CN111258223A - Sliding mode-based switching networked control system safety control method - Google Patents

Sliding mode-based switching networked control system safety control method Download PDF

Info

Publication number
CN111258223A
CN111258223A CN202010170612.0A CN202010170612A CN111258223A CN 111258223 A CN111258223 A CN 111258223A CN 202010170612 A CN202010170612 A CN 202010170612A CN 111258223 A CN111258223 A CN 111258223A
Authority
CN
China
Prior art keywords
sliding mode
switching
sliding
control system
mode
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010170612.0A
Other languages
Chinese (zh)
Inventor
李猛
陈勇
梁洪
郭斌
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
University of Electronic Science and Technology of China
Original Assignee
University of Electronic Science and Technology of China
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by University of Electronic Science and Technology of China filed Critical University of Electronic Science and Technology of China
Priority to CN202010170612.0A priority Critical patent/CN111258223A/en
Publication of CN111258223A publication Critical patent/CN111258223A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

The invention discloses a sliding mode-based switching networked control system security control method, and relates to switching networked control system model establishment under nonlinear and False Data Injection (FDI) attacks and sliding mode-based security control method design. The invention discloses a sliding mode-based security control method aiming at the problems of nonlinearity and FDI attack in a switching networked control system. The technical scheme of the invention comprises the following steps: the method comprises the following steps of system switching, nonlinear and FDI attack modeling, section reduction processing of a control system, sliding mode function design, sliding mode dynamic analysis, sliding mode parameter solving, sliding mode controller design and closed loop system stability proving. The invention can effectively solve the problems of nonlinearity and FDI attack in the switching networked control system, improve the safety of the system and ensure the stable operation of the system.

