A Fast Adaptive Distributed Tracking Method for Reference Signals Based on Adaptive Estimation and Robust Controller

Abstract

This invention belongs to the field of autonomous estimation and fast tracking control of unknown reference signals by multiple agents. Specifically, it relates to a fast adaptive distributed tracking method for reference signals based on adaptive estimation and robust controllers. The method includes: constructing a module for building a model of unknown parameters of the reference signal, reparameterizing the reference signal containing unknown parameters, and converting it into an observable system through filtering and transformation techniques; constructing a fast distributed estimation module for the unknown parameters of the reference signal, designing an adaptive distributed observer for the unknown parameters based on adaptive methods and finite-time theory; constructing a fast robust tracking control module for the reference signal, designing a fast robust tracking controller based on sliding mode algorithm and finite-time theory; and constructing a closed-loop system stability analysis module to prove the finite-time convergence of the multi-agent distributed tracking system. This invention significantly improves the practicality and reliability of multi-agent tracking systems in this field.

Description

Technical Field

[0001] This invention belongs to the field of autonomous estimation and fast tracking control of unknown reference signals by multiple agents, and specifically relates to a fast adaptive distributed tracking method for reference signals based on adaptive estimation and robust controller. Background Technology

[0002] With the development of computer networks, sensor technology, and communication technology, research on multi-agent tracking systems has attracted widespread attention. Compared with single-agent target tracking, multi-agent tracking systems can complete various complex tasks with high performance, high flexibility, and high robustness, such as patrolling, detection, and rescue. In fact, a multi-agent tracking system is a system composed of multiple interacting agents that collaborate to track targets, solving problems that a single agent cannot solve independently. In a multi-agent tracking system, each agent is independent, but through cooperation, they can achieve more complex tasks and objectives. For example, in autonomous driving technology, multiple vehicles need to coordinate their driving to maintain formation and safe distances. In military or civilian missions, multiple drones can share visual or positional information to collaboratively complete tasks covering large areas or form specific flight formations to improve efficiency.

[0003] In multi-agent tracking systems, if all agents can receive target information, multi-agent tracking of the target is easily achieved. However, in real-world scenarios, due to limited communication or insufficient resources, usually only some agents can receive target information, which is currently the main research direction in this field. One approach, given the target reference signal, involves designing a distributed estimator for each agent, enabling each agent to obtain the target's output signal. However, existing methods primarily focus on state estimation of the reference signal, rather than parameter estimation, leading to less than ideal performance when dealing with reference signals containing unknown parameters. Furthermore, when parameter and state estimation need to be performed simultaneously, most methods still suffer from insufficient accuracy in parameter estimation. As research into multi-agent tracking systems deepens, current technology lacks a comprehensive solution that effectively handles reference signals with unknown parameters in multi-agent tracking systems. To address this, it is necessary to design a distributed estimator to autonomously estimate the reference signal and its unknown parameters, while simultaneously designing a tracking controller to enable each agent's output signal to rapidly track the reference signal with unknown parameters within a finite time. Furthermore, in practical applications, the convergence time of a system is a crucial indicator for evaluating controller performance. Currently, most control algorithms can only guarantee asymptotic convergence, failing to meet the high speed and accuracy requirements of real-world applications. Asymptotic stability only guarantees that the system state converges to the neighborhood of the equilibrium point, and the convergence time to the equilibrium point is infinite, which is a drawback of asymptotic stability algorithms. Finite-time control, on the other hand, offers faster convergence and better anti-interference performance than asymptotic stability control, converging to the equilibrium point within a finite time. Therefore, researching multi-agent tracking systems based on finite-time theory is of greater practical significance.

