Attitude control method, system, device and medium for rigid spacecraft

Abstract

This application belongs to the field of spacecraft attitude control technology and discloses a method, system, device and medium for attitude control of a rigid spacecraft. The method includes: S1, constructing a rigid spacecraft attitude dynamics model and kinematic equations; S2, constructing a T-S fuzzy switching model for the rigid spacecraft; S3, introducing a multi-node random communication protocol; S4, designing a fuzzy sliding mode controller based on the T-S fuzzy switching model; S5, forming a closed-loop control system for attitude control: Based on the T-S fuzzy switching model, multi-node random communication protocol and fuzzy sliding mode controller designed in S1-S4, a closed-loop control system is formed to achieve spacecraft attitude control and verify the global stability of the closed-loop control system. This application solves the technical difficulties of strong nonlinearity, large external disturbances, limited communication and high real-time performance in the attitude control of rigid spacecraft, effectively improving the attitude stability and control accuracy of spacecraft in complex space environments.

Description

Technical Field

[0001] The present application belongs to the field of spacecraft attitude control technology, and in particular relates to attitude control methods, systems, devices and media for rigid spacecraft. Background Art

[0002] Spacecraft attitude control is a core technology that ensures stable on-orbit operation and precise mission execution. As a structurally stable aerospace platform, rigid spacecraft must maintain high-precision control of their space attitude in complex orbital environments, such as those subject to disturbances from the geomagnetic field, solar wind, and gravitational gradients. However, this control problem generally faces challenges such as nonlinear dynamics, parameter uncertainty, strong external disturbances, and limited communication resources, placing higher demands on control algorithms.

[0003] Current mainstream control methods, such as PID control, linear optimal control, and classical sliding mode control, perform well under certain conditions. However, they suffer from poor robustness, poor real-time performance, and high communication overhead when dealing with strong nonlinearities and communication scheduling uncertainties. Furthermore, the limited computing resources of onboard equipment necessitate the design of efficient and energy-efficient attitude control strategies. This is especially true when operating with multiple sensor and actuator nodes. Traditional centralized communication mechanisms are no longer suitable for the low-power, low-bandwidth requirements of current space missions. Summary of the Invention

[0004] The technical problem to be solved by this application is to overcome the shortcomings of the existing technology. This application provides a method, system, device and medium for attitude control of rigid spacecraft, which combines TS fuzzy modeling, average dwell time (ADT) switching strategy, sliding mode control law, dynamic event triggering mechanism and multi-node random communication protocol to solve the technical difficulties of strong nonlinearity, large external disturbance, limited communication and high real-time performance in the attitude control of rigid spacecraft, and improve the attitude stability and control accuracy of spacecraft in complex space environments; it has the advantages of high robustness, strong anti-interference, low communication load and easy engineering implementation, and is particularly suitable for on-orbit attitude control tasks of spacecraft with low bandwidth, limited resources and high control accuracy requirements.

[0005] To achieve the above objectives, the present application provides, in a first aspect, a method for attitude control of a rigid spacecraft, comprising the following steps:

[0006] S1. Construct the attitude dynamics model and kinematic equations of rigid spacecraft;

[0007] S2. Construct the TS fuzzy switching model of the rigid spacecraft based on S1:

[0008] The TS fuzzy switching model selects a discrete time system for modeling, splits the overall nonlinear system into multiple fuzzy rule subsystems through local linearization, and performs weighted combination in combination with the switching strategy;

[0009] Introducing uncertainty into each of the fuzzy rule subsystems , the uncertainty Correlation with spacecraft moment of inertia and external disturbances to dynamically correct control inputs;

[0010] S3, based on S2, introduces a multi-node random communication protocol:

[0011] For the TS fuzzy switching model, the multi-node random communication protocol jointly models the multi-node communication resource competition behavior in the S / C and C / A dual-channel communication process through Markov chains;

[0012] S4. Based on S2 and S3, design a fuzzy sliding mode controller:

[0013] Combining the dynamic characteristics of TS fuzzy switching model and multi-node random communication protocol to design sliding mode function , sliding mode matrix Through the dynamic generation of TS fuzzy rules, the sliding mode control parameters are dynamically adjusted with the system mode;

[0014] S5. Form a closed-loop control system for attitude control:

[0015] The TS fuzzy switching model, multi-node random communication protocol and fuzzy sliding mode controller designed by S2-S4 are integrated to form a closed-loop control system to realize spacecraft attitude control and verify the global stability of the closed-loop control system.

[0016] Optionally, the TS fuzzy switching model constructed in S2 includes: the pth fuzzy rule is:

[0017] if is and … and is ,

[0018] So (6);

[0019] in, is the premise variable, … is a fuzzy set, Indicates the switching signal, The switching time interval between is called the dwell time, where is the time when the mth switching occurs, is the moment immediately following the next switch. and denote the state matrix and input matrix of the system respectively, represents uncertainty, is a known constant matrix, The system is The state variables at time , Indicates that the system is The state variables at time , Indicates external interference, Represents the control output, let , the known constant matrix is expressed as , , the uncertainty is expressed as ;

[0020] Through the TS fuzzy reasoning method, the TS fuzzy switching model is obtained, which is expressed as:

[0021] (7);

[0022] in, and denote the state matrix and input matrix of the system respectively, and denote the state matrix and input matrix of the system respectively, is the number of fuzzy rules under the TS fuzzy switching system, is the normalized membership function, Indicates the control output, Indicates external interference and satisfies , is a known constant, and i represents the index number of the fuzzy rule.

[0023] Optionally, the method further includes the step of designing a dynamic event triggering mechanism, wherein the dynamic event triggering mechanism introduces a system state deviation. With internal dynamic variables Build adaptive trigger conditions to determine system status Whether it can be transmitted; including event triggering conditions, expressed as:

[0024] (8);

[0025] in, Indicates the current triggering moment. Indicates the next triggering time. represents a given scalar, , represents the dynamic event trigger weight matrix, represents the system state deviation at time k, represents the internal dynamic variables at time k, represents the transpose of the system state deviation at time k, represents the transpose of the system state at time k, represents the infimum;

[0026] Among them, when the inequality is satisfied The minimum k value is the next trigger moment , and then update the trigger state;

[0027] Among them, the internal dynamic variables It is obtained through dynamic calculation and expressed as:

[0028] (9);

[0029] in, is a known constant, , Indicates the initial value of the internal dynamic variable, Represents a known non-negative constant that is set.

