Bionic machine dolphin multi-mode trajectory tracking control method, device and equipment

Abstract

The application provides a multi-mode trajectory tracking control method, device and equipment of a bionic machine dolphin, wherein the method comprises the following steps: acquiring the current position, current yaw attitude and target trajectory of the bionic machine dolphin; determining the target forward linear velocity and target yaw angular velocity based on the current position, current yaw attitude and target trajectory; obtaining the forward thrust and yaw torque output by a tracking controller based on the speed control law and yaw control law of the tracking controller in the bionic machine dolphin and the target forward linear velocity and target yaw angular velocity; determining the control execution parameter of the tracking controller based on the steering mode, forward thrust and yaw torque of the bionic machine dolphin, and performing trajectory tracking control of the bionic machine dolphin based on the control execution parameter. The method, device, electronic equipment and storage medium provided by the application fully consider the obstacle avoidance distance and heading angle information, improve the smoothness and safety of the obstacle avoidance path, and do not need to carry out method verification through simulation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of underwater robot control, and in particular to a multi-modal trajectory tracking control method, device and equipment for a bionic robot dolphin. BACKGROUND

[0002] The trajectory tracking control problem has always been a research hotspot of underwater robots, and the goal is to enable the robot to start from any point, design a tracking controller, and gradually converge to the target trajectory under time constraints. Trajectory tracking technology is an important part of underwater operations and has important significance for the smooth completion of marine operations.

[0003] However, the trajectory tracking control in the prior art mostly only verifies the method through simulation, and lacks consideration of obstacle avoidance factors in the trajectory tracking process. SUMMARY

[0004] The present application provides a multi-modal trajectory tracking control method, device and equipment for a bionic robot dolphin, to solve the defects in the prior art that trajectory tracking control mostly only verifies the method through simulation and lacks consideration of obstacle avoidance factors in the trajectory tracking process.

[0005] The present application provides a multi-modal trajectory tracking control method for a bionic robot dolphin, comprising:

[0006] Obtaining the current position, current yaw attitude and target trajectory of the bionic robot dolphin;

[0007] Determining the target forward linear velocity and target yaw angular velocity based on the current position, current yaw attitude and target trajectory;

[0008] Based on the speed control law and yaw control law of the tracking controller in the bionic robot dolphin, and the target forward linear velocity and target yaw angular velocity, obtaining the forward thrust and yaw moment output by the tracking controller;

[0009] Based on the steering mode of the bionic robot dolphin, the forward thrust and yaw moment output by the tracking controller, determining the control execution parameters of the tracking controller, and based on the control execution parameters, performing trajectory tracking control of the bionic robot dolphin.

[0010] According to the multi-modal trajectory tracking control method for a bionic robot dolphin provided by the present application, the determination of the control execution parameters of the tracking controller based on the steering mode of the bionic robot dolphin, the forward thrust output by the tracking controller and the yaw moment comprises:

[0011] Determining the forward thrust rate of change based on the forward thrust of the tracking controller, and determining the yaw moment rate of change based on the yaw moment.

[0012] determine a steering mode of the biomimetic robotic dolphin based on the yaw moment;

[0013] determine a control execution parameter of the tracking controller based on the steering mode of the biomimetic robotic dolphin, the forward thrust output by the tracking controller, the yaw moment, the forward thrust rate of change, and the yaw moment rate of change.

[0014] According to the multi-mode trajectory tracking control method of the biomimetic robotic dolphin provided by the application, the control execution parameter of the tracking controller is determined based on the steering mode of the biomimetic robotic dolphin, the forward thrust output by the tracking controller, the yaw moment, the forward thrust rate of change, and the yaw moment rate of change, which comprises:

[0015] determine a tail fin oscillation frequency in the control execution parameter based on the steering mode of the biomimetic robotic dolphin, the forward thrust output by the tracking controller, and the forward thrust rate of change;

[0016] determine a pectoral fin flapping frequency in the control execution parameter based on the steering mode of the biomimetic robotic dolphin, the yaw moment, and the yaw moment rate of change.

[0017] According to the multi-mode trajectory tracking control method of the biomimetic robotic dolphin provided by the application, the steering mode of the biomimetic robotic dolphin is determined based on the yaw moment, which comprises:

[0018] determine the steering mode M of the biomimetic robotic dolphin based on the following formula:

[0019]

[0020] wherein M1 represents a first steering mode, M2 represents a second steering mode, M3 represents a third steering mode, the pectoral fin flapping states of the first steering mode, the second steering mode, and the third steering mode are different, g1 and g2 represent adjustment weight parameters, |τ r | represents the absolute value of the yaw moment, τ rmax represents the maximum value of the yaw moment.

[0021] According to the multi-mode trajectory tracking control method of the biomimetic robotic dolphin provided by the application, the target forward linear velocity and the target yaw angular velocity are determined based on the current position, the current yaw attitude, and the target trajectory, which comprises:

[0022] determine a tracking error based on the current position, the current yaw attitude, and the target trajectory;

[0023] determine the target forward linear velocity and the target yaw angular velocity based on the tracking error.

[0024] According to the application, a multi-mode trajectory tracking control method for a bionic machine dolphin is provided. u is determined based on the following formula:

[0025]

[0026] is determined based on the following formula: r

[0027]

[0028] wherein, denotes the reciprocal of g u (u), denotes the reciprocal of g r (r), denotes a target forward acceleration, denotes a target yaw angular acceleration, e u = u - u d r = r - r e , u d denotes an error variable of the forward linear velocity, e r e denotes an error variable of the yaw angular velocity, k1 and k2 are both positive coefficients, denotes an estimation of the target forward linear velocity, denotes an estimation of the target yaw angular velocity, M = diag(m 11 ,m 22 ,m 33 ) denotes a mass parameter matrix, and D = diag(d 11 ,d 22 ,d 33 ) denotes a damping parameter matrix.

[0029] The application further provides a multi-mode trajectory tracking control device for a bionic machine dolphin, comprising:

[0030] an acquisition unit configured to acquire a current position, a current yaw attitude and a target trajectory of the bionic machine dolphin;

[0031] a determination unit configured to determine a target forward linear velocity and a target yaw angular velocity based on the current position, the current yaw attitude and the target trajectory;

[0032] ​The tracking control unit is used to obtain the forward thrust and yaw torque output by the tracking controller based on the velocity control law and yaw control law of the tracking controller in the bionic robotic dolphin, as well as the forward linear velocity and the yaw angular velocity of the target.

