Robust motion control method and system based on state correlation Riccati equation

Abstract

The invention belongs to the technical field of unmanned underwater vehicles, and particularly relates to a robust motion control method and system based on a state-related Riccati equation, and the robust motion control method based on the state-related Riccati equation is applied to a carrier arm unmanned underwater vehicle. The arm-carrying unmanned underwater vehicle comprises a boat body and an underwater mechanical arm arranged on the boat body, and the method comprises the following steps: establishing a dynamic model of the arm-carrying unmanned underwater vehicle based on a Lagrange energy method; a fuzzy disturbance observer is designed based on the kinetic model, and external disturbance force borne by a boat body and an underwater mechanical arm of the arm-carrying unmanned underwater vehicle in the moving process is compensated through a fuzzy logic system; and designing a robust controller based on a state correlation Riccati equation based on a fuzzy disturbance estimator, and compensating errors by adjusting a state weight matrix, inputting the range of the weight matrix and limiting the upper bound of parameter uncertainty. According to the invention, robust control of the arm-carrying unmanned underwater vehicle is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of boom-carrying unmanned underwater vehicles, and in particular relates to a robust motion control method and system based on a state-dependent Riccati equation. Background Art

[0002] The complexity and uncertainty of the underwater environment bring many challenges to the research of boom-carrying unmanned underwater vehicles.

[0003] Unlike the land environment, the uncertainty and influence of hydrodynamics underwater make it a difficult task to achieve the autonomy and intelligence of the boom-carrying unmanned submersible.

[0004] Although there are motion control solutions for underwater robots in the prior art, these methods usually require a lot of calculations to estimate disturbances and then optimize the controller, which increases the corresponding computational burden. In addition, in the prior art, there will also be local optimal problems when combining observers and control technologies. For example, the update law of the disturbance observer is inconsistent with the parameter adjustment direction of the controller. The system will be limited to exploring the local optimal situation, making it impossible for the adaptive update of the system to promote the convergence of the two.

[0005] Therefore, the problem of the difficulty in achieving robust control of the boom-carrying unmanned submersible in a water disturbance environment and under the conditions of internal parameter uncertainty needs to be solved urgently. Summary of the invention

[0006] In view of the above-mentioned shortcomings of the prior art, an object of the present invention is to provide a robust motion control method and system based on the state-dependent Riccati equation, so as to realize robust control of the boom-carrying unmanned underwater vehicle.

[0007] To achieve the above purpose, the present invention adopts the following technical solution.

[0008] A first aspect of the present invention provides a robust motion control method based on a state-dependent Riccati equation, which is applied to an arm-carrying unmanned submersible, wherein the arm-carrying unmanned submersible includes a hull and an underwater mechanical arm disposed on the hull, including: A dynamic model of the arm-carrying unmanned underwater vehicle is established based on the Lagrangian energy method; A fuzzy disturbance observer is designed based on the dynamic model, and a fuzzy logic system is used to compensate for the external disturbance force on the hull and the underwater mechanical arm of the arm-carrying unmanned underwater vehicle during movement; A robust controller based on the state-dependent Riccati equation is designed based on the fuzzy disturbance estimator, and the error caused by parameter uncertainty is effectively compensated by adjusting the state weight matrix, the range of the input weight matrix and limiting the upper bound of parameter uncertainty.

[0009] As an optional implementation, the dynamic model of the arm-carrying unmanned underwater vehicle established based on the Lagrangian energy method is expressed by the following formula: ; in, , are the position vector and Euler angle vector of the hull; x , y , z They are the position components of the arm-carrying unmanned underwater vehicle in the longitudinal, transverse and vertical directions respectively; They are the attitude components of the boom-carrying unmanned underwater vehicle in the three directions of roll, pitch and bow pitch; is the angle vector of the two-DOF joint robot, and are the speed and acceleration of the arm-carrying unmanned submersible, is the inertia matrix; are the Coriolis and centripetal force matrices; is the water resistance matrix; is the resilience matrix; is the boatman coupling matrix; is the environmental disturbance force matrix; u is the driving force matrix.

