Rigid spacecraft attitude control method, system and device and medium

Abstract

The invention belongs to the technical field of spacecraft attitude control, and discloses a rigid spacecraft attitude control method, system and device and a medium, and the method comprises the steps: S1, constructing a rigid spacecraft attitude dynamics model and a kinematics equation; s2, constructing a T-S fuzzy switching model of 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; and S5, forming a closed-loop control system for attitude control: forming the closed-loop control system based on the T-S fuzzy switching model, the multi-node random communication protocol and the fuzzy sliding mode controller designed in S1 to S4, realizing spacecraft attitude control, and verifying the global stability of the closed-loop control system. The technical problems of high nonlinearity, large external disturbance, limited communication and high real-time performance in rigid spacecraft attitude control are solved, and the attitude stability and control precision of the spacecraft in a complex space environment are effectively improved.

Description

Technical Field

[0001] This application belongs to the technical field of spacecraft attitude control, and particularly relates to an attitude control method, system, device, and medium for rigid spacecraft. Background Art

[0002] Spacecraft attitude control is the core technology to ensure the stable operation of the spacecraft in orbit and the accurate completion of tasks. As a space platform with an invariant structure, a rigid spacecraft must maintain high-precision control of its spatial attitude under the action of disturbances in a complex orbital environment, such as geomagnetism, solar wind, and gravity gradient. However, this control problem generally faces difficulties such as nonlinear dynamics, parameter uncertainty, strong external disturbances, and limited communication resources, which pose higher requirements on control algorithms.

[0003] Current mainstream control methods such as PID control, linear optimal control, and classical sliding mode control perform well under certain specific working conditions, but they have problems such as poor robustness, poor real-time performance, or large communication overhead when dealing with strong nonlinearity and communication scheduling uncertainty. In addition, due to limited computing resources of on-board equipment, it is more necessary to design an efficient and energy-saving attitude control strategy. Especially in the presence of multiple sensors and multiple actuator nodes, the traditional centralized communication mechanism is difficult to adapt to current space missions with low power consumption and low bandwidth requirements. Summary of the Invention

[0004] The technical problem to be solved by this application is to overcome the deficiencies of the prior art. This application provides an attitude control method, system, device, and medium for rigid spacecraft. By combining T-S fuzzy modeling, average dwell time (ADT) switching strategy, sliding mode control law, dynamic event triggering mechanism, and multi-node random communication protocol, it solves the technical problems of strong nonlinearity, large external disturbances, communication constraints, and high real-time performance in rigid spacecraft attitude control, improves the attitude stability and control accuracy of the spacecraft in a complex space environment; has the advantages of high robustness, strong anti-disturbance ability, 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 object, the first aspect of this application provides an attitude control method for rigid spacecraft, including the following steps: S1. Construct the attitude dynamics model and kinematic equation of the rigid spacecraft; S2. Based on S1, construct a T-S fuzzy switching model of the rigid spacecraft: The T-S fuzzy switching model selects a discrete-time system for modeling. Through local linearization, the overall nonlinear system is split into multiple fuzzy rule subsystems and weighted and combined with a switching strategy; Introduce uncertainty into each of the fuzzy rule subsystems , the uncertainty is related to the spacecraft's moment of inertia and external disturbances, and is used to dynamically correct the control input; S3. Based on S2, introduce a multi-node random communication protocol: For the T-S 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 processes through a Markov chain; S4. Based on S2 and S3, design a fuzzy sliding mode controller: Combine the dynamic characteristics of the T-S fuzzy switching model and the multi-node random communication protocol to design a sliding mode function , and the sliding mode matrix is dynamically generated through T-S fuzzy rules, so that the sliding mode control parameters are dynamically adjusted with the system mode; S5. Form a closed-loop control system for attitude control: Integrate the T-S fuzzy switching model, multi-node random communication protocol, and fuzzy sliding mode controller designed in S2-S4 to form a closed-loop control system, realize spacecraft attitude control, and verify the global stability of the closed-loop control system.

[0006] Optionally, the T-S fuzzy switching model constructed in S2 includes: The p-th fuzzy rule is: If is and … and is , Then (6); Wherein, are premise variables, … are fuzzy sets, represents the switching signal, The switching time interval between is called the dwell time, where is the moment when the m-th switching occurs, and respectively represent the state matrix and input matrix of the system, represents the uncertainty, which is a known constant matrix, is the state variable of the system at moment, represents the state variable of the system at moment, represents the external disturbance, represents the control output, let , and represent the known constant matrix as , , the uncertainty is represented as ; Through the T-S fuzzy inference method, a T-S fuzzy switching model is obtained, which is expressed as: (7); Among them, and respectively represent the state matrix and the input matrix of the system, and respectively represent the state matrix and the input matrix of the system, is the number of fuzzy rules under the T-S fuzzy switching system, is the normalized membership function, represents the control output, represents the external disturbance, and satisfies , is a known constant, and i represents the index number of the fuzzy rule.

[0007] Optionally, it further includes the step of designing a dynamic event triggering mechanism, and the dynamic event triggering mechanism constructs an adaptive triggering condition by introducing the system state deviation and the internal dynamic variable to judge whether the system state can be transmitted; it includes an event triggering condition, which is expressed as: (8); Among them, represents the current triggering moment, represents the next triggering moment, represents a given scalar, , represents the dynamic event triggering weight matrix, represents the system state deviation at the kth moment, represents the internal dynamic variable at the kth moment, represents the transpose of the system state deviation at the kth moment, represents the transpose of the system state at the kth moment, represents the infimum; Among them, when the inequality is satisfied, the smallest k value is the next triggering moment , and then the triggering state is updated; Among them, the internal dynamic variable is obtained through dynamic calculation, which is expressed as: (9); Among them, is a known constant, , Represents the initial value of the internal dynamic variable, Represents a set non - negative constant.

