CN-121979282-A - Multi-unmanned aerial vehicle distributed formation safety control method based on dual network

CN121979282ACN 121979282 ACN121979282 ACN 121979282ACN-121979282-A

Abstract

The invention relates to the technical field of unmanned aerial vehicle safety, in particular to a multi-unmanned aerial vehicle distributed formation safety control method based on a dual network, which comprises the steps of establishing a dynamic model, a connectivity index and a DoS attack sequence, defining formation tracking errors and control targets, fusing characteristic parameters to construct continuous comprehensive communication quality evaluation indexes, constructing a hysteresis self-adaptive mode switching rule with anti-jitter characteristics, establishing a self-adaptive shrinkage network controller based on a recursion balance network, and generating a distributed control law adapting to a current mode; based on a shrinkage mapping theory and a multi-Lyapunov function method, the method analyzes the index stability of a switching system, and performs verification operation through a simulation experiment.

Inventors

LI JIANXIN
PANG WEIZHI
Luan Tianyun
YANG YANG
LI MINGQIU
HUANG FUZHONG

Assignees

长春理工大学

Dates

Publication Date: 20260505
Application Date: 20260402

Claims (10)

1. A multi-unmanned aerial vehicle distributed formation safety control method based on a dual network is characterized by comprising the following steps: collecting multi-modal data of a multi-unmanned aerial vehicle cluster, establishing a dynamics model, a connectivity index and a DoS attack sequence based on the multi-modal data, and defining formation tracking errors and control targets; Extracting physical layer channel characteristic parameters and network layer topology characteristic parameters, and fusing the characteristic parameters to construct continuous comprehensive communication quality evaluation indexes; According to the real-time calculation result of the comprehensive communication quality evaluation index, constructing a hysteresis self-adaptive mode switching rule with anti-shake characteristic, and realizing the dynamic switching of a normal mode and an emergency mode; setting up a self-adaptive shrinkage network controller based on a recursion balance network, configuring the scale of a differentiated network according to the requirements of different control modes, and generating a distributed control law adapting to the current mode; Based on a shrink mapping theory and a Polylyapunov function method, a dynamics model and a controller parameter are combined, the exponential stability of a switching system is analyzed, and a steady state error bound is quantized.
2. The dual-network-based multi-unmanned aerial vehicle distributed formation safety control method according to claim 1, wherein the establishment of a dynamics model, connectivity indexes and DoS attack sequences comprises the steps of obtaining multi-mode data consisting of state data, communication link data and external environment data of each unmanned aerial vehicle, establishing a continuous time state space equation of an ith unmanned aerial vehicle based on the state data, wherein the continuous time state space equation comprises a state vector, a control input and external disturbance, establishing a dynamics model in a second-order integrator form for a four-rotor unmanned aerial vehicle platform, wherein a system matrix of the dynamics model is constructed in a block matrix form, and the dynamics model expression is as follows: , Wherein, the Is a state vector, contains the position information and the speed information of the unmanned aerial vehicle, For control input, a thrust control amount acting on the unmanned aerial vehicle, Is an external disturbance and meets the constraint of the limitation A is a system matrix, B is an input matrix, Is an upper limit of the bounded constraint for external disturbances.
3. The dual-network-based multi-unmanned aerial vehicle distributed formation safety control method according to claim 2, wherein the establishing of the dynamics model, the connectivity index and the DoS attack sequence further comprises: Based on communication link data, using directed graph Constructing a communication network topology between unmanned aerial vehicles, wherein a set of nodes Corresponding to unmanned aerial vehicle number, edge set Element characterization of the connectivity status of the communication link between unmanned aerial vehicles, adjacency matrix Element quantization communication link connection weights; Definition matrix Constructing a Laplace matrix by combining the adjacent matrix, and taking a second small eigenvalue of the Laplace matrix as an algebraic connectivity index; establishing DoS attack sequences based on external environment data And introducing frequency constraints And duration constraints ; Wherein, the For the communication degree of the Nth unmanned aerial vehicle, For the start time of the kth DoS attack, For the end time of the kth DoS attack, In order to attack the frequency offset parameter, In order to attack the upper bound parameter of the duty cycle, In order to attack the duration elastic parameter, In order to count the starting moment of time, For the current time of day of the statistics, Is the average attack interval.
4. The method for controlling the distributed formation safety of multiple unmanned aerial vehicles based on dual networks according to claim 1, wherein the steps of extracting physical layer channel characteristic parameters and network layer topology characteristic parameters comprise obtaining signal-to-noise ratio, acquisition packet loss rate and acquisition error rate of a communication link, mapping a typical communication threshold value to a zero interval by adopting a Sigmoid function based on the dimensional difference and numerical range characteristics of the signal-to-noise ratio as a mapping center, controlling mapping sensitivity by adjusting a steep coefficient, carrying out weighted fusion on the normalized signal-to-noise ratio quality index, the complementary quantity of the packet loss rate and the complementary quantity of the error rate, and constructing a physical layer channel quality index, wherein the expression of the physical layer channel quality index is as follows: , Wherein, the In order to normalize the signal-to-noise ratio quality index, In order for the packet loss rate to be the same, In order to achieve a bit error rate, 、 And Is the corresponding weight coefficient.
