An underwater robot formation control method based on dynamic coding and decoding

Abstract

The application discloses a kind of underwater robot formation control methods based on dynamic coding and decoding, comprising the following steps: S100, construct the multi-underwater robot system based on leader robot and follower robot architecture;S200, based on hybrid IVYA-FA optimization algorithm carries out bit rate allocation;S300, dynamic quantization coding and decoding strategy based on error information;S400, finite time domain formation control strategy, the bit rate allocation mechanism in step S200 and the dynamic coding and decoding strategy in step S300 are combined to design the finite time domain formation controller of follower underwater robot, and the linear matrix inequality condition proposed is solved by LMI tool box to obtain the gain of finite time domain formation controller;S500, realize the formation control of multi-underwater robot system.Compared with prior art, the application can effectively improve the optimal utilization of robot to limited channel resource and reduce the saturation error problem caused by quantization saturation phenomenon, improve the stability and robustness of system.

Description

TECHNICAL FIELD

[0001] The present application relates to underwater robot formation control technology, and particularly relates to an underwater robot formation control method based on dynamic encoding and decoding. BACKGROUND

[0002] An underwater multi-autonomous robot system is composed of a plurality of autonomous underwater robots with sensing, computing and execution capabilities, and information exchange and distributed decision making are performed through optical and acoustic communication modules, so as to finally realize efficient underwater group cooperation and group intelligence. However, in the underwater environment such as the sea, the propagation loss, signal propagation delay and multipath effect of seawater limit the bandwidth and transmission rate of underwater communication. At the same time, there are complex factors such as noise, water flow and seabed topography in the underwater environment, which further aggravate the difficulty of communication. The limited underwater communication caused by these complex factors seriously limits the efficient cooperation of the underwater multi-autonomous system.

[0003] The existing research on underwater multi-autonomous robot system formation control under network resource constraints usually starts from the perspective of control theory, considers the packet loss, time delay and other communication phenomena existing in the transmission process between nodes, and designs various types of control strategies to realize the cooperation of the underwater multi-autonomous robot system. However, the existing method lacks consideration of the bandwidth limitation of the underwater network, ignores the coupling relationship between the underwater communication network bandwidth and the cooperative control protocol, and thus cannot effectively utilize the limited bandwidth resources in the underwater communication network to ensure the accuracy of the underwater multi-autonomous robot system communication data and the cooperation of the system.

[0004] In the process of formation cooperation of the underwater multi-autonomous robot system, system information, state / control signals are often transmitted through digital communication channels. These information are usually quantized (i.e. real value signals are mapped to piecewise constant signals taking values in a finite set) before transmission, and then data sharing is realized through an encoder / decoder. However, when the input value exceeds the processing range of the quantizer, the value of the exceeding part will be truncated, which will cause the loss of transmission signal precision and seriously affect the stability of the system. In the existing formation control method for underwater multi-autonomous robot system, the quantization strategy is still to choose a large enough quantization range to weaken the influence of quantization saturation on the system. This quantization method has a large requirement for the number of data quantization levels and cannot be applied to the underwater communication network with limited bandwidth.

[0005] Therefore, it is urgent to research an underwater robot formation control method based on dynamic encoding and decoding to realize efficient cooperation and stability of the underwater multi-autonomous robot system in the network bandwidth limited environment. SUMMARY

[0006] To address the aforementioned shortcomings, the present invention aims to propose a formation control strategy for multiple underwater robot systems in underwater confined bandwidth environments. Compared with existing methods, this strategy effectively improves the optimal utilization of limited channel resources by the robots and reduces saturation error caused by quantization saturation, thereby enhancing the stability and robustness of the system.

[0007] To achieve this objective, the present invention adopts the following technical solution:

[0008] A method for underwater robot formation control based on dynamic encoding and decoding includes the following steps:

[0009] S100. Construct a multi-underwater robot system based on a leader robot and follower robot architecture. One leader interacts with multiple follower robots through an underwater communication network. The leader sends information, and the followers receive information from the leader. Followers send and receive information with each other.

