Networked multi-support system pose cooperative control method with extended function

Abstract

The invention provides a networked multi-support system pose cooperative control method with an extended function, and relates to the technical field of intelligent networked control. The method comprises the following steps: firstly, establishing a linear generalized multi-agent model for describing a multi-support system, determining local information which can be acquired by each agent, and introducing a local information interaction mechanism between adjacent units; carrying out finite pole assignment and eliminating a pulse mode by utilizing measurable output information of the system, and calculating key parameters of a control protocol according to the finite pole assignment and the pulse mode; finally, an extensible cooperative control protocol independent of global communication network information is constructed, and pose synchronization of multiple support systems can be realized in a fixed communication network only needing spanning tree topology. According to the method, the problem of scale vulnerability caused by dependence on global network information in a traditional cooperative control method is solved, and the method is suitable for engineering scenes such as cooperative regulation and control of quakeproof building groups, pose synchronization of water floating platforms and cooperative positioning of large-scale antenna arrays.

Description

Technical Field

[0001] This invention relates to the field of intelligent networked control technology, and in particular to a method for pose coordination control of a networked multi-support system with extended functionality. Background Technology

[0002] As large-scale facilities (such as earthquake-resistant buildings, floating platforms, and large-aperture astronomical telescopes) develop towards ultra-large scale and high precision, unprecedented demands are placed on the collaborative control performance of their support systems. As early as 1994, Shugen Ma et al. proposed a new type of group system—the multi-support system. As an effective solution, the multi-support system, through the collaborative work of multiple distributed support units, jointly undertakes and regulates the position and attitude of large objects, and its application potential in the aforementioned fields is enormous. Specifically, for an earthquake-resistant building, when an earthquake occurs, each device in the multi-support system attempts to maintain the building's horizontal position. After the earthquake, the multi-support system should return to a static and horizontal state; for floating factories, each device must maintain the horizontal position and synchronized height oscillations of its individual components; for large-aperture astronomical telescopes, even if each device is in motion, a certain spatial geometric relationship should be maintained, meaning that each individual should achieve uniformity in velocity, and a certain height difference should be maintained between any two individuals.

[0003] Traditional control methods for multi-support systems typically rely on cooperative control theory for multi-agent systems. However, most existing synchronous control protocols for multi-agent systems suffer from a fundamental flaw—scale vulnerability. The control law design of these protocols heavily depends on global information across the entire communication network, such as the total number of agents, the number of neighboring nodes, or the characteristic spectrum of the Laplace matrix. Once the network size changes dynamically (e.g., adding or removing support units) or the communication topology changes (e.g., link interruption or reconstruction), the original control protocol may fail to maintain stability, leading to performance degradation or even loss of control of the entire system. This is an unacceptable risk for large-scale engineering systems that require long-term stable operation and are likely to be maintained and expanded.

[0004] The concept of scalable control has emerged, with its core objective being to design a control protocol that does not rely on global network information. An ideal scalable control protocol should possess two characteristics: (1) the protocol design is based solely on the agent's own dynamic model; and (2) the control protocol is applicable to any fixed communication graph containing a spanning tree, without needing to consider information about the graph in the control protocol. Currently, research on scalable control mainly focuses on conventional linear multi-agent systems.

[0005] However, the dynamic models of multi-agent systems are more complex due to their unique mechanical structures, and they typically require description using generalized systems, which differ from conventional linear systems. Generalized systems not only involve stability issues but also require handling the stringent mathematical conditions of regularity and impulsivity-free behavior. Directly applying scalable control methods suitable for conventional linear systems to generalized multi-agent systems is ineffective.

[0006] Therefore, within the framework of generalized multi-agent systems, designing a truly scalable cooperative control protocol that can overcome scale vulnerability while simultaneously ensuring the system's regularity, impulsiveness, and stability is crucial for the state synchronization of multi-support systems. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a networked multi-support system pose collaborative control method with extended functionality;

[0008] The technical solution of the present invention is as follows:

[0009] On one hand, the present invention provides a networked multi-support system pose cooperative control method with extended functionality, comprising the following steps:

[0010] Step 1: Obtain the multi-agent system model based on the mechanical structure design of the multi-support system, and construct a linear generalized multi-agent system model;

[0011] Specifically, in a generalized multi-agent system, the dynamic model of agent i is as follows:

[0012] ;

[0013] in, , It is the state variable of agent i. It is the measurement output variable of agent i. It is the control input variable of agent i. They are respectively The coefficient matrix; and express The set of 3D real vector spaces, express A set of 3D real matrices.

