Disclosure of Invention
In order to solve the defects of the prior art, the present disclosure provides a security control method and system of an attacked discrete random distribution control system, so that a closed-loop system is more stable, and effective tracking of a probability function is realized.
In order to achieve the purpose, the following technical scheme is adopted in the disclosure:
a first aspect of the present disclosure provides a security control method for an attacked discrete randomly distributed control system.
A security control method of an attacked discrete random distribution control system comprises the following processes:
acquiring parameter data of a discrete random distribution control system of a sensor under sparse attack;
according to the obtained parameter data, a Luenberger weight observer with a safety preselector is used for carrying out weight estimation to obtain a system weight estimation vector;
obtaining a weight error vector according to a difference value of the system weight estimation vector and a preset reference weight vector;
and obtaining the input control quantity of the discrete random distribution control system according to the weight error vector, the system weight estimation vector and the controller gain, and carrying out safety control according to the obtained input control quantity.
Furthermore, a dynamic relation between the probability density functions of control input and output is described through a B-spline approximation method, and a discrete random distribution control system of the sensor under sparse attack is obtained.
Further, establishing a sufficient condition that the safety weight estimation can be solved, and constructing a Luenberger weight observer with a safety preselector under the sufficient condition.
Furthermore, for the random distribution control system under the s-sparse attack, the following conditions are met:
where s is the number of sensors under attack and q is the total number of sensors.
Further, a controller is constructed according to the weight error vector, the system weight vector and the controller gain, and the output quantity of the controller is used as the input control quantity of the discrete random distribution control system to carry out system safety control, so that the difference between the output probability density function of the closed-loop system and the expected tracking target is within a preset range.
Further, the controller is:
wherein,
and
for the controller gain to be obtained based on the linear matrix inequality,
is the weight value of the observer at the moment k, T
0=t
0I,t
0>0 represents the sample interval, ξ (k) represents the correlation integral term, and W (k-1) represents the weight error vector at the moment k-1.
Furthermore, after the sensor is subjected to sparse attack, extracting the signals which are not attacked by using a sequencing method and a median operator, and designing the Luenberger weight observer with the safety preselector by combining the extracted signals which are not attacked.
A second aspect of the disclosure provides a security control system for an attacked discrete randomly distributed control system.
A security control system for a hacked discrete randomly distributed control system, comprising:
a data acquisition module configured to: acquiring parameter data of a discrete random distribution control system of a sensor under sparse attack;
a system weight estimation module configured to: according to the obtained parameter data, a Luenberger weight observer with a safety preselector is used for carrying out weight estimation to obtain a system weight estimation vector;
a weight error vector calculation module configured to: obtaining a weight error vector according to a difference value of the system weight estimation vector and a preset reference weight vector;
a security control module configured to: and obtaining the input control quantity of the discrete random distribution control system according to the weight error vector, the system weight estimation vector and the controller gain, and carrying out safety control according to the obtained input control quantity.
A third aspect of the present disclosure provides a computer-readable storage medium, on which a program is stored, which when executed by a processor implements the steps in the security control method of the attacked discrete randomly distributed control system according to the first aspect of the present disclosure.
A fourth aspect of the present disclosure provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, where the processor implements the steps in the security control method of the attacked discrete randomly distributed control system according to the first aspect of the present disclosure when executing the program.
Compared with the prior art, the beneficial effect of this disclosure is:
according to the method, the system, the medium or the electronic equipment, after a system sensor is subjected to sparse attack, signals which are not attacked in the middle are extracted by utilizing a sequencing method and a middle value operator; carrying out weight estimation of the system by a Luenberger observer with a safety preselector; and designing an augmented proportional-integral (PI) tracking control strategy according to the obtained weight estimated value, and searching PI control gain to enable a closed-loop system to be more stable and realize effective tracking of a probability function.
Advantages of additional aspects of the disclosure will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the disclosure.
Detailed Description
The present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments in the present disclosure may be combined with each other without conflict.