[0010] S200: Bit rate allocation is performed based on the hybrid IVYA-FA optimization algorithm. According to the characteristics of underwater digital communication networks, the bit rate is introduced to characterize the bandwidth of the underwater network. Based on the communication requirements of different underwater robots, bit rate is allocated to each underwater robot based on the allocation-type MAC protocol, and the allocation is performed through the bit rate allocation mechanism of the hybrid IVYA-FA optimization algorithm.

[0011] S300, an encoding and decoding strategy based on dynamic quantization of error information, the follower underwater robot estimates its own state information through attitude sensors, integrates it into an estimated state vector, and quantizes the estimated state vector; the dynamic quantizer dynamically adjusts parameters to realize the dynamic quantization of the data transmitted by the underwater robot; the processor synthesizes the estimated state vector and the dynamically adjusted parameters into a data packet, which is sent to the communication network through the underwater acoustic sensor;

[0012] S400, Finite-time domain formation control strategy, combined with the bit rate allocation mechanism in step S200 and the dynamic encoding and decoding strategy in step S300, design a finite-time domain formation controller for the follower underwater robot, and obtain the finite-time domain formation controller gain by solving the proposed linear matrix inequality conditions through the LMI toolbox.

[0013] S500: Implement formation control of multiple underwater robot systems. Based on the finite-time domain formation control gain obtained in step S400, design the controller for each follower underwater robot. According to the system state information obtained in the dynamic encoding and decoding process in step S300, calculate the control input for the next motion moment of the underwater robot and transmit it to the actuator of the underwater robot to realize formation control of multiple underwater robot systems in the finite-time domain.

[0014] Preferably, the bit rate allocation mechanism of the hybrid IVYA-FA optimization algorithm in step S200 is as follows:

[0015] S210. Initialize system parameters, considering the system as a multi-underwater robot system with one leader and N followers; the leader's system state variable is x. l The system state variable of the follower is x. i The initial value of the follower's system state is x. i0 Calculate the sum of the initial values ​​s0 of the follower robot's state variables, and set the quantization limit value d of the follower robot. i Set the minimum dynamic adjustment parameters for the follower robot.

[0016] S220. Construct the optimization objective function. Based on the system's preset parameters, construct the following optimization objective function:

[0017]

[0018] Among them, f p (R) is the penalty function. n x Represents the number of elements in the state variable vector, R = [R1, R2, ..., R]. N ];

[0019] S230. Perform optimal bit rate allocation. Optimal bit rate allocation for the follower underwater robot is achieved using a hybrid IVYA-FA optimization algorithm. The optimal bit rate allocation result R is then calculated. best Set the maximum bit rate for the corresponding follower underwater robot.

[0020] Preferably, the hybrid IVYA-FA optimization algorithm includes the following steps:

[0021] S231, Initialize parameters, initialize ivy population parameters: X min X max T max and N pop Let T = 1;

[0022] Initialize firefly population parameters: light absorption intensity γ, maximum attraction θ max Random term coefficient 'a', maximum number of iterations 'T' max And the firefly population size N pop ;

[0023] Calculate the population of ivy The initial value, calculated using the formula: X i =X min +rand(1,D)⊙(X max -Xmin );

[0024] Randomly initialize N pop The location of the firefly Y i (i = 1, 2, ..., N) pop );

[0025] S232. Calculate the growth direction of ivy, and calculate the growth direction ΔGv of ivy. i Calculation formula:

[0026] S233. Calculate the objective function value for all individuals in the initial population, based on the fitness function F(X). i Calculate the objective function values ​​for all individuals in the initial population and sort them from best to worst according to their fitness values. Finally, set X. best =X1;

[0027] S234. Calculate the luminous intensity of the firefly population; calculate N. pop The luminous intensity L of a single firefly i (i = 1, 2, ..., N) pop );

[0028] S235. Iterative optimization using a mixed population, when T is less than or equal to T max At that time, the ivy iterative optimization function, the firefly iterative optimization function, and the mixed population optimization result and construct the optimal allocation set function are executed until T is greater than Tmax;

[0029] S2351. Ivy League Optimization Iteration: i starts from 1, and the value of i is incremented by 1 after each execution of the Ivy League optimization function, until the value of i is greater than N. pop At that time, perform firefly population optimization iteration;