[0014] Step 2: Determine the communication topology of the generalized multi-agent system;

[0015] Specifically: Order Let N be the set of graphs containing N nodes and spanning trees. Then the topology of the communication network is: ;

[0016] Step 3: Determine the information accessible to the agent and introduce local information exchange;

[0017] Communication networks provide information to agent i The details are as follows:

[0018] ;

[0019] Topology diagram of communication network In this context, nodes correspond to agents in the network, and the weights of the edges are... If i=j, then a ij =0, if i≠j, then a ij >0, topology graph Laplace matrix Represented as:

[0020] ;

[0021] but Rewrite it in the following form:

[0022] ;

[0023] Introducing local information exchange between agents, meaning each agent can access local exchange information. The details are as follows:

[0024] ;

[0025] in, These are variables generated within agent i;

[0026] Step 4: Utilize the system's measurable output information By performing finite pole placement and eliminating impulse modes, the key parameters of the scalable generalized cooperative control protocol, namely the nonsingular matrix, are calculated. and ;

[0027] In the nonsingular matrix of state transformation and Under the influence of the matrix pair The equivalent standard decomposition form is transformed as follows:

[0028] ;

[0029] in, Through non-singular matrices and The matrix obtained by the transformation. It is the gain matrix of the output feedback, that is, using measurable output information. Provide output feedback. yes A 3D identity matrix. A matrix. Eigenvalues ​​and matrix pairs Their finite eigenvalues ​​are exactly the same. It is an identity matrix of appropriate dimension.

[0030] Step 5: Design a scalable generalized cooperative control protocol to enable pose synchronization of multiple support systems;

[0031] The scalable generalized cooperative control protocol is as follows:

[0032] ;

[0033] in, It is the relative state information of the generalized multi-agent system. The estimated value, These are variables within the scalable generalized cooperative control protocol, used to achieve state synchronization of this generalized multi-agent system. It is the control input variable of agent i in a generalized multi-agent system. It is the gain matrix of this scalable generalized cooperative control protocol, used for communication between agents. Selected as The superscript · on a parameter indicates its derivative; therefore, each agent obtains local exchange information. The details are as follows:

[0034] ;

[0035] Gain matrix Make the matrix pair No pulse and satisfy , Representing matrix pairs generalized finite eigenvalues, Denotes the closed left half of the complex plane, and the gain matrix. and Designed to make matrix pairs and Regular, pulse-free, and stable.

[0036] On the other hand, the present invention also provides a computer-readable storage medium storing executable instructions that, when executed, cause a processor to perform the aforementioned networked multi-support system pose cooperative control method with extended functionality.

[0037] Thirdly, this application proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the aforementioned networked multi-support system pose collaborative control method with extended functions.

[0038] The beneficial effects of adopting the above technical solution are as follows:

[0039] This invention provides a networked multi-support system pose cooperative control method with scalable functionality. The invention designs an scalable generalized cooperative control protocol for generalized multi-agent systems, thereby achieving scalable state synchronization of the multi-support system. Based on the agent's own information and the relative information between agents, each support module of the multi-support system is controlled in real time to synchronize the horizontal position and height of each module. Furthermore, as long as the communication topology has a spanning tree, the proposed generalized cooperative control protocol can achieve state synchronization of the multi-support system for any number of agents in a communication network. Attached Figure Description

[0040] Figure 1 The flowchart below shows the pose coordination control method for a networked multi-support system with extended functionality according to the present invention.

[0041] Figure 2 This is a multi-support system model of the floating factory building in an embodiment of the present invention;

[0042] Figure 3 This is the scalable generalized state synchronization control protocol architecture of the present invention;

[0043] Figure 4 This is a 6-node communication topology diagram of the multi-support system in an embodiment of the present invention;

[0044] Figure 5 This is a 20-node communication topology diagram of the multi-support system in an embodiment of the present invention;

[0045] Figure 6 This is a state response curve diagram of a 6-node multi-support system in an embodiment of the present invention;

[0046] Figure (a) shows the states of the six agents. The response curves are shown in Figure (b), which shows the states of the six agents. The response curves are shown in Figure (c), which shows the states of the six agents. The response curves are shown in Figure (d), which shows the states of the six agents. The response curve;