[0030] Optionally, the multi-node random communication protocol in S3 jointly models the multi-node communication resource competition behavior in the S / C and C / A dual-channel communication processes through a Markov chain, including:

[0031] The system is defined as having W sensor nodes and V control nodes connected through a network. The communication modeling of the S / C and C / A dual channels includes:

[0032] S / C channel communication modeling:

[0033] The system has a total of W sensor nodes, each of which can measure the system state , at each moment only a maximum of nodes communicate, among which ,S / C channel represents sensor-controller channel;

[0034] Define a binary variable function Indicates whether the w-th sensor node accesses the network at time k, expressed as:

[0035] (10);

[0036] Define the state selection matrix as , then the state signal received by the controller at time k is expressed as:

[0037] (11);

[0038] in, represents the state signal received at time k, represents the access status of the sensor node at time k, and , is the number of all sensor access states, It is the set of all sensor access states;

[0039] Assumptions A discrete-time Markov chain with the following conditional probability is expressed as:

[0040] (12);

[0041] in, represents the conditional probability, ,and , the transition probability matrix is defined as , represents the dimension of the matrix, represents the random variable at time k+1 The probability distribution of the value, P is the probability operator, Indicates the current time k, the state is Under the premise, the next moment Transfer to state The probability of Indicates that the sensor node is The access status at the moment, Respectively represent the current access status and the access status of the sensor node at the next moment, Indicates that s, t belong to the state set ;

[0042] C / A channel communication modeling:

[0043] System Share V control nodes, and a maximum of Nodes receive control signals, where ,C / A channel represents the controller-actuator channel;

[0044] Define the executor to receive the Controller nodes , ;

[0045] The actual control input of the control signal transmitted to the actuator becomes:

[0046] (13);

[0047] in, represents the access status of the controller node at time k, , is the number of all actuator access states, Represents the access status set of all executors, Indicates the actual control input of the control signal transmitted to the actuator, It represents the state combination of the actuator receiving signals from different controller nodes at time k, represents the sliding mode control law;

[0048] A discrete-time Markov chain follows with the following conditional probabilities:

[0049] (14);

[0050] in, represents the random variable at time k + 1 The probability distribution of the value, Indicates that the controller node is The access status at the moment, Respectively represent the access status, represents the conditional probability, ,and , the transition probability matrix is defined as , Represents the dimension of the transition probability matrix.

[0051] Optionally, S3 also includes the introduction of mapping variables The two Markov chains of the S / C channel and the C / A channel are mapped into one Markov chain, including:

[0052] Introducing mapping variables , expressed as:

[0053] (15);

[0054] in, represents a mapping function that merges the states of two Markov chains into a new variable. , , Indicates all The value set of ;

[0055] Determine a unique pair of random variables , expressed as:

[0056] (16);

[0057] in, Represents a mapping function used to map variables Determine the random variable The value of represents the floor function, Represents a mapping function used to map variables Determine the random variable The value of the mapping variable and random variable pairs The corresponding relationship is established through formula (15) and formula (16);

[0058] Get the mapping variable The transition probability is expressed as:

[0059] (17);

[0060] in, represents the mapping variable at time k+1, Representing variables The state at time k+1, Representing variables The state at time k is Indicates that the sensor node is The access status at the moment, Indicates that the state of the S / C channel at time k is At time k + 1, it is transferred to The conditional probability of Indicates that the state of the C / A channel at time k is At time k + 1, it is transferred to The conditional probability of the C / A channel at time k is At time k + 1, it is transferred to The conditional probability of a random variable The transition probability matrix is , Indicates the dimensions of the matrix.

[0061] Optionally, a fuzzy sliding mode controller is designed in S4 based on the TS fuzzy switching model, including:

[0062] Design the sliding mode function, expressed as:

[0063] (18);

[0064] in, It is a sliding mode function. By designing the control law to adjust the system input, the system state is close to the sliding mode surface and the sliding mode is maintained, thus achieving the robustness requirement of sliding mode variable structure control. is the sliding mode matrix, which is used to enhance the robustness of the system to disturbances and modeling uncertainties and is defined as , c, l are scalar parameters, c, l should be selected to ensure It is non-singular under any switching signal. represents the transpose of the system input matrix;

[0065] Design the sliding mode control law, which is expressed as:

[0066] (19);

[0067] in, represents the sliding mode control law at time k, Represents The relevant membership function, express The norm of represents the symbolic function, is the controller gain to be designed to stabilize the linear part of the system, is the value in formula (18) Replace with Income.

[0068] Optionally, a closed-loop control system is constructed in S5, including: the closed-loop control system of the spacecraft is obtained from formulas (7), (13) and (18), which is expressed as:

[0069] (20);

[0070] in, Represents the system state matrix related items, represents the time-varying term related to the system input, and Respectively expressed as:

[0071] ;

[0072] ;

[0073] in, Based on the mapping variable Determine the diagonal matrix of the C / A channel actuator receiving controller signal state combination, Basis Determine the diagonal matrix of the sensor node status information where the S / C channel is selected.

[0074] To achieve the above-mentioned purpose, the second aspect of the present application provides an attitude control system for a rigid spacecraft, the attitude control system comprising:

[0075] The first construction unit is to construct the attitude dynamics model and kinematic equations of the rigid spacecraft;

[0076] The second construction unit builds the TS fuzzy switching model of the rigid spacecraft based on the dynamic model:

[0077] The TS fuzzy switching model selects a discrete time system for modeling, splits the overall nonlinear system into multiple fuzzy rule subsystems through local linearization, and performs weighted combination in combination with the switching strategy;

[0078] Introducing uncertainty into each of the fuzzy rule subsystems , the uncertainty Correlation with spacecraft moment of inertia and external disturbances to dynamically correct control inputs;

[0079] The third building block introduces a multi-node random communication protocol for the TS fuzzy switching model:

[0080] For the TS fuzzy switching model, the multi-node random communication protocol jointly models the multi-node communication resource competition behavior in the S / C and C / A dual-channel communication process through Markov chains;

[0081] The fourth building block designs a fuzzy sliding mode controller based on the TS fuzzy switching model and multi-node random communication protocol:

[0082] Combining the dynamic characteristics of TS fuzzy switching model and multi-node random communication protocol to design sliding mode function The sliding mode matrix Through the dynamic generation of TS fuzzy rules, the sliding mode control parameters are dynamically adjusted with the system mode;

[0083] Closed-loop control unit forms a closed-loop control system for attitude control:

[0084] The TS fuzzy switching model, multi-node random communication protocol and fuzzy sliding mode controller constructed by the above building blocks are integrated to form a closed-loop control system to realize spacecraft attitude control and verify the global stability of the closed-loop control system.

[0085] To achieve the above-mentioned purpose, the third aspect of the present application provides an attitude control device for a rigid spacecraft, comprising a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method described above is implemented.

[0086] To achieve the above-mentioned objectives, the fourth aspect of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, is used to implement the method described above.

[0087] After adopting the above technical solution, this application has the following beneficial effects compared with the prior art:

[0088] In this application, the TS fuzzy model is used to describe the nonlinear dynamic characteristics of the spacecraft, and the dynamic response of the TS fuzzy switching system in different working modes is modeled in the form of fuzzy rules, and the switching strategy is combined to realize the robust modeling and control of the entire system; a multi-node random communication protocol is introduced, and the communication behavior of each node is modeled through the Markov process, which effectively characterizes the dynamic changes of the network state and realizes the simultaneous transmission of data through multiple communication channels; a dynamic event triggering mechanism is designed to reduce the communication frequency and energy consumption; a sliding mode control method suitable for spacecraft attitude adjustment tasks with strong disturbances is adopted to improve the adaptability and stability of the control system, effectively improve the robustness of the system to external disturbances and parameter uncertainties, and at the same time reduce the computational complexity to ensure the high efficiency and real-time performance of attitude control.

