Parameter identification method for kinetic model of pectoral fin propelling bionic robotic fish

Abstract

The invention relates to the technical field of biomimetic robot parameter identification, in particular to a parameter identification method for a kinetic model of a pectoral fin propelled biomimetic robotic fish, which comprises the following steps: dividing the motion state of the biomimetic robotic fish into a flapping state and a sliding state; hydrodynamic parameters of the bionic robotic fish in the sliding state are obtained; and fluid power parameters of the bionic robotic fish in the flapping state are obtained. According to the method, the problem that the calculation amount of computational fluid mechanics simulation is exploded due to flexible pectoral fin flapping is avoided, the initial value of the particle swarm algorithm is defined, the adverse effect of the initial value on parameter optimization is reduced, the model simulation result is closer to the real situation in combination with experimental data identification parameters, and the method is suitable for large-scale popularization and application. Therefore, the accuracy of parameter identification is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of bionic robot parameter identification, and particularly to a method for identifying parameters of a dynamic model of a pectoral fin propulsion bionic robotic fish. Background Art

[0002] The progress of bionic underwater robot technology is of great significance to the development and protection of marine resources. Facing increasingly complex task requirements, more and more bionic robotic fish have been created, which mimic the nearly perfect underwater motion characteristics of fish, such as high efficiency, speed, stability, and maneuverability. Among them, pectoral fin propulsion robotic fish have become the new favorites of engineers and scientists due to their stronger stability and load capacity, and their dynamic models are of great significance for the theoretical analysis and motion control of their motions.

[0003] The hydrodynamic parameters of the dynamic model of bionic robotic fish are crucial for the simulation of the model and model-based motion control. It directly affects the accuracy of the model and is also one of the keys to whether the model can reflect the motion of real bionic robotic fish. For bionic robotic fish with flexible pectoral fins, their pectoral fins will deform when moving underwater due to the interaction with the surrounding fluid, and it is challenging to obtain accurate values of their hydrodynamic parameters. There are three common methods for parameter determination: hydrodynamic experiments, computational fluid dynamics software simulation, and parameter identification based on experimental data. Among them, hydrodynamic experiments require building relevant experimental platforms and involve a large amount of experimental work. In addition, although computational fluid dynamics software simulation can more accurately reflect the swimming details, due to the flexible deformation of the pectoral fins, it is prone to cause mesh distortion during simulation, resulting in calculation failure or explosion of the calculation amount. Some researchers reduce the workload of parameter identification based on experimental data through appropriate model simplification, but overly simplified models are difficult to accurately describe the motion state of bionic machines. Currently, it is quite difficult to obtain relatively accurate parameters for bionic robotic fish with flexible pectoral fins only through pure experimental or simulation methods.

[0004] Therefore, developing an identification method that can determine the hydrodynamic parameters of the dynamic model of pectoral fin propulsion bionic robotic fish has become the focus of current research. Summary of the Invention

[0005] The present invention provides a method for identifying parameters of a dynamic model of a pectoral fin propulsion bionic robotic fish to solve the problem that it is difficult to obtain accurate parameters of the dynamic model of bionic robotic fish in the existing technology.

[0006] The method for identifying parameters of a dynamic model of a pectoral fin propulsion bionic robotic fish of the present invention adopts the following technical solutions, including: Dividing the motion state of the bionic robotic fish into a flapping state and a gliding state; According to the method of hydrodynamic simulation, obtain the hydrodynamic parameters of the bionic robotic fish in the gliding state; Take the hydrodynamic parameters in the gliding state as the initial values, and perform particle swarm parameter optimization based on the experimental data of the bionic robotic fish in the flapping state. Take the optimized parameters as the hydrodynamic parameters of the bionic robotic fish in the flapping state.

[0007] Preferably, the flapping state is that the pectoral fins of the bionic robotic fish flap to make the bionic robotic fish swim forward; the gliding state is that the pectoral fins of the bionic robotic fish stop flapping, and the bionic robotic fish glides forward at the initial forward swimming speed.