Description

Sliding mode-based switching networked control system safety control method
Technical Field
The invention belongs to the technical field of switching networked control systems under attack, and relates to modeling of a switching networked control system under nonlinear and FDI attack and design of a sliding mode-based security control method, in particular to a sliding mode-based switching networked control system security control method.
Background
A switching system is a hybrid system in which system parameters or system structure are randomly varied. Since the switching system shows significant advantages in terms of modeling system structure change, system topology change, system node or component failure, etc., the switching system is widely used in various industrial controls. However, in the switching system, the controlled system may become unstable or the control performance may be degraded due to the switching characteristics of the system. In particular, for switching networked control systems, the system is not only affected by some common problems, such as system nonlinearities, uncertainties, unmodeled dynamics, interference, etc. And the network attack also brings huge threat to the networked control system. Recently, the network security control problem has attracted a lot of attention, and various network attack events are frequently reported. In particular, for FDI attacks, an adversary can inject false data into sensors and actuators, or tamper with transmitted data, thereby destroying the control performance of the system and destabilizing or disabling the controlled system. For a multi-agent system with sensor FDI attack, a state estimation method based on Distributed optimization is provided in the document [ "Distributed secure state estimation for cell-physical systems under sensors (Liwei An, Guang-Hong Yang, Automatica, 2019, 107: 526-. The document "State estimation under fault data analysis attacks: Security analysis and System protection" (Liang Hu, Zidong Wang, Qi-Long Han, Xiaohui Liu, Automatica,2018, 87: 176-. However, to date, research on security control issues is in the initiative, and most of the reported literature focuses on attack detection, filtering, and state estimation, and lacks an effective security defense strategy.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a sliding mode-based switching networked control system security control method aiming at the problems of nonlinearity and FDI attack in a switching networked control system so as to improve the security of the system and ensure the stable operation of the system.
In order to achieve the above object, the invention provides a sliding mode-based switching networked control system security control method, which is characterized by comprising the following steps:
(1) aiming at the modeling problem of a switching networked control system with nonlinear and FDI attacks, a half Markov switching signal is designed to describe the switching characteristic of the system, and nonlinear time-varying functions are simultaneously and respectively designed to describe the nonlinear characteristic of the system and dummy data injected by a hacker;
(2) aiming at the problem of sensor false data injection attack in the system, the system is subjected to section reduction processing, a sliding mode function is designed, and a sliding mode dynamic system is obtained;
(3) aiming at the problem of parameter solution in the sliding mode function, a switching Lyapunov function is designed to obtain the stable condition of the sliding mode dynamic system, and the parameter in the sliding mode function is solved by adopting a linear matrix inequality technology;
(4) aiming at the problems of nonlinearity in a system and executor false data injection attack, a sliding mode controller is designed.
The switching networked control system modeling with the nonlinear and false data injection attacks and the sliding mode-based security control method design comprise system switching characteristics, nonlinear and FDI attack modeling, section-reducing processing of the system, sliding mode function design, section-reducing sliding mode dynamic analysis, sliding mode parameter solving and sliding mode controller design.
Modeling of the system, employing a random variable α (t) to describe the switching characteristics of the system, which obeys a semi-Markov distribution and satisfies a conditional probability distribution:
Figure BDA0002409050900000021
wherein
Figure BDA0002409050900000022
And limΔt→0o(h)/h＝0。
The design function g (z, t),
Figure BDA0002409050900000023
and ξa(t) represents the nonlinearity of the system and spurious data injected by hackers into the actuators and sensors, respectively, assuming they are both time-varying energy-bounded nonlinear functions;
the reducing process of the system considers the system state variable z (t), and for the ith subsystem, an invertible matrix gamma is searchediSuch thatiBi＝(0 Bi,2)TAnd Bi,2Reversibly, further, a new state variable x (t) ═ Γ is definediz(t)。
The sliding mode function is designed, the asynchronization of a system node and a controller node is considered, the relation between the system node and the controller node is established through a hidden Markov model, the states of the system node and the controller node at the time t are respectively represented by variables α (t) and β (t), the variables (α (t) and β (t)) form the hidden Markov model, and the conditional probability that Pr { β (t) ═ k | α (t) ═ i } - [ tau ] (t) ], is metikWherein 0 is not more than τ ik1 or less and
Figure BDA0002409050900000031
further, a sliding mode function is designed: s (t) ═ Ki，kx1(t)+x2(t) wherein Ki,kRepresenting a sliding mode parameter matrix.
The analysis of the sliding mode dynamics requires that the system meets the following two conditions (1) the system is ξaUnder the condition that (t) is 0, x is an arbitrary initial condition1(0) And α (0), there is a constant
Figure BDA0002409050900000032
So that the conditions
Figure BDA0002409050900000033