[0089] The specific implementation methods of the present application are further described in detail below with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0090] The accompanying drawings are part of this application and are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application but do not constitute an undue limitation of this application. Obviously, the drawings described below are only some embodiments. For those of ordinary skill in the art, other drawings can be derived from these drawings without inventive effort.

[0091] In the drawings of the specification:

[0092] Figure 1 is a flow chart of a rigid spacecraft attitude control method in this specific embodiment;

[0093] Figure 2 is a logic diagram of the rigid spacecraft attitude control method in this specific embodiment;

[0094] Figure 3 It is the S / C channel scheduling signal in this specific implementation method Schematic diagram of;

[0095] Figure 4 It is the C / A channel scheduling signal in this specific implementation method Schematic diagram of;

[0096] Figure 5 is a schematic diagram of subsystem switching frequency based on average dwell time in this specific implementation manner;

[0097] Figure 6 : is a schematic diagram of a simulation curve of the angular velocity ω response of the spacecraft in the three rotation axis directions in this specific embodiment;

[0098] Figure 7is a schematic diagram of a simulation curve of the control input u(k) of the spacecraft in this specific implementation mode;

[0099] Figure 8 3 is a schematic diagram of a simulation curve showing the change of the sliding mode variable s(k) of the spacecraft system under the action of the sliding mode controller in this specific embodiment. DETAILED DESCRIPTION

[0100] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. The following embodiments are used to illustrate the present application but are not used to limit the scope of the present application.

[0101] See Figure 1 and Figure 2 Based on this, the present application provides a method for attitude control of a rigid spacecraft, comprising the following steps:

[0102] S1. Construct the attitude dynamics model and kinematic equations of rigid spacecraft;

[0103] S2. Construct the TS fuzzy switching model of the rigid spacecraft based on S1:

[0104] The TS fuzzy switching model selects a discrete time system for modeling. By means of local linearization, the overall nonlinear system is split into multiple fuzzy rule subsystems, and then weighted combinations are performed in combination with the switching strategy.

[0105] Introducing uncertainty into each fuzzy rule subsystem , uncertainty Correlation with spacecraft moment of inertia and external disturbances to dynamically correct control inputs;

[0106] S3, based on S2, introduces a multi-node random communication protocol:

[0107] Aiming at the TS fuzzy switching model, the multi-node random communication protocol jointly models the multi-node communication resource competition behavior in the S / C and C / A dual-channel communication process through Markov chains;

[0108] S4. Based on S2 and S3, design a fuzzy sliding mode controller:

[0109] Combining the dynamic characteristics of TS fuzzy switching model and multi-node random communication protocol to design sliding surface , sliding surface parameters Through the dynamic generation of TS fuzzy rules, the sliding mode control parameters are dynamically adjusted with the system mode;

[0110] S5. Form a closed-loop control system for attitude control:

[0111] The TS fuzzy switching model, multi-node random communication protocol and fuzzy sliding mode controller designed by S2-S4 are integrated to form a closed-loop control system to realize spacecraft attitude control and verify the global stability of the closed-loop control system.

[0112] It should be noted that the attitude control method for a rigid spacecraft in this embodiment is performed by the attitude control device of the rigid spacecraft. This device can be an electronic device, a component of an electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. For example, mobile electronic devices can include mobile phones, tablet computers, laptop computers, PDAs, in-vehicle electronic devices, wearable devices, etc. Non-mobile electronic devices can include servers and personal computers, etc., although this application does not impose specific limitations. The following describes the attitude control method for a rigid spacecraft in this embodiment, using a server as an example.

[0113] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of this application, the meaning of "plurality" is two or more, unless otherwise clearly specified.

[0114] See Figure 1 In one achievable implementation, it includes S1, constructing a rigid spacecraft attitude dynamics model and kinematic equations.

[0115] Specifically, the dynamic equations of a spacecraft describe the evolution of its angular velocity under the action of various torques. If the spacecraft is regarded as a rigid body, its dynamic equations can be derived according to the laws of rigid body dynamics as follows:

[0116] (1);

[0117] in, is the positive definite symmetric matrix of the spacecraft's moment of inertia, is the angular velocity of the three axes of the spacecraft in the body coordinate system, To control the torque, External interference is expressed as follows:

[0118] (2);

[0119] in, represents the angular velocity, They represent the angular velocities of the three axes of the spacecraft in the body coordinate system, represents the transpose of the angular velocity corresponding to the three axes, Respectively represent the control outputs of the three axes. represents the transpose of the control output corresponding to the three axes, represents the moment of inertia of the spacecraft around the x-axis of the body coordinate system, represents the moment of inertia around the y-axis, represents the moment of inertia around the z-axis, It reflects the coupled moment of inertia of the spacecraft mass distribution with respect to the x-axis and y-axis. It reflects the coupled moment of inertia of the spacecraft mass distribution with respect to the x-axis and z-axis. It reflects the coupled moment of inertia of the spacecraft mass distribution with respect to the y-axis and z-axis. Is an operation symbol, which can be used to obtain an antisymmetric matrix through a vector, such as in formula (1) The specific form is:

[0120] (3);

[0121] In the Cartesian coordinate system, the rotational motion of a rigid body can be decomposed into angular changes along the three coordinate axes, which are usually described by Euler angles. Euler angles are composed of three rotations performed in sequence to determine the posture of the rigid body in space. These three rotations are performed around the X, Y, and Z axes respectively, and the corresponding rotation angles are called nutation angles. , precession angle , rotation angle ; The rotation matrix of the rigid body can be obtained as:

[0122] ;

[0123] (4);

[0124] ;

[0125] The kinematic equation of the spacecraft is expressed as:

[0126] (5).

[0127] See Figure 1 and Figure 2 In one feasible implementation, the TS fuzzy switching model constructed in S2 includes: the pth fuzzy rule is:

[0128] Fuzzy rule p: If is and … and is ,

[0129] So (6);

[0130] in, is the premise variable, ,…, is a fuzzy set, Indicates switching signal;

[0131] in, 、 、 、 , respectively expressed as:

[0132] ;

[0133] ;

[0134] ;

[0135] ;

[0136] ;

[0137] in, , is a known constant matrix, is an unknown time-varying matrix that satisfies , I is the identity matrix; , 1;

[0138] in, represents the angular velocity, They represent the angular velocities of the three axes of the spacecraft in the body coordinate system, represents the transpose of the angular velocity corresponding to the three axes, Respectively represent the control outputs of the three axes. represents the transpose of the control output corresponding to the three axes, represents the moment of inertia of the spacecraft around the x-axis of the body coordinate system, represents the moment of inertia around the y-axis, represents the moment of inertia around the z-axis, It reflects the coupled moment of inertia of the spacecraft mass distribution with respect to the x-axis and y-axis. It reflects the coupled moment of inertia of the spacecraft mass distribution with respect to the x-axis and z-axis. It reflects the coupled moment of inertia of the spacecraft mass distribution with respect to the y-axis and z-axis. represents the membership function, Indicates the Precondition variables, represents the total number of premise variables, Indicates the index of the premise variable;

[0139] Through the TS fuzzy reasoning method, the TS fuzzy switching model is obtained, which is expressed as:

[0140] (7);

[0141] in, and denote the state matrix and input matrix of the system respectively, is the number of fuzzy rules under the TS fuzzy switching system, is the normalized membership function, The system is The state variables at time , represents the state variable of the system at time k+1, Indicates the control output, Indicates external interference and satisfies , is a known constant, i represents the index number of the fuzzy rule, Indicates uncertainty.