[0030] S2352, Ivy League Optimization: Selecting the Best Neighbor X li Sort the members of population X from highest to lowest fitness value to form a new set: The optimal neighbor is selected as follows:

[0031]

[0032] Calculate β = (2 + rand) / 2 and ΔGv i =rand 2 ·(N(1,D)⊙ΔGv i );

[0033] If F(X) is satisfied i )<β·F(X best If F(X) is not satisfied, then equation (1) is executed. i )<β·F(Xbest If ), then execute formula (2);

[0034]

[0035] calculate fitness value and calculate

[0036] Will Add it to set X as the i-th element. new In the middle, and set its growth direction to

[0037] S2353. Firefly population optimization iteration: j starts from 1, and the value of j is incremented by 1 after each firefly population optimization iteration, until the value of j is greater than N. pop ;

[0038] S2354. Firefly population optimization, if L is satisfied i <L j If an Ivy League optimization iteration is executed, then no action is taken; otherwise, no action is taken. The value of i starts from 1, and is incremented by 1 after each Ivy League optimization iteration, until the value of i is greater than N. pop ;

[0039] Execution formula (3)

[0040]

[0041] Where, r ij The Cartesian distance between the two fireflies;

[0042] S2355. Find the optimal allocation set from the mixed population optimization results and merge the two populations X. new and Y new , represented as M = {X new ,Y new};

[0043] Calculate the fitness value of each member in M ​​according to the fitness function, and sort them from high to low to form a new set M. s Subsequently, the order

[0044] S236. Output the optimal bit rate allocation result. Iterate through the mixed population until T is greater than Tmax, then exit the loop and, based on the last optimization result in S5, output the optimal bit rate allocation result, thus obtaining the final result.

[0045] Preferably, in step S300, the estimated state vector is: The parameter is dynamically adjusted to μ. iThe selection rules for dynamically adjusting parameters are as follows:

[0046]

[0047] in, The setting rules satisfy: Usually chosen Because the decoding end also needs to obtain the dynamically adjusted parameter μ in real time. i The value of μ is required, therefore, the encoder needs to adjust the value of μ. i Perform binary encoding; dynamically adjust parameter μ i This can be expressed by the following formula:

[0048]

[0049] Where, α i and β i This is represented by the encoder's dynamic adjustment parameter μ. i (k) The number of bits used in binary encoding;

[0050] Due to μ i The value of (k) is not unique; therefore, the following settings are made for μ. i The rules for choosing the value of (k):

[0051]

[0052] Considering the limited bandwidth of communication, let in and The value is set according to the number of bits allocated in the actual situation.

[0053] Furthermore, in step S400,

[0054] The finite-time formation controller for a follower underwater robot is designed in the following form:

[0055]

[0056] Among them, K i Let a be the control gain of the i-th follower underwater robot. ij b is the connection weight between follower underwater robot i and follower underwater robot j; i The connection weights between the leader and follower underwater robots;

[0057] Furthermore, the communication topology of the follower layer subsystem of a multi-underwater robot system consists of a symmetric and irreducible matrix. The description is as follows:

[0058]

[0059] If there is a coupling relationship between the follower underwater robots i and j, then there exists a ij =a ji ≠0; if there is no coupling relationship, then a ij =a ji =0;

[0060] To obtain the optimal gain of the finite-time-domain formation controller for the follower underwater robot, an optimization problem is solved using the LMI toolbox to obtain the finite-time-domain formation controller gain K.

[0061] The specific steps are as follows:

[0062] 1) Define matrix variables by using the lmivar function to define variables μ≥1, ... Variable matrix And positive definite variable matrices P and Q;

[0063] 2) Construct LMI constraints. Based on the system parameters, construct the following inequality conditions:

[0064]

[0065] in, I is the identity matrix. and These represent the squares of the dot product of the initial state differences between the followers and the leader, the squares of the dot product of the external disturbance vectors, and the squares of the dot product of the decoding error vectors, respectively.

[0066] 3) Set optimization target ∈ 2 The optimization objective is: min(∈ 2 );

[0067] 4) Solve the LMI optimization problem using the solver provided in the LMI toolbox to obtain the optimal matrix. Finally, the following formula is used for calculation:

[0068]

[0069] The gain K of the finite-time-domain formation controller is obtained.