[0008] Optionally, 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 processes through a Markov chain; including: Define that the system has 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, and each node can measure the system state , and at most only nodes are allowed to communicate at each moment, where , the S / C channel represents the sensor - controller channel; Define the binary variable function Indicates whether the w - th sensor node accesses the network at time k, expressed as: (10); Define the state selection matrix as , then the state signal received by the controller at time k is expressed as: (11); Among them, Represents the state signal received at time k, Represents the access state of the sensor node at time k, and , is the number of all sensor access states, is the set of all sensor access states; Assume obeys a discrete - time Markov chain with the following conditional probability, expressed as: (12); Among them, Represents the conditional probability, , and , the transition probability matrix is defined as , Represents the dimension of this matrix, Represents the probability distribution of the random variable taking values at time k + 1, P is the probability operator, Represents the current time k, given that the state is , the probability that at the next time transitions to the state , Represents the sensor node at The access status at a moment respectively represent the current access status of the sensor node and the access status at the next moment indicating that s and t belong to the state set ; C / A channel communication modeling: The system has a total of V control nodes, and at most nodes are allowed to receive control signals each time. Among them, the C / A channel represents the controller-actuator channel; Define the situation where the actuator receives a signal from the th controller node at time k , ; The actual control input for the control signal transmitted to the actuator becomes: (13); Among them, represents the access status of the controller node at time k, , is the number of all actuator access statuses, represents the set of all actuator access statuses, represents the actual control input for 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; Follow the discrete-time Markov chain with the following conditional probability: (14); Among them, represents the probability distribution of the random variable taking values at time k + 1, represents the access status of the controller node at time, 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.

[0009] Optionally, S3 also includes introducing a mapping variable to map the two Markov chains of the S / C channel and the C / A channel into one Markov chain, including: Introduce a mapping variable , expressed as: (15); Among them, represents a mapping function for combining the states of two Markov chains into a new variable, , , represents all value sets; Determine a unique pair of random variables , expressed as: (16); Among them, represents a mapping function for determining the value of the random variable according to the mapping variable value, represents the floor function, represents a mapping function for determining the value of the random variable according to the mapping variable value, the mapping variable and the random variable pair are established corresponding relationships through formula (15) and formula (16); Obtain the transition probability of the mapping variable , expressed as: (17); Among them, represents the mapping variable at time k + 1, represents the state of the variable at time k + 1, represents the state of the variable at time k, represents the access state of the sensor node at time, represents the conditional probability that the state of the S / C channel at time k is and transfers to at time k + 1, represents the conditional probability that the state of the C / A channel at time k is and transfers to at time k + 1, the conditional probability that the state of the C / A channel at time k is and transfers to at time k + 1, the transition probability matrix of the random variable is , represents the dimension of this matrix.

[0010] Optionally, in S4, based on the T-S fuzzy switching model, design a fuzzy sliding mode controller, including: Design a sliding mode function, expressed as: (18); Wherein, is the sliding mode function, and the system input is adjusted by designing the control law to make the system state approach the sliding mode surface and maintain the sliding mode, so as to meet the robustness requirements of the 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, and the selection of c, l should ensure that is non-singular under any switching signal, represents the transpose of the system input matrix; Design the sliding mode control law, expressed as: (19); Wherein, represents the sliding mode control law at time k, represents the membership function related to , represents norm of, represents the sign function, is the controller gain to be designed, which is used to stabilize the linear part of the system, is in formula (18) replaced by obtained.

[0011] Optionally, in S5, construct a closed-loop control system, including: the closed-loop control system of the spacecraft obtained from formulas (7), (13) and (18), expressed as: (20); Wherein, represents the relevant item of the system state matrix, represents the time-varying item related to the system input, and are respectively expressed as: ; ; Wherein, is the diagonal matrix that determines the state combination of the C / A channel actuator receiving the controller signal according to the mapping variable , represents the diagonal matrix that determines the state information of the selected sensor node of the S / C channel according to .

[0012] To achieve the above object, the second aspect of the present application provides an attitude control system for a rigid spacecraft, and the attitude control system includes: A first construction unit for constructing an attitude dynamics model and a kinematic equation of the rigid spacecraft; A second construction unit for constructing a T-S fuzzy switching model of the rigid spacecraft based on the dynamics model: The T-S fuzzy switching model selects a discrete-time system for modeling. Through local linearization, the overall nonlinear system is split into multiple fuzzy rule subsystems and weighted combination is performed in combination with a switching strategy; Introduce uncertainty into each of the fuzzy rule subsystems , the uncertainty is associated with the spacecraft moment of inertia and external disturbances and is used to dynamically correct the control input; A third construction unit for introducing a multi-node random communication protocol for the T-S fuzzy switching model: For the T-S 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 processes through a Markov chain; A fourth construction unit for designing a fuzzy sliding mode controller based on the T-S fuzzy switching model and the multi-node random communication protocol: Design a sliding mode function by combining the dynamic characteristics of the T-S fuzzy switching model and the multi-node random communication protocol The sliding mode matrix of is dynamically generated by T-S fuzzy rules, so that the sliding mode control parameters are dynamically adjusted according to the system mode; A closed-loop control unit for forming a closed-loop control system for attitude control: Integrate the T-S fuzzy switching model, the multi-node random communication protocol, and the fuzzy sliding mode controller constructed by the above construction units to form a closed-loop control system, realize spacecraft attitude control, and verify the global stability of the closed-loop control system.

[0013] To achieve the above object, the third aspect of the present application provides an attitude control device for a rigid spacecraft, including a processor and a memory. A computer program is stored on the memory, and when the computer program is executed by the processor, the method described above is implemented.

[0014] To achieve the above object, the fourth aspect of the present application provides a computer-readable storage medium storing a computer program, which is used to implement the method described above when executed by a processor.

[0015] After adopting the above technical solutions, the present application has the following beneficial effects compared with the prior art: In this application, the T-S fuzzy model is used to describe the non-linear dynamic characteristics of the spacecraft. The dynamic responses of the T-S fuzzy switching system under different working modes are modeled in the form of fuzzy rules, and the overall robust modeling control of the system is realized by combining the switching strategy. A multi-node random communication protocol is introduced, and the communication behaviors of each node are modeled through a Markov process to effectively characterize the dynamic changes of the network state and enable simultaneous data transmission on multiple communication channels. A dynamic event-triggering mechanism is designed to reduce communication frequency and energy consumption. A sliding mode control method suitable for the spacecraft attitude adjustment task with strong disturbances is adopted to improve the adaptability and stability of the control system, effectively enhancing the robustness of the system to external disturbances and parameter uncertainties, while reducing the computational complexity to ensure the efficiency and real-time performance of attitude control.