[0010] As an optional implementation, the fuzzy disturbance observer designed based on the dynamic model is expressed by the following formula: ; in, L >0, L is the observer gain matrix chosen by the designer, and is the estimate of the observer parameters, is the estimate of the external disturbance; Will is defined as the observation error of the fuzzy disturbance observer, and Defined as the parameter error of the fuzzy disturbance observer; According to the approximate discrete time linear model, it is expressed by the following formula: ; in, and Prepare the parameter matrix for suboptimal control of the UUV with a boom, N is the weight vector matrix corresponding to the perturbation, is the collective disturbance value of the fuzzy disturbance observer; Therefore, the dynamic equation of the fuzzy disturbance observer error is expressed as follows: .

[0011] As an optional implementation, the fuzzy logic system is used to compensate for the external interference force on the hull and the underwater mechanical arm of the arm-carrying unmanned underwater vehicle during movement, including: The observer parameters are adjusted by the adaptive law, which is expressed as follows: ; Among them, the adjustment parameters and is a positive constant matrix, and .

[0012] As an optional implementation, the equivalent state correlation coefficient form of the system compensated by the fuzzy disturbance observer is expressed by the following formula: ; in, u r is the nominal controller without considering external disturbances and parameter uncertainties, that is, the control input, u s is the compensation input to handle external disturbances.

[0013] As an optional implementation, the robust controller based on the state-dependent Riccati equation designed based on the fuzzy disturbance estimator is expressed by the following formula: ; in, Q is the symmetric positive definite semidefinite weight matrix of the state, R is the symmetric positive definite weight matrix of the input; The control input of the reference system is set to: ; in, p ( x ) is a symmetric positive definite matrix, and its solution system is defined as: .

[0014] As an optional implementation, the reference form of the established uncertainty system is expressed by the following formula: ; in, x r is the state vector, is the nominal input; According to the actual system and reference form, the closed-loop error can be expressed as follows: ; in, is defined as: .

[0015] As an optional implementation, the control input of the reference system is set to: ; in, p ( x ) is a symmetric positive definite matrix, and its solution system is defined as: .

[0016] The second aspect of the present invention provides a robust motion control system based on state-dependent Riccati equations, which is applied to an arm-carrying unmanned submersible. The arm-carrying unmanned submersible includes a hull and an underwater mechanical arm arranged on the hull. The system is applied to the robust motion control method based on the state-dependent Riccati equations described in the first aspect of the present invention.

[0017] A third aspect of the present invention provides a robust motion control system based on a state-dependent Riccati equation, which is applied to an arm-carrying unmanned submersible, wherein the arm-carrying unmanned submersible includes a hull and an underwater mechanical arm disposed on the hull, including: The first unit is at least used to establish a dynamic model of the arm-carrying unmanned underwater vehicle based on the Lagrangian energy method; The second unit is at least used to design a fuzzy disturbance estimator based on the dynamic model, and use a fuzzy logic system to compensate for the external interference force on the hull and the underwater mechanical arm of the arm-carrying unmanned underwater vehicle during the movement; The third unit is at least used to design a robust controller based on the state-dependent Riccati equation based on the fuzzy disturbance estimator, and effectively compensate for the error caused by parameter uncertainty by adjusting the state weight matrix, the range of the input weight matrix and limiting the upper bound of parameter uncertainty.

[0018] In summary, compared with the prior art, the present invention can achieve robust control of the arm-carrying unmanned submersible while considering the uncertainty of the system model and the water flow disturbance. It can also be widely used in the system design of the arm-carrying unmanned submersible, provide new ideas and methods for the design and application of underwater manipulator arms and robot systems, and promote the further development of underwater operation technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 It is a flowchart of a robust motion control method based on the state-dependent Riccati equation according to an embodiment of the present invention.