[0070] One of the above technical solutions includes the following beneficial effects: 1. The bit rate allocation mechanism based on the hybrid IVYA-FA optimization algorithm proposed in this patent can achieve optimal bit rate allocation for robots with different characteristics in underwater limited bandwidth environments. Compared with existing methods, it can effectively improve the optimal utilization of limited channel resources by robots and improve system stability. 2. The encoding and decoding strategy based on dynamic quantization of error information proposed in this patent can minimize the impact of quantization saturation on system stability under limited channel resources. Compared with existing methods, it can make full use of limited underwater channel resources, weaken the impact of signal errors caused by quantization saturation on the system, and improve the robustness and cooperative performance of multi-underwater robot systems in limited bit rate environments. 3. The finite-time domain formation control strategy designed in this patent can achieve stable control performance of underwater robot formations within a preset time domain. Compared with existing methods, this patent designs a finite-time domain formation control method for multi-underwater robot systems in limited bit rate environments, improving the safety and stability of formation control of multi-underwater robot systems in a specific time domain. Attached Figure Description

[0071] Figure 1 This is a schematic diagram of the overall steps of one embodiment of the present invention;

[0072] Figure 2 This is a schematic diagram of a bit rate allocation mechanism according to an embodiment of the present invention;

[0073] Figure 3 This is a schematic diagram of the steps of a hybrid IVYA-FA optimization algorithm according to an embodiment of the present invention;

[0074] Figure 4 This is a schematic diagram of the hybrid population iterative optimization steps according to an embodiment of the present invention. Detailed Implementation

[0075] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0076] like Figure 1 As shown, an underwater robot formation control method based on dynamic encoding and decoding includes the following steps:

[0077] S100. Construct a multi-underwater robot system based on a leader robot and follower robot architecture. One leader interacts with multiple follower robots through an underwater communication network. The leader sends information, and the followers receive information from the leader. Followers send and receive information with each other.

[0078] S200: Bit rate allocation is performed based on the hybrid IVYA-FA optimization algorithm. According to the characteristics of underwater digital communication networks, the bit rate is introduced to characterize the bandwidth of the underwater network. Based on the communication requirements of different underwater robots, bit rate is allocated to each underwater robot based on the allocation-type MAC protocol, and the allocation is performed through the bit rate allocation mechanism of the hybrid IVYA-FA optimization algorithm.

[0079] S300, an encoding and decoding strategy based on dynamic quantization of error information, the follower underwater robot estimates its own state information through attitude sensors, integrates it into an estimated state vector, and quantizes the estimated state vector; the dynamic quantizer dynamically adjusts parameters to realize the dynamic quantization of the data transmitted by the underwater robot; the processor synthesizes the estimated state vector and the dynamically adjusted parameters into a data packet, which is sent to the communication network through the underwater acoustic sensor;

[0080] S400, Finite-time domain formation control strategy, combined with the bit rate allocation mechanism in step S200 and the dynamic encoding and decoding strategy in step S300, design a finite-time domain formation controller for the follower underwater robot, and obtain the finite-time domain formation controller gain by solving the proposed linear matrix inequality conditions through the LMI toolbox.

[0081] S500: Implement formation control of multiple underwater robot systems. Based on the finite-time domain formation control gain obtained in step S400, design the controller for each follower underwater robot. According to the system state information obtained in the dynamic encoding and decoding process in step S300, calculate the control input for the next motion moment of the underwater robot and transmit it to the actuator of the underwater robot to realize formation control of multiple underwater robot systems in the finite-time domain.