If true; (2) at x1(0) Under the condition of 0, inequality
Figure BDA0002409050900000034
Figure BDA0002409050900000035
Wherein γ > 0 represents an inhibitory factor. Further, the condition for giving the sliding mode dynamic random stability is as follows:
Figure BDA0002409050900000036
wherein
Figure BDA0002409050900000037
And
Figure BDA0002409050900000038
solving parameters in the sliding mode function, carrying out matrix change and variable substitution on stability conditions in sliding mode dynamics, and solving a sliding mode parameter matrix:
Figure BDA0002409050900000039
designing the sliding mode controller according to the sliding mode parameters obtained by solving
Figure BDA00024090509000000310
Wherein
Figure BDA00024090509000000311
The stability of the closed-loop system proves that the state track of the closed-loop system can be converged on a sliding mode surface within a limited time under a designed sliding mode controller. By constructing the Lyapunov function
Figure BDA00024090509000000312
The weak infinitesimal operator is proved to meet the following conditions:
Figure BDA00024090509000000313
the object of the invention is thus achieved.
The invention relates to switching networked control system model establishment under nonlinear and False Data Injection (FDI) attacks and a sliding mode-based security control method design. The invention discloses a sliding mode-based security control method aiming at the problems of nonlinearity and FDI attack in a switching networked control system. The technical scheme of the invention comprises the following steps: the method comprises the following steps of system switching, nonlinear and FDI attack modeling, section reduction processing of a control system, sliding mode function design, sliding mode dynamic analysis, sliding mode parameter solving, sliding mode controller design and closed loop system stability proving. The invention can effectively solve the problems of nonlinearity and FDI attack in the switching networked control system, improve the safety of the system and ensure the stable operation of the system.
Drawings
Fig. 1 is a schematic diagram of a specific embodiment of a sliding mode-based switching networked control system security control method according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided in order to better understand the present invention for those skilled in the art with reference to the accompanying drawings. It is to be expressly noted that in the following description, a detailed description of known functions and designs will be omitted when it may obscure the subject matter of the present invention.
As shown in FIG. 1, the invention relates to the establishment of a switching networked control system model with nonlinear and FDI attacks and the design of a sliding mode-based security control method. The method comprises the steps of considering the system switching characteristic, the system nonlinearity and the modeling of FDI attack, the section reduction processing of the system, the sliding mode function design, the sliding mode dynamic analysis, the sliding mode parameter solving, the sliding mode controller design and the closed-loop system stability verification.
Modeling of system switching characteristics, non-linearities and FDI attacks
Consider the following non-linear switching networked control system:
Figure BDA0002409050900000041
wherein
Figure BDA0002409050900000042
And
Figure BDA0002409050900000043
respectively representing the state of the system and the control inputs,
Figure BDA0002409050900000044
and
Figure BDA0002409050900000045
a matrix of the system is represented,
Figure BDA0002409050900000046
represents a non-linear function and satisfies the following condition:
||g(z,t)||≤g1||z(t)|| (2)
wherein g is1In equation (1), α (t) represents the switching signal, which takes the values:
Figure BDA0002409050900000047
and obey half Markov distribution, satisfy following conditional probability:
Figure BDA0002409050900000048
wherein λij(h) Representing the transition probability (from state i to state j), h represents the residence time of the state, and
Figure BDA0002409050900000049
and limΔt→0For convenience of description, α (t) is represented by aiAnd BiRepresenting a (α (t)) and B (α (t)), respectively, while considering the effector spurious data injection attack, the system (1) can be described as:
Figure BDA00024090509000000410
in the system (4), it is assumed that for any
Figure BDA00024090509000000411
BiIs column full rank, and
Figure BDA00024090509000000412
and theta1＞0。
System reduction process
For matrix BiIs decomposed to make
Figure BDA0002409050900000051
So that
Figure BDA0002409050900000052
And Bi，2Is reversible. Constructing a reversible matrix
Figure BDA0002409050900000053
Thereby to obtain
Figure BDA0002409050900000054
Further, a new state variable x (t) ═ Γ is definediz (t), the system (4) becomes of the form:
Figure BDA0002409050900000055
wherein
Figure BDA0002409050900000056
Order to
Figure BDA0002409050900000057
And is
Figure BDA0002409050900000058
And
Figure BDA0002409050900000059
a system like a drop node can then be obtained:
Figure BDA00024090509000000510
sliding mode function design
Defining a random variable β (t) (taking on a value of
Figure BDA00024090509000000511
Representing the controller nodes, the variables (α (t), β (t)) together form a hidden Markov model with a conditional probability τikThe definition is as follows:
Pr{β(t)＝k|α(t)＝i}＝τik(7)
wherein 0 is not less than tau ik1 or less and
Figure BDA00024090509000000512
according to the conditional probability (7) formula, the following sliding mode function is designed:
S(t)＝Ki，kx1(t)+x2(t) (8)
wherein
Figure BDA00024090509000000513
Representing the sliding mode parameters.
According to the sliding mode control theory, when the motion track of the system reaches the sliding mode surface, S (t) is 0, and x is obtained2(t)＝-Ki，kx1(t), however, considering sensor spurious data injection attacks, the following sliding mode equations of motion are available:
Figure BDA00024090509000000514
