护作用的智能微网分布式动态跟踪方法 - Google Patents
一种具有隐私保护作用的智能微网分布式动态跟踪方法 Download PDFInfo
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- the present invention relates to the technical field of smart microgrid energy dispatching, and in particular to a smart microgrid distributed dynamic tracking method with privacy protection function.
- microgrids are defined as system units consisting of a group of micro power sources, loads, energy storage systems, and control devices. It is an autonomous system that can achieve self-control, protection, and management. It can be connected to the external power grid or run in isolation.
- the smart microgrid As a new type of network composed of multiple distributed power sources and their related components in a certain topological structure, the smart microgrid is more robust and cost-effective than the traditional centralized power grid, but it is necessary to focus on the effective management of the microgrid power supply and demand load; since the smart microgrid is a new type of distributed network structure, the traditional centralized control method is no longer applicable, so a technology called dynamic average tracking is introduced into the field of smart microgrid research.
- the dynamic average tracking technology aims to design a distributed collaborative algorithm so that each node in the network can track the average value of the time-varying reference signal of all nodes only by exchanging information with its neighbor nodes.
- each node In the traditional dynamic tracking collaborative algorithm, each node must communicate honestly with its neighbor nodes to reach a consensus on a variable of interest. However, in some practical applications, this direct information exchange may lead to the leakage of sensitive information; therefore, some malicious or curious attackers can easily obtain such data by monitoring the channel, and then use the accessed power consumption data to infer the detailed home or business activities of the target node.
- the purpose of the present invention is to develop a distributed dynamic tracking method with privacy protection based on a random number perturbation mechanism to solve the supply and demand load balancing problem in smart microgrids, so as to reasonably plan the power production, storage and scheduling links.
- a distributed dynamic tracking method for a smart microgrid with privacy protection function comprising the following steps:
- the dynamic tracking algorithm is designed based on the random number perturbation mechanism as follows
- z i (t), x i (t) and u i (t) represent the node internal state, estimated state and control input respectively;
- parameter 0 ⁇ i ⁇ 1 is the regularization weight of agent i, ⁇ is the control gain, and ⁇ >0 is the design parameter;
- each of the nodes i generates a set of random numbers according to the number of its neighbor nodes j.
- the number of random numbers generated is equal to the number of its neighbor nodes j, and then a random number is sent to each of its neighbor nodes; each node i receives a random number sent by neighbor node j After that, according to its own state estimate x i (t), the random number set generated by itself And the received random number set Compute virtual states for information transfer
- Step 1 Algorithm initialization, including:
- Each node i generates Ni random numbers Where Ni is the number of its neighbor nodes;
- Node i transmits the random number to its adjacent nodes (e.g. is passed to i's neighbor node i 1 ), and receives the random number generated and passed by its neighbor node j
- Step 3 Node i calculates the state based on the random number set generated by itself
- Step 4 Node i calculates the virtual state based on the random number set passed by the neighboring node
- Step 5 Adjacent nodes exchange information using their respective virtual states, and each node i updates the control input as follows:
- Step 6 Node i updates its internal state z i (t+1) and estimator state x i (t+1) as follows:
- zi (t+1) zi (t)+h( - ⁇ zi (t)+ ui (t)),
- h is the step size the algorithm uses to iterate.
- Step 7 Update the algorithm iteration time and determine whether the iteration is complete, including:
- the algorithm proposed by the present invention makes the information exchange between nodes no longer rely on the real state value x i (t), but use the virtual state value disturbed by random numbers.
- Mutual information exchange is carried out, thus avoiding the leakage of the real information of the node, thereby playing a role in protecting the privacy of the sensitive information of the node;
- the random number perturbation mechanism used in the present invention is carefully designed to ensure that even if the information exchange between the nodes has been perturbed by random numbers, accurate dynamic target tracking can still be achieved in the end;
- the distributed dynamic tracking algorithm based on the random number perturbation mechanism can ensure that the sensitive information of the nodes in the microgrid is not leaked, while enabling each node to accurately track the weighted average of the time-varying power consumption data of all nodes in the entire network, and then obtain the total power consumption data, providing a decision-making reference for supply and demand load balancing control.
- Fig. 1 is a flow chart of the steps of the present invention
- FIG. 2 is a diagram showing the specific execution steps of the algorithm in S3 of the present invention.