[0082] Beneficial Effects: 1. The bit rate allocation mechanism based on the hybrid IVYA-FA optimization algorithm proposed in this patent can achieve optimal bit rate allocation for robots with different characteristics in underwater bandwidth-constrained environments. Compared with existing methods, it can effectively improve the optimal utilization of limited channel resources by robots and improve system stability. 2. The encoding and decoding strategy based on dynamic quantization of error information proposed in this patent can minimize the impact of quantization saturation on system stability under limited channel resources. Compared with existing methods, it can make full use of limited underwater channel resources, weaken the impact of signal errors caused by quantization saturation on the system, and improve the robustness and cooperative performance of multi-underwater robot systems in limited bit rate environments. 3. The finite-time domain formation control strategy designed in this patent can achieve stable control performance of underwater robot formations within a preset time domain. Compared with existing methods, this patent designs a finite-time domain formation control method for multi-underwater robot systems in limited bit rate environments, improving the safety and stability of formation control of multi-underwater robot systems in a specific time domain.

[0083] like Figure 2 As shown, the bit rate allocation mechanism of the hybrid IVYA-FA optimization algorithm in step S200 is as follows:

[0084] S210. Initialize system parameters, considering the system as a multi-underwater robot system with one leader and N followers; the leader's system state variable is x. l The system state variable of the follower is x. i The initial value of the follower's system state is x. i0 Calculate the sum of the initial values ​​s0 of the follower robot's state variables, and set the quantization limit value d of the follower robot. i Set the minimum dynamic adjustment parameters for the follower robot.

[0085] S220. Construct the optimization objective function. Based on the system's preset parameters, construct the following optimization objective function:

[0086]

[0087] Among them, f p (R) is the penalty function. n x Represents the number of elements in the state variable vector, R = [R1, R2, ..., R]. N ];

[0088] S230. Perform optimal bit rate allocation. Optimal bit rate allocation for the follower underwater robot is achieved using a hybrid IVYA-FA optimization algorithm. The optimal bit rate allocation result R is then calculated. bestSet the maximum bit rate for the corresponding follower underwater robot.

[0089] like Figure 3 As shown, the hybrid IVYA-FA optimization algorithm includes the following steps:

[0090] S231, Initialize parameters, initialize ivy population parameters: X min X max T max and N po p; Let T = 1;

[0091] Initialize firefly population parameters: light absorption intensity γ, maximum attraction θ max Random term coefficient 'a', maximum number of iterations 'T' max And the firefly population size N pop ;

[0092] Calculate the population of ivy The initial value, calculated using the formula: X i =X min +rand(1,D)⊙(X max -X min );

[0093] Randomly initialize N pop The location of the firefly Y i (i = 1, 2, ..., N) pop );

[0094] S232. Calculate the growth direction of ivy, and calculate the growth direction ΔGv of ivy. i Calculation formula:

[0095] S233. Calculate the objective function value for all individuals in the initial population, based on the fitness function F(X). i Calculate the objective function values ​​for all individuals in the initial population and sort them from best to worst according to their fitness values. Finally, set X. best =X1;

[0096] S234. Calculate the luminous intensity of the firefly population; calculate N. pop The luminous intensity L of a single firefly i (i = 1, 2, ..., N) pop );

[0097] like Figure 4 As shown, S235, mixed population iterative optimization, when T is less than or equal to T max At that time, the ivy iterative optimization function, the firefly iterative optimization function, and the mixed population optimization result and construct the optimal allocation set function are executed until T is greater than Tmax;

[0098] S2351. Ivy League Optimization Iteration: i starts from 1, and the value of i is incremented by 1 after each execution of the Ivy League optimization function, until the value of i is greater than N. pop At that time, perform firefly population optimization iteration;

[0099] S2352, Ivy League Optimization: Selecting the Best Neighbor X li Sort the members of population X from highest to lowest fitness value to form a new set: The optimal neighbor is selected as follows:

[0100]

[0101] Calculate β = (2 + rand) / 2 and ΔGv i =rand 2 ·(N(1,D)⊙ΔGv i );

[0102] If F(X) is satisfied i )<β·F(X best If F(X) is not satisfied, then equation (1) is executed. i )<β·F(X best If ), then execute formula (2);

[0103]

[0104] calculate fitness value and calculate

[0105] Will Add it to set X as the i-th element. new In the middle, and set its growth direction to

[0106] S2353. Firefly population optimization iteration: j starts from 1, and the value of j is incremented by 1 after each firefly population optimization iteration, until the value of j is greater than N. pop ;