[0033] The trajectory tracking control unit is used to determine the control execution parameters of the tracking controller based on the steering mode of the bionic robotic dolphin, the forward thrust output by the tracking controller, and the yaw torque, and to perform trajectory tracking control of the bionic robotic dolphin based on the control execution parameters.

[0034] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the multimodal trajectory tracking control method for the biomimetic robotic dolphin as described above.

[0035] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the multimodal trajectory tracking control method for the biomimetic robotic dolphin as described above.

[0036] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the multimodal trajectory tracking control method for the biomimetic robotic dolphin as described above.

[0037] The present invention provides a multimodal trajectory tracking control method, apparatus, and device for a biomimetic robotic dolphin. Based on the velocity control law and yaw control law of the tracking controller in the biomimetic robotic dolphin, as well as the target's forward linear velocity and target yaw angular velocity, the forward thrust and yaw torque output by the tracking controller are obtained. Then, based on the turning mode of the biomimetic robotic dolphin, the forward thrust output by the tracking controller, and the yaw torque, the control execution parameters of the tracking controller are determined. Based on the control execution parameters, the trajectory tracking control of the biomimetic robotic dolphin is performed. This process fully considers obstacle avoidance distance and heading angle information, improves the smoothness and safety of the obstacle avoidance path, eliminates the need for method verification through simulation, and determines the target's forward linear velocity and target yaw angular velocity based on a nonlinear prediction model planner, improving anti-interference capability and further enhancing the accuracy and precision of the trajectory tracking control of the biomimetic robotic dolphin. Attached Figure Description

[0038] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0039] Figure 1 is a flowchart of a multi-modal trajectory tracking control method of a bionic machine dolphin provided by the present application;

[0040] Figure 2 is a schematic diagram of trajectory tracking control in a complex environment provided by the present application;

[0041] Figure 3 is a structural schematic diagram of a bionic machine dolphin provided by the present application;

[0042] Figure 4 is a structural schematic diagram of a multi-modal trajectory tracking control device of a bionic machine dolphin provided by the present application;

[0043] Figure 5 is a structural schematic diagram of an electronic device provided by the present application. DETAILED DESCRIPTION

[0044] In order to make the objects, technical solutions and advantages of the present application clearer, the technical solutions in the present application will be described clearly and completely below with reference to the drawings in the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0045] The terms "first", "second", and the like in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of a kind.

[0046] With the development of economy and underwater technology, human beings are more and more eager to explore the unknown ocean. In recent years, underwater autonomous operation such as seabed exploration, ocean observation, ocean search and rescue has attracted more and more attention of scientists and engineers. In order to successfully and efficiently complete the above tasks, it is a key link to realize stable and effective tracking control in complex and narrow underwater environment. The traditional autonomous underwater vehicle (AUV) driven by propeller has the advantages of compact structure and simple control, and plays an important role in underwater autonomous operation. However, the traditional AUV has inherent defects of large noise, poor environmental friendliness and poor maneuverability. In recent years, with the help of emerging bionics and robot technology, underwater bionic robots have emerged. By imitating the shape and movement mechanism of natural organisms, underwater bionic robots have the advantages of high efficiency, high maneuverability and strong concealment. Therefore, compared with the traditional AUV, the underwater bionic robot is more suitable for autonomous operation in complex and narrow environment.

[0047] The trajectory tracking control problem has always been a research hotspot of underwater robots, and the goal is to enable the robot to start from any point, design a tracking controller, and gradually converge to the target trajectory under time constraints. The trajectory tracking technology is an important part of underwater operation, and has important significance for the smooth completion of ocean operation.

[0048] In the prior art, some express the trajectory tracking problem as a convex optimization problem, and apply a dynamic controller with a disturbance observer to realize target tracking. Some propose a trajectory tracking control method based on robust adaptation, which combines a robust sliding mode controller and an adaptive law. Some use yaw dynamics without continuous excitation to solve the trajectory tracking problem, and use a finite time uncertainty observer to estimate the uncertainty, and realize trajectory tracking.

[0049] However, the trajectory tracking control in the prior art mostly only carries out method verification through simulation, and lacks consideration of obstacle avoidance factors in the trajectory tracking process.

[0050] Based on the above problems, the present application provides a multi-modal trajectory tracking control method of a bionic robot dolphin, Figure 1 The flowchart of the multi-modal trajectory tracking control method of the bionic robot dolphin provided by the present application is shown in Figure 1 As shown in the figure, the method comprises:

[0051] Step 110, obtaining the current position, current yaw attitude and target trajectory of the bionic robot dolphin.

[0052] Specifically, considering that when performing underwater operation, complex and narrow environment is often encountered, such as reef cluster, uneven terrain or water area with obstacles. The bionic machine dolphin has high maneuverability, and can complete tracking operation in complex environment by virtue of its multi-modal movement form.

[0053] The bionic machine dolphin is a simplified model of high-speed swimming marine organism dolphin, and is provided with a planner, a tracking controller and a modal distributor. The bionic machine dolphin is mainly composed of a waist tail device and a pectoral fin device, wherein the waist tail joints are all driven by motors, and the two sides of the pectoral fin are driven by rudders. In the embodiment of the application, the Body and / or Caudal Fin (BCF) of the waist tail device is mainly used to provide thrust, and the Median and / or Paired Fin (MPF) of the pectoral fin device is used to generate yawing moment.

[0054] The current position, the current yawing posture and the target trajectory of the bionic machine dolphin can be obtained, wherein the current position of the bionic machine dolphin refers to the position coordinate of the bionic machine dolphin at present, and the current position of the bionic machine dolphin can be represented as (x, y). The current yawing posture reflects the degree of deviation of the current position of the bionic machine dolphin from the target trajectory, and the current yawing posture can include yawing angle, planar linear velocity and yawing angular velocity, the yawing angle can be represented as ψ, and the planar linear velocity and the yawing angular velocity can be represented as (u, v, r), wherein u and v represent the planar linear velocity, and r represents the yawing angular velocity.

[0055] Thus, the kinematics equation of the bionic machine dolphin can be derived as follows:

[0056]

[0057]

[0058]

[0059] The target trajectory refers to the trajectory of the target to be tracked by the bionic machine dolphin, and the target trajectory can be represented as p d (t)=(x d (t),y d (t)) represents.

[0060] In step 120, the target forward linear velocity and the target yawing angular velocity are determined based on the current position, the current yawing posture and the target trajectory.