[0004] In summary, the current research has the following main problems: (1) Most algorithms in the existing technology have a slow convergence speed, such as the asymptotic convergence algorithm, which is not conducive to the practical application of drone formation or autonomous vehicle fleet that requires fast response; (2) Existing technology methods usually require some understanding of some states or parameters of the reference signal, which is not applicable when the parameters of the reference signal are completely unknown; (3) Existing technology may not be able to maintain good stability when facing external interference such as wind or sound waves, and is easily affected by external environmental factors; To this end, we propose a fast adaptive distributed tracking method for reference signals based on adaptive estimation and robust controller. Summary of the Invention

[0005] The purpose of this invention is to provide a fast adaptive distributed tracking method for reference signals based on adaptive estimation and robust controllers. A multi-agent fast adaptive distributed tracking system model for reference signals is constructed, considering the unknown parameters in the reference signal and the impact of external interference such as wind, interference signals, and sound waves on intelligent agent systems such as UAVs and unmanned vehicles. Furthermore, the convergence speed of the system is improved based on finite-time theory. This invention provides a new modeling approach for the autonomous estimation and fast tracking control of unknown reference signals by multi-agent systems, enriching the research methods in this field. The designed distributed observer and fast robust tracking controller significantly improve the practicality and reliability of multi-agent tracking systems in this area.

[0006] The specific technical solution adopted by this invention is as follows:

[0007] A fast adaptive distributed tracking method for reference signals based on adaptive estimation and robust controllers is characterized by the following steps:

[0008] Step 1: Construct a multi-agent reference signal fast adaptive distributed tracking system based on an unknown parameter observer and a robust controller. The multi-agent reference signal fast adaptive distributed tracking system based on an unknown parameter observer and a robust controller includes: a reference signal unknown parameter model construction module, a reference signal unknown parameter fast distributed estimation module, a reference signal fast robust tracking control module, and a closed-loop system stability analysis module.

[0009] Step 2: In the unknown parameter model construction module of the reference signal, establish an observable model of the reference signal containing unknown parameters;

[0010] Step 3: In the fast distributed estimation module for unknown parameters of the reference signal, an adaptive distributed observer for unknown parameters is designed based on the adaptive method and finite time theory for the observable model generated in Step 2. When only some agents can detect the reference signal, each agent can autonomously estimate the reference signal and its unknown parameters within a finite time using only limited communication information.

[0011] Step 4: In the reference signal fast robust tracking control module, using the reference signal information estimated in Step 3, a fast robust tracking controller is designed based on the sliding mode algorithm and finite time theory, so that the output signal of each agent can achieve fast tracking of the reference signal containing unknown parameters within a finite time.

[0012] Step 5: In the closed-loop system stability analysis module, provide a finite-time convergence proof for a multi-agent reference signal fast adaptive distributed tracking system; based on steps 1 to 4, by designing a Lyapunov function, prove that the multi-agent reference signal fast adaptive distributed tracking system based on an unknown parameter observer and a robust controller, under the action of the distributed observer designed in step 3 and the robust tracking controller designed in step 4, enables each agent to achieve fast estimation and stable tracking of the reference signal within a finite time when only some agents can detect the reference signal.

[0013] Preferably, in step 1: the reference signal unknown parameter model construction module is mainly used to establish an observable model for a reference signal containing unknown parameters;

[0014] The fast distributed estimation module for unknown parameters of the reference signal enables each agent to autonomously estimate the unknown parameters of the reference signal based on the state information obtained from sensors and communication networks.

[0015] The reference signal fast and robust tracking control module enables the intelligent agent to achieve safe and stable tracking of the reference signal under the influence of external disturbances.

[0016] The closed-loop system stability analysis module is mainly used to provide finite-time convergence proofs for fast adaptive distributed tracking systems for multi-agent reference signals.

[0017] Preferably, in step 2, since sinusoidal signals can form various periodic signals, the reference signal to be tracked is specifically represented as the sum of sinusoidal signals with unknown frequency, amplitude and initial phase; the reference signal containing unknown parameters is re-parameterized through parameter transformation method, and converted into an observable system that is easier to control and observe through filtering transformation technology.