[0107] S2354. Firefly population optimization, if L is satisfied i <L j If an Ivy League optimization iteration is executed, then no action is taken; otherwise, no action is taken. The value of i starts from 1, and is incremented by 1 after each Ivy League optimization iteration, until the value of i is greater than N. pop ;

[0108] Execution formula (3)

[0109]

[0110] Where, rij The Cartesian distance between the two fireflies;

[0111] S2355. Find the optimal allocation set from the mixed population optimization results and merge the two populations X. new and Y new , represented as M = {X new ,Y new};

[0112] Calculate the fitness value of each member in M ​​according to the fitness function, and sort them from high to low to form a new set M. s Subsequently, the order

[0113] S236. Output the optimal bit rate allocation result. Iterate through the mixed population until T is greater than Tmax, then exit the loop and, based on the last optimization result in S5, output the optimal bit rate allocation result, thus obtaining the final result.

[0114] The hybrid IVYA-FA optimization algorithm effectively enhances the overall efficiency of the system in searching for the optimal solution by increasing population diversity. It helps to quickly find the optimal bit rate allocation scheme based on the differences in the characteristics of system nodes, thereby making full use of the limited underwater bandwidth resources to achieve formation control of multiple underwater robot systems.

[0115] Furthermore, in step S300, the estimated state vector is... The parameter is dynamically adjusted to μ. i The selection rules for dynamically adjusting parameters are as follows:

[0116]

[0117] in, The setting rules satisfy: Usually chosen Because the decoding end also needs to obtain the dynamically adjusted parameter μ in real time. i The value of μ is required, therefore, the encoder needs to adjust the value of μ. i Perform binary encoding; dynamically adjust parameter μ i This can be expressed by the following formula:

[0118]

[0119] Where, α i and β i This is represented by the encoder's dynamic adjustment parameter μ. i (k) The number of bits used in binary encoding;

[0120] Due to μ i The value of (k) is not unique; therefore, the following settings are made for μ.i The rules for choosing the value of (k):

[0121]

[0122] Considering the limited bandwidth of communication, let in and The value is set according to the number of bits allocated in the actual situation.

[0123] To address the quantization saturation problem that exists when underwater robots transmit their own state values, this patent proposes a dynamic encoding and decoding strategy based on a limited bit rate. This strategy performs dual-end encoding and decoding processing on the dynamic adjustment factor based on state information, which can effectively reduce the quantization error caused by quantization saturation and thus improve the formation control performance of the entire multi-underwater robot system under limited communication resources.

[0124] In addition, in step S400,

[0125] The finite-time formation controller for a follower underwater robot is designed in the following form:

[0126]

[0127] Among them, K i Let a be the control gain of the i-th follower underwater robot. ij b is the connection weight between follower underwater robot i and follower underwater robot j; i The connection weights between the leader and follower underwater robots;

[0128] Furthermore, the communication topology of the follower layer subsystem of a multi-underwater robot system consists of a symmetric and irreducible matrix. The description is as follows:

[0129]

[0130] If there is a coupling relationship between the follower underwater robots i and j, then there exists a ij =a ji ≠0; if there is no coupling relationship, then a ij =a ji =0;

[0131] To obtain the optimal gain of the finite-time-domain formation controller for the follower underwater robot, an optimization problem is solved using the LMI toolbox to obtain the finite-time-domain formation controller gain K.

[0132] The specific steps are as follows:

[0133] 1) Define matrix variables by using the lmivar function to define variables μ≥1, ... Variable matrix And positive definite variable matrices P and Q;

[0134] 2) Construct LMI constraints. Based on the system parameters, construct the following inequality conditions:

[0135]

[0136] in, I is the identity matrix. and These represent the squares of the vector inner products of the initial state differences between the followers and the leader.

[0137] Square of the dot product of external interference vectors and square of the dot product of decoding error vectors;

[0138] 3) Set optimization target ∈ 2 The optimization objective is: min(∈ 2 );

[0139] 4) Solve the LMI optimization problem using the solver provided in the LMI toolbox to obtain the optimal matrix. Finally, the following formula is used for calculation:

[0140]

[0141] The gain K of the finite-time-domain formation controller is obtained.