Example 1:
as described in the background art, in the prior art, there are weight estimation and security control problems of an attacked random distribution control system, and in order to solve the above technical problems, this embodiment proposes a security control method of an attacked discrete random distribution control system, as shown in fig. 1 and fig. 2, which includes the following processes:
s1: constructing a dynamics model of a randomly distributed control system
A discrete time linear system with a sensor under sparse attack is described as follows:
wherein V (k) e R
n-1Is the weight vector of the system, u (k) e R
mFor the control input vector of the system, a and B represent known parameter matrices, C (y) ═ C
1(y),C
2(y)...C
n-1(y)]∈R
q×n-1Is a vector of basis functions that is pre-specified by the system,
is determined by a neural network B-spline basis function, η (k) [. eta. ]
1(k),η
2(k)...η
q(k)]
T∈R
qRepresents an attack signal injected by an attacker under the sensor, and gamma (y, k) is the interval [ a, b ] of y]The measurement output probability density function which can be damaged is obtained, and y is an independent variable of a B spline basis function of the neural network.
s-sparse attack definition: suppose that the attack signal eta (k) belongs to R
qIn (1) is an s-sparse attack (s ≦ q) and satisfies | | | | η (k) | ≦ D, where D is a known normal number. If there are q sensors, sensor/e {1,2.. q } is attacked, then η
l(k) Is a non-zero constant, otherwise sensor l is not attacked, s ∈ N is the number of attacked sensors, and a set exists
And in the set
Q-s is the number of non-attacked sensors. For all k, eta
j(k) 0, j ∈ l, and the other s elements of η (k) can be arbitrarily operated.
S2: distributed discrete time security weight estimation
In order to solve the problem of the security weight estimation of the randomly distributed control system in equation (1), a sufficient condition is proposed as shown below.
Condition 1: for a random distribution control system which is subjected to s-sparse attack in the formula (1), the following conditions are met:
based on condition 1, this embodiment designs a distributed strategy to obtain an attack-free sensor in the system, where given is an output probability density function γ (y, k) instead of using the output y (k), and arranges its elements by size to obtain a new vector x ═ x1,x2...xq]Satisfy x1≤x2≤...≤xqThe median operator for the output γ (y, k) is as follows:
wherein, if q is an odd number, ω is 1/2(q +1), if q is an even number, ω is 1/2q, and when condition 1 is established, the distributed safety measurement preselector of the random distribution control system is designed as follows:
ηj(k)=Med[γ(y,k)] (4)
by analyzing the above, when condition 1 is satisfied, the "middle" non-attacked signal is extracted by analyzing the difference of the multiplexed signals by using the sorting method and the median operator, so that ηj(k)=0。
By utilizing the Luenberger observability, aiming at the attacked random distribution control system, a discrete Luenberger weight observer is designed:
wherein,
is the weight of the observer and is,
is the output of an observerOut of wherein
Residual error
σ(y)∈R
PIs defined in [ a, b]The above pre-specified weight vector, η
j(k) Representing an "intermediate" non-attacked signal, i.e. eta, resolved when condition 1 holds
j(k)=0,L
0Is to make (A-L)
0Σ) parameters of the stable design.
The tracking error can be described as:
then the
Δe(k)=e(k+1)-e(k) (7)
Substituting equation (1) and equation (5) into equation (7) yields:
Constructing a Lyapunov function:
Φ(k)=eT(k)Pe(k) (9)
according to the formula (9):
using the Young inequality, 2bTd≤1/ε1bTb+ε1dTd, wherein epsilon1>0 is any scalar, b and d are real vectors, so,
then, further from the formula (10):
wherein (A-L)
0Σ)
T(P-1/ε
1P
TP)(A-L
0Σ)-P=-I,
Because | | | eta (k) | | is less than or equal to D, therefore,
thus, from the above it can be demonstrated that:
when | | e (k) | non-woven2>φ,ΔΦ(k)<-||e(k)||2+φ
Wherein e (k) converges to a small set of | | e (k) | tory cells according to the Lyapunov theory of stability
2Phi is less than or equal to phi, therefore, the weight estimation error e (k) is consistently bounded, and the weight V (k) approximation is obtained through the above demonstration
S3: and according to the obtained weight estimation, subtracting the reference weight vector by using the weight vector to obtain a weight error vector, and designing an augmented PI controller.
The weight dynamic model can be known from (1):
about
The desired tracking objective may be defined as:
g(y)=C(y)Vg+L(y)+η(k) (12)
wherein VgIs as desiredAnd (5) weight vectors.