[0016] The following further describes in detail the specific implementation manners of this application with reference to the accompanying drawings. Description of the Drawings

[0017] The accompanying drawings, as part of this application, are used to provide a further understanding of this application. The schematic embodiments and descriptions thereof of this application are used to explain this application, but do not constitute an improper limitation to this application. Obviously, the accompanying drawings in the following description are only some embodiments, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0018] In the specification drawings: Figure 1 is a schematic flow diagram of the rigid spacecraft attitude control method in this specific implementation manner; Figure 2 is a schematic logical diagram of the rigid spacecraft attitude control method in this specific implementation manner; Figure 3 is the S / C channel scheduling signal in this specific implementation manner schematic diagram; Figure 4 is the C / A channel scheduling signal in this specific implementation manner schematic diagram; Figure 5 is a schematic diagram of the subsystem switching frequency based on the average dwell time in this specific implementation manner; Figure 6 is a schematic diagram of the simulation curve of the angular velocity ω response of the spacecraft in three rotational axis directions in this specific implementation manner; Figure 7 is a schematic diagram of the simulation curve of the control input u(k) of the spacecraft in this specific implementation manner; Figure 8 is a schematic diagram of the simulation curve of the change of the sliding mode variable s(k) of the spacecraft system under the action of the sliding mode controller in this specific implementation manner. Detailed implementation manners

[0019] To make the objectives, 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 with reference to the accompanying 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.

[0020] Please refer to Figure 1 and Figure 2 , based on this, the present application provides an attitude control method for a rigid spacecraft, including the following steps: S1. Construct an attitude dynamics model and kinematic equations of the rigid spacecraft; S2. Based on S1, construct a T-S fuzzy switching model of the rigid spacecraft: The T-S fuzzy switching model selects a discrete-time system for modeling. Through local linearization, the overall nonlinear system is split into multiple fuzzy rule subsystems and weighted combination is performed in combination with a switching strategy; Introduce uncertainty into each fuzzy rule subsystem , uncertainty is associated with the spacecraft moment of inertia and external disturbances and is used to dynamically correct the control input; S3. Based on S2, introduce a multi-node random communication protocol: For the T-S 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 processes through a Markov chain; S4. Based on S2 and S3, design a fuzzy sliding mode controller: Combine the dynamic characteristics of the T-S fuzzy switching model and the multi-node random communication protocol to design a sliding mode surface , the sliding mode surface parameters are dynamically generated by T-S fuzzy rules, so that the sliding mode control parameters are dynamically adjusted with the system mode; S5. Form a closed-loop control system for attitude control: Integrate the T-S fuzzy switching model, multi-node random communication protocol, and fuzzy sliding mode controller designed in S2-S4 to form a closed-loop control system, realize spacecraft attitude control, and verify the global stability of the closed-loop control system.

[0021] It should be noted that in this embodiment, the execution subject of the attitude control method for a rigid spacecraft is the attitude control device of the rigid spacecraft, and this device can be an electronic device, a component in an electronic device, an integrated circuit, or a chip. The electronic device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a handheld computer, a vehicle-mounted electronic device, a wearable device, etc., and the non-mobile electronic device can be a server and a personal computer, etc., which are not specifically limited in this application. Hereinafter, taking the execution subject as a server as an example, the attitude control method for the rigid spacecraft in this embodiment will be described.

[0022] In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of this application, "a plurality" means two or more, unless otherwise specifically defined.

[0023] Please refer to Figure 1 , in an implementable embodiment, it includes S1: constructing a rigid spacecraft attitude dynamics model and kinematic equations.

[0024] Specifically, the dynamic equation of the spacecraft describes the evolution law of its angular velocity under various torques. If the spacecraft is regarded as a rigid body, its dynamic equation can be obtained according to the rigid body dynamics law as follows: (1); Among them, is the positive definite symmetric matrix of the moment of inertia of the spacecraft, is the angular velocity of the three axes of the spacecraft in the body coordinate system, is the control torque, is the external disturbance, and its specific expression form is as follows: (2); Among them, represents the angular velocity, respectively 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 corresponding to the three axes, represents the transpose of the control output corresponding to the three axes, represents the moment of inertia of the spacecraft about the x-axis of the body coordinate system, represents the moment of inertia about the y-axis, represents the moment of inertia about the z-axis, It reflects the coupling moment of inertia of the spacecraft's mass distribution with respect to the x-axis and y-axis. It reflects the coupling moment of inertia of the spacecraft's mass distribution with respect to the x-axis and z-axis. It reflects the coupling moment of inertia of the spacecraft's mass distribution with respect to the y-axis and z-axis. is an operation symbol that can obtain an anti-symmetric matrix from a vector. For example, in formula (1), The specific form of (3); In the Cartesian coordinate system, the rotational motion of a general rigid body can be decomposed into angular changes along three coordinate axes, usually described by Euler angles. Euler angles consist of three successive rotations used to determine the attitude of the rigid body in space. These three rotations are performed about the X, Y, and Z axes respectively, and the corresponding rotation angles are called the nutation angle , precession angle , and spin angle ; The rotation matrix of the rigid body can be obtained as: ; (4); ; The kinematic equation of the spacecraft is expressed as: (5).

[0025] Please refer to Figure 1 and Figure 2 , in a realizable implementation, the T-S fuzzy switching model constructed in S2 includes: The p-th fuzzy rule is: Fuzzy rule p: If is and … and is , Then (6); Where are premise variables, , …, are fuzzy sets, represents the switching signal; Where , , , , are respectively expressed as: ; ; ; ; ; Among them, , is a known constant matrix, is an unknown time-varying matrix that satisfies , where I is the identity matrix; , 1; Among them, represents the angular velocity, respectively represent the angular velocities of the spacecraft along the three axes in the body coordinate system, represents the transpose of the angular velocities corresponding to the three axes, respectively represent the control outputs corresponding to the three axes, represents the transpose of the control outputs corresponding to the three axes, represents the moment of inertia of the spacecraft about the x-axis in the body coordinate system, represents the moment of inertia about the y-axis, represents the moment of inertia about the z-axis, reflects the coupled moment of inertia of the spacecraft's mass distribution with respect to the x-axis and y-axis, reflects the coupled moment of inertia of the spacecraft's mass distribution with respect to the x-axis and z-axis, reflects the coupled moment of inertia of the spacecraft's mass distribution with respect to the y-axis and z-axis, represents the membership function, represents the th premise variable, represents the total number of premise variables, represents the index of the premise variable; Through the T-S fuzzy inference method, a T-S fuzzy switching model is obtained, which is expressed as: (7); Among them, and respectively represent the state matrix and input matrix of the system, is the number of fuzzy rules in the T-S fuzzy switching system, is the normalized membership function, is the state variable of the system at time, represents the state variable of the system at time k + 1, represents the control output, represents the external disturbance and satisfies , is a known constant, i represents the index number of the fuzzy rule, Indicates uncertainty.