and (3) obtaining a descending section sliding mode dynamic equation by combining the descending section system (6):
Figure BDA00024090509000000515
wherein
Figure BDA0002409050900000061
Representing false data injected into the sensor by a hacker.
Further, in order to analyze the sliding mode dynamic stability, an output equation of a descending section system is given as follows:
y1(t)＝Cix1(t) (11)
wherein C isiAn output matrix is represented.
Sliding mode dynamic analysis
Analyzing the random stability of the sliding mode dynamic system (10), namely satisfying the following two conditions:
(1) at ξaUnder the condition that (t) is 0, x is an arbitrary initial condition1(0) And α (0), there is a constant
Figure BDA0002409050900000062
So that the following condition is satisfied.
Figure BDA0002409050900000063
(2) At x1(0) For any ξ under the condition of 0a(t)∈L2[0, ∞), the following inequality holds.
Figure BDA0002409050900000064
Where γ > 0 represents an inhibitor.
For a given suppression factor γ > 0, if a symmetric matrix P is presenti，j> 0, matrix Ki，kAnd a scalar λijAnd τikWherein
Figure BDA0002409050900000065
So that
Figure BDA0002409050900000066
The sliding mode dynamic system (10) is randomly stable.
(i) When ξaWhen (t) is 0, the following lyapunov function is constructed:
Figure BDA0002409050900000067
for function V1(x1(t), α (t)), defining the following weak infinitesimal operator
Figure BDA0002409050900000068
Figure BDA0002409050900000069
Figure BDA00024090509000000610
Where at represents a very small positive number.
According to a first order Taylor expansion
Figure BDA00024090509000000611
Definition Fi(h) Is a distribution function of residence time h at node i, due to Fi(h+Δt)-Fi(h) → 0(Δ t → 0) and
Figure BDA0002409050900000071
thus can obtain
Figure BDA0002409050900000072
Wherein piijRepresenting the probability density of switching from state i to state j.
Definition of lambdai(h) And fi(h) Respectively, the transition probability and the probability density function of the residence time h at the node i. Order to
Figure BDA0002409050900000073
And λij(h)＝πijλi(h) I ≠ j, thus
Figure BDA0002409050900000074
At condition ξa(t) is 0 or less, and (10) is substituted for (10) to obtain
Figure BDA0002409050900000075
Wherein
Figure BDA0002409050900000076
Inequality (14) indicates
Figure BDA0002409050900000077
That is to say, the position of the nozzle is,
Figure BDA0002409050900000078
factor(s)
Figure BDA0002409050900000079
Wherein λmax(. cndot.) represents the matrix maximum eigenvalue operator. Due to the fact that
Figure BDA00024090509000000710
When the factor t → ∞ is satisfied, the compound is obtained
Figure BDA00024090509000000711
Wherein
Figure BDA00024090509000000712
This indicates that the condition (12) is established.
(ii) When x is1(0) When 0, for any ξa(t)∈L2[0, ∞) definition
Figure BDA00024090509000000713
Thus, the
Figure BDA00024090509000000714
Order to
Figure BDA00024090509000000715
Can be pushed out
Figure BDA0002409050900000081
Wherein
Figure BDA0002409050900000082
Inequality (14) indicates that J < 0, and thus conditional (13) is satisfied.
From the analysis of (i) and (ii), it can be seen that the sliding mode dynamic system (10) is randomly stable.
Sliding mode parameter solution
For a given suppression factor γ > 0, if a symmetric matrix P is presenti，j> 0, matrix Mi，kAnd Ki，kAnd a scalar λijAnd τikWherein
Figure BDA0002409050900000083
So that
Figure BDA0002409050900000084
Wherein
Figure BDA0002409050900000085
Figure BDA0002409050900000086
The sliding mode dynamics (10) are then randomly stable and the sliding mode parameters can be solved as:
Figure BDA0002409050900000087
by applying Shur's complement theory to (14), the product can be obtained
Figure BDA0002409050900000088
Definition matrix
Figure BDA0002409050900000089
For any i e {1,2
Figure BDA00024090509000000810
By means of matrices
Figure BDA00024090509000000811
The left and right are multiplied by (27) simultaneously to obtain
Figure BDA00024090509000000812
To process in (28) formula
Figure BDA00024090509000000813
Using Shur's complement theory again on (28) to obtain
Figure BDA0002409050900000091
Further, using variable substitution, the matrix (29) expression may be converted to a matrix (25) expression. It follows that the sliding mode dynamics (10) are randomly stable under condition (25) and the sliding mode parameters can be solved as equation (26).
Sliding mode controller design and closed loop system stability certification
For a section-reducing system, the bandwidth of a quasi-sliding mode is defined as follows:
Figure BDA0002409050900000092
where κ > 0. Thus, the following slide mode controller is designed
Figure BDA0002409050900000093
Wherein
Figure BDA0002409050900000094
Given scalar g1And theta1For the section reducing system (6), a sliding mode function (8) is designed, and a sliding mode parameter K is solved through theorem (2)i，k. The trajectory of the closed loop system can converge on the sliding surface within a limited time under the influence of the controller (30).
The following Lyapunov function was constructed
Figure BDA0002409050900000095
For function V2(t) solving for a weak infinitesimal operator to obtain
Figure BDA0002409050900000096
Substituting equation (5) into equation (32) can derive
Figure BDA0002409050900000097
Further, substituting the formula (30) into the formula (33) to obtain
Figure BDA0002409050900000098
From the formula (34), it can be seen that
Figure BDA0002409050900000101
This is true. Thus, the trajectory of the closed-loop system can converge on the slip-form face in a limited time.
Although illustrative embodiments of the present invention have been described above to facilitate the understanding of the present invention by those skilled in the art, it should be understood that the present invention is not limited to the scope of the embodiments, and various changes may be made apparent to those skilled in the art as long as they are within the spirit and scope of the present invention as defined and defined by the appended claims, and all matters of the invention which utilize the inventive concepts are protected.