[0018] Preferably, the specific steps in step 2 are as follows:

[0019] Step 201: Represent the reference signal to be tracked as the sum of sinusoidal signals with unknown frequency, amplitude, and initial phase:

[0020]

[0021] in, and These represent the amplitude, frequency, and phase of a sinusoidal signal, respectively. It is the output signal of the system to be tracked;

[0022] Step 202: Based on the mathematical expression of the reference signal to be tracked proposed in Step 201, the reference signal containing unknown parameters is re-parameterized through parameter transformation. That is, the reference sinusoidal signal to be tracked (1) can be regarded as the output of the following virtual linear system:

[0023]

[0024] Among them, and ,in , , ,and Based on One unknown parameter Reversible reparameterization;

[0025] Step 203: Based on the virtual linear system (2) proposed in step 2.2), (2) is transformed into an observable system through filtering transformation:

[0026]

[0027] in, , It is the filter transform vector.

[0028] ,

[0029] ,

[0030] ,

[0031] .

[0032] Preferably, the specific method in step 3 is as follows:

[0033] Step 301: In the adaptive method, define the adaptive gain. The expression is as follows:

[0034]

[0035] in, and It is any constant that is greater than 0;

[0036] Step 302: Using an adaptive method and finite-time theory, based on the observable system proposed in Step 2 and the adaptive gain defined in Step 301... The following design is used for an adaptive distributed observer with unknown parameters:

[0037] ;

[0038] ;

[0039] ;

[0040]

[0041] in, ,for , and They are and The estimated value, and , , , , , , , , Represents the Kronecker product. It is a symbolic function; furthermore, , , , .

[0042] Preferably, the specific method of step 4 is as follows:

[0043] Step 401: Define the dynamic equations of the multi-agent system, which can be used as follows: The mathematical expression for the order integrator is as follows:

[0044]

[0045] in , ; , , These are the state vector, control input, and agent output, respectively.

[0046] Step 402: For Define function as follows:

[0047]

[0048] Step 403: Design a fast and robust tracking controller based on sliding mode algorithm and finite-time theory as follows:

[0049] ;

[0050]

[0051] in , ,and satisfy .

[0052] Preferably, the specific steps of step 5 are as follows:

[0053] Step 501: Estimation error analysis of the reference signal and its unknown parameters;

[0054] Step 502: Analysis of the intelligent agent tracking the reference signal.

[0055] Preferably, step 501 specifically includes the following steps:

[0056] definition , , , These are the filter transformation vectors in step 2. and parameters and output signal The estimated value; for Define the estimation error: , , , ; Define tracking error: The error dynamics of the system are expressed as:

[0057] ;

[0058] ;

[0059] ;

[0060]

[0061] make , , , Consider the following Lyapunov function:

[0062]

[0063] in , , , , , , , Then, according to Differentiating equation (18) above, we get:

[0064]

[0065] if ,but Then, define

[0066]

[0067] in ,make , There exists a positive constant. Make , Then equation (20) can be written as:

[0068]

[0069] when hour, ,but Therefore, the estimation error , , , In a limited time Internal convergence; after the estimation error converges, i.e. , There is a point in time. For all , , , ,and All are true;

[0070] Consider the observation system of equation (1), for any initial value , , and It exists for a limited time. This makes the estimation error , , , The convergence is established by finite-time distributed observer equations (4) and (6)-(9), where , , , and .

[0071] Preferably, step 502 specifically includes the following steps:

[0072] make and According to equation (13), we can obtain:

[0073] ;

[0074]

[0075] Considering system equations (22) and (23), the estimation error will not be outside the neighborhood of 0 in a finite time, thus there exists a point in time, namely This makes for , Then consider the controller-based (13), system-based (22), and system-based (23) formulas, which are transformed into:

[0076]

[0077] In the interval Internally, the closed-loop system transforms into:

[0078] ;

[0079]

[0080] in ;and then, and It is bounded and exists within a finite time. It then converges to 0; let It is a closed-loop system (25) and a system (26). ,in Furthermore, the system is Globally stable over a finite time;

[0081] Therefore, considering the observation system equations (1), (10) and the directed graph For any , , and , , Choose any , , and And for , Satisfying equation (4); through distributed observers (6)-(9) and robust tracking controllers (12)-(13), each agent can achieve rapid estimation and stable tracking of the reference signal within a finite time, even when only some agents can detect the reference signal.