The dynamic reference system is designed as follows:
wherein A is
m∈R
(n-1)×(n-1),B
m∈R
(n-1)×m,C
m∈R
(n-1)×(n-1)Is a pre-specified matrix, R ∈ R
mIs a pre-specified constant vector. To ensure
It is assumed here that A
mIs a stable matrix, V
g=C
m(I-A
m)
-1B
mr。
The probability density function tracking control error can be described as:
e(y,k)=γ(y,k)-g(y) (14)
the weight error vector is defined as:
the probability density function tracking control problem is converted into a weight error
Based on the above analysis, the following controller was constructed in order to simplify the design procedure and provide a strict control theory:
wherein T is0=t0I,t0>0 denotes the sample interval and ξ (k) denotes the correlation integral term.
Substituting equation (16) into equations (1) and (5) results in the following closed loop system:
wherein
V (k), ξ (k) and z (k) have the same dimensionality, C
mIs reversible, defining:
solving the matrix inequality psi to obtain Qi>0(i ═ 1,2) and M, the algebraic matrix inequality satisfies:
wherein
Definition of Q
1=diag{Q
11,Q
12,Q
13},
β
1,β
2Is a matrix with suitable dimensions.
Stability: to prove that condition (20) holds, Schur's theorem can be used to obtain if Ψ<Proof of 0
Solving Linear Matrix Inequality (LMI) Ψ by MATLAB<0, Q can be obtained
i(i ═ 1,2) and M, and further using M to obtain the controller gain
And
definition Q ═ diag { Q
1,Q
2On the basis of Schur's theorem, if Ψ
1<0, then
This is true. Here, a sufficiently small normal number delta is first defined, which satisfies
Under system (17), the appropriate Lipapp stability theorem was applied. The Lyapunov function was chosen as:
Ξ(Π(k),k)=ΠT(k)Q-1Π(k) (22)
then the
Inequality (23), which may be further converted to a polynomial in the form of a sum of perfect squares on | | | | Π (k) | |, so:
Therefore, Δ Π (k) <0, proving that the system (17) is stable.
Tracking performance: considering two tracking performances theta of the system (17)1(k) And theta2(k)。
Definition of θ (k +1) ═ Anθ(k),θ(k)=θ1(k)-θ2(k);
Designing a Lyapunov function:
S(θ(k),k)=θT(k)Q-1θ(k) (24)
the same can prove that:
the above results demonstrate that
So Δ S (k)<-δ||θ(k)||
2<0, as can be seen from the Lyapunov stability criterion, there must be a stable equilibrium point for the system (17):
thus can be easily verified
In fact, the output probability density function γ (y, k) can be as close as possible to the given distribution function g (y), thus achieving tracking performance.
Obtaining controller gain by solving linear matrix inequality through MATLAB
And
gain the controller
And
substituting the controller u (k) into the system model, and further calculating a weight and a probability density function, so that the design of the random distribution control system modeling, the observer and the PI controller is completed.
At present, no similar scheme is proposed for modeling, estimating and controlling an attacked random distribution control system, but after a sensor is sparsely attacked, an attacked signal can be detected, a weight of the system is estimated through an observer, a weight error vector and a system control law are constructed, an augmented PI controller is constructed, and the system is stable and tracking performance is realized by searching PI control gain.
Example 2:
an embodiment 2 of the present disclosure provides a security control system of an attacked discrete randomly distributed control system, including:
a data acquisition module configured to: acquiring parameter data of a discrete random distribution control system of a sensor under sparse attack;
a system weight estimation module configured to: according to the obtained parameter data, a Luenberger weight observer with a safety preselector is used for carrying out weight estimation to obtain a system weight estimation vector;
a weight error vector calculation module configured to: obtaining a weight error vector according to a difference value of the system weight estimation vector and a preset reference weight vector;
a security control module configured to: and obtaining the input control quantity of the discrete random distribution control system according to the weight error vector, the system weight estimation vector and the controller gain, and carrying out safety control according to the obtained input control quantity.
The working method of the system is the same as the security control method of the attacked discrete random distribution control system provided in embodiment 1, and details are not repeated here.
Example 3:
the embodiment 3 of the present disclosure provides a computer-readable storage medium, on which a program is stored, which when executed by a processor, implements the steps in the security control method of the attacked discrete random distribution control system according to the embodiment 1 of the present disclosure.
Example 4:
the embodiment 4 of the present disclosure provides an electronic device, which includes a memory, a processor, and a program stored in the memory and capable of running on the processor, where the processor implements the steps in the security control method of the attacked discrete random distribution control system according to embodiment 1 of the present disclosure when executing the program.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.