[0021] Figure 2 It is a control block diagram of a robust motion control method based on the state-dependent Riccati equation according to an embodiment of the present invention.

[0022] Figure 3 This is a simulation effect diagram of a hull path tracking using a robust controller according to an embodiment of the present invention, wherein: Figure 3 In ( a ) is a hull path using a robust controller according to an embodiment of the present invention. x Direction position tracking simulation effect diagram; Figure 3 In ( b ) is a simulation effect diagram of a hull path roll direction position tracking using a robust controller according to an embodiment of the present invention; Figure 3 In ( c ) is a hull path using a robust controller according to an embodiment of the present invention. y Direction position tracking simulation effect diagram; Figure 3 In ( d ) is a simulation effect diagram of a pitch direction position tracking of a hull path using a robust controller according to an embodiment of the present invention; Figure 3 In ( e ) is a hull path using a robust controller according to an embodiment of the present invention. z Direction position tracking simulation effect diagram; Figure 3 In ( f ) is a diagram showing the simulation effect of yaw direction position tracking of a hull path using a robust controller according to an embodiment of the present invention.

[0023] Figure 4 This is a pose result diagram of an end effector using a robust controller according to an embodiment of the present invention, wherein: Figure 4 In ( a ) is an end effector using a robust controller according to an embodiment of the present invention x Direction position tracking simulation effect diagram; Figure 4 In ( b ) is a simulation effect diagram of position tracking in the roll direction of an end effector using a robust controller according to an embodiment of the present invention; Figure 4 In ( c ) is an end effector using a robust controller according to an embodiment of the present invention y Direction position tracking simulation effect diagram; Figure 4 In ( d) is a simulation result diagram of the pitch direction position tracking of the end effector using a robust controller according to an embodiment of the present invention; Figure 4 In ( e ) is an end effector using a robust controller according to an embodiment of the present invention z Direction position tracking simulation effect diagram; Figure 4 In ( f ) is a diagram showing the simulation effect of yaw direction position tracking of an end effector using a robust controller according to an embodiment of the present invention.

[0024] Figure 5 It is a block diagram of a robust motion control system based on the state-dependent Riccati equation according to an embodiment of the present invention. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application. In addition, it should be understood that the specific implementation methods described herein are only used to illustrate and explain the present application, and are not used to limit the present application.

[0026] It should be noted that the description order of the following embodiments is not intended to limit the preferred order of the embodiments of the present application. In addition, in the following embodiments, the description of each embodiment has its own emphasis, and for parts not described in detail in one embodiment, reference can be made to the relevant description of other embodiments.

[0027] like Figure 1 and Figure 2 As shown, the first aspect of the present invention provides a robust motion control method based on the state-dependent Riccati equation, which is applied to an arm-carrying unmanned submersible (hereinafter referred to as the submersible), wherein the submersible includes a hull and an underwater mechanical arm arranged on the hull, and includes the following steps.

[0028] Step S100: establishing a dynamic model of the arm-carrying unmanned underwater vehicle based on the Lagrangian energy method.

[0029] Specifically, in step S100, considering the influence of external disturbance and internal parameter uncertainty on the dynamics of the arm-carrying unmanned underwater vehicle, the dynamics model of the arm-carrying unmanned underwater vehicle established based on the Lagrangian energy method is expressed by the following formula: ; in, represents the position vector of the submersible, are the position vector and Euler angle vector of the hull, x, y , z They are the position components of the hull in the longitudinal, transverse and vertical directions respectively; They are the attitude components of the hull in the three directions of roll, pitch and pitch, is the angle vector of the two-DOF joint robot; and are the speed and acceleration of the submersible, is the inertia matrix, are the Coriolis and centripetal force matrices; is the water resistance matrix; is the resilience matrix; is the boatman coupling matrix; is the environmental disturbance force matrix, where Including model uncertainty and external disturbances ; u is the driving force matrix.