[0082] The technical effects achieved by this invention are as follows:

[0083] This invention constructs a multi-agent reference signal fast adaptive distributed tracking system model, considering the unknown parameters in the reference signal and the impact of external interference such as wind, interference signals, and sound waves on intelligent agent systems such as UAVs and unmanned vehicles. Furthermore, it improves the system's convergence speed based on finite-time theory. This provides a new modeling approach for the autonomous estimation and fast tracking control of unknown reference signals by multi-agent systems, enriching the research methods in this field. The designed distributed observer and fast robust tracking controller significantly improve the practicality and reliability of multi-agent tracking systems in this area.

[0084] The distributed observer and tracking controller proposed in this invention enable rapid estimation and stable tracking of the reference signal by multiple agents even when only some agents can detect the reference signal, thereby improving the system's flexibility and applicability.

[0085] This invention takes into account the impact of external interference such as wind, interference signals and sound waves on multi-agent tracking systems. The robust controller based on sliding mode algorithm designed in this invention shows better anti-interference characteristics, especially in uncertain and disturbed environments, and can effectively suppress the impact of external interference.

[0086] This invention, based on finite-time theory, designs a robust tracking controller that achieves finite-time convergence, significantly improving the convergence speed. By utilizing distributed computing and finite-time theory, it enhances both convergence speed and estimation accuracy, demonstrating higher operational efficiency. Attached Figure Description

[0087] Figure 1 This is a flowchart of the fast adaptive distributed tracking method for reference signals based on adaptive estimation and robust controllers provided by the present invention.

[0088] Figure 2 This is a graph showing the estimation errors of five intelligent agents for an unknown reference signal in an embodiment of the present invention.

[0089] Figure 3 This is a tracking error diagram of five intelligent agents for an unknown reference signal in an embodiment of the present invention.

[0090] Figure 4 This is an estimation diagram of unknown parameters of the reference signal by five intelligent agents in an embodiment of the present invention.

[0091] Figure 5 This is a graph showing the estimation errors of the five intelligent agents for unknown parameters of the reference signal in an embodiment of the present invention.

[0092] Figure 6 This is an estimation diagram of unknown frequencies in a reference signal by five intelligent agents in an embodiment of the present invention.

[0093] Figure 7This is an adaptive gain diagram of the five intelligent agents in an embodiment of the present invention. Detailed Implementation

[0094] To make the objectives and advantages of this invention clearer, the invention will be specifically described below with reference to embodiments. It should be understood that the following text is merely used to describe one or more specific embodiments of the invention and does not strictly limit the scope of protection specifically claimed by the invention.

[0095] like Figure 1 As shown, a fast adaptive distributed tracking method for reference signals based on adaptive estimation and robust controllers is characterized by the following steps:

[0096] Step 1: Construct a multi-agent reference signal fast adaptive distributed tracking system based on an unknown parameter observer and a robust controller. The multi-agent reference signal fast adaptive distributed tracking system based on an unknown parameter observer and a robust controller includes: a reference signal unknown parameter model construction module, a reference signal unknown parameter fast distributed estimation module, a reference signal fast robust tracking control module, and a closed-loop system stability analysis module.

[0097] Step 2: In the unknown parameter model construction module of the reference signal, establish an observable model of the reference signal containing unknown parameters;

[0098] Step 3: In the fast distributed estimation module of unknown parameters of the reference signal, an adaptive distributed observer for unknown parameters is designed based on the adaptive method and finite time theory for the observable model generated in Step 2. Under the condition that only some agents can detect the reference signal, each agent can realize the autonomous estimation of the reference signal and its unknown parameters within a finite time by using only limited communication information.

[0099] Step 4: In the reference signal fast robust tracking control module, for example, considering the influence of external interference factors such as wind, interference signals, and sound waves on intelligent agent systems such as UAVs and unmanned vehicles, the reference signal information estimated in Step 3 is used to design a fast robust tracking controller based on sliding mode algorithm and finite time theory, so that the output signal of each intelligent agent can achieve fast tracking of the reference signal containing unknown parameters within a finite time.