[0047] Figure 7 This is a synchronization error response curve of a 6-node multi-support system in an embodiment of the present invention;

[0048] Figure 8 This is a state response curve diagram of a 20-node multi-support system in an embodiment of the present invention;

[0049] Figure (a) shows the states of the 20 agents. The response curves are shown in Figure (b), which shows the states of the 20 agents. The response curves are shown in Figure (c), which shows the states of the 20 agents. The response curves are shown in Figure (d), which shows the states of the 20 agents. The response curve;

[0050] Figure 9 This is a synchronization error response curve of a 20-node multi-support system in an embodiment of the present invention. Detailed Implementation

[0051] The specific implementation methods of this application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0052] Example 1:

[0053] On the one hand, this invention provides a networked multi-support system pose cooperative control method with extended functionality, such as... Figure 1 As shown, it includes the following steps:

[0054] Step 1: Obtain the multi-agent system model based on the mechanical structure design of the multi-support system, and construct a linear generalized multi-agent system model;

[0055] In this embodiment, each main body in a multi-support system is supported by two columns, referred to as Unit I and Unit II, respectively. Figure 2 As shown, where It's about quality. It is the damping coefficient. It is the stiffness coefficient. It's about altitude.

[0056] For each agent's unit I, we have:

[0057]

[0058] in, It is an intelligent agent The input force. For element II, its height is constrained by element I. Let , , and The height and velocity of unit I and unit II are represented respectively.

[0059] The multi-support system is modeled as a linear, generalized multi-agent system, where the agents are... The specific dynamic model is as follows:

[0060] ;

[0061] in, It is the first The state variables of each agent are specifically:

[0062]

[0063] It is the first The control input variables of the intelligent agent, It is the first The measurement output variables of each agent The coefficient matrices for each of the above variables are shown below:

[0064]

[0065] Consider a general linear and introspective generalized multi-agent system, the first... The specific agent models are as follows:

[0066] ;

[0067] in, It is the first The state variables of an agent, It is the first The measurement output variables of each agent It is the first The control input variables of the intelligent agent, These are the coefficient matrices for the variables mentioned above.

[0068] Step 2: Determine the communication topology of the generalized multi-agent system;

[0069] Specifically: Order Let N be the set of graphs containing N nodes and spanning trees. Then the topology of the communication network is: ;

[0070] Step 3: Determine the information accessible to the agent and introduce local information exchange;

[0071] A multi-agent system is introspective if, in addition to having relative information from the network, the agent also performs an absolute measurement of its own dynamics; specifically, the communication network provides information to agent i. The details are as follows:

[0072] ;

[0073] This information is a linear combination of its own output relative to the outputs of other agents; the topology of the communication network. In this context, nodes correspond to agents in the network, and the weights of the edges are... If i=j, then a ij =0, if i≠j, then a ij >0, topology graph Laplace matrix Represented as:

[0074] ;

[0075] but Rewrite it in the following form:

[0076] ;

[0077] Introducing local information exchange between agents, meaning each agent can access local information. The details are as follows:

[0078] ;

[0079] in, These are variables generated within agent i;

[0080] Step 4: Utilize the system's measurable output information By performing finite pole placement and eliminating impulse modes, the key parameters of the scalable generalized cooperative control protocol, namely the nonsingular matrix, are calculated. and ;

[0081] intelligent agent model It is sedative, detectable, has controllable pulses, and has observable pulses.

[0082] Since the intelligent agent possesses absolute output measurement information, then there exists As output feedback, thus generating matrix pairs Matrix pairs Through a non-singular state transformation, it is transformed into an equivalent standard decomposition form, as shown in the following expression:

[0083] ;

[0084] in, Through non-singular matrices and The matrix obtained by the transformation. It is the gain matrix of the output feedback, that is, using measurable output information. Provide output feedback. yes A 3D identity matrix. A matrix. Eigenvalues ​​and matrix pairs Their finite eigenvalues ​​are exactly the same. It is an identity matrix of appropriate dimension. This yields the parameters used for control protocol design. and At this point, the block matrix... The eigenvalues ​​are matrix pairs The generalized finite eigenvalues ​​are located in the closed left complex plane.