- a distributed dynamic tracking method of a smart microgrid with privacy protection function comprises the following steps:
- the smart microgrid consists of N physical nodes.
- the real-time power consumption data of the i-th node is ⁇ i (t).
- the dynamic average tracking problem solves how to enable each node to track the average value of all power consumption data ⁇ i (t). That is, the tracking target can be expressed as
- the above problem is also called the absolute average tracking problem, because the tracking target is the absolute average value of all ⁇ i (t); however, in the actual microgrid scenario, since the importance of each physical node is different, the target value to be tracked is not necessarily the absolute average value; at this time, in order to reflect the different importance of each node, a weight coefficient w i is introduced for each node i, and the tracking target becomes the weighted average sum of all ⁇ i (t), that is,
- z i (t), x i (t) and ui (t) represent the node internal state, estimated state and control input respectively.
- the algorithm has good performance in terms of convergence speed and convergence accuracy, the target it finally tracks is the absolute average of all time-varying reference signals ⁇ i (t).
- the algorithm requires all nodes in the network to communicate directly with their neighboring nodes, that is, the true state of each node is transmitted directly through the channel, which brings a very high risk of leakage of sensitive node information.
- this paper proposes a dynamic weighted average tracking algorithm with privacy protection based on a random number perturbation mechanism:
- z i (t), x i (t) and ui (t) represent the node internal state, estimated state and control input respectively.
- ⁇ is the control gain
- ⁇ >0 is the design parameter.
- each node i first generates a set of random numbers according to the number of its neighbors The number of random numbers generated is equal to the number of its neighboring nodes, and then a random number is sent to each of its neighboring nodes.
- Each node i receives a random number from a neighboring node. After that, according to its own state estimate x i (t), the random number set generated by itself And the received random number set Compute virtual states for information transfer Once all nodes have completed the computation of their respective virtual states, the virtual states are exchanged between neighboring nodes in the network.
- step 1 algorithm initialization, specifically including:
- Each node i generates Ni random numbers Where Ni is the number of its neighbor nodes;
- Node i transmits the random number to its adjacent nodes (e.g. is passed to i's neighbor node i 1 ), and receives the random number generated and passed by its neighbor node j
- Step 3 Node i calculates the state based on the random number set generated by itself
- Step 4 Node i calculates the virtual state based on the random number set passed by the neighboring node
- Step 5 Adjacent nodes exchange information using their respective virtual states, and each node i updates the control input as follows:
- Step 6 Node i updates its internal state z i (t+1) and estimator state x i (t+1) as follows:
- zi (t+1) zi (t)+h( - ⁇ zi (t)+ ui (t)),
- h is the step size the algorithm uses to iterate.
- Step 7 Update the algorithm iteration time and determine whether the iteration is complete, including:
- the algorithm proposed in the present invention can ensure that the virtual state x + (t) after random number perturbation can achieve the weighted average signal within a limited time.
- Theorem 1 above shows that the convergence time of the algorithm is bounded and exponential, and the corresponding lower bound t * is mainly determined by the design parameter ⁇ , the weight matrix W and the initial steady-state error e(t 0 ).
- the lower bound of the control gain ⁇ depends on the reference signal ⁇ (t) and its time derivative
- the global information of the reference signal and its upper bound of time derivative can be obtained by running the maximum consistency algorithm in the initialization phase of the algorithm.
- node i can protect the privacy of its estimator state x i (t) and time-varying reference signal ⁇ i (t) from being leaked unless all neighbors of node i cooperate with each other to infer the relevant state information of node i.
- the algorithm proposed in the present invention can no longer protect the sensitive information of the target node. On the contrary, as long as there is at least one neighboring node with a non-cooperative attitude, the proposed algorithm can ensure the privacy of the sensitive state of the target node.
- the attacker does not have the network topology information of the system, it cannot launch an effective attack on the target because it cannot determine whether a certain node is a neighboring node of its attack target, and it cannot collect the random numbers generated by all neighboring nodes of the target and further calculate and infer the real sensitive information of the attack target.