[0100] Step 5: In the closed-loop system stability analysis module, provide a finite-time convergence proof for a fast adaptive distributed tracking system for multi-agent reference signals; based on steps 1-4, by designing a Lyapunov function, prove that the fast adaptive distributed tracking system for multi-agent reference signals based on an unknown parameter observer and a robust controller, under the action of the distributed observer designed in step 3 and the robust tracking controller designed in step 4, enables each agent to achieve fast estimation and stable tracking of the reference signal within a finite time when only some agents can detect the reference signal.

[0101] In summary, the multi-agent reference signal fast adaptive distributed tracking system model constructed in this invention considers the case of unknown parameters in the reference signal, as well as the impact of external interference such as wind, interference signals, and sound waves on intelligent agent systems such as UAVs and unmanned vehicles. Furthermore, it improves the system's convergence speed based on finite-time theory. This provides a new modeling approach for the autonomous estimation and fast tracking control of unknown reference signals by multi-agent systems, enriching the research methods in this field. The designed distributed observer and fast robust tracking controller significantly improve the practicality and reliability of multi-agent tracking systems in this area.

[0102] In step 1, the unknown parameter model building module for the reference signal is mainly used to establish an observable model for a reference signal containing unknown parameters.

[0103] The fast distributed estimation module for unknown parameters of the reference signal enables each agent to autonomously estimate the unknown parameters of the reference signal based on the state information obtained from sensors and communication networks.

[0104] The reference signal fast and robust tracking control module enables the intelligent agent to achieve safe and stable tracking of the reference signal under the influence of external disturbances.

[0105] The closed-loop system stability analysis module is mainly used to provide finite-time convergence proofs for fast adaptive distributed tracking systems for multi-agent reference signals.

[0106] In step 2, since sinusoidal signals can form various periodic signals, the reference signal to be tracked is specifically represented as the sum of sinusoidal signals with unknown frequency, amplitude and initial phase. Through parameter transformation, the reference signal containing unknown parameters is re-parameterized and transformed into an observable system that is easier to control and observe through filtering transformation technology.

[0107] The specific steps in step 2 are as follows:

[0108] Step 201: Represent the reference signal to be tracked as the sum of sinusoidal signals with unknown frequency, amplitude, and initial phase:

[0109]

[0110] in, and These represent the amplitude, frequency, and phase of a sinusoidal signal, respectively. It is the output signal of the system to be tracked;

[0111] Step 202: Based on the mathematical expression of the reference signal to be tracked proposed in Step 201, the reference signal containing unknown parameters is re-parameterized through parameter transformation. That is, the reference sinusoidal signal to be tracked (1) can be regarded as the output of the following virtual linear system:

[0112]

[0113] Among them, and ,in , , ,and Based on One unknown parameter Reversible reparameterization;

[0114] Step 203: Based on the virtual linear system (2) proposed in step 2.2), (2) is transformed into an observable system through filtering transformation:

[0115]

[0116] in, , It is the filter transform vector.

[0117] ,

[0118] ,

[0119] ,

[0120] .

[0121] The specific method in step 3 is as follows:

[0122] Step 301: In the adaptive method, define the adaptive gain. The expression is as follows:

[0123]

[0124] in, and It is any constant that is greater than 0;

[0125] Step 302: Using an adaptive method and finite-time theory, based on the observable system proposed in Step 2 and the adaptive gain defined in Step 301... The following design is used for an adaptive distributed observer with unknown parameters:

[0126] ;

[0127] ;

[0128] ;

[0129]

[0130] in, ,for , and They are and The estimated value, and , , , , , , , , Represents the Kronecker product. It is a symbolic function; furthermore, , , , .

[0131] The specific method for step 4 is as follows:

[0132] Step 401: Define the dynamic equations of the multi-agent system, which can be used as follows: The mathematical expression for the order integrator is as follows:

[0133]

[0134] in , ; , , These are the state vector, control input, and agent output, respectively.