Claims (8)

1. A switching networking control system safety control method based on a sliding mode is characterized by comprising the following steps:
(1) aiming at the modeling problem of a switching networked control system with nonlinear and FDI attacks, a half Markov switching signal is designed to describe the switching characteristic of the system, and nonlinear time-varying functions are simultaneously and respectively designed to describe the nonlinear characteristic of the system and dummy data injected by a hacker;
(2) aiming at the problem of sensor false data injection attack in the system, the system is subjected to section reduction processing, a sliding mode function is designed, and a sliding mode dynamic system is obtained;
(3) aiming at the problem of parameter solution in the sliding mode function, a switching Lyapunov function is designed to obtain the stable condition of the sliding mode dynamic system, and the parameter in the sliding mode function is solved by adopting a linear matrix inequality technology;
(4) aiming at the problems of nonlinearity in a system and executor false data injection attack, a sliding mode controller is designed.
2. The sliding-mode-based switching networked control system security control method according to claim 1, wherein the switching networked control system with nonlinear and FDI attacks is modeled by:
the switching rule of the system is subject to half Markov switching and satisfies the conditional probability distribution of Pr { α (t + h) ═ j
Figure FDA0002409050890000011
Wherein
Figure FDA0002409050890000012
And limΔt→0o (h)/h ═ 0. In addition, the non-linearity of the system g (z, t) and the spurious data injected by hackers into actuators and sensors
Figure FDA0002409050890000013
And ξa(t) are both energy-limited, i.e., both the nonlinear function and the attack injection data are bounded.
3. The sliding-mode-based switching networked control system safety control method according to claim 1, wherein the section reduction processing of the control system is characterized by comprising the following steps: considering the system state variable z (t), for the ith subsystem, find the invertible matrix ΓiSuch thatiBi＝(0 Bi，2)TAnd Bi，2Reversibly, further, define the new state variables as: x (t) ═ Γiz(t)。
4. The sliding-mode-based switching networked control system safety control method according to claim 1, characterized in that the sliding-mode function design is implemented by establishing a relation between a system node and a controller node through a hidden Markov model in consideration of non-synchronization of the system node and the controller node, respectively representing states of the system node and the controller node at time t by variables α (t) and β (t), and satisfying a conditional probability of Pr { β (t) ═ k | α (t) ═ i } - [ tau ] }ikWherein 0 is not more than τik1 or less and
Figure FDA0002409050890000014
further, a sliding mode function is designed: s (t) ═ Ki,kx1(t)+x2(t) wherein Ki,kRepresenting a sliding mode parameter matrix.
5. The sliding-mode-based switching networked control system safety control method according to claim 1, characterized in that a designed sliding mode function is substituted into a section-reducing system, so that a section-reducing sliding mode dynamic is obtained:
Figure FDA0002409050890000021
the system is then analyzed for random stability, i.e., the system satisfies the two conditions (1) at ξaUnder the condition that (t) is 0, x is an arbitrary initial condition1(0) And α (0), there is a constantNumber of
Figure FDA0002409050890000022
So that the conditions
Figure FDA0002409050890000023
If true; (2) at x1(0) Under the condition of 0, inequality
Figure FDA0002409050890000024
Wherein γ > 0 represents an inhibitory factor. Further, the condition for giving the sliding mode dynamic random stability is as follows:
Figure FDA0002409050890000025
wherein
Figure FDA0002409050890000026
And
Figure FDA0002409050890000027
6. the sliding-mode-based switching networked control system safety control method according to claim 5, characterized in that the sliding-mode parameter solution: the stability condition is subjected to matrix change and variable substitution, and a sliding mode parameter matrix can be solved:
Figure FDA0002409050890000028
7. the sliding-mode-based switching networked control system safety control method according to claim 1, characterized in that the sliding-mode controller is designed to: designing a sliding mode controller according to the sliding mode parameters obtained by solving
Figure FDA0002409050890000029
Wherein
Figure FDA00024090508900000210
8. The sliding-mode-based switching networked control system safety control method according to claim 1, characterized in that a closed-loop system stability certification: the state track of the closed-loop system can be converged on a sliding mode surface within a limited time under a designed sliding mode controller; by constructing the Lyapunov function
Figure FDA00024090508900000211
The weak infinitesimal operator is proved to meet the following conditions:
Figure FDA00024090508900000212
CN202010170612.0A 2020-03-12 2020-03-12 Sliding mode-based switching networked control system safety control method Pending CN111258223A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010170612.0A CN111258223A (en) 2020-03-12 2020-03-12 Sliding mode-based switching networked control system safety control method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010170612.0A CN111258223A (en) 2020-03-12 2020-03-12 Sliding mode-based switching networked control system safety control method