[0061] Specifically, considering the trajectory tracking accuracy and safety obstacle avoidance double factors, the planner is arranged in the bionic machine dolphin. After the current position, the current yaw attitude and the target trajectory are obtained, the target forward linear velocity and the target yaw angular velocity can be determined based on the current position, the current yaw attitude and the target trajectory.

[0062] The target forward linear velocity here refers to the expected forward linear velocity of the bionic machine dolphin, and the target forward linear velocity can be expressed as u d . The target yaw angular velocity refers to the expected yaw angular velocity of the bionic machine dolphin, and the target yaw angular velocity can be expressed as r d .

[0063] That is, the input of the planner can be the current position, the current yaw attitude and the target trajectory, and the output of the planner can be the target forward linear velocity and the target yaw angular velocity, that is, the output of the planner is u f =(u d ,r d ).

[0064] Therefore, the tracking error can be expressed as p e (t) = (x e (t), y e (t)) = (x d -x, y d -y). Further, considering that the velocity planning is an optimization problem with multiple objectives and motion constraints, the planner based on the nonlinear model prediction method is designed to complete the real-time task, and the optimization problem can be expressed as the following minimum cost function:

[0065]

[0066] J(p e , D ob , u f , t k ) = J(p e (t k ), D ob (t k ), u f (t k ))

[0067] L = p e (τ|t k ) T Qp e (τ|t k ) + D ob (τ|t k ) T HD ob (τ|t k ) + uf (τ|t k ) T Ru f (τ|t k )

[0068] g=Ξ T KΞ

[0069] Ξ=(p e (t k +T|t k ),D ob (t k +T|t k ))

[0070] u f ∈[u fmin ,u fmax ]

[0071] Wherein, T represents a prediction period, a control period is set to half of the prediction period, L represents a trajectory tracking cost quantity, g represents a terminal cost, Q, R, H and K represent positive coefficient matrices, D ob represents a designed nonlinear obstacle avoidance term, J(p e , D ob , u f , t k ) represents a cost function, is an objective of the entire optimization problem to be solved, and the value of the cost function J(p e , D ob , u f , t k ) is expected to be minimized under continuous adjustment of the value of u f , p e (t k ) represents a tracking error between a current trajectory and an expected trajectory, u f represents a control output, which is a two-dimensional vector, u f includes a target forward linear velocity and a target yaw angular velocity, and t k represents a kth control time, i.e., a time at which the biomimetic dolphin currently prepares to optimize the control output.

[0072] This term fully considers the obstacle avoidance distance and the heading angle information, improves the smoothness and safety of the obstacle avoidance path, and the specific solving process is as follows:

[0073] Firstly, input n obstacle coordinates

[0074] Secondly, poll to find the nearest obstacle And the direction vector

[0075] Step 4, calculate the relative azimuth angle between the real-time position and the nearest obstacle d ob (t) = ||ξ ob (t) ||.

[0076] Step 5,

[0077] d(t) = c1·d max

[0078] else if |ξ ob (t) || > d thresh

[0079] d(t) = c1·d max

[0080] else

[0081] Step 6, calculate the final obstacle penalty term:

[0082] where d thresh and d max represent the distance threshold and the maximum safety distance respectively (both are normal numbers), and c1 and c2 represent two weight coefficients respectively (both are normal numbers).

[0083] Then, by solving the above optimization problem, the optimal control sequence can be obtained. Further, by applying the first value of the control sequence to the planner of the biomimetic dolphin and repeating the process, the target forward linear velocity and the target yaw angular velocity are obtained.

[0084] Step 130, based on the speed control law and the yaw control law of the tracking controller in the biomimetic dolphin, and the target forward linear velocity and the target yaw angular velocity, the forward thrust and the yaw moment of the tracking controller output are obtained.

[0085] Specifically, after obtaining the target forward linear velocity and the target yaw angular velocity, the forward thrust and the yaw moment of the tracking controller output can be obtained based on the speed control law and the yaw control law of the tracking controller in the biomimetic dolphin, and the target forward linear velocity and the target yaw angular velocity. The speed control law here is used to control the speed of the biomimetic dolphin, and the yaw control law here is used to control the yaw attitude of the biomimetic dolphin. The forward thrust of the tracking controller here refers to the forward thrust required for the biomimetic dolphin to perform tracking control, and the yaw moment here refers to the general term of a type of moment that changes the yaw angle of the biomimetic dolphin.

[0086] First, ignoring the pitch and roll motions of the bionic machine dolphin, the dynamics equation can be obtained as follows:

[0087]

[0088]

[0089]

[0090] where M = diag(m 11 ,m 22 ,m 33 ) represents a mass parameter matrix; D = diag(d 11 ,d 22 ,d 33 ) represents a damping parameter matrix; diag(·) represents a diagonal matrix; τ u and τ r respectively represent forward thrust and yaw moment of the tracking controller; (δ u , δ v , δ r ) represents external disturbance.

[0091] In order to express concisely, the above dynamics in the forward speed and yaw angular velocity dimensions can be rearranged as follows:

[0092]

[0093]

[0094] Figure 2 The schematic diagram of trajectory tracking control in a complex environment provided by the present application is shown in Figure 1, and further, in order to reduce the influence of external disturbance on tracking control, the embodiment of the present application applies a nonlinear observer to estimate external disturbance to compensate for tracking control, and the observer is in the form as follows: Figure 2

[0095]

[0096]

[0097]

[0098] where j = u, r respectively represent forward and yaw sub-items; represents an estimated value, the present application assumes δ v = 0; l j (j) represents an observer gain value, which can be calculated by represents an estimation error, and the estimation error ​​Under certain conditions, it can be proved that the gradual convergence can be achieved, for example, the conditions are: if l j The selection of (j) satisfies Thus, the following can be obtained Where B j are all normal numbers.

[0099] Finally, the tracking controller mainly takes the design of Lyapunov function as the criterion, and derives the backstepping control law while ensuring the convergence of the system. First, define the error variable u e = u-u d , r e = r-r d , and then define the Lyapunov function as follows:

[0100]

[0101] By substituting the above speed and yaw dynamics, the derivative form can be obtained:

[0102]

[0103] Further, the embodiment of the present application designs the speed control law τ u and the yaw control law τ r , as follows:

[0104]

[0105]

[0106] Wherein, represents the inverse of g u (u), and represents the inverse of g r (r), represents the target forward acceleration, represents the target yaw angle acceleration, u e = u-u d , r e = r-r d , u e represents the error variable of the forward linear velocity, r e represents the error variable of the yaw angle velocity, k1 and k2 are both positive coefficients, represents the estimated value of the target forward linear velocity, represents the estimated value of the target yaw angle velocity, M = diag(m 11 , m 22 , m 33 ) represents the mass parameter matrix, and D = diag(d 11 , d 22 , d33 ) denotes a damping parameter matrix.