[0135] Step 402: For Define function as follows:

[0136]

[0137] Step 403: Design a fast and robust tracking controller based on sliding mode algorithm and finite-time theory as follows:

[0138] ;

[0139]

[0140] in , ,and satisfy .

[0141] The specific steps of step 5 are as follows:

[0142] Step 501: Estimation error analysis of the reference signal and its unknown parameters;

[0143] Step 502: Analysis of the intelligent agent tracking the reference signal.

[0144] Step 501 specifically includes the following steps:

[0145] definition , , , These are the filter transformation vectors in step 2. and parameters and output signal The estimated value; for Define the estimation error: , , , ; Define tracking error: The error dynamics of the system are expressed as:

[0146] ;

[0147] ;

[0148] ;

[0149]

[0150] make , , , Consider the following Lyapunov function:

[0151]

[0152] in , , , , , , , Then, according to Differentiating equation (18) above, we get:

[0153]

[0154] if ,but Then, define

[0155]

[0156] in ,make , There exists a positive constant. Make , Then equation (20) can be written as:

[0157]

[0158] when hour, ,but Therefore, the estimation error , , , In a limited time Internal convergence; after the estimation error converges, i.e. , There is a point in time. For all , , , ,and All are true;

[0159] Consider the observation system of equation (1), for any initial value , , and It exists for a limited time. This makes the estimation error , , , The convergence is established by finite-time distributed observer equations (4) and (6)-(9), where , , , and .

[0160] The specific steps of step 502 are as follows:

[0161] make and According to equation (13), we can obtain:

[0162] ;

[0163]

[0164] Considering system equations (22) and (23), the estimation error will not be outside the neighborhood of 0 in a finite time, thus there exists a point in time, namely This makes for , Then consider the controller-based (13), system-based (22), and system-based (23) formulas, which are transformed into:

[0165]

[0166] In the interval Internally, the closed-loop system transforms into:

[0167] ;

[0168]

[0169] in ;and then, and It is bounded and exists within a finite time. It then converges to 0; let It is a closed-loop system (25) and a system (26). ,in Furthermore, the system is Globally stable over a finite time;

[0170] Therefore, considering the observation system equations (1), (10) and the directed graph For any , , and , , Choose any , , and And for , Satisfying equation (4); through distributed observers (6)-(9) and robust tracking controllers (12)-(13), each agent can achieve rapid estimation and stable tracking of the reference signal within a finite time, even when only some agents can detect the reference signal.

[0171] In this embodiment, we use five intelligent agents as an example for simulation. The constructed multi-agent reference signal fast adaptive distributed tracking system model considers the unknown parameters in the reference signal, as well as the impact of external interference such as wind, interference signals, and sound waves on intelligent agent systems such as drones and unmanned vehicles. Figure 2-7 The experimental results in Figure 2 This is a diagram showing the estimation error of the unknown reference signal by the five intelligent agents in this embodiment of the invention; Figure 3 This is a tracking error diagram of five intelligent agents for an unknown reference signal in an embodiment of the present invention; Figure 4 This is an estimation diagram of unknown parameters of the reference signal by five intelligent agents in an embodiment of the present invention; Figure 5 This is a diagram showing the estimation errors of the five intelligent agents for unknown parameters of the reference signal in an embodiment of the present invention. Figure 6 This is an estimation diagram of unknown frequencies in a reference signal by five intelligent agents in an embodiment of the present invention; Figure 7 This is an adaptive gain diagram of five agents in an embodiment of the present invention. As can be seen from the diagram, the finite-time theory improves the convergence speed of the system, enhances the convergence speed and estimation accuracy, and demonstrates higher operational efficiency. It enables rapid estimation and stable tracking of the reference signal by multiple agents even when only some agents can detect the reference signal. It improves the flexibility and applicability of the system and shows better anti-interference characteristics, especially in uncertain and disturbed environments, where it can effectively suppress the influence of external interference.