[0026] It should be noted that spacecraft attitude dynamics usually have the characteristics of non-linearity and parameter uncertainty. For example, the dynamic characteristics change due to the change of moment of inertia. The T-S fuzzy switching model provided in this embodiment splits the overall non-linear system into multiple fuzzy rule subsystems through local linearization, and combines the switching strategy for weighted combination, which can effectively cope with the dynamic changes of rigid spacecraft in different mission modes and improve the adaptability and robustness of the control strategy.

[0027] It is worth noting that the T-S fuzzy switching system introduces a switching mechanism of multiple subsystems on the basis of the traditional T-S fuzzy model, enabling the system to select different fuzzy models for control in different working modes. Compared with a single T-S fuzzy model, the T-S fuzzy switching model provided in this embodiment has stronger modeling ability and higher control flexibility, and has greater advantages in dealing with complex rigid spacecraft systems.

[0028] This embodiment uses a discrete-time system for modeling. The discrete-time system is more suitable for digital implementation and anti-interference than the continuous-time system in the prior art. It can provide high-precision and reconfigurable computing capabilities in rigid spacecraft attitude control, reduce the hardware complexity at the same time, and is also more convenient to combine with event-triggered and communication scheduling mechanisms to effectively reduce bandwidth and energy consumption.

[0029] Please refer to Figure 1 and Figure 2 , in a realizable embodiment, it further includes the step of designing a dynamic event-triggered mechanism. The dynamic event-triggered mechanism constructs an adaptive trigger condition by introducing the system state deviation and the dynamic variable to judge whether the system state can be transmitted.

[0030] Specifically, the dynamic event-triggered mechanism introduces an internal dynamic variable , and comprehensively considers the system state and historical information when judging whether to trigger signal transmission, so as to achieve a more flexible trigger strategy. Compared with the traditional static event-triggered mechanism, which only depends on the current state deviation for judgment, it can effectively reduce unnecessary communication and improve the robustness and resource utilization efficiency of the system.

[0031] It should be noted that in a rigid spacecraft, the angular velocity state variable and the control signal need to be transmitted through the on-board communication bus, but limited by bandwidth and energy supply, they cannot be continuously transmitted at a high frequency. Introducing the dynamic event-triggered mechanism enables the control system to communicate only when the "state changes significantly" or "the disturbance increases", thereby significantly reducing the communication frequency, extending the on-orbit operation life of the spacecraft, and improving the communication resource utilization efficiency of key nodes.

[0032] Specifically, let represent the current angular velocity state and the deviation from the nearest trigger state . Subsequently, the subsequent trigger time is determined according to the following event trigger conditions, expressed as: (8); where represents a given scalar, , represents the dynamic event trigger weight matrix, represents the system state deviation, represents the internal dynamic variable, represents the transpose of the system state deviation, represents the transpose of the system state, represents the current trigger time, represents the next trigger time, represents the infimum; where, when the inequality is satisfied, the smallest k value is the next trigger time , and then the trigger state is updated; where, the internal dynamic variable is obtained through dynamic calculation, expressed as: (9); where is a known constant, .

[0033] Specifically, the dynamic event trigger mechanism provided in this embodiment introduces the internal dynamic variable , and the trigger condition depends on the weighted state energy term and the deviation energy term for dynamic adjustment. At the same time, the internal dynamic variable is updated through formula (9). The constants and in formula (9) can be selected and adjusted accordingly according to specific scenario requirements, which is more flexible and adaptable.

[0034] It should be noted that the dynamic event trigger mechanism determines whether communication is required based on the current system state deviation and the internal dynamic variable; formula (8) controls the trigger threshold, and only when the state change is large enough or the trigger variable growth exceeds the threshold, the communication will be activated, thus significantly reducing the communication frequency; the internal variable shown in formula (9) reflects the "memory" of the system, which can avoid extreme situations of frequent triggering or long-term silence.

[0035] It should be noted that the dynamic event-triggering mechanism adopted in this embodiment constructs an adaptive triggering condition by introducing system state deviation and dynamic variables, which can effectively reduce unnecessary communication frequency and is particularly suitable for scenarios where spacecraft communication resources are limited. The dynamic event-triggering mechanism provided in this embodiment has a simple structure, is easy to implement in a discrete-time system, and is deeply coupled with the T-S fuzzy switching model, enabling the triggering behavior to adapt to the dynamic changes of different fuzzy modes. In contrast, the event-triggering strategies in the prior art are relatively basic and do not fully reflect the system structure coupling.

[0036] Please refer to Figure 1 and Figure 2 , in practical applications, including S3, 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 processes through a Markov chain.

[0037] In an actual rigid spacecraft attitude control system, there are often multiple gyroscopes, star sensors (sensor nodes), and multiple reaction wheels or control moment gyroscopes (actuator nodes). Due to the randomness of the communication scheduling mechanism and physical bandwidth limitations, sensor nodes and actuator nodes cannot simultaneously send / receive all state and control data completely. Most of the prior art only allows one node to access the communication channel at the same time. In fact, the network environment sometimes allows multiple nodes to access the communication channel, and multi-node transmission of information is usually more beneficial to improving the performance of the system. This embodiment adopts a multi-node communication protocol to simulate the behavior of multiple nodes "competing for communication resources" through a Markov chain, combined with sensor-controller (S / C) and controller-actuator (C / A) channel modeling, to improve the system's ability to depict the real space network environment.