[0107] Therefore, based on the Young inequality,

[0108]

[0109] where κ = min{2k1-1,2k2-1},

[0110] The embodiment of the present application can make the tracking error achieve consistent final boundedness by adjusting the parameters k1 and k2, so that κ>0.

[0111] In step 140, the control execution parameter of the tracking controller is determined based on the steering mode of the biomimetic machine dolphin, the forward thrust output by the tracking controller and the yawing moment, and the trajectory tracking control of the biomimetic machine dolphin is performed based on the control execution parameter.

[0112] Specifically, after obtaining the forward thrust and the yawing moment of the tracking controller, the control execution parameter of the tracking controller can be determined based on the steering mode of the biomimetic machine dolphin, the forward thrust output by the tracking controller and the yawing moment.

[0113] The steering mode of the biomimetic machine dolphin here refers to the pectoral fin flapping state of the biomimetic machine dolphin, and the steering mode of the biomimetic machine dolphin can be obtained based on the mode distributor of the biomimetic machine dolphin. The steering mode of the biomimetic machine dolphin can include three steering modes M1, M2 and M3.

[0114] The steering mode M1 is that one side of the pectoral fin of the biomimetic machine dolphin is kept at zero position and the other side of the pectoral fin flaps. Flapping the pectoral fin on one side can generate forward thrust to provide yawing moment for the body.

[0115] The steering mode M2 is that one side of the pectoral fin of the biomimetic machine dolphin is biased by 90° and the other side of the pectoral fin flaps. The biased pectoral fin can generate resistance on one side to increase the differential moment on both sides.

[0116] The steering mode M3 is that one side of the pectoral fin of the biomimetic machine dolphin flaps and the other side of the pectoral fin flaps reversely. The differential moment generated by the flapping on both sides is used for steering. The flapping reversely of the other side of the pectoral fin refers to first deflecting the pectoral fin by 180° and then flapping.

[0117] Obviously, the maneuverability of the above three steering modes is in the order of M3>M2>M1, and in particular, the differential flapping mode can basically achieve steering in place.

[0118] The control execution parameter of the tracking controller here refers to the parameter used by the biomimetic machine dolphin for trajectory tracking control, and the control execution parameter can include the tail fin flapping frequency and the pectoral fin flapping frequency.

[0119] In addition, in determining the control execution parameter of the tracking controller, a forward thrust rate of change determined based on the forward thrust of the tracking controller and a yaw moment rate of change determined based on the yaw moment can also be combined.

[0120] After the control execution parameter of the tracking controller is determined, trajectory tracking control of the biomimetic robotic dolphin can be performed based on the control execution parameter. That is, the control execution parameter can be directly applied to the biomimetic robotic dolphin, so as to perform trajectory tracking control of the biomimetic robotic dolphin.

[0121] The method provided by the embodiment of the application obtains the forward thrust and the yaw moment output by the tracking controller based on the speed control law and the yaw control law of the tracking controller in the biomimetic robotic dolphin and the target forward linear velocity and the target yaw angular velocity, determines the control execution parameter of the tracking controller based on the steering mode of the biomimetic robotic dolphin, the forward thrust and the yaw moment output by the tracking controller, and performs trajectory tracking control of the biomimetic robotic dolphin based on the control execution parameter. This process fully considers the obstacle avoidance distance and the heading angle information, improves the smoothness and safety of the obstacle avoidance path, does not need to carry out method verification through simulation, and determines the target forward linear velocity and the target yaw angular velocity based on the planner of the nonlinear prediction model, improves the anti-interference ability, and further improves the precision and accuracy of the trajectory tracking control of the biomimetic robotic dolphin.

[0122] Based on the above embodiment, step 140 comprises:

[0123] Step 141, determining a forward thrust rate of change based on the forward thrust of the tracking controller and a yaw moment rate of change based on the yaw moment;

[0124] Step 142, determining the steering mode of the biomimetic robotic dolphin based on the yaw moment;

[0125] Step 143, determining the control execution parameter of the tracking controller based on the steering mode of the biomimetic robotic dolphin, the forward thrust output by the tracking controller, the yaw moment, the forward thrust rate of change, and the yaw moment rate of change.

[0126] Specifically, on the one hand, the biomimetic robotic dolphin can realize various yaw modes through the coordinated movement of the two chest fins, and the steering design needs to be performed according to the maneuvering performance of different modes. On the other hand, the joint movement is derived from the rhythm signal generated by the central pattern generator (including the dorsal-ventral propulsion of the tail fin and the propulsion of the chest fin), and the forward thrust and the yaw moment need to be mapped to the control execution parameter.

[0127] After the forward thrust and the yawing moment are obtained, considering the actual application, the tracking controller is calculated periodically by using an embedded chip, so according to the control period defined by a user, the forward thrust difference is obtained by subtracting the forward thrust of the last control period from the current forward thrust, and then the forward thrust variation rate is obtained by dividing the forward thrust difference by the control period. Similarly, the yawing moment variation rate can also be obtained in this way. That is, the forward thrust variation rate can be determined based on the forward thrust of the tracking controller, and the yawing moment variation rate can be determined based on the yawing moment.

[0128] Then, the steering mode of the bionic machine dolphin can be determined based on the yawing moment.

[0129] That is, the steering mode M of the bionic machine dolphin can be determined based on the following formula:

[0130]

[0131] Wherein, M1 represents the first steering mode, M2 represents the second steering mode, and M3 represents the third steering mode, the flapping states of the pectoral fins of the first steering mode, the second steering mode and the third steering mode are different, g1 and g2 represent the adjustment weight parameters, |τ r | represents the absolute value of the yawing moment, τ rmax represents the maximum value of the yawing moment.

[0132] The steering mode M1 here: the bionic machine dolphin's unilateral pectoral fin zero position is kept, and the other side pectoral fin flaps. Flapping the unilateral pectoral fin can generate forward thrust, thereby providing the body with a yawing moment.

[0133] The steering mode M2: the bionic machine dolphin's unilateral pectoral fin is 90° biased, and the other side pectoral fin flaps. The biased pectoral fin can generate resistance on one side, thereby increasing the differential moment of the two sides.

[0134] The steering mode M3: the bionic machine dolphin's unilateral pectoral fin flaps, and the other side pectoral fin reversely flaps, and the steering is performed by the differential moment generated by the flapping of the two sides. Wherein, the other side pectoral fin reversely flaps means that the pectoral fin is first deflected by 180°, and then flapped. The steering mode M3 is taken as the main mode in the embodiment of the application.