[0085] Step 5: Design a scalable generalized cooperative control protocol to enable pose synchronization of multiple support systems;

[0086] The scalable generalized cooperative control protocol is as follows:

[0087] ;

[0088] in, It is the relative state information of the generalized multi-agent system. The estimated value, These are variables within the scalable generalized cooperative control protocol, used to achieve state synchronization of this generalized multi-agent system. It is the control input variable of agent i in a generalized multi-agent system. It is the gain matrix of this scalable generalized cooperative control protocol, used for communication between agents. Selected as The superscript · on a parameter indicates its derivative; therefore, each agent obtains local information. The details are as follows:

[0089] ;

[0090] Gain matrix Make the matrix pair No pulse and satisfy , Representing matrix pairs generalized finite eigenvalues, Denotes the closed left half of the complex plane, and the gain matrix. and Designed to make matrix pairs and Regular, pulse-free, and stable.

[0091] From the designed control protocol Figure 3 middle:

[0092]

[0093]

[0094] Based on the non-singular transformation matrix obtained in step 4, perform the following coordinate transformation:

[0095] ;

[0096] Further order:

[0097] ;

[0098] as well as ;

[0099] The equivalent synchronization error system is obtained as follows:

[0100] ;

[0101] in, The synchronization error system is found to be regular, pulse-free, and stable.

[0102] The following implementation case simulation illustrates the effectiveness of the implementation plan.

[0103] This invention utilizes the MATLAB simulation platform for testing, obtaining position and velocity variation curves in a multi-support system, thereby verifying the proposed networked multi-support system pose cooperative control method with extended functionality. Specific embodiments are given below:

[0104] Considering that all individual flotation devices in a floating plant need to remain horizontal, each individual flotation device is treated as an intelligent agent, allowing... Let represent the absolute height of each agent. It can be observed that if all agents reach a consensus on absolute height and each device reaches a horizontal position, then the entire floating plant will remain synchronized; that is, there exists a function... Make ,Right now ,and Since the water's disturbance to the device is constant, the synchronization function... It is time-varying; in this embodiment, the synchronization function is set to continuous oscillation.

[0105] This embodiment employs a multi-support system consisting of 6 and 20 individual water-floating devices, respectively, with the network topology as follows: Figure 4 and Figure 5 As shown, circles represent agents, connections between agents represent communication relationships, and each edge has a weight of 1. It can be seen that both the 6-node and 20-node topologies are weighted graphs with spanning trees.

[0106] Based on the communication topology, the specific expression for the Laplace matrix of the 6-node topology graph can be calculated as follows:

[0107]

[0108] The specific expression for the Laplace matrix of a 20-node topology graph is:

[0109]

[0110] The parameters for each individual water flotation device were set to... , , The specific expression for the coefficient matrix of the constructed generalized multi-agent system is as follows:

[0111]

[0112] Select the output feedback gain matrix The specific expression is:

[0113] ;

[0114] The generalized finite poles of each agent are assigned to the imaginary axis of the complex plane, specifically as follows:

[0115] Pair the matrices The equivalent standard decomposition form is shown in the following expression:

[0116]

[0117] The state transformation matrix is ​​then obtained as follows:

[0118]

[0119] Selecting the gain matrix and for:

[0120]

[0121] In the simulation, due to the large number of nodes, the initial states of the system are not listed individually. The simulation results are as follows: Figures 6-9 As shown. By Figure 6 (a), (b), (c), (d) and Figure 7 It can be seen that in the 6-node communication topology, the four states of the six agents are synchronized, and the synchronization error is... Ultimately, the velocity tends to 0. This means that the position and velocity of each of the six individual buoyancy devices have been synchronized. Using the same control protocol parameters described above... ,Depend on Figure 8(a), (b), (c), (d) and Figure 9 It can be seen that, in a 20-node communication topology, the positions and velocities of the 20 individual water-floating devices are also synchronized. Therefore, the control protocol designed in this invention is applicable to any communication topology containing a spanning tree and has no limitation on the number of agents.

[0122] Example 2:

[0123] This embodiment proposes a computer-readable storage medium that stores executable instructions. When these instructions are executed, if they are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium.

[0124] The computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the networked multi-support system pose cooperative control method with extended functions described in the various embodiments of this application.

[0125] The aforementioned storage media include: flash memory, hard disk, multimedia card, card-type memory (e.g., SD (Secure Digital Memory Card) or DX (Memory Data Register, MDR) memory, random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, server, APP (Application) application store, and other media capable of storing program verification codes, on which computer programs are stored. When the computer program is executed by a processor, it can implement the various steps of the above-mentioned networked multi-support system pose collaborative control method with extended functions.