[0142] It should be noted that spacecraft attitude dynamics typically exhibit nonlinearities and parametric uncertainty, such as changes in dynamic characteristics caused by variations in moment of inertia. The TS fuzzy switching model provided in this embodiment uses local linearization to split the overall nonlinear system into multiple fuzzy rule subsystems. These subsystems are then weighted together using a switching strategy. This effectively addresses the dynamic changes of rigid spacecraft under different mission modes, improving the adaptability and robustness of the control strategy.

[0143] It's worth noting that the TS fuzzy switching system introduces a switching mechanism for multiple subsystems based on the traditional TS fuzzy model, enabling the system to select different fuzzy models for control in different operating modes. Compared to a single TS fuzzy model, the TS fuzzy switching model provided in this embodiment offers stronger modeling capabilities and greater control flexibility, offering significant advantages in handling complex rigid spacecraft systems.

[0144] This embodiment uses a discrete-time system for modeling. Compared with the continuous-time system in the prior art, the discrete-time system is more suitable for digital implementation and anti-interference. It can provide high-precision, reconfigurable computing capabilities in rigid spacecraft attitude control, while reducing hardware complexity. It is also easier to combine event triggering and communication scheduling mechanisms, effectively reducing bandwidth and energy consumption.

[0145] See Figure 1 and Figure 2In an achievable implementation, the step of designing a dynamic event triggering mechanism is also included. The dynamic event triggering mechanism introduces system state deviation With dynamic variables Build adaptive trigger conditions to determine system status Whether it can be transmitted.

[0146] Specifically, the dynamic event triggering mechanism introduces an internal dynamic variable , comprehensively considers system status and historical information when determining whether to trigger signal transmission, thereby achieving a more flexible triggering strategy. Compared with the traditional static event triggering mechanism, which relies solely on the current state deviation judgment, it can effectively reduce unnecessary communication and improve system robustness and resource utilization efficiency.

[0147] It should be noted that in rigid spacecraft, angular velocity state variables and control signals must be transmitted via an onboard communication bus. However, due to bandwidth and energy constraints, continuous high-frequency transmission is not possible. By introducing a dynamic event trigger mechanism, the control system only communicates when there is a significant change in state or an increase in disturbance. This significantly reduces communication frequency, extends the spacecraft's on-orbit lifespan, and improves the efficiency of communication resources at key nodes.

[0148] Specifically, Indicates the current angular velocity state With the most recent trigger state The subsequent trigger time is determined according to the following event trigger conditions, expressed as:

[0149] (8);

[0150] in, represents a given scalar, , represents the dynamic event trigger weight matrix, Indicates the system state deviation, Represents internal dynamic variables, represents the transpose of the system state deviation, represents the transpose of the system state, Indicates the current triggering moment. Indicates the next triggering time. represents the infimum;

[0151] Among them, when the inequality is satisfied The minimum k value is the next trigger moment , and then update the trigger state;

[0152] Among them, the internal dynamic variables It is obtained through dynamic calculation and expressed as:

[0153] (9);

[0154] in, is a known constant, .

[0155] Specifically, the dynamic event triggering mechanism provided in this embodiment introduces internal dynamic variables , the trigger condition depends on the weighted state energy term and the deviation energy term Dynamic adjustment, while internal dynamic variables Updated by formula (9), the constant in formula (9) and They can be selected and adjusted accordingly according to the needs of specific scenarios, which is more flexible and adaptable.

[0156] It should be noted that the dynamic event trigger mechanism determines whether communication is needed based on the current system state deviation and internal dynamic variables; Formula (8) controls the trigger threshold. Communication will only be activated when the state change is large enough or the trigger variable increases beyond the threshold, thereby significantly reducing the communication frequency; the internal variables shown in Formula (9) reflect the system's "memory" and can avoid extreme situations such as frequent triggering or long-term silence.

[0157] Notably, the dynamic event triggering mechanism employed in this embodiment effectively reduces unnecessary communication frequency by incorporating system state deviations and dynamic variables into adaptive triggering conditions. This mechanism is particularly well-suited for scenarios where spacecraft communication resources are limited. The dynamic event triggering mechanism provided in this embodiment boasts a simple structure and is easily implemented in discrete-time systems. Deeply coupled with the TS fuzzy switching model, this allows triggering behavior to adapt to the dynamic changes of different fuzzy modes. In contrast, existing event triggering strategies are relatively basic and lack sufficient system structure coupling.

[0158] See Figure 1 and Figure 2 ,In practical applications, including S3, multi-node random communication ,protocols jointly model the multi-node communication resource competition ,behavior in the S / C and C / A dual-channel communication processes through Markov ,chains.

[0159] Actual rigid spacecraft attitude control systems are often equipped with multiple gyroscopes, star sensors (sensor nodes), and reaction flywheels or control torque gyroscopes (actuator nodes). Due to the random nature of communication scheduling mechanisms and physical bandwidth limitations, sensor and actuator nodes cannot simultaneously and completely transmit and receive all status and control data. Existing technologies often only allow one node to access a communication channel at a time. In reality, network environments can sometimes allow multiple nodes to access a communication channel, and multi-node information transmission is generally more beneficial for improving system performance. This embodiment uses a multi-node communication protocol to simulate the behavior of multiple nodes competing for communication resources using a Markov chain approach. This, combined with sensor-controller (S / C) and controller-actuator (C / A) channel modeling, improves the system's ability to characterize real-world aerospace network environments.