[0030] In one embodiment of the present invention, in order to define the model uncertainty of the system , which is convenient for control design, the uncertainty of the dynamic model can be expressed as follows: ; in, is the true value, is the estimated value of the established dynamic model, is the dynamic error due to model uncertainty; Specifically, the true values ​​of the Coriolis force and centripetal force matrices are established. are the estimates of the Coriolis and centripetal force matrices established, is the dynamic error of the established Coriolis and centripetal force matrices; Specifically, it is the true value of the water resistance matrix, is an estimate of the water resistance matrix, is the dynamic error of the water resistance matrix, and so on.

[0031] Furthermore, the dynamic model considering external disturbances and model uncertainty is expressed as follows: ; Among them, the model uncertainty of the system .

[0032] Step S200: designing a fuzzy disturbance observer based on the dynamic model, and using a fuzzy logic system to compensate for the external disturbance force exerted on the hull and the underwater mechanical arm of the arm-carrying unmanned submersible during the movement.

[0033] Specifically, in step S200, the fuzzy disturbance observer designed based on the dynamic model is expressed by the following formula: ; in, L >0, L is the observer gain matrix chosen by the designer, and is the estimate of the observer parameters, is an estimate of the external disturbance, is the basis function of the fuzzy logic system.

[0034] Further, is defined as the observation error of the fuzzy disturbance observer, and Defined as the parameter error of the fuzzy disturbance observer; According to the approximate discrete time linear model, it is expressed by the following formula: ; in, and They are the parameter matrices of the arm-carrying unmanned underwater vehicle system prepared for implementing suboptimal control, N is the weight vector matrix corresponding to the perturbation, is the collective disturbance estimate of the fuzzy disturbance observer.

[0035] Therefore, the dynamic equation of the fuzzy disturbance observer error is expressed as follows: .

[0036] In one embodiment of the present invention, the fuzzy logic system is used to compensate for the external interference force on the hull and the underwater mechanical arm of the arm-carrying unmanned underwater vehicle during movement, including: The observer parameters are adjusted by the adaptive law, which is expressed as follows: ; Among them, the adjustment parameters γ (learning rate) and β (damping coefficients) are all positive constant matrices, is the basis function of the fuzzy logic system.

[0037] Furthermore, the adaptive law must satisfy and , in order to ensure the optimality of the observer under ideal conditions.

[0038] In addition, for a specific deterministic nonlinear function, the optimal parameter Uniquely determined by the designed fuzzy logic system, satisfying =0.

[0039] Step S300: Design a robust controller based on the state-dependent Riccati equation based on the fuzzy disturbance estimator, and effectively compensate for the error caused by parameter uncertainty by adjusting the state weight matrix, the range of the input weight matrix and limiting the upper bound of parameter uncertainty.

[0040] Specifically, in step S300, the robust controller based on the state-dependent Riccati equation designed based on the fuzzy disturbance estimator is expressed by the following formula: ; in, Q is the symmetric positive definite semidefinite weight matrix of the state, R is the symmetric positive definite weight matrix of the input.

[0041] Furthermore, the control input of the reference system is set as: ; in, P(x) is a symmetric positive definite matrix, and its solution system is defined as: .

[0042] In one embodiment of the present invention, the reference form of the established uncertainty system is expressed by the following formula: ; in, x r is the state vector, is the nominal input; According to the actual system and reference form, the closed-loop error can be expressed as follows: ; In order to converge the tracking error to zero, the correction input is adopted under the approximation given by Eq. ,therefore, is defined as: .

[0043] In one embodiment of the present invention, a nonlinear control system is considered and a robust state-dependent Riccati controller is designed. The external disturbance satisfies the assumption. In this case, the tracking error needs to be converged to the desired threshold by selecting appropriate parameters. The parameters are defined as: .