[0038] Specifically, it is defined that the system has 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, and each node can measure the system state , and at most only nodes are allowed to communicate at each moment, where , and the S / C channel represents the sensor-controller channel; Define the binary variable function to represent whether the w-th sensor node accesses the network at time k, which 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); Among them, represents the status signal received at time k, represents the access status of the sensor node at time k, and , is the number of all sensor access statuses, is the set of all sensor access statuses; Suppose obeys a discrete-time Markov chain with the following conditional probability, denoted as: (12); Among them, represents the conditional probability of transitioning from access status s to status t, , and , the transition probability matrix is defined as , represents the dimension of this matrix, represents the probability distribution of the random variable taking values at time k + 1, P is the probability operator, represents the current time k, with the status being on the premise that the next time transitions to the status probability, represents the access status of the sensor node at time, respectively represent the current access status and the access status at the next time of the sensor node, indicates that s and t belong to the status set ; C / A channel communication modeling: The system has V control nodes, and at most nodes are allowed to receive control signals each time. Among them, , the C / A channel represents the controller-actuator channel; Define the situation where the actuator receives a signal from the th controller node at time k , ; The actual control input for the control signal transmitted to the actuator becomes: (13); Among them, , represents the actual control input for the control signal transmitted to the actuator, represents the status combination of the actuator receiving signals from different controller nodes at time k, represents the sliding mode control law, Denotes the access status of the controller node at time k, Denotes the set of all actuator access statuses; Follows a discrete-time Markov chain with the following conditional probability: (14); Where, Denotes that at the current time k, the state is On the premise that, the probability of transitioning to state at the next time k + 1, Denotes the access status of the controller node at time, Denote the current access status and the access status at the next time of the actuator node respectively, Denotes the conditional probability from state to state is, , and , the transition probability matrix is defined as , Denotes the dimension of the matrix.

[0039] It should be noted that the multi-node random communication protocol adopted in this embodiment is a random communication protocol used for the transmission of control information within a single spacecraft internal system, and a random communication protocol is added to both the sensor / controller node and the controller / actuator node, and a mapping technique is used to map the Markov chains of the two channels into a single Markov chain, which is more convenient and flexible to control.

[0040] The multi-node random communication protocol adopted in this embodiment is more complex than the prior art. The multi-node random communication protocol allows multiple nodes to transmit information simultaneously, which better meets the requirements of multi-node transmission in practical applications, can greatly improve communication efficiency, and save communication resources.

[0041] Please refer to Figure 1 and Figure 2 , in a realizable embodiment, S3 further includes introducing a mapping variable to map the two Markov chains of the S / C channel and the C / A channel into one Markov chain.

[0042] 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 respectively. If processed separately, it will lead to a highly complex system model. In the above modeling process, the access of the sensor and actuator nodes is controlled by two separate Markov chains. By introducing a mapping variable , the two scheduling chains are unified into a single-variable scheduling model, and this variable is related to the Markov chain through the mapping technique and A one-to-one correspondence is established, which not only reduces the dimension of the control law design but also facilitates the mathematical processing of subsequent Lyapunov stability analysis, especially suitable for spaceborne computing platforms with limited resources.

[0043] Specifically, a mapping variable is introduced, expressed as: (15); where represents a mapping function for combining the states of two Markov chains into a new variable, , , represents the set of all values; Determine a unique pair of random variables , expressed as: (16); where represents a mapping function for determining the value of the random variable based on the mapping variable , represents the modulo operation, represents a mapping function for determining the value of the random variable based on the mapping variable , the mapping variable and the random variable pair are in correspondence through equations (15) and (16); Obtain the transition probability of the mapping variable , expressed as: (17); where represents the mapping variable at time k + 1, represents the state of the variable at time k + 1, represents the state of the variable at time k, represents the access state of the sensor node at time, represents the conditional probability that the state of the S / C channel at time k is and transfers to at time k + 1, represents the conditional probability that the state of the C / A channel at time k is and transfers to at time k + 1, the random variable The transition probability matrix is .

[0044] It should be noted that the mapping method (15) was used above to map the random variable pair to a common scheduling signal . This variable can simultaneously reflect the network access conditions of the state signal and the control signal. Therefore, the controller gain only depends on the new random variable , which also avoids the complexity brought about by simultaneously depending on the random variable pair .

[0045] Please refer to Figure 1 and Figure 2 . In practical applications, including S4, a fuzzy sliding mode controller is designed based on the T-S fuzzy switching model.

[0046] 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. By constructing a sliding mode surface and a nonlinear switching control law, the system state can quickly converge and maintain operation near the sliding mode surface, effectively improving the control accuracy and system robustness, and is applicable to related spacecraft flight missions with extremely high stability requirements.

[0047] Specifically, design the sliding mode surface, expressed as: (18); where is the sliding mode surface. By adjusting this term, the system state approaches the sliding mode surface and maintains the sliding mode. is the sliding mode surface parameter, which is used to enhance the robustness of the system to disturbances and modeling uncertainties, and is defined as . c and l are selected according to the specific situation of the spacecraft. represents the transpose of the system input matrix; Design the sliding mode control law. This control law combines the advantages of linear feedback and nonlinear sliding mode control and is applicable to the aerospace environment with limited communication and strong disturbances; it is expressed as: (19); where represents the sliding mode control law at time k. represents the membership function related to . represents the norm of. represents the sign function. is the controller gain to be designed, which is used to stabilize the linear part of the system. is in formula (18) replaced by obtained

[0048] It should be noted that the sliding mode 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 dynamically adjusted according to the system mode. The sign function term is introduced into the control law to enhance the robustness to external disturbances, such as space environment interference. The fuzzy switching is used to improve the adaptability of the sliding mode control and suppress the chattering problem of the traditional sliding mode, while ensuring the spacecraft attitude control accuracy.

[0049] The greatest advantage of this embodiment in sliding mode control lies in the deep integration of sliding mode control with T-S fuzzy switching modeling, scheduling mechanism, and event-triggered control. It not only has the robustness of traditional sliding mode control but also enhances the adaptability to communication scheduling uncertainty and fuzzy structure changes. By designing an adjustable sliding mode surface and design and scheduling signals, and performing subsequent stability reachability analysis on the Lyapunov function related to the event-triggered internal dynamic variables, the system can maintain high precision, low communication frequency, and fast response in a disturbed environment, and is particularly suitable for scenarios with extremely high requirements for stability and energy consumption, such as rigid spacecraft attitude control.

[0050] Please refer to Figure 1 and Figure 2 . In a realizable embodiment, it includes S5, constructing a closed-loop control system. The closed-loop control system of the spacecraft is obtained from formulas (7), (13), and (18), and is expressed as: (20); where, represents the relevant term of the system state matrix, represents the time-varying term related to the system input, and are respectively expressed as: ; ; where, is a diagonal matrix that determines the state combination of the actuator receiving the controller signal in the C / A channel according to the mapping variable , represents a diagonal matrix that determines the state information of the selected sensor node in the S / C channel according to , represents the controller gain.