- the proposed algorithm enables each node to accurately track the weighted average value of the time-varying reference signal instead of the simple absolute average value;
- a set of privacy protection schemes are carefully designed for the dynamic tracking algorithm, so that the algorithm can accurately track the target point while avoiding the leakage of sensitive information of participating nodes;
- the distributed dynamic tracking algorithm based on the random number perturbation mechanism proposed in the present invention can ensure that the sensitive information of nodes in the microgrid is not leaked, while enabling each node to accurately track the weighted average value of the time-varying power consumption data of all nodes in the entire network, and then obtain the total power consumption data, providing a decision reference for supply and demand load balancing control.
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Abstract
本发明涉及智能微电网能源调度技术领域，尤其涉及一种具有隐私保护作用的智能微网分布式动态跟踪方法，该方法包括以下步骤：S1、确定智能微网实体节点总量为N，第i个节点的实时电力消耗数据为φ i(t)，再结合各个节点权重系数w i，得到电力消耗的求解目标函数模型；S2、基于随机数扰动机制设计动态跟踪算法；S3、具体阐述S2中算法的执行过程；S4、对算法参数的确定与验证；采用本发明提出的基于随机数扰动机制的分布式动态跟踪算法，可以在确保微网中节点敏感信息不被泄露的同时，使得每个节点都能够准确跟踪到整个网络中所有节点时变电力消耗数据的加权平均值，进而得出电力消耗总数据，为供需负载平衡控制提供决策参考。
Description
本发明涉及智能微电网能源调度技术领域，尤其涉及一种具有隐私保护作用的智能微网分布式动态跟踪方法。
随着光伏、风电等可再生能源发电技术的发展，分布式发电逐渐成为满足负荷增长需求、提高能源综合利用效率、提高供电可靠性的一种有效途径，并在配电网中得到广泛的应用。为整合分布式发电优势，研究人员提出了智能微网的概念，其被定义为由一组微电源、负载、储能系统和控制装置构成的系统单元，是一个能够实现自我控制、保护和管理的自治系统，既可以与外部电网并网运行，也可以孤立运行；
智能微网作为一种由多个分布式电源及其相关组件按照一定的拓扑结构组成的新型网络，相比传统集中式电网更具有鲁棒性且成本效益高，但需要重点关注微网电力供需负载的有效管理；由于智能微网是一种分布式的新型网络结构，传统的集中式控制方法已不再适用，因此，一种称为动态平均跟踪的技术就被引入到智能微网研究领域。动态平均跟踪技术旨在设计一个分布式协同算法，使得网络中的每个节点仅通过与其邻居节点交换信息就能够跟踪到所有节点时变 参考信号的平均值。在传统的动态跟踪协同算法中，每个节点必须与其邻居节点诚实地通信才能针对某个感兴趣的变量达成共识。然而，在某实际应用中，这种直接的信息交换可能会导致敏感信息的泄露；因此一些恶意或好奇的攻击者就可以很容易通过监听信道获得此类数据，进而能够使用访问到的电力消耗数据推断出目标节点的详细家庭或商业活动。