[0126] Example 3:

[0127] This embodiment proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the aforementioned networked multi-support system pose collaborative control method with extended functions.

[0128] Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or part of the technical solution, can be embodied in the form of a computer program product.

[0129] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0130] The scope of protection of this application is not limited to the embodiments described above. Obviously, those skilled in the art can make various modifications and variations to this disclosure without departing from the scope and spirit of this disclosure. If such modifications and variations fall within the scope of the methods disclosed herein and their equivalents, then the intent of this disclosure also includes such modifications and variations.

Claims

1. A networked multi-support system pose collaborative control method with extended functionality, characterized in that, Includes the following steps: Step 1: Obtain the multi-agent system model based on the mechanical structure design of the multi-support system, and construct a linear generalized multi-agent system model; Step 2: Determine the communication topology of the generalized multi-agent system; Step 3: Determine the information accessible to the agent and introduce local information exchange; Step 4: Utilize the system's measurable output information By performing finite pole placement and eliminating impulse modes, the key parameters of the scalable generalized cooperative control protocol, namely the nonsingular matrix, are calculated. and ; Step 5: Design a scalable generalized cooperative control protocol to enable the pose synchronization of multiple support systems.

2. The networked multi-support system pose collaborative control method with extended functionality according to claim 1, characterized in that, Step 1 specifically involves the following: In the generalized multi-agent system, the dynamic model of agent i is as follows: ； in, , It is the state variable of agent i. It is the measurement output variable of agent i. It is the control input variable of agent i. They are respectively The coefficient matrix; and express The set of 3D real vector spaces, express A set of 3D real matrices.

3. The networked multi-support system pose collaborative control method with extended functionality according to claim 1, characterized in that, Step 2 specifically involves: Let Let N be the set of graphs containing N nodes and spanning trees. Then the topology of the communication network is: .

4. The networked multi-support system pose collaborative control method with extended functionality according to claim 1, characterized in that, Step 3 specifically involves: the communication network providing information to agent i. The details are as follows: ； Topology diagram of communication network In this context, nodes correspond to agents in the network, and the weights of the edges are... If i=j, then a ij =0, if i≠j, then a ij >0, topology graph Laplace matrix Represented as: ； but Rewrite it in the following form: ； Introducing local information exchange between agents, meaning each agent can access local exchange information. The details are as follows: ； in, These are variables generated within agent i.

5. The networked multi-support system pose collaborative control method with extended functionality according to claim 1, characterized in that, Step 4 specifically involves: in the state transformation non-singular matrix and Under the influence of the matrix pair The equivalent standard decomposition form is transformed as follows: ； in, Through non-singular matrices and The matrix obtained by the transformation; It is the gain matrix of the output feedback, that is, using measurable output information. Provide output feedback; yes An identity matrix of dimensionality; a matrix Eigenvalues ​​and matrix pairs Their finite eigenvalues ​​are exactly the same; It is an identity matrix of appropriate dimension.

6. The networked multi-support system pose collaborative control method with extended functionality according to claim 1, characterized in that, The scalable generalized cooperative control protocol described in step 5 is as follows: ； in, It is the relative state information of the generalized multi-agent system. The estimated value, These are variables within the scalable generalized cooperative control protocol, used to achieve state synchronization of this generalized multi-agent system. It is the control input variable of agent i in a generalized multi-agent system. This is the gain matrix of the scalable generalized cooperative control protocol, used for communication between agents. Selected as The superscript · on a parameter indicates its derivative; therefore, each agent obtains local exchange information. The details are as follows: ； Gain matrix Make the matrix pair No pulse and satisfy , Representing matrix pairs generalized finite eigenvalues, Denotes the closed left half of the complex plane, and the gain matrix. and The matrix pairs were designed to be used separately. and Regular, pulse-free, and stable.

7. The networked multi-support system pose collaborative control method with extended functionality according to claim 1, implemented based on a computer-readable storage medium, is characterized in that... The system stores executable instructions that, when executed, cause the processor to perform the aforementioned networked multi-support system pose cooperative control method with extended functionality.

8. The networked multi-support system pose collaborative control method with extended functionality according to claim 1, implemented through a computer program product, is characterized in that... It includes a computer program or instructions that, when executed by a processor, implement the aforementioned networked multi-support system pose collaborative control method with extended functionality.

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