[0051] Please refer to Figure 2 . In a realizable embodiment, S5 also includes the step of verifying the global stability of the closed-loop control system.

[0052] To ensure the stable operation of the spacecraft during long-term orbital missions, it is necessary to verify the mean-square exponential stability of the control system under different scheduling states through mathematical tools. Based on the Lyapunov function method that depends on the scheduling signal, the system can be ensured to converge stably in the desired sense.

[0053] Specifically, analyze the stability of the closed-loop control system and select a Lyapunov function that depends on the scheduling signal as: (21); where, is a symmetric positive definite matrix related to the scheduling signal , and this function is used to evaluate the energy level of the current state of the system. Its expected decrease can be used to judge the stability of the system.

[0054] Calculate the expected difference of the Lyapunov function, expressed as: (22); where, represents a known constant, reflecting the decay rate, and satisfies , represents the scheduling signal, represents the mathematical expectation operator, represents the mathematical expectation of the Lyapunov function at the next moment under the premise of knowing the current system state and the scheduling signal ; Verify and obtain, expressed as: (28); where, represents the mathematical expectation of; Obtained through the iterative equation, expressed as: (29); where, represents the exponential decay factor related to the current switching path, represents the value of the Lyapunov function at the switching moment and the corresponding switching signal , represents the switching moment, represents the switching signal at the switching moment, represents a positive constant, represents the average dwell time; represents the number of times the switching signal σ switches within the time interval ; Obtained based on formula (21), expressed as: (30); (31); (23); Where a and b are respectively the minimum and maximum amplification factors between the Lyapunov function and the system state norm, used to establish the upper and lower bound relationship between them to assist in stability analysis; If , , then the closed-loop control system is exponentially stable in mean square.

[0055] Please refer to Figure 2 , in an implementable embodiment, it includes the steps of sliding mode reachability analysis, proving 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 sense of mean square. The sliding mode reachability analysis further shows that the state of the rigid spacecraft system can approach the target control surface within a finite time, with good convergence and engineering feasibility.

[0056] Specifically, the sliding mode domain , expressed as: (24); Where is the norm of , represents the upper bound term caused by disturbances and communication scheduling, defined as , the sliding mode domain represents the small area near the sliding mode surface into which the state will be driven in the sense of mean square, represents the minimum eigenvalue of the corresponding positive definite matrix , used to define the lower limit of the growth of the Lyapunov function, represents the intermediate variable in the LMI condition related to the sliding mode surface, used to control the energy of the disturbance term, m is the total number of actuators, represents the intermediate variable related to the control signal scheduling matrix , represents the diagonal matrix that determines the state information of the selected sensor nodes in the S / C channel according to , represents the square of the norm, represents the square of the norm of the system state ; From formula (18) and formula (20), we can obtain: (25); Select the Lyapunov function as: (26); where, represents a positive definite matrix related to ; Calculate , and it can be verified that: (27); where, represents the mathematical expectation of the Lyapunov function at the next moment under the premise of , represents of the expectation Therefore, the Lyapunov function is monotonically decreasing outside the sliding mode domain, which means that the state trajectory of the system will enter and converge to the sliding mode domain within a finite time .

[0057] Please refer to Figure 3 , Figure 4 , Figure 5 , Figure 6 and Figure 7 , in practical applications, the rigid spacecraft system parameters are given to further illustrate this embodiment.

[0058] Specifically, select the inertia matrix of the spacecraft as: ; Select the initial angular velocity as .

[0059] Specifically, select a T-S fuzzy switching system with two switching subsystems, and each subsystem has two fuzzy rules. The system parameters are as follows: Subsystem 1 (low-speed flight mode): ; ; ; ; , , , , ; Subsystem 2 (high-speed flight mode): ; ; ; ; , , , , ; In Figure 4 the abscissa represents the simulation time in seconds, and the ordinate represents the mode, which is the subsystem number. It can be seen from the figure that the system dynamically switches between two subsystems, and the switching frequency is restricted by the average dwell time constraint. There is no frequent high-frequency jump in the whole switching process. Figure 4 This indicates that the controller structure has good switching logic stability under the average dwell time control, effectively avoiding unstable behaviors caused by too fast switching.

[0060] The membership function is selected as: ; ; where are the endpoints of the fuzzy intervals of two fuzzy rules respectively, defining the range of the fuzzy intervals and used to calculate the activation strength of each fuzzy rule; The external disturbance is: ; In addition, the parameters in the dynamic event triggering are selected as , and the parameters in the stability analysis are selected as .

[0061] Specifically, assume that only two sensors transmit data at each moment in the S / C channel. Therefore, there are three transmission situations as: , and its transition probability matrix is: ; Assume that in the C / A channel, only two sensors are allowed to transmit data at each moment: , and its transition probability matrix is: ; The relationship between the random variable pair and the new random variable is shown in Table 1 below: Table 1: The relationship between the random variable pair and the new random variable ; Therefore, the new random variable The transition probability matrix can be calculated as follows: ; Under the above simulation conditions, the system is simulated to verify the attitude control ability of the system.

[0062] Figure 3 It shows the communication situation of the S / C channel. The abscissa represents the simulation time in seconds; the ordinate is the scheduling signal of the S / C channel, where represents the system state, that is, the spacecraft angular velocity and are transmitted at the current moment, while and respectively represent the transmission of the system states { , } and the states { , }. It shows that the selection of the communication channel of the S / C channel is randomly selected among multiple nodes.

[0063] Figure 4 It shows the communication situation of the C / A channel. The abscissa represents the simulation time in seconds; the ordinate is the scheduling signal of the C / A channel, , and respectively represent the transmission of the control signals { }, { } and { } at the current moment, showing that the communication channel selection mode of the system is randomly selected.

[0064] Figure 5 It shows the subsystem switching frequency based on the average dwell time. The abscissa represents the simulation time in seconds; the ordinate is the mode, that is, the subsystem number. It can be seen from the figure that the system dynamically switches between two subsystems, and the switching frequency is restricted by the average dwell time. There is no frequent high-frequency jump in the whole switching process. Figure 4 It shows that the controller structure has good switching logic stability under the average dwell time control, effectively avoiding unstable behaviors caused by too fast switching.