发明内容
本发明目的是针对智能微网中的供需负载平衡问题，基于随机数扰动机制开发一种具有隐私保护作用的分布式动态跟踪方法，以便合理地规划电力生产、存储和调度环节。
一种具有隐私保护作用的智能微网分布式动态跟踪方法，该方法包括以下步骤：
S1、确定智能微网实体节点总量为N，第i个和节点的实时电力消耗数据为φ
i(t)，再结合各个节点权重系数w
i，得到电力消耗的求解目标函数模型
S2、根据S1中目标函数模型，基于随机数扰动机制设计动态跟踪算法如下
其使用被随机数扰乱后的虚拟状态
进行互相的信息交换，这样就避免了节点真实信息的泄露；
S3、具体阐述S2中算法的执行过程；
S4、对算法参数的确定与验证；
进一步的，所述S2中z
i(t)，x
i(t)和u
i(t)分别表示节点内部状态、估计状态和控制输入；参数0＜ω
i＜1，
是智能体i的正则化权重，α是控制增益，γ＞0是设计参数；
表示由节点i生成并传递给邻居节点j的随机数，
表示节点i从邻居节点j接收到的随机数，
可看作是估计状态x
i(t)被随机数扰乱后的虚拟状态；
进一步的，每个所述节点i根据自己的邻居节点j的数量生成一组随机数
生成的随机数个数等于其邻居节点j的个数，然后分别向其邻居节点各发送一个随机数；每个节点i收到来自邻居节点j发出的随机数
后，根据自身的状态估计x
i(t)、自身生成的随机数集合
和接收到的随机数集合
计算用于信息传输的虚拟状态
5.进一步的，S3阐述具体的算法流程如下：
步骤1：算法初始化，具体包括：
(1-1)每个节点i生成N
i个随机数
其中N
i是其邻居节点数量；
(1-2)节点i将随机数分别传递给它相邻的节点(例如
被传递给i的邻节点i
1)，并接收其邻节点j生成并传递过来的随机数
步骤2：初始化算法迭代时间t＝0，并重复以下步骤；
步骤3：节点i基于自身生成的随机数集合计算状态
步骤4：节点i基于邻居节点传递过来的随机数集合计算虚拟状态
步骤5：相邻节点间利用各自的虚拟状态进行信息交换，每个节点i进行控制输入更新如下：
步骤6：节点i更新内部状态z
i(t+1)和估计器状态x
i(t+1)如下：
z
i(t+1)＝z
i(t)+h(-γz
i(t)+u
i(t)),
其中h是算法用于迭代的步长。
步骤7：更新算法迭代时间，并判断迭代是否结束，具体包括：
(7-1)更新迭代时间t＝t+1；
(7-2)判断迭代是否结束，计算最近两次状态估计值的误差||ε(t)||＝||x(t)-x(t-1)||，如果该误差小于给定的阈值，即||ε(t)||＜δ，那么算法结束；否则，继续执行步骤3-步骤7，直到算法结束；
本发明的有益效果是：
采用本发明，提出的算法使得节点之间的信息交换不再依赖真实的状态值x
i(t)，而是使用被随机数扰乱后的虚拟状态
进行互相的信息交换，这样就避免了节点真实信息的泄露，从而起到节点敏感信息隐私保护的作用；同时，本发明所使用的随机数扰动机制是经过精心设计的，从而保证即便节点之间的信息交换经过了随机数的扰动， 但最终仍能够实现精确的动态目标跟踪；基于随机数扰动机制的分布式动态跟踪算法，可以在确保微网中节点敏感信息不被泄露的同时，使得每个节点都能够准确跟踪到整个网络中所有节点时变电力消耗数据的加权平均值，进而得出电力消耗总数据，为供需负载平衡控制提供决策参考。
图1是本发明的步骤流程图；
图2是本发明的S3中算法的具体执行步骤。
下面将结合本发明实施例中的附图，对本发明实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本发明一部分实施例，而不是全部的实施例。
参考图1-2，一种具有隐私保护作用的智能微网分布式动态跟踪方法，该方法包括以下步骤：
S1、智能微网由N个实体节点组成，第i个节点的实时电力消耗数据为φ
i(t)，则动态平均跟踪问题解决的是如何让每个节点都能跟踪到所有电力消耗数据φ
i(t)的平均值，即跟踪目标可以表示为
上述求解问题也称为绝对平均跟踪问题，因为跟踪目标是所有φ
i(t)的绝对平均值；然而，在实际微网场景中，由于每个实体节点的 重要性是不一样的，要跟踪的目标值不一定是绝对平均值；此时，为了体现每个节点重要性的不同，就要为每个节点i引入一个权重系数w
i，则跟踪目标就变成了所有φ
i(t)的加权平均和，即