[0172] In summary, this invention improves the convergence speed of the system based on finite-time theory; it provides a new modeling approach for multi-agent autonomous estimation and fast tracking control of unknown reference signals, enriching the research methods in this field; the designed distributed observer and fast robust tracking controller significantly improve the practicality and reliability of multi-agent tracking systems in this field; the distributed observer and tracking controller proposed in this invention enable rapid estimation and stable tracking of reference signals by multiple agents even when only some agents can detect the reference signal, improving the system's flexibility and applicability; this invention considers the impact of external interference such as wind, interference signals, and sound waves on multi-agent tracking systems, and the robust controller based on sliding mode algorithm shows better anti-interference characteristics, especially in uncertain and disturbed environments, effectively suppressing the impact of external interference; based on finite-time theory, the robust tracking controller designed in this invention achieves finite-time convergence, significantly improving the convergence speed. By using distributed computing and finite-time theory, the convergence speed and estimation accuracy are improved, demonstrating higher operational efficiency.

[0173] The above description is merely a preferred embodiment of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention. Structures, devices, and operating methods not specifically described or explained in this invention are implemented according to conventional methods in the art unless otherwise specified or limited.

Claims

1. A reference signal fast adaptive distributed tracking method based on adaptive estimation and robust controller, characterized in that: Comprising the following steps: Step 1: Constructing a multi-agent reference signal fast adaptive distributed tracking system based on an unknown parameter observer and a robust controller, comprising: a reference signal unknown parameter model construction module, a reference signal unknown parameter fast distributed estimation module, a reference signal fast robust tracking control module, and a closed-loop system stability analysis module; Step 2: In the reference signal unknown parameter model construction module, an observable model of the reference signal containing unknown parameters is established; Step 3: In the reference signal unknown parameter fast distributed estimation module, based on the adaptive method and the finite time theory, an unknown parameter adaptive distributed observer is designed for the observable model generated in step 2, so that each agent can autonomously estimate the reference signal and its unknown parameters within a limited time using only limited communication information, even if only part of the agents can detect the reference signal; The specific method in step 3 is as follows: Step 301 : In the adaptive method, define the adaptive gain The expression for the adaptive gain is as follows: ； wherein and are arbitrary and greater than 0 constants; Step 302: Using the adaptive method and finite time theory, the unknown parameter adaptive distributed observer is designed as follows based on the observable system proposed in step 2 and the adaptive gain defined in step 301 , design unknown parameter adaptive distributed observer as follows: ； ； ； ； wherein , for , and are and are , , , , , , , , denotes the Kronecker product, is the sign function; furthermore, , , , ; Step 4: In the reference signal fast robust tracking control module, the reference signal information estimated in step 3 is used to design a fast robust tracking controller based on the sliding mode algorithm and the finite time theory, so that the output signal of each agent can track the reference signal containing unknown parameters within a limited time; The specific method of step 4 is as follows: Step 401 : Define the dynamics equation of multi-agent, which can be described by The mathematical expression of the 2nd order integrator is as follows: ； wherein , ; , , are the state vector, the control input and the output of the agent, respectively; Step 402: For each of the functions , define a function as follows: ； Step 403: Design a fast robust tracking controller based on the sliding mode algorithm and the finite time theory as follows: ； ； wherein , , and satisfies ; Step 5: In the closed-loop system stability analysis module, provide a finite time convergence proof for the multi-agent reference signal fast adaptive distributed tracking system; based on steps 1-4, by designing a Lyapunov function, it is proved that the multi-agent reference signal fast adaptive distributed tracking system based on the unknown parameter observer and the robust controller can make each agent estimate and track the reference signal within a limited time under the action of the distributed observer designed in step 3 and the robust tracking controller designed in step 4, even if only part of the agents can detect the reference signal.