[0065] Figure 6 It reflects the angular velocity response of the three rotational axes of the spacecraft. The abscissa represents the simulation time in seconds; the ordinate is the angular velocity in rad / s. The dashed curve represents the angular velocity of the spacecraft on the x-axis in the body coordinate system, the dash-dotted curve represents the angular velocity on the y-axis, and the solid curve represents the angular velocity on the z-axis. Figure 6It shows that the controller has good disturbance rejection ability and attitude stabilization performance, and can achieve efficient and fast attitude stabilization control under the conditions of uncertain disturbances and incomplete communication scheduling.

[0066] Figure 7 Shows the control input of the spacecraft. The abscissa represents the simulation time in seconds; the ordinate is the control input. The dashed curve represents the control input , the dash-dotted curve represents the control input , and the solid 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 require long-term and large-amplitude control, thus achieving low-energy consumption control, which is suitable for the control of energy-constrained rigid spacecraft. Figure 7 Indicates that the sliding mode control of this application has a faster response speed and stronger anti-disturbance ability.

[0067] Figure 8 Reflects the change of the sliding mode variable of the spacecraft system under the action of the sliding mode controller. The abscissa represents the simulation time in seconds; the ordinate is the sliding mode variable. The dashed curve represents the sliding mode variable , the dash-dotted curve represents the sliding mode variable , and the solid curve represents the sliding mode variable . The rapid convergence of the sliding mode variable represents that the system state can quickly approach the sliding mode surface, and the system enters the sliding mode dynamic process. Figure 8 Illustrates that this application has good anti-parameter disturbance ability and system robustness, and the control strategy is reliable.

[0068] Based on the same inventive concept, this application also provides an attitude control system for a rigid spacecraft, and the attitude control system includes: The first construction unit constructs the attitude dynamics model and kinematic equation of the rigid spacecraft; The second construction unit constructs a T-S fuzzy switching model of the rigid spacecraft based on the dynamics model: The T-S 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 weighted combination is carried out in combination with the switching strategy; Introduce uncertainty into each fuzzy rule subsystem , uncertainty Is associated with the moment of inertia of the spacecraft and external disturbances, and is used to dynamically correct the control input; The third construction unit introduces a multi-node random communication protocol for the T-S fuzzy switching model: For the T-S 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 processes through a Markov chain; The fourth construction unit designs a fuzzy sliding mode controller based on the T-S fuzzy switching model and the multi-node random communication protocol: Design a sliding mode surface by combining the dynamic characteristics of the T-S fuzzy switching model and the multi-node random communication protocol The sliding mode surface parameters Are dynamically generated by T-S fuzzy rules, enabling the sliding mode control parameters to be dynamically adjusted with the system mode; The closed-loop control unit forms a closed-loop control system for attitude control: Integrate the T-S fuzzy switching model, the multi-node random communication protocol, and the fuzzy sliding mode controller constructed by the above construction units to form a closed-loop control system, realize the attitude control of the spacecraft, and verify the global stability of the closed-loop control system.

[0069] Based on the same inventive concept, the present application also provides an attitude control device for a rigid spacecraft, including a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, the method described above is implemented.

[0070] 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.

[0071] The program product for implementing the above method in the present application can adopt a portable compact disc read-only memory and includes program code, and can run on a terminal device, such as a personal computer. However, the program product of the present application is not limited to this. In the present application, the readable storage medium can be any tangible medium containing or storing a program, and this program can be used by or in combination with an instruction execution system, device, or device.

[0072] It should be noted that the computer-readable storage medium can include a data signal in a baseband or as part of a carrier wave, which carries the readable program code. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than the readable storage medium, and this readable medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted by any appropriate medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0073] The above are only the preferred embodiments of the present application, and do not impose any formal restrictions on the present application. Although the present application has been disclosed above with the preferred embodiments, it is not intended to limit the present application. Any person skilled in the art of the present application can make some changes or modifications to equivalent embodiments of equivalent changes by using the technical content prompted above within 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, as long as the content does not depart from the technical solution of the present application, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application still belong to the scope of the present application solution.

Claims

1. A method for attitude control of a rigid spacecraft, characterized in that, It includes the following steps: S1. Construct the attitude dynamics model and kinematic equations of a rigid spacecraft; S2. Based on S1, construct the T-S fuzzy switching model of the rigid spacecraft: The T-S fuzzy switching model selects a discrete-time system for modeling. Through local linearization, the overall nonlinear system is split into multiple fuzzy rule subsystems and combined with a switching strategy for weighted combination; Introduce uncertainty into each of the fuzzy rule subsystems , the uncertainty is associated with the spacecraft's moment of inertia and external disturbances and is used to dynamically correct the control input; S3. Based on S2, introduce a multi-node random communication protocol: For the T-S 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 processes through a Markov chain; S4. Based on S2 and S3, design a fuzzy sliding mode controller: Design a sliding mode function by combining the dynamic characteristics of the T-S fuzzy switching model and the multi-node random communication protocol , the sliding mode matrix is dynamically generated by T-S fuzzy rules, so that the sliding mode control parameters are dynamically adjusted with the system mode; S5. Form a closed-loop control system for attitude control: Integrate the T-S fuzzy switching model, multi-node random communication protocol, and fuzzy sliding mode controller designed in S2-S4 to form a closed-loop control system, realize spacecraft attitude control, and verify the global stability of the closed-loop control system.

2. The method according to claim 1, wherein The T-S fuzzy switching model constructed in S2 includes: The p-th fuzzy rule is: If is and … and is , Then (6); Among them, is a premise variable, … is a fuzzy set, represents a switching signal, The switching time interval between is the moment when the m-th switching occurs, is the moment when the next switching occurs immediately, and represent the state matrix and input matrix of the system respectively, represents uncertainty, which is a known constant matrix, is the state variable of the system at moment, represents the state variable of the system at moment, represents external disturbance, represents the control output. Let , and represent the known constant matrix as , , and represent uncertainty as ; Through the T-S fuzzy inference method, the T-S fuzzy switching model is obtained, expressed as: （7）； Among them, and represent the state matrix and the input matrix of the system respectively, is the number of fuzzy rules under the T-S fuzzy switching system, is the normalized membership function, represents the uncertainty, represents the external disturbance, 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, It also includes the step of designing a dynamic event triggering mechanism, which constructs an adaptive triggering condition by introducing system state deviation and internal dynamic variables to determine whether the system state can be transmitted; including an event triggering condition, expressed as: （8）； Among them, represents the current trigger time, represents the next trigger time, represents a given scalar, , represents the dynamic event-triggering weight matrix, represents the system state deviation at time k, represents the internal dynamic variable 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 value of k is the next trigger time , and then the trigger state is updated; Among them, the internal dynamic variable is obtained through dynamic calculation and is expressed as: （9）； Among them, is a known constant, represents the internal dynamic variable at time , represents the initial value of the internal dynamic variable, represents a set known non - negative constant.