[0008] Preferably, the dynamic model of the bionic robotic fish adopts a classical ship dynamic model.

[0009] Preferably, among them, the thrust in the classical ship dynamic model is defined as the thrust generated by the pectoral fins of the bionic robotic fish.

[0010] Preferably, the steps to obtain the hydrodynamic parameters of the bionic robotic fish in the gliding state are as follows: Import the shape parameters of the bionic robotic fish into the simulation software of computational fluid dynamics, and perform simulation after parameter setting; Perform non-dimensionalization calculation on the simulation results to obtain the hydrodynamic parameters of the bionic robotic fish in the gliding state.

[0011] Preferably, the shape parameters of the bionic robotic fish include: the body length, width, height, mass and volume of the bionic robotic fish.

[0012] Preferably, the steps to obtain the experimental data of the bionic robotic fish in the flapping state are as follows: Conduct a forward swimming experiment on the flapping state of the pectoral fin propulsion bionic robotic fish in the experimental pool, and obtain the initial experimental data of the bionic robotic fish in the flapping state through a motion capture system; Perform filtering processing on the initial experimental data to obtain the experimental data of the bionic robotic fish in the flapping state.

[0013] Preferably, it further includes: Input the hydrodynamic parameters in the gliding state and the dynamic parameters in the flapping state into the dynamic model respectively for simulation to obtain the first forward swimming speed; Conduct a forward swimming experiment on the alternating flapping state and gliding state of the pectoral fin propulsion bionic robotic fish in the pool, and record the second forward swimming speed; Calculate the speed error according to the stabilized first average forward swimming speed and the stabilized second average forward swimming speed. Take the hydrodynamic parameters in the gliding state and the dynamic parameters in the flapping state when the speed error is less than or equal to the preset speed error threshold as the final dynamic parameters.

[0014] Preferably, the speed error threshold is 0.05 m / s.

[0015] A parameter identification system for the dynamics model of a pectoral fin propulsion bionic robotic fish, comprising: A state division module for dividing the motion state of the bionic robotic fish into a flapping state and a gliding state; And a parameter identification module for obtaining the hydrodynamic parameters of the bionic robotic fish in the gliding state according to the method of computational fluid dynamics simulation; using the hydrodynamic parameters in the gliding state as the initial values, and performing particle swarm parameter optimization based on the experimental data of the bionic robotic fish in the flapping state, and taking the optimized parameters as the hydrodynamic parameters of the bionic robotic fish in the flapping state.

[0016] The beneficial effects of the present invention are: The present invention first divides the motion state of the pectoral fin propulsion bionic robotic fish into a flapping state and a gliding state, and then calculates the hydrodynamic parameters of the gliding state by using the method of computational fluid dynamics simulation. Since the pectoral fin does not move in the gliding state, it avoids the explosion of the computational amount of computational fluid dynamics simulation caused by the flapping of the flexible pectoral fin, and thus relatively accurate hydrodynamic parameters can be obtained with a relatively small computational amount. Under these parameters, the model can more realistically describe the gliding motion of the pectoral fin propulsion bionic robotic fish; secondly, using the hydrodynamic parameters of the gliding state as the initial values, the hydrodynamic parameters applicable in the flapping state are identified and updated by using the particle swarm algorithm based on the experimental data, reducing the adverse impact of the initial values on parameter optimization, and combining the experimental data to identify the parameters makes the model simulation results closer to the real situation, thereby ensuring the accuracy of parameter identification. Description of the Drawings

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0018] Figure 1 It is the overall structural schematic diagram of an embodiment of the parameter identification method for the dynamics model of a pectoral fin propulsion bionic robotic fish of the present invention; Figure 2 It is the speed curve graph of the first forward swimming speed obtained by respectively inputting the hydrodynamic parameters in the gliding state and the dynamic parameters in the flapping state into the dynamics model for simulation in the embodiment of the present invention; Figure 3 It is the speed curve graph of the second forward swimming speed obtained by conducting a forward swimming experiment of alternately flapping and gliding states of the pectoral fin propulsion bionic robotic fish in a pool in the embodiment of the present invention. Detailed implementation manners