5. The dual-network-based multi-unmanned aerial vehicle distributed formation safety control method according to claim 1, wherein the step of fusing the characteristic parameters to construct a continuous comprehensive communication quality evaluation index comprises the steps of calculating algebraic connectivity characteristic values of a current communication topology in real time based on a Laplacian matrix, carrying out ratio operation on the algebraic connectivity characteristic values and the maximum algebraic connectivity characteristic values under nominal conditions to obtain a network layer topology quality index, and integrating a physical layer channel quality index and the network layer topology quality index into the comprehensive communication quality evaluation index through a hierarchical weighted fusion mechanism, wherein the expression of the network layer topology quality index is as follows: , Wherein, the For the maximum degree of algebraic connectivity, Is the second small eigenvalue of the current laplace matrix.
6. The dual-network-based multi-unmanned aerial vehicle distributed formation safety control method according to claim 1, wherein the construction of a hysteresis adaptive mode switching rule with anti-jitter characteristics comprises setting a resume switching threshold value And emergency switch threshold Forming hysteresis bandwidth Based on the hysteresis bandwidth, establishing a quantitative relation between average residence time and hysteresis bandwidth and quality change rate, wherein a recovery switching threshold represents a quality recovery level required for returning to a normal mode from an emergency mode, the emergency switching threshold represents a quality degradation limit tolerated by switching from the normal mode to the emergency mode, and a sectional mode switching logic rule is established: , Wherein, the In the form of a mode signal, In order to switch the mode state at the moment before the switch, In the normal mode of operation, In the case of an emergency mode, Is an integrated communication quality evaluation index.
7. The dual-network-based multi-unmanned aerial vehicle distributed formation security control method according to claim 6, wherein the generating a distributed control law adapted to the current mode comprises constructing an adaptive systolic network controller by using a recursion balanced network architecture, defining an L-layer network structure, and the third step of The layer realizes forward propagation of information through a weight matrix and an activation function, an input layer receives unmanned aerial vehicle state deviation signals, an output layer generates nonlinear control mapping signals, different network scale parameters are configured based on a normal mode and an emergency mode, and the normal mode adopts a first neuron number In order to fully utilize neighbor cooperative information, the emergency mode adopts the second neuron number And (2) and Based on recursion balance network mapping function and mode dependent control gain, generating a distributed control law adapting to the current mode, wherein the control law is constructed in a weighted summation form of neighbor connection weight and network mapping output, and the expression is as follows: , Wherein, the In order to control the gain in a mode dependent manner, As a set of neighbors, In order to connect the weights, the weight of the connection, For the purpose of recursively balancing the network mapping functions, And The status vectors of the ith and jth drones, respectively.
8. The dual-network-based multi-unmanned aerial vehicle distributed formation safety control method according to claim 7, wherein the mode-dependent control gain determination method comprises recursively balancing the shrinkage characteristics of a network based on shrinkage mapping theory analysis, determining a shrinkage factor of the network mapping, wherein the shrinkage factor is determined by a power form of a product of an activation function liphatz constant and a weight matrix spectral norm, constructing a lyapunov function consisting of an augmentation system matrix, a atlas matrix and a mode-dependent positive weighting matrix, determining a control gain lower bound constraint according to the lyapunov function, and the control gain satisfies the following bound constraint condition: , Wherein, the Is of mode The control gain of the lower part of the control signal, In order to augment the system matrix, For a pattern dependent positive weighting matrix, In order to rely on the laplace matrix for the pattern, In order to augment the input matrix, Is a network contraction factor and meets , Is that Is used to determine the non-zero minimum feature value of (c), And the same is the maximum eigenvalue.
9. The dual-network-based multi-unmanned aerial vehicle distributed formation safety control method according to claim 8, wherein the analysis of the shrinkage characteristics of the recursion balance network based on the shrinkage mapping theory comprises the steps of constructing the recursion balance network by adopting an activation function meeting the lipschz continuous condition, wherein the lipschz continuous condition is characterized in that for any input vector, the deviation of the output of the activation function is limited by a linear function of the input deviation, the coefficient of the linear function is lipschz constant, the upper limit constraint of the spectrum norms of weight matrixes of all layers of the recursion balance network is set, and the shrinkage factors of the recursion balance network map are obtained based on the lipschz constant and the upper limit of the spectrum norms, and the recursion balance network map meets the shrinkage condition: , Wherein, the For the purpose of recursively balancing the network mapping functions, To activate the liplitz constant of the function, As the upper bound of the spectral norms of the weight matrix, For the number of layers of the network, For recursively balancing any one of the input vectors of the network, Another arbitrary input vector for the recursively balanced network.
10. The dual network-based multi-unmanned aerial vehicle distributed formation safety control method according to claim 9, wherein the analyzing the exponential stability of the switching system comprises: Based on a normal mode and an emergency mode, respectively constructing a Lyapunov function, wherein the Lyapunov function is constructed by taking an augmented formation error vector as an independent variable and adopting a quadratic matrix form, calculating the derivative of the Lyapunov function along a system track, and establishing a sufficient condition of Lyapunov function exponential decay in the duration of each mode by combining with the constraint of a control gain, wherein the sufficient condition is used for determining the jump ratio of the Lyapunov function at the mode switching moment so as to quantify the switching gain; establishing a full average residence time condition for global index stabilization of a switching system according to the constraint condition of the average residence time, wherein the full average residence time condition is provided with a limited external disturbance Based on the derivative constraint and the contraction mapping property of the Lyapunov function, the steady-state upper bound analysis is obtained.