[0135] Finally, the steering mode of the bionic machine dolphin can be determined first, and after the steering mode of the bionic machine dolphin is determined, the control execution parameters of the tracking controller can be determined based on the forward thrust, the yawing moment, the forward thrust variation rate and the yawing moment variation rate output by the tracking controller.

[0136] Here, the control execution parameter of the tracking controller can be determined based on the forward thrust, the yaw moment, the rate of change of the forward thrust and the rate of change of the yaw moment output by the tracking controller, and the fuzzy rule table.

[0137] The method provided by the embodiment of the application determines the steering mode of the bionic machine dolphin based on the yaw moment, and determines the control execution parameter of the tracking controller based on the steering mode of the bionic machine dolphin, the forward thrust, the yaw moment, the rate of change of the forward thrust and the rate of change of the yaw moment output by the tracking controller, which can avoid damaging the mechanical structure due to frequent mode switching, and can select the main mode to realize the yaw motion by adjusting the weight parameters g1 and g2.

[0138] Based on the above embodiment, step 143 comprises:

[0139] Step 1431 determines the tail fin swing frequency in the control execution parameter based on the steering mode of the bionic machine dolphin, the forward thrust output by the tracking controller and the rate of change of the forward thrust.

[0140] Step 1432 determines the pectoral fin flap frequency in the control execution parameter based on the steering mode of the bionic machine dolphin, the yaw moment and the rate of change of the yaw moment.

[0141] Specifically, the steering mode of the bionic machine dolphin can be determined first, and then the method of fuzzy reasoning is used to map the control execution parameter after the steering mode of the bionic machine is determined, and the main steps of the method are fuzzification, fuzzy rule design and defuzzification. Fuzzification aims to determine the basic universe of the input and output variables. The fuzzy reasoning structure with double inputs and single output is used in the embodiment of the application, the inputs include the forward thrust (unit: N), the yaw moment (unit: Nm) and the rate of change thereof, and the output is two motion frequencies (unit: Hz). The design of the fuzzy rule base is a key step of the fuzzy reasoning algorithm, and mainly consists of data and fuzzy language rules. The fuzzy language of the embodiment of the application is “IF-THEN”, and the fuzzy rule table is as follows:

[0142] Table 1. Fuzzy rule table for determining pectoral fin flap frequency

[0143]

[0144] From Table 1, 1) if the yaw moment = NB and the rate of change of the yaw moment = NB, then the pectoral fin flap frequency = NB;

[0145] 2) if the yaw moment = NB and the rate of change of the yaw moment = NM, then the pectoral fin flap frequency = NB;

[0146] 3) if yaw moment = NB and rate of change of yaw moment = NS, then pectoral fin beat frequency = NM;

[0147] 4) if yaw moment = NB and rate of change of yaw moment = ZO, then pectoral fin beat frequency = NM;

[0148] 5) if yaw moment = NB and rate of change of yaw moment = PS, then pectoral fin beat frequency = NS;

[0149] 6) if yaw moment = NB and rate of change of yaw moment = PM, then pectoral fin beat frequency = NS;

[0150] 7) if yaw moment = NB and rate of change of yaw moment = PB, then pectoral fin beat frequency = ZE, which will not be described here.

[0151] In Table 1, the yaw moment has 7 values, which are NB, NM, NS, ZE, PS, PM and PB, and the rate of change of the yaw moment also has 7 values, which are NB, NM, NS, ZO, PS, PM and PB, therefore, there are 7*7=49 rules.

[0152] Here, NB (negative big) means "negative and large", NM (negative middle) means "negative and medium", NS (negative small) means "negative and small", ZO (Zero) means "not negative and not positive", PS (positive small) means "positive and small", PM (positive middle) means "positive and medium", and PB (positive big) means "positive and large". Here, the so-called positive and negative, as well as large and small, are imprecise or vague descriptions of numbers, which can be understood by analogy in a popular sense, for example, his height is high, height is generally high, height is not very high, height is generally high, height is relatively low, and the like. Moving to control is that the faucet switch is twisted more (PB), twisted not very much (PS), and the like, which will not be described here.

[0153] After outputting the membership function of the pectoral fin beat frequency from Table 1, the membership function of the pectoral fin beat frequency can be weighted and averaged, and finally the pectoral fin beat frequency in the final control execution parameter is defuzzified.

[0154] The membership function of the pectoral fin beat frequency here can be a triangular membership function, or a trapezoidal membership function, etc., which is not specifically limited by the embodiments of the present application.

[0155] Table 2. Fuzzy rule table for determining tail fin swing frequency

[0156]

[0157]

[0158] From table 2, 1) if forward thrust = ZE and forward thrust rate of change = NB, then tail fin oscillation frequency = ZE;

[0159] 2) if forward thrust = ZE and forward thrust rate of change = NM, then tail fin oscillation frequency = ZE;

[0160] 3) if forward thrust = ZE and forward thrust rate of change = NS, then tail fin oscillation frequency = ZE;

[0161] 4) if forward thrust = ZE and forward thrust rate of change = ZE, then tail fin oscillation frequency = ZE;

[0162] 5) if forward thrust = ZE and forward thrust rate of change = PS, then tail fin oscillation frequency = PS;

[0163] 6) if forward thrust = ZE and forward thrust rate of change = PM, then tail fin oscillation frequency = PS;

[0164] 7) if forward thrust = ZE and forward thrust rate of change = PB, then tail fin oscillation frequency = PM, which will not be repeated here.

[0165] In table 2, the forward thrust has 4 values, which are ZE, PS, PM and PB, and the forward thrust rate of change has 7 values, which are NB, NM, NS, ZE, PS, PM and PB, therefore, there are 4*7=28 cases.

[0166] After the membership function of the tail fin oscillation frequency is output from table 2, the membership function of the tail fin oscillation frequency can be weighted and averaged, and the tail fin oscillation frequency in the final control execution parameter is finally defuzzified.

[0167] The membership function of the tail fin oscillation frequency here can be a triangular membership function, or a trapezoidal membership function, etc., which is not specifically limited by the embodiments of the present application.