[0019] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0020] An embodiment of the parameter identification method for the dynamic model of a pectoral fin propulsion bionic robot fish of the present invention is as Figure 1 shown and includes: S1. Divide the motion state of the bionic robot fish into a flapping state and a gliding state; Among them, the flapping state means that the pectoral fins of the bionic robot fish flap to make the bionic robot fish swim forward; the gliding state means that the pectoral fins of the bionic robot fish stop flapping, and the bionic robot fish glides forward at the initial forward swimming speed.

[0021] In an exemplary embodiment, the dynamic model of the bionic robot fish adopts a classical ship dynamic model, that is, the Fossen equation is used to express the dynamic model of the bionic robot fish, and the source of the thrust in the Fossen equation is defined as the thrust generated by the pectoral fins of the bionic robot fish. Specifically, the expression of the dynamic model is:

[0022] In the formula, represents the pose of the bionic robot fish; represents the generalized velocity of the bionic robot fish (including forward swimming velocity, lateral velocity, vertical velocity, roll angular velocity, pitch angular velocity, yaw angular velocity); represents the rotation matrix; represents the inertia matrix including added mass; is the Coriolis and centripetal matrix; represents the hydrodynamic damping matrix; is the restoring force and restoring moment; represents the input force and input moment generated by the pectoral fins of the bionic robot fish; represents the hydrodynamic parameters to be identified. Among them, ; among them, represents the drag coefficient with the maximum cross-sectional area as the reference area; represents the bionic robot fish at the th motion state, the position derivative of the lift coefficient with respect to the angle of attack ; represents the bionic robot fish at the th motion state, the pitch moment coefficient with respect to the angle of attack The position derivative; Denote the position derivative of the lift coefficient with respect to the sideslip angle when the biomimetic robotic fish is in the th motion state; Denote the position derivative of the yaw moment coefficient with respect to the sideslip angle when the biomimetic robotic fish is in the th motion state; Denote the position derivative of the lift coefficient with respect to the horizontal rudder angle when the biomimetic robotic fish is in the th motion state; Denote the position derivative of the pitch moment coefficient with respect to the horizontal rudder angle when the biomimetic robotic fish is in the th motion state; Denote the rotational derivative of the lift coefficient with respect to when the biomimetic robotic fish is in the th motion state; Denote the rotational derivative of the lift coefficient with respect to ; Denote the rotational derivative of the pitch moment coefficient with respect to when the biomimetic robotic fish is in the th motion state; Denote the rotational derivative of the pitch moment coefficient with respect to when the biomimetic robotic fish is in the th motion state; Denote the position derivative of the roll moment coefficient with respect to the sideslip angle when the biomimetic robotic fish is in the th motion state; Denote the rotational derivative of the roll moment coefficient with respect to when the biomimetic robotic fish is in the th motion state; Denote the rotational derivative of the roll moment coefficient with respect to when the biomimetic robotic fish is in the th motion state, where takes (1, 2), when takes 1, it represents that the motion state of the biomimetic robotic fish is the gliding state;

[0023] S2. Obtain the hydrodynamic parameters of the biomimetic robotic fish in the gliding state; Specifically, according to the method of hydrodynamic simulation, obtain the hydrodynamic parameters of the biomimetic robotic fish in the gliding state.

[0024] In an exemplary embodiment, according to the method of hydrodynamic simulation, the steps to obtain the hydrodynamic parameters of the bionic robotic fish in the gliding state are as follows: Import the shape parameters of the bionic robotic fish into the simulation software of computational fluid dynamics. In this embodiment, Fluent software is used. Set the number of grids, the incoming flow velocity, the angle of attack of the robotic fish, and the sideslip angle of the robotic fish in the Fluent software and perform the simulation; perform non-dimensionalization calculation on the simulation results to obtain the hydrodynamic parameters of the bionic robotic fish in the gliding state. Among them, the shape parameters of the bionic robotic fish include: the body length, width, height, mass, and volume of the bionic robotic fish.