Description

Multi-unmanned aerial vehicle distributed formation safety control method based on dual network Technical Field The invention relates to the technical field of unmanned aerial vehicle safety, in particular to a multi-unmanned aerial vehicle distributed formation safety control method based on a dual network. Background The distributed collaborative operation of the multi-unmanned aerial vehicle cluster has been widely applied in the fields of low-altitude operation, collaborative reconnaissance, disaster relief and the like by virtue of the advantages of flexibility and high efficiency, the stable operation of the multi-unmanned aerial vehicle cluster is highly dependent on a wireless communication link, however, the open characteristic of the communication link makes the unmanned aerial vehicle cluster easily subject to DoS attack, signal interference and other antagonistic threats, especially in a disputed airspace or complex electromagnetic environment, the communication safety becomes a core bottleneck restricting the reliability of tasks, and the unmanned aerial vehicle cluster is different from a stable communication infrastructure of a ground system, needs to cope with complex situations such as rapid change of channel conditions, intermittent interruption of connection, deliberate interference and the like, and the communication quality always presents progressive degradation characteristics. The existing formation control method has three core defects that firstly, communication quality continuous evaluation and self-adaptive control mechanism are lacked, most of the existing formation control method relies on binary fault detection logic to judge communication states, the gradual degradation process of communication quality from excellent to poor cannot be reflected, the control strategy adjustment lacks accurate quality basis, secondly, the stability theory guarantee of a switching system is insufficient, for a mode switching mechanism possibly introduced, a perfect stability analysis system is not established in the prior art, the quality threshold design of mode switching lacks theoretical basis, the optimization of switching gain and Lyapunov function weight matrix is free of systemization method, a significant gap exists between an experimental result and theoretical analysis, the stability of a system in the switching process cannot be ensured, finally, the calculation complexity and the real-time requirement are in conflict, the calculation cost of a control algorithm is required to be reduced under an emergency scene with serious degradation of communication quality, the calculation complexity of a traditional full-connection neural network is difficult to meet the real-time control requirement of an embedded platform, even if a part of the method adopts a recursion a balance network to reduce the complexity to a certain extent, the experiment result and the theoretical analysis is different from theoretical analysis, the stability of the unmanned aerial vehicle is still required to be controlled in a bottleneck-level control method based on a plurality of stages. Disclosure of Invention The invention provides a multi-unmanned aerial vehicle distributed formation safety control method based on a dual network, which aims to solve the problems that formation is easy to be unstable and control precision is low due to the fact that the stability of a switching system is lack of theoretical guarantee and the computational complexity and instantaneity are difficult to balance in the existing multi-unmanned aerial vehicle distributed formation control. The invention provides a multi-unmanned aerial vehicle distributed formation safety control method based on a dual network, which adopts the following technical scheme: A multi-unmanned aerial vehicle distributed formation safety control method based on dual network comprises the following steps: collecting multi-modal data of a multi-unmanned aerial vehicle cluster, establishing a dynamics model, a connectivity index and a DoS attack sequence based on the multi-modal data, and defining formation tracking errors and control targets; Extracting physical layer channel characteristic parameters and network layer topology characteristic parameters, and fusing the characteristic parameters to construct continuous comprehensive communication quality evaluation indexes; According to the real-time calculation result of the comprehensive communication quality evaluation index, constructing a hysteresis self-adaptive mode switching rule with anti-shake characteristic, and realizing the dynamic switching of a normal mode and an emergency mode; setting up a self-adaptive shrinkage network controller based on a recursion balance network, configuring the scale of a differentiated network according to the requirements of different control modes, and generating a distributed control law adapting to the current mode; Based on a shrink mapping theory and a Polylyapu