Publications (1)

Publication Number Publication Date
CN111258223A true CN111258223A (en) 2020-06-09

Family

ID=70947702

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010170612.0A Pending CN111258223A (en) 2020-03-12 2020-03-12 Sliding mode-based switching networked control system safety control method

Country Status (1)

Country Link
CN (1) CN111258223A (en)

Cited By (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111650835A (en) * 2020-06-16 2020-09-11 电子科技大学 Self-adaptive event-triggered asynchronous sliding mode control method of random jump system
CN112596387A (en) * 2020-12-14 2021-04-02 电子科技大学 Networked system security control method based on extended observer
CN113341725A (en) * 2021-06-18 2021-09-03 曲阜师范大学 Sliding mode control method of multi-mode electronic throttle valve
CN113900378A (en) * 2021-10-20 2022-01-07 深圳职业技术学院 Asynchronous sliding mode control method for random nonlinear system
CN114055463A (en) * 2021-09-26 2022-02-18 曲阜师范大学 Fuzzy sliding mode control method of networked mechanical arm system
CN114326481A (en) * 2021-12-04 2022-04-12 曲阜师范大学 Safety control method of multi-mode aircraft system
CN114710436A (en) * 2022-04-19 2022-07-05 电子科技大学 Topology reconstruction method of multi-domain unmanned system under topology attack

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3276521A1 (en) * 2016-07-25 2018-01-31 General Electric Company Methods and system for detecting false data injection attacks
CN108919647A (en) * 2018-07-23 2018-11-30 哈尔滨理工大学 A kind of sliding-mode control with Nonlinear Stochastic disturbance
CN109375517A (en) * 2018-12-12 2019-02-22 哈尔滨理工大学 The sliding-mode control of uncertain probability of happening situation lower network networked control systems
CN110647033A (en) * 2019-09-02 2020-01-03 浙江工业大学 Networked motion control system attack identification method based on class-Longberger observer
CN110855683A (en) * 2019-11-18 2020-02-28 东北电力大学 Method for carrying out attack detection and reconstruction on electric power information physical system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP3276521A1 (en) * 2016-07-25 2018-01-31 General Electric Company Methods and system for detecting false data injection attacks
CN108919647A (en) * 2018-07-23 2018-11-30 哈尔滨理工大学 A kind of sliding-mode control with Nonlinear Stochastic disturbance
CN109375517A (en) * 2018-12-12 2019-02-22 哈尔滨理工大学 The sliding-mode control of uncertain probability of happening situation lower network networked control systems
CN110647033A (en) * 2019-09-02 2020-01-03 浙江工业大学 Networked motion control system attack identification method based on class-Longberger observer
CN110855683A (en) * 2019-11-18 2020-02-28 东北电力大学 Method for carrying out attack detection and reconstruction on electric power information physical system