[0168] The method provided by the embodiment of the application determines the tail fin swing frequency in the control execution parameter based on the steering mode of the bionic machine dolphin, the forward thrust output by the tracking controller and the forward thrust change rate, determines the pectoral fin flap frequency in the control execution parameter based on the steering mode of the bionic machine dolphin, the yawing moment and the yawing moment change rate, improves the accuracy of the determination of the tail fin swing frequency and the pectoral fin flap frequency, uses the tail fin swing frequency and the pectoral fin flap frequency for trajectory tracking control, and further improves the accuracy of subsequent trajectory tracking control, and can be used for trajectory tracking control in a complex and narrow environment.

[0169] Based on the above embodiment, step 120 comprises:

[0170] Step 121, determining a tracking error based on the current position, the current yawing posture and the target trajectory.

[0171] Step 122, determining the target forward linear velocity and the target yawing angular velocity based on the tracking error.

[0172] Specifically, the current position of the bionic machine dolphin can be represented as (x, y), the current yawing posture can include a yawing angle, a planar linear velocity and a yawing angular velocity, the yawing angle can be represented as ψ, and the planar linear velocity and the yawing angular velocity can be represented as (u, v, r), where u and v represent the planar linear velocity, and r represents the yawing angular velocity. The target trajectory can be represented as p d (t) = (x d (t), y d (t)) represents.

[0173] Then the tracking error can be represented as p e (t) = (x e (t), y e (t)) = (x d -x, y d -y).

[0174] Substitute the tracking error into the minimization cost function:

[0175]

[0176] J(p e ,D ob ,u f ,t k ) = J(p e (t k ), D ob (t k ), u f (t k ))

[0177] L = p e (τ | tk ) T Qp e (τ|t k )+D ob (τ|t k ) T HD ob (τ|t k )+u f (τ|t k ) T Ru f (τ|t k )

[0178] g=Ξ T KΞ

[0179] Ξ=(p e (t k +T|t k ),D ob (t k 6T|t k ))

[0180] u f ∈[u fmin ,u fmax ]

[0181] where u f includes the target forward linear velocity and the target yaw angular velocity.

[0182] Based on the above embodiment, the velocity control law τ u is determined based on the following equation:

[0183]

[0184] The yaw control law τ r is determined based on the following equation:

[0185]

[0186] where, denotes the inverse of g u (u), denotes the inverse of g r (r), denotes the target forward linear acceleration, denotes the target yaw angular acceleration, u e = u - u d , r e = r - r d , u e denotes the error variable of the forward linear velocity, r ean error variable representing a yaw rate, k1 and k2 are positive coefficients, an estimator of a target forward linear velocity, an estimator of a target yaw rate, M = diag(m 11 ,m 22 ,m 33 ) represents a mass parameter matrix, D = diag(d 11 ,d 22 ,d 33 ) represents a damping parameter matrix.

[0187] Based on the above embodiment, the present application provides a schematic diagram of a bionic machine dolphin, Figure 3 is a structural schematic diagram of a bionic machine dolphin provided by the present application, as Figure 3 shown, the bionic machine dolphin includes a planner, a tracking controller and a modal allocator, the planner is designed considering the double factors of trajectory tracking accuracy and safe obstacle avoidance, the planner is provided with a target function, an optimizer and a kinematics model, and is constrained by linear velocity and angular velocity.

[0188] The tracking controller includes a compensator, a controller and a dynamics model, wherein the compensator is used for nonlinear disturbance observation, so that the bionic machine dolphin can be used for tracking control in complex environment.

[0189] The modal allocator includes a modal allocator, after determining the steering modal, the chest fin flapping frequency and the tail fin swinging frequency in the control execution parameter can be determined.

[0190] In addition, the modal allocator also feeds back the state to the tracking controller and the planner, so that the tracking controller and the planner update the parameters.

[0191] Based on any of the above embodiments, a multi-modal trajectory tracking control method of a bionic machine dolphin, the steps are as follows:

[0192] Firstly, the current position, the current yaw attitude and the target trajectory of the bionic machine dolphin are obtained.

[0193] Secondly, based on the current position, the current yaw attitude and the target trajectory, the tracking error is determined. Based on the tracking error, the target forward linear velocity and the target yaw rate are determined.

[0194] Thirdly, based on the speed control law and the yaw control law of the tracking controller in the bionic machine dolphin, and the target forward linear velocity and the target yaw rate, the forward thrust and the yaw torque output by the tracking controller are obtained.

[0195] Fourthly, the forward thrust rate of change is determined based on the forward thrust of the tracking controller, and the yaw torque rate of change is determined based on the yaw torque.

[0196] In the fifth step, the turning mode of the biomimetic robotic dolphin is determined based on the yaw moment.

[0197] In the sixth step, the tail fin oscillation frequency in the control execution parameter is determined based on the turning mode of the biomimetic robotic dolphin, the forward thrust and the forward thrust rate of change of the tracking controller output.

[0198] In the seventh step, the pectoral fin flapping frequency in the control execution parameter is determined based on the turning mode of the biomimetic robotic dolphin, the yaw moment and the yaw moment rate of change.

[0199] In the eighth step, the trajectory tracking control of the biomimetic robotic dolphin is performed based on the control execution parameter.

[0200] The biomimetic robotic dolphin multi-mode trajectory tracking control device provided by the present application is described below, and the biomimetic robotic dolphin multi-mode trajectory tracking control device described below can be correspondingly referred to the biomimetic robotic dolphin multi-mode trajectory tracking control method described above.

[0201] Based on any of the above embodiments, the present application provides a biomimetic robotic dolphin multi-mode trajectory tracking control device, Figure 4 is a structural schematic diagram of the biomimetic robotic dolphin multi-mode trajectory tracking control device provided by the present application, as Figure 4 shown, the device comprises:

[0202] The acquisition unit 410 is configured to acquire the current position, the current yaw attitude and the target trajectory of the biomimetic robotic dolphin.

[0203] The determination unit 420 is configured to determine the target forward linear velocity and the target yaw angular velocity based on the current position, the current yaw attitude and the target trajectory.

[0204] The tracking control unit 430 is configured to obtain the forward thrust and the yaw moment of the tracking controller output based on the speed control law and the yaw control law of the tracking controller in the biomimetic robotic dolphin, and the target forward linear velocity and the target yaw angular velocity.

[0205] The trajectory tracking control unit 440 is configured to determine the control execution parameter of the tracking controller based on the turning mode of the biomimetic robotic dolphin, the forward thrust of the tracking controller output and the yaw moment, and perform the trajectory tracking control of the biomimetic robotic dolphin based on the control execution parameter.