[0025] In an exemplary embodiment, the hydrodynamic parameters of the bionic robotic fish in the gliding state obtained after non-dimensional calculation are shown in Table 1.

[0026] Table 1

[0027] S3. Obtain the hydrodynamic parameters of the bionic robotic fish in the flapping state; Specifically, use the hydrodynamic parameters in the gliding state obtained in step S2 as the initial values, and perform particle swarm parameter optimization based on the experimental data of the bionic robotic fish in the flapping state. Use the optimized parameters as the hydrodynamic parameters of the bionic robotic fish in the flapping state.

[0028] In an exemplary embodiment, the steps to obtain the experimental data of the bionic robotic fish in the flapping state are as follows: Conduct a forward swimming experiment on the pectoral fin propulsion bionic robotic fish in the flapping state in an experimental pool, and obtain the initial experimental data of the bionic robotic fish in the flapping state through a motion capture system; perform Kalman filtering on the initial experimental data to obtain the experimental data of the bionic robotic fish in the flapping state.

[0029] In an exemplary embodiment, the hydrodynamic parameters of the bionic robotic fish in the flapping state are shown in Table 2.

[0030] Table 2

[0031] Based on steps S1, S2, and S3, it further includes: Input the hydrodynamic parameters in the gliding state and the dynamic parameters in the flapping state into the dynamic model respectively for simulation to obtain the first forward swimming speed. The curve graph of the first forward swimming speed is as Figure 2 shown; Conduct a forward swimming experiment on the pectoral fin propulsion bionic robotic fish in the flapping state and the gliding state alternately in the pool, and record to obtain as Figure 3The curve graph of the second forward swimming speed shown; calculate the speed error based on the stabilized first average forward swimming speed and the stabilized second average forward swimming speed, and use the hydrodynamic parameters in the gliding state and the dynamic parameters in the flapping state when the speed error is less than the preset speed error threshold as the final dynamic parameters.

[0032] By Figure 2 and Figure 3 Comparing, it can be seen that the error between the first average forward swimming speed and the second average forward swimming speed is small after the forward swimming speed is stabilized, and the error is 0.05 m / s. The hydrodynamic parameters in the gliding state and the dynamic parameters in the flapping state identified in this embodiment are effective. Therefore, the hydrodynamic parameters in the gliding state and the dynamic parameters in the flapping state are used as the final dynamic parameters.

[0033] A parameter identification system for the dynamic model of a pectoral fin propulsion bionic robot fish, including: a state division module and a parameter identification module. The state division module is used to divide the motion state of the bionic robot fish into a flapping state and a gliding state; the parameter identification module is used to obtain the hydrodynamic parameters of the bionic robot fish in the gliding state according to the method of computational fluid dynamics simulation; use the hydrodynamic parameters in the gliding state as the initial value, and perform particle swarm parameter optimization based on the experimental data of the bionic robot fish in the flapping state, and use the optimized parameters as the hydrodynamic parameters of the bionic robot fish in the flapping state.

[0034] In summary, due to the fact that the pectoral fins of the pectoral fin propulsion bionic robot fish will undergo flexible deformation in the pectoral fin flapping state, the method of computational fluid dynamics simulation is prone to grid distortion or computational explosion. At the same time, the method of determining hydrodynamic parameters based on experimental data using particle swarm algorithms and the like is relatively dependent on the initial value of the parameters. Therefore, the parameter identification method for the dynamic model of a pectoral fin propulsion bionic robot fish provided by the embodiments of the present invention first; calculates the hydrodynamic parameters in the gliding state using the method of computational fluid dynamics simulation, and then based on this parameter, uses the particle swarm algorithm to identify and update the applicable hydrodynamic parameters in the flapping state based on experimental data, so as to ensure the accuracy of parameter identification.