[0160] Specifically, the system is defined as having W sensor nodes and V control nodes connected through a network. The communication modeling of the S / C and C / A dual channels includes:

[0161] S / C channel communication modeling:

[0162] The system has a total of W sensor nodes, each of which can measure the system state , at each moment only a maximum of nodes communicate, among which ,S / C channel represents sensor-controller channel;

[0163] Define a binary variable function Indicates whether the w-th sensor node accesses the network at time k, expressed as:

[0164] (10);

[0165] Define the state selection matrix as , then the state signal received by the controller at time k is expressed as:

[0166] (11);

[0167] in, represents the state signal received at time k, represents the access status of the sensor node at time k, and , is the number of all sensor access states, It is the set of all sensor access states;

[0168] Assumptions A discrete-time Markov chain with the following conditional probability is expressed as:

[0169] (12);

[0170] in, represents the conditional probability of transitioning to state t when visiting state s, ,and , the transition probability matrix is defined as , represents the dimension of the matrix, represents the random variable at time k+1 The probability distribution of the value, P is the probability operator, Indicates the current time k, the state is Under the premise, the next moment Transfer to state The probability of Indicates that the sensor node is The access status at the moment, Respectively represent the current access status and the access status of the sensor node at the next moment, Indicates that s, t belong to the state set ;

[0171] C / A channel communication modeling:

[0172] The system has V control nodes, and a maximum of Nodes receive control signals, where ,C / A channel represents the controller-actuator channel;

[0173] Define the executor to receive the Controller nodes , ;

[0174] The actual control input of the control signal transmitted to the actuator becomes:

[0175] (13);

[0176] in, , Indicates the actual control input of the control signal transmitted to the actuator, represents the state combination of the actuator receiving signals from different controller nodes at time k, represents the sliding mode control law, represents the access status of the controller node at time k, Represents the access status set of all executors;

[0177] A discrete-time Markov chain follows with the following conditional probabilities:

[0178] (14);

[0179] in, Indicates that at the current time k, the state is Under the premise, at the next moment k+1 transfers to state The probability of Indicates that the controller node is The access status at the moment, Respectively represent the current access status of the executor node and the access status at the next moment, Indicates the slave state To status The conditional probability of ,and , the transition probability matrix is defined as , Indicates the dimensions of the matrix.

[0180] It should be noted that the multi-node random communication protocol used in this embodiment is a random communication protocol used when a single spacecraft internal system transmits control information, and the random communication protocol is added to both the sensor / controller node and the controller / actuator node, and mapping technology is used to map the Markov chains of the two channels into a single Markov chain, which makes control more convenient and flexible.

[0181] The multi-node random communication protocol adopted in this embodiment is more complex than the existing technology. The multi-node random communication protocol allows multiple nodes to transmit information simultaneously, which is more in line with the needs of multi-node transmission in actual applications, can greatly improve communication efficiency and save communication resources.

[0182] See Figure 1 and Figure 2 In one achievable implementation, S3 also includes introducing mapping variables The two Markov chains of the S / C channel and the C / A channel are mapped into one Markov chain.

[0183] It should be noted that spacecraft multi-node communication often involves multiple random variables, such as the scheduling status of the S / C channel and the C / A channel. If they are handled separately, the system model will be highly complex. In the above modeling process, the access to the sensor and actuator nodes is controlled by two separate Markov chains. By introducing the mapping variable , the two scheduling chains are unified into a single variable scheduling model, which is mapped to the Markov chain through the mapping technique. and A one-to-one correspondence is established, which not only reduces the dimensionality of control law design but also facilitates the mathematical processing of subsequent Lyapunov stability analysis, and is particularly suitable for resource-constrained onboard computing platforms.

[0184] Specifically, introduce mapping variables , expressed as:

[0185] (15);

[0186] in, represents a mapping function that merges the states of two Markov chains into a new variable. , , Indicates all The value set of ;

[0187] Determine a unique pair of random variables , expressed as:

[0188] (16);

[0189] in, Represents a mapping function used to map variables Determine the random variable The value of represents the modulo operation, Represents a mapping function used to map variables Determine the random variable The value of the mapping variable and random variable pairs The corresponding relationship is established through formula (15) and formula (16);

[0190] Get the mapping variable The transition probability is expressed as:

[0191] (17);

[0192] in, represents the mapping variable at time k+1, Representing variables The state at time k+1, Representing variables The state at time k is Indicates that the sensor node is The access status at the moment, Indicates that the state of the S / C channel at time k is At time k + 1, it is transferred to The conditional probability of Indicates that the state of the C / A channel at time k is At time k + 1, it is transferred to The conditional probability of a random variable The transition probability matrix is .

[0193] It should be noted that the mapping method (15) used above transforms the random variables into Mapped to a common scheduling signal , this variable can reflect the network access status of both the state signal and the control signal. Therefore, the controller gain Depends only on the new random variable , which also avoids the simultaneous dependence of random variables on The complexity brought about.

[0194] See Figure 1 and Figure 2 ,In practical applications, including S4, a fuzzy sliding mode controller is designed based on the TS fuzzy switching ,model.

[0195] Spacecraft attitude control needs to face continuous disturbances such as solar wind pressure and geomagnetic torque. The sliding mode controller has good anti-disturbance ability. and nonlinear switching control laws, so that the system state can converge quickly and maintain operation near the sliding surface, effectively improving the control accuracy and system robustness, and is suitable for related spacecraft flight missions with extremely high stability requirements.

[0196] Specifically, the sliding surface is designed and expressed as:

[0197] (18);

[0198] in, Is the sliding surface. By adjusting this item, the system state is made close to the sliding surface and the sliding mode is maintained. is the sliding surface parameter, which is used to enhance the robustness of the system to disturbances and modeling uncertainties and is defined as , c, l are selected according to the specific conditions of the spacecraft, represents the transpose of the system input matrix;

[0199] A sliding mode control law is designed, which combines the advantages of linear feedback and nonlinear sliding mode control and is suitable for aerospace environments with limited communication and strong disturbances. It is expressed as:

[0200] (19);

[0201] in, represents the sliding mode control law at time k, Represents The relevant membership function, express The norm of represents the symbolic function, is the controller gain to be designed to stabilize the linear part of the system, is the value in formula (18) Replace with Income.

[0202] It should be noted that the sliding surface is designed as , where G is generated by the weighted sum of fuzzy rules and switching subsystems, that is: , so that the sliding mode control parameters are adjusted dynamically with the system mode. The symbolic function term is introduced into the control law , enhancing robustness to external disturbances, such as space environment interference. Utilizing fuzzy switching to improve the adaptability of sliding mode control, suppress the chattering problem of traditional sliding mode, while ensuring the accuracy of spacecraft attitude control.

[0203] The greatest advantage of this embodiment in sliding mode control lies in its deep integration of sliding mode control with TS fuzzy switching modeling, scheduling mechanisms, and event-triggered control. This approach not only offers the robustness of traditional sliding mode control but also enhances adaptability to communication scheduling uncertainties and fuzzy structure changes. By designing an adjustable sliding surface and designing Lyapunov functions related to scheduling signals and event-triggered internal dynamic variables for subsequent stability and reachability analysis, the system can maintain high precision, low communication frequency, and rapid response in disturbed environments. This makes it particularly suitable for scenarios with extremely high stability and energy requirements, such as rigid spacecraft attitude control.

[0204] See Figure 1 and Figure 2 In one feasible implementation, S5 is included, and a closed-loop control system is constructed. The closed-loop control system of the spacecraft is obtained from formulas (7), (13) and (18), which is expressed as:

[0205] (20);

[0206] in, Represents the system state matrix related items, represents the time-varying term related to the system input, and Respectively expressed as:

[0207] ;

[0208] ;

[0209] in, Based on the mapping variable Determine the diagonal matrix of the C / A channel actuator receiving controller signal state combination, Basis Determine the diagonal matrix of the sensor node status information where the S / C channel is selected, represents the controller gain.