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
BEI CHEN等: "Adaptive sliding mode control for Markov jump system against false data injection attack", 《2019 CHINESE CONTROL CONFERENCE (CCC)》 *
李猛: "具有干扰和不确定性的网络化控制系统研究及应用", 《中国博士学位论文全文数据库 信息科技辑》 *

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111650835A (en) * 2020-06-16 2020-09-11 电子科技大学 Self-adaptive event-triggered asynchronous sliding mode control method of random jump system
CN111650835B (en) * 2020-06-16 2021-10-22 电子科技大学 Self-adaptive event-triggered asynchronous sliding mode control method of random jump system
CN112596387A (en) * 2020-12-14 2021-04-02 电子科技大学 Networked system security control method based on extended observer
CN113341725A (en) * 2021-06-18 2021-09-03 曲阜师范大学 Sliding mode control method of multi-mode electronic throttle valve
CN114055463A (en) * 2021-09-26 2022-02-18 曲阜师范大学 Fuzzy sliding mode control method of networked mechanical arm system
CN114055463B (en) * 2021-09-26 2023-04-18 曲阜师范大学 Fuzzy sliding mode control method of networked mechanical arm system
CN113900378A (en) * 2021-10-20 2022-01-07 深圳职业技术学院 Asynchronous sliding mode control method for random nonlinear system
CN113900378B (en) * 2021-10-20 2023-08-25 深圳职业技术学院 Random nonlinear system-oriented asynchronous sliding mode control method
CN114326481A (en) * 2021-12-04 2022-04-12 曲阜师范大学 Safety control method of multi-mode aircraft system
CN114326481B (en) * 2021-12-04 2024-05-24 曲阜师范大学 Safety control method for multi-mode aircraft system
CN114710436A (en) * 2022-04-19 2022-07-05 电子科技大学 Topology reconstruction method of multi-domain unmanned system under topology attack

Similar Documents

Publication Publication Date Title
CN111258223A (en) Sliding mode-based switching networked control system safety control method
Li et al. Event-triggered consensus control for multi-agent systems against false data-injection attacks
Li et al. Dynamic event-triggered security control for networked control systems with cyber-attacks: A model predictive control approach
CN109683477B (en) Design method and system of finite time controller of random multi-agent system
Gao et al. Finite‐time stabilization for a class of switched stochastic nonlinear systems with dead‐zone input nonlinearities
Liu et al. Consensus problem of high‐order multi‐agent systems with external disturbances: An H∞ analysis approach
CN110865616B (en) Design method of event trigger zone memory DOF controller under random FDI attack
Wu et al. On designing event-triggered schemes for networked control systems subject to one-step packet dropout
Liu et al. Event-triggered state estimation for T–S fuzzy neural networks with stochastic cyber-attacks
Wang et al. Resilient adaptive intermittent control of nonlinear systems under deception attacks
Kato et al. Security analysis of linearization for nonlinear networked control systems under DoS
CN112596387A (en) Networked system security control method based on extended observer
Dadras et al. Resilient control design for vehicular platooning in an adversarial environment
Shao et al. Neural-network-based adaptive secure control for nonstrict-feedback nonlinear interconnected systems under DoS attacks
Sathishkumar et al. Resilient annular finite-time bounded and adaptive event-triggered control for networked switched systems with deception attacks
Wang et al. Event-triggered cooperative adaptive neural control for cyber–physical systems with unknown state time delays and deception attacks
Chatterjee et al. Fixed-time event-triggered control under denial-of-service attacks
Wang et al. Finite‐time asynchronous control of linear time‐varying switched systems
Ma et al. Event‐triggered adaptive finite‐time secure control for nonlinear cyber‐physical systems against unknown deception attacks
Ma et al. Consensus tracking control for uncertain non‐strict feedback multi‐agent system under cyber attack via resilient neuroadaptive approach
Yang et al. Sliding mode fuzzy control of stochastic nonlinear systems under cyber-attacks
Wang et al. Adaptive neural networks event-triggered fault-tolerant consensus control for a class of nonlinear multi-agent systems
Yang et al. Adaptive control for cyber-physical systems against actuator attacks
Gorbachev et al. Distributed secure consensus control of nonlinear multi-agent systems under sensor and actuator attacks
Kato et al. Stabilization of nonlinear networked control systems under denial-of-service attacks: A linearization approach

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20200609