[0142] By combining the optimal bit rate allocation results with the constraints of dynamically adjusted parameters, the optimization problem of minimizing the upper bound of the error is solved using the LMI toolbox, resulting in the finite-time domain formation controller gain K. This solved controller gain K fully considers the limited communication resources and quantization saturation issues under bit rate constraints, ensuring the stability and robustness of the multi-underwater robot system during formation control.

[0143] The technical principles of the present invention have been described above with reference to specific embodiments. These descriptions are merely for explaining the principles of the invention and should not be construed as limiting the scope of protection of the invention in any way. Based on this explanation, those skilled in the art can readily conceive of other specific embodiments of the invention without inventive effort, and these embodiments will all fall within the scope of protection of the present invention.

Claims

1. A method for underwater robot formation control based on dynamic encoding and decoding, characterized in that, Includes the following steps: S100. Construct a multi-underwater robot system based on a leader robot and follower robot architecture. One leader interacts with multiple follower robots through an underwater communication network. The leader sends information, and the followers receive information from the leader. Followers send and receive information with each other. S200: Bit rate allocation is performed based on the hybrid IVYA-FA optimization algorithm. According to the characteristics of underwater digital communication networks, the bit rate is introduced to characterize the bandwidth of the underwater network. Based on the communication requirements of different underwater robots, bit rate is allocated to each underwater robot based on the allocation-type MAC protocol, and the allocation is performed through the bit rate allocation mechanism of the hybrid IVYA-FA optimization algorithm. S300, an encoding and decoding strategy based on dynamic quantization of error information, the follower underwater robot estimates its own state information through attitude sensors, integrates it into an estimated state vector, and quantizes the estimated state vector; the dynamic quantizer dynamically adjusts parameters to realize the dynamic quantization of the data transmitted by the underwater robot; the processor synthesizes the estimated state vector and the dynamically adjusted parameters into a data packet, which is sent to the communication network through the underwater acoustic sensor; S400, Finite-time domain formation control strategy, combined with the bit rate allocation mechanism in step S200 and the dynamic encoding and decoding strategy in step S300, design a finite-time domain formation controller for the follower underwater robot, and obtain the finite-time domain formation controller gain by solving the proposed linear matrix inequality conditions through the LMI toolbox. In step S400, The finite-time formation controller for a follower underwater robot is designed in the following form: ; in, For the first Control gain of a follower underwater robot underwater robot as a follower With follower underwater robot The connection weights between them; The connection weights between the leader and follower underwater robots; Furthermore, the communication topology of the follower layer subsystem of a multi-underwater robot system consists of a symmetric and irreducible matrix. The description is as follows: ; If the underwater robot is followed and If there is a coupling relationship, then there exists If there is no coupling relationship, then we have ; To obtain the optimal gain of the finite-time formation controller for the follower underwater robot, an optimization problem is solved using the LMI toolbox to obtain the finite-time formation controller gain. ; The specific steps are as follows: 1) Define matrix variables by using the lmivar function to define the variables separately. , , Variable matrix and positive definite variable matrix and ; 2) Construct LMI constraints. Based on the system parameters, construct the following inequality conditions: ; in, , , It is the identity matrix. , and These represent the squares of the dot product of the initial state differences between the followers and the leader, the squares of the dot product of the external disturbance vectors, and the squares of the dot product of the decoding error vectors, respectively. 3) Set optimization goals The optimization objective is: ; 4) Solve the LMI optimization problem using the solver provided in the LMI toolbox to obtain the optimal matrix. Finally, the following formula is used for calculation: ; Gain of the finite-time array controller ; S500: Implement formation control of multiple underwater robot systems. Based on the finite-time domain formation control gain obtained in step S400, design the controller for each follower underwater robot. According to the system state information obtained in the dynamic encoding and decoding process in step S300, calculate the control input for the next motion moment of the underwater robot and transmit it to the actuator of the underwater robot to realize formation control of multiple underwater robot systems in the finite-time domain.