[0210] See Figure 2 In one feasible implementation, S5 further includes a step of verifying the global stability of the closed-loop control system.

[0211] To ensure stable operation of spacecraft during long-term orbital missions, mathematical tools are required to verify the mean square exponential stability of the control system under different scheduling states. Lyapunov function methods based on scheduling signals can ensure that the system converges stably in the desired sense.

[0212] Specifically, analyze the stability of the closed-loop control system and select the control signal that depends on the scheduling signal. The Lyapunov function is:

[0213] (twenty one);

[0214] in, Is with the dispatch signal The related symmetric positive definite matrix is used to evaluate the energy level of the current state of the system, and its expected decrease can be used to judge the stability of the system.

[0215] Calculate the expected difference of the Lyapunov function, expressed as:

[0216] (twenty two);

[0217] in, represents a known constant that reflects the decay rate and satisfies , Indicates the scheduling signal, represents the mathematical expectation operator, Indicates that the current system status is known and dispatch signals Under the premise of the next moment, the Lyapunov function The mathematical expectation of

[0218] Verified, expressed as:

[0219] (28);

[0220] in, express The mathematical expectation of

[0221] It is obtained by iterative equation, which is expressed as:

[0222] (29);

[0223] in, Represents the exponential decay factor related to the current switching path, Indicates the switching time and the corresponding switching signal The Lyapunov function value under , Indicates the switching time, The switching signal indicating the switching moment, It is a positive constant. represents the average residence time; Indicates the time interval The number of times the internal switching signal σ switches;

[0224] Based on formula (21), it is expressed as:

[0225] (30);

[0226] (31);

[0227] (twenty three);

[0228] Where a and b are the minimum and maximum amplification factors between the Lyapunov function and the system state norm, respectively, which are used to establish the upper and lower bound relationship between them and assist in stability analysis;

[0229] If satisfied , , then the closed-loop control system is mean square exponentially stable.

[0230] See Figure 2 In a feasible implementation, the step of sliding mode domain reachability analysis is included to prove that the state trajectory of the closed-loop control system (20) can reach the neighborhood of the specified sliding surface s(k) = 0 in the mean square sense. The sliding mode domain reachability analysis further illustrates that the state of the rigid spacecraft system can approach the target control surface within a finite time, and has good convergence and engineering feasibility.

[0231] Specifically, the sliding mode domain , expressed as:

[0232] (twenty four);

[0233] in, yes The norm of represents the upper bound term caused by disturbance and communication scheduling, defined as , sliding mode domain represents the small region near the sliding surface into which the state will be driven in the mean square sense, express The corresponding positive definite matrix The minimum eigenvalue of is used to define the lower limit of the Lyapunov function growth. represents the intermediate variable in the LMI condition related to the sliding surface, which is used to control the energy of the disturbance term. m is the total number of actuators. Representation and control signal scheduling matrix The relevant intermediate variables, Basis Determine the diagonal matrix of the sensor node status information where the S / C channel is selected, express The square of the norm, Indicates system status The square of the norm;

[0234] From formula (18) and formula (20), we can get:

[0235] (25);

[0236] Choose the Lyapunov function as:

[0237] (26);

[0238] in, Represents Correlated positive definite matrices;

[0239] calculate , it can be verified that:

[0240] (27);

[0241] in, Indicates Under the premise of the next moment, the Lyapunov function The mathematical expectation of express Expectations

[0242] Therefore, the Lyapunov function is monotonically decreasing outside the sliding mode domain, which means that the state trajectory of the system will converge into the sliding mode domain in a finite time. middle.

[0243] See Figure 3 、 Figure 4 、 Figure 5 、 Figure 6 and Figure 7 ,In practical applications, this embodiment is further illustrated by giving ,rigid spacecraft system parameters.

[0244] Specifically, the inertia matrix of the spacecraft is converted to:

[0245] ;

[0246] Select the initial angular velocity as .

[0247] Specifically, a TS fuzzy switching system with two switching subsystems and two fuzzy rules in each subsystem is selected. The system parameters are as follows:

[0248] Subsystem 1 (low-speed flight mode):

[0249] ;

[0250] ;

[0251] ;

[0252] ;

[0253] , , , , ;

[0254] Subsystem 2 (high-speed flight mode):

[0255] ;

[0256] ;

[0257] ;

[0258] ;

[0259] , , , , ;

[0260] exist Figure 4In the figure, the horizontal axis represents simulation time in seconds, while the vertical axis represents the mode, or subsystem number. The figure shows that the system dynamically switches between the two subsystems, with the switching frequency constrained by the average dwell time. The entire switching process does not experience frequent high-frequency jumps. Figure 4 It shows that the controller structure has good switching logic stability under the average residence time control, and effectively avoids unstable behavior caused by too fast switching.

[0261] The membership function is chosen as:

[0262] ;

[0263] ;

[0264] in, are the endpoints of the fuzzy intervals of the two fuzzy rules, defining the range of the fuzzy intervals and used to calculate the activation strength of each fuzzy rule;

[0265] The external interference is:

[0266] ;

[0267] In addition, the parameters in the dynamic event trigger are selected as , select the parameters in the stability analysis .

[0268] Specifically, assuming that only two sensors transmit data on the S / C channel at each moment, there are three transmission scenarios: , its transition probability matrix is:

[0269] ;

[0270] Assume that only two sensors are allowed to transmit data at each moment in the C / A channel: , its transition probability matrix is:

[0271] ;

[0272] Pair of random variables and the new random variable The relationship is shown in Table 1 below:

[0273] Table 1: Pairs of random variables and the new random variable relationship

[0274] ;

[0275] Therefore, the new random variable The transition probability matrix can be calculated as:

[0276] ;

[0277] According to the above simulation conditions, the system is simulated to verify the system's attitude control capability.

[0278] Figure 3 The communication status of the S / C channel is shown. The horizontal axis represents the simulation time in seconds; the vertical axis represents the simulation time in seconds. is the scheduling signal of the S / C channel, where Represents the system state, that is, the spacecraft angular velocity and Transmitted at the current moment, and and Respectively represent the transmission system state { , } and state { , It shows that the selection of the communication channel of the S / C channel is random by multiple nodes.

[0279] Figure 4 The communication status of the C / A channel is shown. The horizontal axis represents the simulation time in seconds; the vertical axis represents the simulation time in seconds. is the scheduling signal of the C / A channel, , and Respectively represent the current moment transmission control signal { }, { }and{ }, showing that the communication channel selection pattern of the system is randomly selected.

[0280] Figure 5 This figure shows the subsystem switching frequency based on the average dwell time. The horizontal axis represents the simulation time in seconds, while the vertical axis represents the mode, which is the subsystem number. The figure shows that the system dynamically switches between the two subsystems, and the switching frequency is constrained by the average dwell time. The entire switching process does not experience frequent high-frequency jumps. Figure 4 This shows that the controller structure has good switching logic stability under the average residence time control, and effectively avoids unstable behavior caused by too fast switching.