[0206] The device provided by the embodiment of the application is based on the speed control law and the yaw control law of the tracking controller in the bionic machine dolphin, and target forward linear velocity and target yaw angular velocity, to obtain forward thrust and yaw moment of the tracking controller output, and then based on the steering mode of the bionic machine dolphin, the forward thrust and the yaw moment of the tracking controller output, to determine the control execution parameter of the tracking controller, and based on the control execution parameter, to perform trajectory tracking control of the bionic machine dolphin, which fully considers the obstacle avoidance distance and the heading angle information, improves the smoothness and safety of the obstacle avoidance path, does not need to carry out method verification through simulation, and determines the target forward linear velocity and the target yaw angular velocity based on the planner of the nonlinear prediction model, improves the anti-interference ability, and further improves the precision and accuracy of the trajectory tracking control of the bionic machine dolphin.

[0207] Based on any of the above embodiments, the trajectory tracking control unit is specifically used for:

[0208] The determining rate unit is used for determining a forward thrust rate based on the forward thrust of the tracking controller, and determining a yaw moment rate based on the yaw moment;

[0209] The determining steering mode unit is used for determining the steering mode of the bionic machine dolphin based on the yaw moment;

[0210] The determining control execution parameter unit is used for determining the control execution parameter of the tracking controller based on the steering mode of the bionic machine dolphin, the forward thrust output by the tracking controller, the yaw moment, the forward thrust rate and the yaw moment rate.

[0211] Based on any of the above embodiments, the determining control execution parameter unit is specifically used for:

[0212] Based on the steering mode of the bionic machine dolphin, the forward thrust output by the tracking controller and the forward thrust rate, to determine the tail fin swing frequency in the control execution parameter;

[0213] Based on the steering mode of the bionic machine dolphin, the yaw moment and the yaw moment rate, to determine the pectoral fin flap frequency in the control execution parameter.

[0214] Based on any of the above embodiments, the determining steering mode unit is specifically used for:

[0215] Based on the following formula, to determine the steering mode M of the bionic machine dolphin:

[0216]

[0217] Wherein, M1 represents a first steering mode, M2 represents a second steering mode, M3 represents a third steering mode, the fin flapping states of the first steering mode, the second steering mode and the third steering mode are different, g1 and g2 represent adjustment weight parameters, |τ r | represents the absolute value of the yaw moment, τ rmax represents the maximum value of the yaw moment.

[0218] Based on any of the above embodiments, the determination unit is specifically configured to:

[0219] Based on the current position, the current yaw attitude and the target trajectory, a tracking error is determined.

[0220] Based on the tracking error, the target forward linear velocity and the target yaw angular velocity are determined.

[0221] Based on any of the above embodiments, the speed control law τ u is determined based on the following formula:

[0222]

[0223] The yaw control law τ r is determined based on the following formula:

[0224]

[0225] Wherein, represents the reciprocal of g u (u), represents the reciprocal of g r (r), represents the target forward acceleration, represents the target yaw angular acceleration, u e = u - u d , r e = r - r d , u e represents the error variable of the forward linear velocity, r e represents the error variable of the yaw angular velocity, k1 and k2 are both positive coefficients, represents the estimation of the target forward linear velocity, represents the estimation of the target yaw angular velocity, M = diag(m 11 , m 22 , m 33 ) represents a mass parameter matrix, D = diag(d 11 , d 22 , d 33 ) represents a damping parameter matrix.

[0226] Figure 5An example of a schematic diagram of a physical structure of an electronic device is shown in FIG. 1. Figure 5 As shown in the figure, the electronic device can include a processor 510, a communications interface 520, a memory 530, and a communications bus 540, wherein the processor 510, the communications interface 520, and the memory 530 can communicate with each other through the communications bus 540. The processor 510 can invoke the logic instructions in the memory 530 to execute a multi-modal trajectory tracking control method of a biomimetic robotic dolphin, the method including: obtaining a current position, a current yaw attitude, and a target trajectory of the biomimetic robotic dolphin; determining a target forward linear velocity and a target yaw angular velocity based on the current position, the current yaw attitude, and the target trajectory; obtaining a forward thrust and a yaw moment of a tracking controller based on a velocity control law and a yaw control law of the tracking controller in the biomimetic robotic dolphin, and the target forward linear velocity and the target yaw angular velocity; determining a control execution parameter of the tracking controller based on a turning mode of the biomimetic robotic dolphin, the forward thrust of the tracking controller, and the yaw moment, and performing trajectory tracking control of the biomimetic robotic dolphin based on the control execution parameter.

[0227] In addition, the logic instructions in the memory 530 described above can be implemented in the form of a software functional unit and sold or used as an independent product, which can be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the present application or the part of the technical solutions that essentially contribute to the prior art or the part of the technical solutions can be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0228] In another aspect, the present application also provides a computer program product comprising a computer program, which can be stored on a non-transitory computer readable storage medium, and the computer program can be executed by a processor to enable the computer to perform the multi-modal trajectory tracking control method of the biomimetic robotic dolphin provided by the above method, which comprises: obtaining a current position, a current yaw attitude and a target trajectory of the biomimetic robotic dolphin; determining a target forward linear velocity and a target yaw angular velocity based on the current position, the current yaw attitude and the target trajectory; obtaining a forward thrust and a yaw moment of a tracking controller in the biomimetic robotic dolphin based on a velocity control law and a yaw control law of the tracking controller and the target forward linear velocity and the target yaw angular velocity; determining a control execution parameter of the tracking controller based on a steering mode of the biomimetic robotic dolphin, the forward thrust of the tracking controller and the yaw moment, and performing trajectory tracking control of the biomimetic robotic dolphin based on the control execution parameter.

[0229] In yet another aspect, the present application also provides a non-transitory computer readable storage medium having a computer program stored thereon, which can be executed by a processor to implement the multi-modal trajectory tracking control method of the biomimetic robotic dolphin provided by the above method, which comprises: obtaining a current position, a current yaw attitude and a target trajectory of the biomimetic robotic dolphin; determining a target forward linear velocity and a target yaw angular velocity based on the current position, the current yaw attitude and the target trajectory; obtaining a forward thrust and a yaw moment of a tracking controller in the biomimetic robotic dolphin based on a velocity control law and a yaw control law of the tracking controller and the target forward linear velocity and the target yaw angular velocity; determining a control execution parameter of the tracking controller based on a steering mode of the biomimetic robotic dolphin, the forward thrust of the tracking controller and the yaw moment, and performing trajectory tracking control of the biomimetic robotic dolphin based on the control execution parameter.