需要注意的是，如果将所有的权重系数设置为w
i＝1/N，那么动态加权平均跟踪问题就会退化成绝对平均跟踪问题，这就说明绝对平均跟踪仅是加权平均跟踪的一个特殊情况，同时也说明动态加权平均跟踪有着更好的普适性；
S2、为了解决绝对平均跟踪问题，已有工作提出了如下的动态跟踪算法：
x
i(t)＝z
i(t)+φ
i(t)
其中z
i(t)，x
i(t)和u
i(t)分别表示节点内部状态、估计状态和控制输入。尽管该算法在收敛速度和收敛精确度方面有着比较好的性能，但其最终跟踪到的目标是所有时变参考信号φ
i(t)的绝对平均值。另一方面，该算法要求网络中的所有节点跟自己的邻居节点直接进行通信，也就是说每个节点的真实状态是直接通过信道进行传输的，这就为节点敏感信息的泄露带来了非常大的风险。
为了克服上述算法存在的不足，本发明基于随机数扰动机制提出了一种具有隐私保护作用的动态加权平均跟踪算法：
其中z
i(t)，x
i(t)和u
i(t)分别表示节点内部状态、估计状态和控制输入。参数0＜ω
i＜1，
是智能体i的正则化权重，α是控制增益，γ＞0是设计参数。
表示由节点i生成并传递给邻居节点j的随机数，
表示节点i从邻居节点j接收到的随机数，
可看作是估计状态x
i(t)被随机数扰乱后的虚拟状态。
具体来说，每个节点i首先根据自己的邻居个数生成一组随机数
生成的随机数个数等于其邻节点的个数，然后分别向其邻居节点各发送一个随机数。每个节点i收到来自邻居节点发出的随机数
后，根据自身的状态估计x
i(t)、自身生成的随机数集合
和接收到的随机数集合
计算用于信息传输的虚拟状态
一旦所有节点完成各自虚拟状态的计算，虚拟状态将在网络中的相邻节点之间进行交换。
从通信网络的角度来看，节点自身生成的随机数和他们对应邻居节点生成随机数的组合使用在确保实现精确目标跟踪方面起着关键作用，因为它们在计算各自虚拟状态并进行状态融合时正好能达到互相抵消的效果；值得强调的是，引入的随机数扰动机制对算法执行增加的通信负载是非常小的，因为随机数的生成和交换只在算法执行的初始化阶段发生并且是一次性的操作。但是需要注意的是，一旦通信网络拓扑发生改变，包括随机数生成和随机数交换在内的扰动机制必 须重新执行并初始化；
S3、步骤1：算法初始化，具体包括：
(1-1)每个节点i生成N
i个随机数
其中N
i是其邻居节点数量；
(1-2)节点i将随机数分别传递给它相邻的节点(例如
被传递给i的邻结点i
1)，并接收其邻节点j生成并传递过来的随机数
步骤2：初始化算法迭代时间t＝0，并重复以下步骤；
步骤3：节点i基于自身生成的随机数集合计算状态
步骤4：节点i基于邻居节点传递过来的随机数集合计算虚拟状态
步骤5：相邻节点间利用各自的虚拟状态进行信息交换，每个节点i进行控制输入更新如下：
步骤6：节点i更新内部状态z
i(t+1)和估计器状态x
i(t+1)如下：
z
i(t+1)＝z
i(t)+h(-γz
i(t)+u
i(t)),
其中h是算法用于迭代的步长。
步骤7：更新算法迭代时间，并判断迭代是否结束，具体包括：
(7-1)更新迭代时间t＝t+1；
(7-2)判断迭代是否结束，计算最近两次状态估计值的误差||ε(t)||＝||x(t)-x(t-1)||，如果该误差小于给定的阈值，即||ε(t)||＜δ，那么 算法结束；否则，继续执行步骤3-步骤7，直到算法结束；
S4、对算法参数的确定与验证；
假设1(连通性)：假设N个节点构成的网络拓扑图是双向通信，并且是连通的。
假设2(有界性)：对于网络拓扑中任意节点i∈{1,...,N}，其时变参考信号φ
i(t)和其导数
都是有界的，即存在正常数
和σ使得：
定理1：在假设1、2成立的基础上，若控制增益α满足：
其中
λ
2是通信拓扑图对应的Lapacian矩阵的第二小特征值(即非零最小特征值)，那么本发明提出的算法能够保证经过随机数扰动后的虚拟状态x
+(t)在有限的时间内实现对加权平均信号
的精确跟踪，即：
其对应的时间下界t
*为：
其中e(t
0)为初始稳态误差。
上述定理1表明，算法的收敛时间是有界的且为指数形式，对应的时间下界t
*主要由设计参数γ、权重矩阵W和初始稳态误差e(t
0)决定。此外，控制增益α的下界依赖于参考信号φ(t)及其时间导数
的全局信息。在实际应用中，参考信号及其时间导数上界的全局信息可 以在算法初始化阶段通过运行最大一致性算法来获得。