[0281] Figure 6 It reflects the angular velocity response of the three rotation axes of the spacecraft. The horizontal axis represents the simulation time in seconds; the vertical axis is the angular velocity in rad / s. The dotted curve represents the angular velocity of the spacecraft on the x-axis in the body coordinate system. , the dot-dash curve represents the angular velocity on the y-axis , the solid line curve represents the angular velocity on the z-axis . Figure 6 This shows that the controller has good disturbance suppression capability and attitude stability performance, and can achieve efficient and fast attitude stabilization control under conditions of uncertain disturbances and incomplete communication scheduling.

[0282] Figure 7 The control input of the spacecraft is shown. The horizontal axis represents the simulation time in seconds; the vertical axis represents the control input. The dotted curve represents the control input , the dot-dash curve represents the control input , the solid line curve represents the control input Under the initial disturbance, the controller applies a large control input to the system to quickly suppress the disturbance, and then quickly approaches 0, indicating that the system does not need long-term and large-scale control, thus achieving low-energy control, which is suitable for energy-constrained rigid spacecraft control. Figure 7 This shows that the sliding mode control of this application has faster response speed and stronger anti-disturbance ability.

[0283] Figure 8 This figure reflects the change of the sliding mode variable of the spacecraft system under the action of the sliding mode controller. The horizontal axis represents the simulation time in seconds; the vertical axis represents the sliding mode variable. The dotted curve represents the sliding mode variable , the dot-dashed curve represents the sliding mode variable , the solid line curve represents the sliding mode variable The rapid convergence of the sliding mode variables means that the system state can quickly approach the sliding mode surface, and the system enters the sliding mode dynamic process. Figure 8 This shows that the application has good anti-parameter disturbance capability and system robustness, and the control strategy is reliable.

[0284] Based on the same inventive concept, the present application also provides an attitude control system for a rigid spacecraft, the attitude control system comprising:

[0285] The first construction unit is to construct the attitude dynamics model and kinematic equations of the rigid spacecraft;

[0286] The second construction unit builds the TS fuzzy switching model of the rigid spacecraft based on the dynamic model:

[0287] The TS fuzzy switching model selects a discrete time system for modeling. By means of local linearization, the overall nonlinear system is split into multiple fuzzy rule subsystems, and then weighted combinations are performed in combination with the switching strategy.

[0288] Introducing uncertainty into each fuzzy rule subsystem , uncertainty Correlation with spacecraft moment of inertia and external disturbances to dynamically correct control inputs;

[0289] The third building block introduces a multi-node random communication protocol for the TS fuzzy switching model:

[0290] Aiming at the TS fuzzy switching model, the multi-node random communication protocol jointly models the multi-node communication resource competition behavior in the S / C and C / A dual-channel communication process through Markov chains;

[0291] The fourth building block designs a fuzzy sliding mode controller based on the TS fuzzy switching model and multi-node random communication protocol:

[0292] Combining the dynamic characteristics of TS fuzzy switching model and multi-node random communication protocol to design sliding surface Sliding surface parameters Through the dynamic generation of TS fuzzy rules, the sliding mode control parameters are dynamically adjusted with the system mode;

[0293] Closed-loop control unit forms a closed-loop control system for attitude control:

[0294] The TS fuzzy switching model, multi-node random communication protocol and fuzzy sliding mode controller constructed by the above building blocks are integrated to form a closed-loop control system to realize spacecraft attitude control and verify the global stability of the closed-loop control system.

[0295] Based on the same inventive concept, the present application also provides an attitude control device for a rigid spacecraft, comprising a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method described above is implemented.

[0296] Based on the same inventive concept, the present application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the method described above is implemented.

[0297] The program product of the present application for implementing the above-mentioned method may be a portable compact disk read-only memory and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present application is not limited thereto. In the present application, a readable storage medium may be any tangible medium containing or storing a program, which may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0298] It should be noted that a computer-readable storage medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0299] The above is only a preferred embodiment of the present application and does not constitute any form of limitation to the present application. Although the present application has been disclosed as above with preferred embodiments, it is not intended to limit the present application. Any technician familiar with the present application can make some changes or modifications to equivalent embodiments with equivalent changes using the technical content suggested above without departing from the scope of the technical solution of the present application. The implementation schemes in the above embodiments can also be further combined or replaced. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present application that do not depart from the content of the technical solution of the present application still fall within the scope of the solution of the present application.

Claims

1. A method for attitude control of a rigid spacecraft, characterized in that: The following steps are involved: S1. Construct the attitude dynamics model and kinematic equations of rigid spacecraft; S2. Construct the TS fuzzy switching model of the rigid spacecraft based on S1: The TS fuzzy switching model selects a discrete time system for modeling, splits the overall nonlinear system into multiple fuzzy rule subsystems through local linearization, and performs weighted combination in combination with the switching strategy; Introducing uncertainty into each of the fuzzy rule subsystems , the uncertainty Correlation with spacecraft moment of inertia and external disturbances to dynamically correct control inputs; S3, based on S2, introduces a multi-node random communication protocol: For the TS fuzzy switching model, the multi-node random communication protocol jointly models the multi-node communication resource competition behavior in the S / C and C / A dual-channel communication process through Markov chains; S4. Based on S2 and S3, design a fuzzy sliding mode controller: Combining the dynamic characteristics of TS fuzzy switching model and multi-node random communication protocol to design sliding mode function , sliding mode matrix Through the dynamic generation of TS fuzzy rules, the sliding mode control parameters are dynamically adjusted with the system mode; S5. Form a closed-loop control system for attitude control: The TS fuzzy switching model, multi-node random communication protocol and fuzzy sliding mode controller designed by S2-S4 are integrated to form a closed-loop control system to realize spacecraft attitude control and verify the global stability of the closed-loop control system.

2. The method according to claim 1, characterized in that The TS fuzzy switching model constructed in S2 includes: the pth fuzzy rule is: if is and … and is , So (6); in, is the premise variable, … is a fuzzy set, Indicates the switching signal, The switching time interval between is called the dwell time, where is the time when the mth switching occurs, is the moment immediately following the next switch. and denote the state matrix and input matrix of the system respectively, represents uncertainty, is a known constant matrix, The system is The state variables at time , Indicates that the system is The state variables at time , Indicates external interference, Represents the control output, let , the known constant matrix is expressed as , , the uncertainty is expressed as ; Through the TS fuzzy reasoning method, the TS fuzzy switching model is obtained, which is expressed as: （7）； in, and denote the state matrix and input matrix of the system respectively, is the number of fuzzy rules under the TS fuzzy switching system, is the normalized membership function, Indicates uncertainty, Indicates external interference and satisfies , is a known constant, and i represents the index number of the fuzzy rule.