[0230] The device embodiments described above are only schematic, wherein the units shown as separate components can or can not be physically separate, and the components shown as units can or can not be physical units, i.e., can be located in one place, or can be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the present embodiment scheme. Those skilled in the art can understand and implement without creative labor.

[0231] Those skilled in the art can clearly understand the technical solutions of the various embodiments from the above description of the embodiments, and the various embodiments can be implemented by means of software with the necessary general hardware platforms, and of course, can also be implemented by hardware. Based on such understanding, the above technical solutions, essentially or in other words, the part of the prior art that makes a contribution, can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, and the like, and includes a number of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0232] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacement for some technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-modal trajectory tracking control method for a biomimetic robotic dolphin, characterized by, The method comprises the following steps: obtaining the current position, the current yaw attitude and the target trajectory of the biomimetic machine dolphin; determining a target forward linear velocity and a target yaw angular velocity based on the current position, the current yaw attitude and the target trajectory; obtaining a forward thrust and a yaw moment output by a tracking controller in the biomimetic machine dolphin based on a speed control law and a yaw control law of the tracking controller and the target forward linear velocity and the target yaw angular velocity; determining a control execution parameter of the tracking controller based on a turning mode of the biomimetic machine dolphin, the forward thrust and the yaw moment output by the tracking controller, and performing trajectory tracking control of the biomimetic machine dolphin based on the control execution parameter; The speed control law τ u is determined based on the equation: The yaw control law τ r is determined based on the equation: wherein denotes g u the inverse of (u), denotes g r the inverse of (r), denotes the target forward acceleration, denotes the target yaw angle acceleration, u e = u - u d , r e = r - r d , u e denotes the error variable of the forward linear velocity, r e denotes the error variable of the yaw angle velocity, k1 and k2 are both positive coefficients, denotes the estimation of the target forward linear velocity, denotes the estimation of the target yaw angle velocity, M = diag(m 11 , m 22 , m 33 ) denotes the mass parameter matrix, D = diag(d 11 , d 22 , d 33 ) denotes the damping parameter matrix.

2. The multi-modal trajectory tracking control method of the biomimetic robotic dolphin according to claim 1, wherein, the step of determining the control execution parameter of the tracking controller based on the turning mode of the biomimetic machine dolphin, the forward thrust and the yaw moment output by the tracking controller comprises the following steps: determining a forward thrust change rate based on the forward thrust of the tracking controller and a yaw moment change rate based on the yaw moment; determining the turning mode of the biomimetic machine dolphin based on the yaw moment; determining the control execution parameter of the tracking controller based on the turning mode of the biomimetic machine dolphin, the forward thrust, the yaw moment, the forward thrust change rate and the yaw moment change rate output by the tracking controller.

3. The multi-modal trajectory tracking control method of the biomimetic robotic dolphin according to claim 2, wherein, the step of determining the control execution parameter of the tracking controller based on the turning mode of the biomimetic machine dolphin, the forward thrust, the yaw moment, the forward thrust change rate and the yaw moment change rate output by the tracking controller comprises the following steps: determining a tail fin oscillation frequency in the control execution parameter based on the turning mode of the biomimetic machine dolphin, the forward thrust and the forward thrust change rate output by the tracking controller; determining a pectoral fin flapping frequency in the control execution parameter based on the turning mode of the biomimetic machine dolphin, the yaw moment and the yaw moment change rate.

4. The multi-modal trajectory tracking control method of the biomimetic robotic dolphin according to claim 2, wherein, the step of determining the turning mode of the biomimetic machine dolphin based on the yaw moment comprises the following steps: determining the turning mode M of the biomimetic machine dolphin based on the following formula: Wherein M1 represents a first turning mode, M2 represents a second turning mode, M3 represents a third turning mode, the fin flapping states of the first turning mode, the second turning mode and the third turning mode are different, g1 and g2 represent adjustment weight parameters, |τ r | represents the absolute value of the yawing moment, τ rmax represents the maximum value of the yawing moment.

5. The multi-modal trajectory tracking control method of the biomimetic robotic dolphin according to any one of claims 1 to 4, characterized in that, the step of determining the target forward linear velocity and the target yaw angular velocity based on the current position, the current yaw attitude and the target trajectory comprises the following steps: determining a tracking error based on the current position, the current yaw attitude and the target trajectory; determining the target forward linear velocity and the target yaw angular velocity based on the tracking error.

6. A multi-modal trajectory tracking control device of a biomimetic robotic dolphin, characterized by, The method comprises the following steps: an obtaining unit, configured to obtain the current position, the current yaw attitude and the target trajectory of the biomimetic machine dolphin; a determining unit, configured to determine a target forward linear velocity and a target yaw angular velocity based on the current position, the current yaw attitude and the target trajectory; a tracking control unit, configured to obtain a forward thrust and a yaw moment output by a tracking controller in the biomimetic machine dolphin based on a speed control law and a yaw control law of the tracking controller and the target forward linear velocity and the target yaw angular velocity; a trajectory tracking control unit configured to determine a control execution parameter of the tracking controller based on a steering mode of the biomimetic robotic dolphin, a forward thrust output by the tracking controller, and a yaw moment, and to perform trajectory tracking control of the biomimetic robotic dolphin based on the control execution parameter; The speed control law τ u is determined based on the equation: The yaw control law τ r is determined based on the equation: wherein denotes g u the inverse of (u), denotes g r the inverse of (r), denotes the target forward acceleration, denotes the target yaw angle acceleration, u e = u - u d , r e = r - r d , u e denotes the error variable of the forward linear velocity, r e denotes the error variable of the yaw angle velocity, k1 and k2 are both positive coefficients, denotes the estimation of the target forward linear velocity, denotes the estimation of the target yaw angle velocity, M = diag(m 11 , m 22 , m 33 ) denotes the mass parameter matrix, D = diag(d 11 , d 22 , d 33 ) denotes the damping parameter matrix.

7. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, the processor implements the multi-modal trajectory tracking control method of the biomimetic robotic dolphin according to any one of claims 1 to 5 when executing the program.

8. A non-transitory computer-readable storage medium having stored thereon a computer program, characterized in that, the computer program, when executed by the processor, implements the multi-modal trajectory tracking control method of the biomimetic robotic dolphin according to any one of claims 1 to 5.

9. A computer program product comprising a computer program, characterized in that, the computer program, when executed by the processor, implements the multi-modal trajectory tracking control method of the biomimetic robotic dolphin according to any one of claims 1 to 5. the computer program, when executed by the processor, implements the multi-modal trajectory tracking control method of the biomimetic robotic dolphin according to any one of claims 1 to 5.

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