4. The method according to claim 2, wherein 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: Define that the system has W sensor nodes and V control nodes connected through a network. The S / C and C / A dual-channel communication modeling includes: S / C channel communication modeling: The system has a total of W sensor nodes, and each node can measure the system state , and at most only nodes are allowed to communicate at each moment. Among them, , the S / C channel represents the sensor-controller channel; Define the binary variable function Indicates whether the w th sensor node accesses the network at time k, expressed as: （10）； Define the state selection matrix as , then the state signal received by the controller at time k is expressed as: （11）； Among them, represents the state signal received by the controller at time k, represents the access state of the sensor node at time k, and , is the number of all sensor access states, is the set of all sensor access states; Hypothesis A discrete-time Markov chain that follows the following conditional probability is denoted as: （12）； Among them, represents the conditional probability of transferring from state s to state t when accessed, , and , the transition probability matrix is defined as , represents the dimension of this matrix, represents the probability distribution of the random variable taking values at time k + 1, P is the probability operator, represents the current time k, with the state being On the premise that transfers to state at the next time represents the access state of the sensor node at time, respectively represent the current access state and the access state at the next time of the sensor node, represents that s and t belong to the state set ; C / A channel communication modeling: The system has a total of control nodes, and at most nodes are allowed to receive control signals each time. Among them, , the C / A channel represents the controller-actuator channel; Define the situation where the actuator receives information from the th controller node at time k , ; The actual control input for the control signal transmitted to the actuator becomes: （13）； Among them, represents the access status of the controller node at time k, , is the number of all actuator access statuses, represents the set of all actuator access statuses, represents the actual control input for transmitting the control signal 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; Follow the discrete-time Markov chain with the following conditional probabilities: （14）； Among them, represents that at the current moment k, given that the state is , the probability of transitioning to state at the next moment k + 1, represents the access state of the controller node at moment, respectively represent the current access state and the access state at the next moment of the actuator node, represents the conditional probability from state to state , , and , the transition probability matrix is defined as , represents the dimension of the transition probability matrix.

5. The method according to claim 4, wherein S3 also includes introducing a mapping variable Mapping two Markov chains of the S / C channel and the C / A channel into one Markov chain includes: Introduce a mapping variable , expressed as: （15）； Among them, represents a mapping function, which is used to combine the states of two Markov chains into a new variable, , , represents all value sets; Determine a unique pair of random variables , denoted as: （16）； Among them, represents a mapping function for determining the value of a random variable according to a mapping variable , wherein represents a floor function represents a mapping function for determining the value of a random variable according to a mapping variable , wherein the mapping variable and the random variable pair are in a corresponding relationship through formulas (15) and (16); Obtain the mapping variable The transition probability, expressed as: （17）； Among them, represents the mapping variable at time k + 1, represents the variable at the state at time k + 1, represents the variable at the state at time k, represents the conditional probability of transferring to at time k + 1 when the state at time k in the S / C channel is , represents the conditional probability of transferring to at time k + 1 when the state at time k in the C / A channel is , and the transition probability matrix of the random variable is , represents the dimension of this matrix.

6. The method according to claim 5, wherein In S4, based on the T-S fuzzy switching model, design a fuzzy sliding mode controller, including: Design the sliding mode function, expressed as: （18）； Among them, is the sliding mode function. By designing the control law to adjust the system input, the system state approaches the sliding mode surface and maintains the sliding mode, realizing the robustness requirements 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 , where c and l are scalar parameters. The selection of c and l should ensure that is nonsingular under any switching signal. represents the transpose of the system input matrix; Design the sliding mode control law, expressed as: （19）； Among them, represents the sliding mode control law at time k, represents the membership function related to represents the norm of represents the sign function, is the controller gain to be designed, which is used to stabilize the linear part of the system, is in formula (18) replaced by obtained.

7. The method according to claim 6, wherein In S5, construct a closed-loop control system, including: The closed-loop control system of the spacecraft is obtained from formulas (7), (13), and (18), expressed as: （20）； Among them, represents the relevant term of the system state matrix, represents the time-varying term related to the system input, Among them, and are respectively expressed as: ； ； Among them, is a diagonal matrix that determines the combined state of the controller signals received by the C / A channel actuator according to the mapping variable , represents a diagonal matrix that determines the state information of the selected sensor nodes in the S / C channel according to .

8. Attitude control system for a rigid spacecraft, characterized in that, The attitude control system includes: The first construction unit constructs the attitude dynamics model and kinematic equations of a rigid spacecraft; The second construction unit constructs the T-S fuzzy switching model of the rigid spacecraft based on the dynamics model: The T-S fuzzy switching model selects a discrete-time system for modeling. Through local linearization, the overall nonlinear system is split into multiple fuzzy rule subsystems and combined with a switching strategy for weighted combination; Introduce uncertainty into each of the fuzzy rule subsystems , the uncertainty is associated with the spacecraft moment of inertia and external disturbances and is used to dynamically correct the control input; The third construction unit introduces a multi-node random communication protocol for the T-S fuzzy switching model: For the T-S 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 processes through a Markov chain; The fourth construction unit designs a fuzzy sliding mode controller based on the T-S fuzzy switching model and the multi-node random communication protocol: Design a sliding mode function by combining the dynamic characteristics of the T-S fuzzy switching model and the multi-node random communication protocol The sliding mode matrix is dynamically generated by T-S fuzzy rules, enabling the sliding mode control parameters to be dynamically adjusted with the system mode; The closed-loop control unit forms a closed-loop control system for attitude control: Integrate the T-S fuzzy switching model, multi-node random communication protocol, and fuzzy sliding mode controller constructed by the above building blocks to form a closed-loop control system, realize spacecraft attitude control, and verify the global stability of the closed-loop control system.

9. Attitude control device for a rigid spacecraft, characterized in that, It includes a processor and a memory, and a computer program is stored on the memory. When the computer program is executed by the processor, the method described in any one of claims 1-7 is implemented.

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

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