2. The reference signal fast adaptive distributed tracking method based on adaptive estimation and robust controller according to claim 1, characterized in that: In step 1: the reference signal unknown parameter model construction module is mainly used to establish an observable model for the reference signal containing unknown parameters; The reference signal unknown parameter fast distributed estimation module enables each agent to autonomously estimate the unknown parameters of the reference signal based on the state information obtained from the sensor and the communication network; The reference signal fast robust tracking control module enables the agent to achieve safe and stable tracking of the reference signal under the influence of external disturbances; The closed-loop system stability analysis module is mainly used to provide a finite time convergence proof for the multi-agent reference signal fast adaptive distributed tracking system.

3. The reference signal fast adaptive distributed tracking method based on adaptive estimation and robust controller according to claim 1, characterized in that: In step 2, since the sinusoidal signal can constitute various periodic signals, the reference signal to be tracked is specifically expressed as the sum of sinusoidal signals with unknown frequency, amplitude and initial phase; through the parameter transformation method, the reference signal containing unknown parameters is re-parameterized, and the filter transformation technology is used to convert it into an observable system which is easier to control and observe.

4. The reference signal fast adaptive distributed tracking method based on adaptive estimation and robust controller according to claim 3, characterized in that: The specific steps in step 2 are as follows: Step 201: Express the reference signal to be tracked as the sum of sinusoidal signals with unknown frequency, amplitude and initial phase: ； wherein, and A, f and φ represent the amplitude, frequency and phase of the sinusoidal signal, respectively, is the output signal of the system to be tracked; Step 202: Based on the mathematical expression of the reference signal to be tracked proposed in step 201, the reference signal containing unknown parameters is re-parameterized through parameter transformation, that is, the reference sinusoidal signal (1) to be tracked can be regarded as the output of the following virtual linear system: ； wherein, wherein and wherein , , and is a reversible reparameterization based on unknown parameters . Step 203: Based on the virtual linear system (2) proposed in step 2.2), (2) is converted into an observable system through filter transformation: ； wherein , is a filter transform vector, ， ， ， 。 5. The reference signal fast adaptive distributed tracking method based on adaptive estimation and robust controller according to claim 1, and the specific steps of step 5 are as follows: Step 501: Analysis of the estimation error of the reference signal and its unknown parameters; Step 502: Analysis of the agent tracking reference signal.

6. The reference signal fast adaptive distributed tracking method based on adaptive estimation and robust controller according to claim 5, characterized in that: The step 501 specifically includes the following steps: Definitions , , , are the filtered transformed vectors of step 2 and the estimates of the parameters and the output signal ; for , the estimation error is defined as: , , , ; the tracking error is defined as: ; the error dynamics of the system are given by: ； ； ； ； Let , , , ; consider the Lyapunov function ； wherein , , , , , , , ; then, according to , the derivative of the above equation (18) is ； If then ; then, define ； wherein , let , , there is a positive constant such that , then equation (20) can be written as: ； When , , then , the estimation error , , , converges in finite time ; after the estimation error converges, i.e. , , there exists a time point such that for all , , , , and hold; Consider the observation system of equation (1) for any initial value , , and there exists a finite time such that the convergence of the estimation error , , , is established by the finite time distributed observer equations (4) and (6)-(9) where , , , and .

7. The reference signal fast adaptive distributed tracking method based on adaptive estimation and robust controller according to claim 6, characterized in that: The specific steps of the step 502 are as follows: Let and According to equation (13), we have: ； ； Considering system (22) and system (23), the estimation error will not be outside the neighborhood of 0 in finite time, and there is a time point, i.e. such that for , Then considering the controller (13), system (22) and system (23) are transformed into: ； Within the interval the closed loop system transforms into: ； ； where ; furthermore, and are bounded and converge to 0 after a finite time ; let be the closed-loop system of equations (25) and (26) where ; furthermore, the system is globally finite-time stable; Thus, considering the observation system (1), (10) and the directed graph , for any , , and , , , selecting any , , and , and for , , the equation (4) is satisfied; through the distributed observer (6)-(9) and the robust tracking controller (12)-(13), each agent realizes the estimation and tracking of the reference signal in a limited time under the condition that only part of the agents can detect the reference signal.

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