2. The underwater robot formation control method based on dynamic encoding and decoding according to claim 1, characterized in that, The bit rate allocation mechanism of the hybrid IVYA-FA optimization algorithm in step S200 is as follows: S210. Initialize system parameters, assuming the system contains a leader. A multi-submersible robot system with several followers; the system state variable of the leader is... The system state variable of the follower is The initial value of the follower's system state is Calculate the sum of the initial values ​​of each state variable of the follower robot. Set the quantization threshold value for the follower robot. Set the minimum dynamic adjustment parameters for the follower robot. ; S220. Construct the optimization objective function. Based on the system's preset parameters, construct the following optimization objective function: ; in, For the penalty function, ; This represents the number of elements in the state variable vector. ; S230. Perform optimal bit rate allocation. Optimal bit rate allocation for the follower underwater robot is achieved using a hybrid IVYA-FA optimization algorithm. The optimal bit rate allocation result is then used as the basis for the final allocation. Set the maximum bit rate for the corresponding follower underwater robot.

3. The underwater robot formation control method based on dynamic encoding and decoding according to claim 1, characterized in that, The hybrid IVYA-FA optimization algorithm includes the following steps: S231. Initialize the maximum number of iterations. ,make Initialize ivy population parameters: , and ; Initialize firefly population parameters: light absorption intensity The greatest attraction The random term coefficient 'a' and the firefly population size ; Calculate the population of ivy The initial value is calculated using the following formula: ; Random initialization The location of fireflies ; S232. Calculate the growth direction of ivy. Calculation formula: ; S233. Calculate the objective function value for all individuals in the initial population, based on the fitness function. Calculate the objective function values ​​for all individuals in the initial population and sort them from best to worst according to their fitness values. Finally, set... ; S234. Calculate the luminous intensity of the firefly population; calculate... The luminous intensity of a firefly ; S235, Iterative optimization of mixed populations, when Less than or equal to At that time, the iterative optimization functions of ivy, firefly, and mixed population are executed, and the optimal allocation set is constructed based on the optimization results, until... Greater than until; S2351, Evergreen Optimization Iteration Starting from 1, after each execution of the Ivy Search function... The value is incremented by 1 until... The value is greater than At that time, perform firefly population optimization iteration; S2352, Ivy League Optimization: Selecting the Best Neighbor , will the population The members within the set are sorted from highest to lowest fitness value to form a new set: The optimal neighbor is selected as follows: ； calculate and ; If satisfied If then, execute equation (1); if not satisfied... If so, then execute formula (2); Equation (1) Equation (2) calculate fitness value and calculate ; Will As the first Add elements to the collection In the middle, and set its growth direction to ; S2353, Firefly Population Optimization and Iteration Starting from 1, after each firefly population optimization exercise is completed... The value is incremented by 1 until... The value is greater than ; S2354. Firefly population optimization, if the following conditions are met... If the ivy-like optimization iteration is executed, then no operation is performed; otherwise, no operation is performed. Starting from 1, after each iteration of the Ivy League optimization search, The value is incremented by 1 until... The value is greater than ; Execution formula (3) , Equation (3) in, The Cartesian distance between the two fireflies; S2355. Calculate the results of mixed population optimization and construct the optimal allocation set, merging the two populations. and , represented as ; Calculated based on fitness function The fitness value of each member is used to form a new set, sorted from highest to lowest. Subsequently, the order ; S236. Output the optimal bit rate allocation result, and iterate the mixed population until... Greater than Exit the loop and, based on the last optimization result in S5, output the optimal bit rate allocation result, thus obtaining the final result. .

4. The underwater robot formation control method based on dynamic encoding and decoding according to claim 1, characterized in that, In step S300, the estimated state vector is: The parameters are dynamically adjusted as follows: The selection rules for dynamically adjusting parameters are as follows: ; in, , The setting rules satisfy: Because the decoding end also needs to obtain and dynamically adjust parameters in real time. The encoder needs to specify the value of , therefore, the encoder needs to... Perform binary encoding; dynamically adjust parameters This can be expressed by the following formula: ; in, and This represents the encoder's dynamic adjustment parameters. The number of bits used in binary encoding; because The value of is not unique; therefore, the following settings are made for . The rules for determining the value: ; ； ； Considering the limited bandwidth of communication, let , ,in and The value is set according to the number of bits allocated in the actual situation.

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