定理2：本发明所提出的算法，节点i可以保护其估计器状态x
i(t)、时变参考信号φ
i(t)的隐私不被泄露，除非节点i的所有邻居互相合作来推断节点i的相关状态信息。
如果目标节点的所有邻节点都愿意相互合作来攻击该节点，那么本发明提出的算法就不再能够保护目标节点的敏感信息，相反，只要存在至少一个邻居节点是非合作的态度，那么所提出的算法就能够保证目标节点敏感状态的隐私性；此外，如果攻击者没有系统的网络拓扑信息，那它也无法对目标发起有效攻击，因为它无法确定某个节点是否属于其攻击目标的邻居节点，也就无法收集目标所有邻居节点产生的随机数和进一步计算、推断攻击目标的真实敏感信息；
本发明在具体实施过程中(1)通过给系统中的每个节点引入一个权重系数，提出的算法使得每个节点能够准确跟踪到时变参考信号的加权平均值而不是简单的绝对平均值；(2)基于随机数扰动机制为动态跟踪算法精心设计了一套隐私保护方案，使得算法能够准确跟踪到目标点的同时避免参与节点敏感信息的泄露；本发明提出的基于随机数扰动机制的分布式动态跟踪算法，可以在确保微网中节点敏感信息不被泄露的同时，使得每个节点都能够准确跟踪到整个网络中所有节点时变电力消耗数据的加权平均值，进而得出电力消耗总数据，为供需负载平衡控制提供决策参考。
采用本发明本文中所描述的具体实施例仅仅是对本发明精神作举例说明；本发明所属技术领域的技术人员可以对所描述的具体实施 例做各种各样的修改或补充或采用类似的方式替代，但并不会偏离本发明的精神所定义的范围；
Claims (4)
- 一种具有隐私保护作用的智能微网分布式动态跟踪方法，其特征在于，该方法包括以下步骤：S1、确定智能微网实体节点总量为N，第i和节点的实时电力消耗数据为φ i(t)，再结合各个节点权重系数w i，得到电力消耗的求解目标函数模型
S2、根据S1中目标函数模型，基于随机数扰动机制设计分布式动态跟踪算法如下：
其使用被随机数扰乱后的虚拟状态进行互相的信息交换，这样就避免了节点真实信息的泄露；
S3、具体阐述S2中算法的执行过程；S4、对算法参数的确定与验证。 - 根据权利要求1所述的一种具有隐私保护作用的智能微网分布式动态跟踪方法，其特征在于，所述S2中z i(t)，x i(t)和u i(t)分别表示节点内部状态、估计状态和控制输入；参数是智能体i的正则化权重，α是控制增益，γ＞0是设计参数；
表示由节点i生成并传递给邻居节点j的随机数，
表示节点i从邻居节点j接收到的随机数，
可看作是估计状态x i(t)被随机数扰乱后的虚拟 状态。
- 根据权利要求2所述的一种具有隐私保护作用的智能微网分布式动态跟踪方法，其特征在于，每个所述节点i根据自己的邻居节点j的个数生成一组随机数生成的随机数个数等于其邻居节点j的个数，然后分别向其邻居节点各发送一个随机数；每个节点i收到来自邻居节点j发出的随机数
后，根据自身的状态估计x i(t)、自身生成的随机数集合
和接收到的随机数集合
计算用于信息传输的虚拟状态
- 根据权利要求3所述的一种具有隐私保护作用的智能微网分布式动态跟踪方法，其特征在于，S3阐述了具体的算法流程如下：步骤1：算法初始化，具体包括：(1-1)每个节点i生成N i个随机数其中N i是其邻居节点数量；
(1-2)节点i将随机数分别传递给它相邻的节点(例如被传递给i的邻节点i 1)，并接收其邻节点j生成并传递过来的随机数
步骤2：初始化算法迭代时间t＝0，并重复以下步骤；步骤3：节点i基于自身生成的随机数集合计算状态
步骤4：节点i基于邻居节点传递过来的随机数集合计算虚拟状态
步骤5：相邻节点间利用各自的虚拟状态进行信息交换，每个节 点i进行控制输入更新如下：
步骤6：节点i更新内部状态z i(t+1)和估计器状态x i(t+1)如下：z i(t+1)＝z i(t)+h(-γz i(t)+u i(t)),
其中h是算法用于迭代的步长。步骤7：更新算法迭代时间，并判断迭代是否结束，具体包括：(7-1)更新迭代时间t＝t+1；(7-2)判断迭代是否结束，计算最近两次状态估计值的误差||ε(t)||＝||x(t)-x(t-1)||，如果该误差小于给定的阈值，即||ε(t)||＜δ，那么算法结束；否则，继续执行步骤3-步骤7，直到算法结束。
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