[0035] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A parameter identification method for a pectoral fin propulsion bionic robotic fish dynamics model, characterized in that: include: The motion state of the bionic robot fish is divided into flapping state and gliding state; According to the method of fluid mechanics simulation, the fluid dynamic parameters of the bionic robot fish in the gliding state are obtained; The fluid dynamic parameters in the gliding state are taken as the initial values, and the particle swarm parameters are optimized based on the experimental data of the bionic robotic fish in the flapping state, and the optimized parameters are used as the fluid dynamic parameters of the bionic robotic fish in the flapping state.

2. The parameter identification method of the pectoral fin propulsion bionic robotic fish dynamics model according to claim 1 is characterized in that: The flapping state is when the pectoral fins of the bionic robotic fish flap, causing the bionic robotic fish to swim forward; the gliding state is when the pectoral fins of the bionic robotic fish stop flapping, and the bionic robotic fish glides forward at the initial forward swimming speed.

3. The parameter identification method of the pectoral fin propulsion bionic robotic fish dynamics model according to claim 1 is characterized in that: The dynamic model of the bionic robotic fish adopts the classic ship dynamic model.

4. The parameter identification method of the pectoral fin propulsion bionic robotic fish dynamics model according to claim 3 is characterized in that: in, The thrust in the classic ship dynamics model is defined as the thrust generated by the pectoral fins of the bionic robotic fish.

5. The parameter identification method of the pectoral fin propulsion bionic robotic fish dynamics model according to claim 1 is characterized in that: The steps to obtain the fluid dynamic parameters of the bionic robotic fish in the gliding state are: Import the shape parameters of the bionic robot fish into the simulation software of computational fluid dynamics, and perform simulation after setting the parameters; The simulation results are dimensionlessly calculated to obtain the fluid dynamic parameters of the bionic robotic fish in the gliding state.

6. The parameter identification method of the pectoral fin propulsion bionic robotic fish dynamics model according to claim 5 is characterized in that: The appearance parameters of the bionic robot fish include: length, width, height, mass and volume of the bionic robot fish.

7. The parameter identification method of the pectoral fin propulsion bionic robotic fish dynamics model according to claim 1 is characterized in that: The steps to obtain the experimental data of the bionic robot fish in the flapping state are: A forward swimming experiment of the bionic robot fish in a flapping state with pectoral fin propulsion was conducted in the experimental pool, and the initial experimental data of the bionic robot fish in the flapping state was obtained through the motion capture system; The initial experimental data were filtered to obtain the experimental data of the bionic robotic fish in the flapping state.

8. The parameter identification method of the pectoral fin propulsion bionic robotic fish dynamics model according to claim 1 is characterized in that: Also includes: The fluid dynamic parameters in the gliding state and the dynamic parameters in the flapping state are respectively input into the dynamic model for simulation to obtain the first forward swimming speed; Conduct a forward swimming experiment in a pool with the pectoral fin-propelled bionic robotic fish alternating between flapping and gliding states, and record the second forward swimming speed; The speed error is calculated according to the stabilized first average forward swimming speed and the stabilized second average forward swimming speed, and the fluid dynamic parameters in the gliding state and the dynamic parameters in the flapping state when the speed error is less than or equal to the preset speed error threshold are taken as the final dynamic parameters.

9. The parameter identification method of the pectoral fin propulsion bionic robotic fish dynamics model according to claim 8, characterized in that: The speed error threshold is 0.05m / s.

10. A parameter identification system for a pectoral fin propulsion bionic robotic fish dynamics model, characterized in that: include: A state division module, used for dividing the motion state of the bionic robot fish into a flapping state and a gliding state; and a parameter identification module, for obtaining the fluid dynamic parameters of the bionic robotic fish in a gliding state according to a fluid mechanics simulation method; The fluid dynamic parameters in the gliding state are taken as the initial values, and the particle swarm parameters are optimized based on the experimental data of the bionic robotic fish in the flapping state, and the optimized parameters are used as the fluid dynamic parameters of the bionic robotic fish in the flapping state.

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