3. The method according to claim 1, characterized in that The invention also includes the step of designing a dynamic event triggering mechanism, wherein the dynamic event triggering mechanism introduces a system state deviation. With internal dynamic variables Build adaptive trigger conditions to determine system status Whether it can be transmitted; including event triggering conditions, expressed as: （8）； in, Indicates the current triggering moment. Indicates the next triggering time. represents a given scalar, , represents the dynamic event trigger weight matrix, represents the system state deviation at time k, represents the internal dynamic variables at time k, represents the transpose of the system state deviation at time k, represents the transpose of the system state at time k, represents the infimum; Among them, when the inequality is satisfied The minimum k value is the next trigger moment , and then update the trigger state; Among them, the internal dynamic variables It is obtained through dynamic calculation and expressed as: （9）； in, is a known constant, express Internal dynamic variables at the moment, , Indicates the initial value of the internal dynamic variable, Represents a known non-negative constant that is set.

4. The method according to claim 2, characterized in that The multi-node random communication protocol described in S3 jointly models the multi-node communication resource competition behavior in the S / C and C / A dual-channel communication process through Markov chains; including: The system is defined as having W sensor nodes and V control nodes connected through a network. The communication modeling of the S / C and C / A dual channels includes: S / C channel communication modeling: The system has a total of W sensor nodes, each of which can measure the system state , at each moment only a maximum of nodes communicate, among which ,S / C channel represents sensor-controller channel; Define a binary variable function Indicates the w Whether a sensor node accesses the network at time k is expressed as: （10）； Define the state selection matrix as , then the state signal received by the controller at time k is expressed as: （11）； in, represents the state signal received by the controller at time k, represents the access status of the sensor node at time k, and , is the number of all sensor access states, It is the set of all sensor access states; Assumptions A discrete-time Markov chain with the following conditional probability is expressed as: （12）； in, represents the conditional probability of transitioning to state t when visiting state s, ,and , the transition probability matrix is defined as , represents the dimension of the matrix, represents the random variable at time k+1 The probability distribution of the value, P is the probability operator, Indicates the current time k, the state is Under the premise, the next moment Transfer to state The probability of Indicates that the sensor node is The access status at the moment, Respectively represent the current access status and the access status of the sensor node at the next moment, Indicates that s, t belong to the state set ; C / A channel communication modeling: System Share control nodes, and a maximum of Nodes receive control signals, where ,C / A channel represents the controller-actuator channel; Define the executor to receive the Controller nodes , ; The actual control input of the control signal transmitted to the actuator becomes: （13）； in, represents the access status of the controller node at time k, , is the number of all actuator access states, Represents the access status set of all executors, Indicates the actual control input of the control signal transmitted to the actuator, represents the state combination of the actuator receiving signals from different controller nodes at time k, represents the sliding mode control law; A discrete-time Markov chain follows with the following conditional probabilities: （14）； in, Indicates that at the current time k, the state is Under the premise, at the next moment k+1 transfers to state The probability of Indicates that the controller node is The access status at the moment, Respectively represent the current access status of the executor node and the access status at the next moment, Indicates the slave state To status The conditional probability of ,and , the transition probability matrix is defined as , Represents the dimension of the transition probability matrix.

5. The method according to claim 4, characterized in that S3 also includes the introduction of mapping variables The two Markov chains of the S / C channel and the C / A channel are mapped into one Markov chain, including: Introducing mapping variables , expressed as: （15）； in, represents a mapping function that merges the states of two Markov chains into a new variable. , , Indicates all The value set of ; Determine a unique pair of random variables , expressed as: （16）； in, Represents a mapping function used to map variables Determine the random variable The value of represents the floor function, Represents a mapping function used to map variables Determine the random variable The value of the mapping variable and random variable pairs The corresponding relationship is established through formula (15) and formula (16); Get the mapping variable The transition probability is expressed as: （17）； in, represents the mapping variable at time k+1, Representing variables The state at time k+1, Representing variables The state at time k is Indicates that the state of the S / C channel at time k is At time k + 1, it is transferred to The conditional probability of Indicates that the state of the C / A channel at time k is At time k + 1, it is transferred to The conditional probability of a random variable The transition probability matrix is , Indicates the dimensions of the matrix.

6. The method according to claim 5, characterized in that In S4, a fuzzy sliding mode controller is designed based on the TS fuzzy switching model, including: Design the sliding mode function, expressed as: （18）； in, It is a sliding mode function. By designing the control law to adjust the system input, the system state is close to the sliding mode surface and the sliding mode is maintained, thus achieving the robustness requirement of sliding mode variable structure control. is the sliding mode matrix, which is used to enhance the robustness of the system to disturbances and modeling uncertainties and is defined as , c, l are scalar parameters, c, l should be selected to ensure It is non-singular under any switching signal. represents the transpose of the system input matrix; Design the sliding mode control law, which is expressed as: （19）； in, represents the sliding mode control law at time k, Represents The relevant membership function, express The norm of represents the symbolic function, is the controller gain to be designed to stabilize the linear part of the system, is the value in formula (18) Replace with Income.

7. The method according to claim 6, characterized in that The closed-loop control system is constructed in S5, including: the closed-loop control system of the spacecraft is obtained from formulas (7), (13) and (18), which is expressed as: （20）； in, Represents the system state matrix related items, represents the time-varying term related to the system input, in, and Respectively expressed as: ； ； in, Based on the mapping variable Determine the diagonal matrix of the C / A channel actuator receiving controller signal state combination, Basis Determine the diagonal matrix of the sensor node status information where the S / C channel is selected.

8. An attitude control system for a rigid spacecraft, characterized in that The posture control system includes: The first construction unit is to construct the attitude dynamics model and kinematic equations of the rigid spacecraft; The second construction unit builds the TS fuzzy switching model of the rigid spacecraft based on the dynamic model: The TS fuzzy switching model selects a discrete time system for modeling, splits the overall nonlinear system into multiple fuzzy rule subsystems through local linearization, and performs weighted combination in combination with the switching strategy; Introducing uncertainty into each of the fuzzy rule subsystems , the uncertainty Correlation with spacecraft moment of inertia and external disturbances to dynamically correct control inputs; The third building block introduces a multi-node random communication protocol for the TS fuzzy switching model: For the TS fuzzy switching model, the multi-node random communication protocol jointly models the multi-node communication resource competition behavior in the S / C and C / A dual-channel communication process through Markov chains; The fourth building block designs a fuzzy sliding mode controller based on the TS fuzzy switching model and multi-node random communication protocol: Combining the dynamic characteristics of TS fuzzy switching model and multi-node random communication protocol to design sliding mode function The sliding mode matrix Through the dynamic generation of TS fuzzy rules, the sliding mode control parameters are dynamically adjusted with the system mode; Closed-loop control unit forms a closed-loop control system for attitude control: The TS fuzzy switching model, multi-node random communication protocol and fuzzy sliding mode controller constructed by the above building blocks are integrated to form a closed-loop control system to realize spacecraft attitude control and verify the global stability of the closed-loop control system.

9. An attitude control device for a rigid spacecraft, characterized in that: The method comprises a processor and a memory, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the method according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, it is used to implement the method according to any one of claims 1 to 7.

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