[0044] In order to better express the content to be protected by the present invention, the present invention conducts relevant simulation experiments based on MATLAB / SIMULINK to verify the effectiveness of the proposed coordinated control method for submersibles. The simulation time is 40 seconds. The simulation results are as follows: Figure 3 and Figure 4 As shown in the figure, the roll angle (roll) is the angle of rotation of the object around the Z axis, which is manifested as a "rolling" action; the pitch angle (pitch) is the angle of rotation of the object around the X axis, which is manifested as a "nodding" action; the yaw angle (yaw) is the angle of rotation of the object around the Y axis, which is manifested as a "shaking head" action; the red curve represents the expected trajectory, and the blue curve represents the actual trajectory.

[0045] Please continue reading Figure 3 In the simulation, the robust control method of the submersible system is verified from the perspective of hull path tracking, where: Figure 3 In ( a ) represents the hull path x Direction position tracking effect, Figure 3 In ( b ) represents the tracking effect of the boat path in the roll direction. Figure 3 In ( c ) represents the hull path y Direction position tracking effect, Figure 3 In ( d ) represents the pitch direction position tracking effect of the boat path, Figure 3 In ( e ) represents the hull path z Direction position tracking effect, Figure 3 In ( f ) represents the tracking effect of the yaw direction position of the boat path.

[0046] First, the expected trajectory of the hull system state is input in the simulation environment. This trajectory represents the motion path that the submersible should follow under ideal conditions. Then, the corresponding control input is calculated according to the current state of the system through the designed robust controller, and it is applied to the dynamic model of the submersible. During the simulation process, the system makes feedback adjustments based on the control input, and finally obtains the actual state trajectory of the submersible. By comparing with the preset expected trajectory, it is observed that the actual state trajectory of the submersible is highly consistent with the expected trajectory, and the tracking error is always kept within the set allowable range. This shows that the robust controller can effectively maintain system performance and ensure that the system state runs stably along the expected trajectory when facing system uncertainties, external disturbances and possible modeling errors.

[0047] Please continue reading Figure 4 In the simulation, the robust control method of the submersible system is verified from the perspective of the end effector posture information, where: Figure 4 In ( a ) represents the end effector x Direction position tracking effect, Figure 4 In ( b) represents the position tracking effect of the end effector in the roll direction. Figure 4 In ( c ) represents the end effector y Direction position tracking effect, Figure 4 In ( d ) represents the position tracking effect of the end effector in pitch direction. Figure 4 In ( e ) represents the end effector z Direction position tracking effect, Figure 4 In ( f ) represents the position tracking effect of the end effector in the yaw direction.

[0048] In order to evaluate the effectiveness of the proposed controller in tracking the trajectory in the task space, the Newton-Euler equation is used to convert the posture data of the hull and the manipulator joint into the position information of the end effector in the simulation. By comparing with the preset trajectory, it is observed that the error between the trajectory of the end effector and the expected trajectory is always kept within the allowable error range, meeting the robust control requirements.

[0049] The present invention realizes robust control of a submersible under system parameter uncertainty and water flow disturbance by integrating a fuzzy disturbance observer with the state-related Riccati equation.

[0050] A second aspect of the present invention provides a robust motion control system based on state-dependent Riccati equations, which is applied to an arm-carrying unmanned submersible, wherein the arm-carrying unmanned submersible includes a hull and an underwater mechanical arm arranged on the hull. The system is applied to the robust motion control method based on the state-dependent Riccati equations described in any of the above embodiments.

[0051] like Figure 5 As shown, the third aspect of the present invention provides a robust motion control system based on the state-dependent Riccati equation, which is applied to an arm-carrying unmanned submersible, wherein the arm-carrying unmanned submersible includes a hull and an underwater mechanical arm arranged on the hull, including: The first unit is at least used to establish a dynamic model of the arm-carrying unmanned underwater vehicle based on the Lagrangian energy method; The second unit is at least used to design a fuzzy disturbance estimator based on the dynamic model, and use a fuzzy logic system to compensate for the external interference force on the hull and the underwater mechanical arm of the arm-carrying unmanned underwater vehicle during the movement; The third unit is at least used to design a robust controller based on the state-dependent Riccati equation based on the fuzzy disturbance estimator, and effectively compensate for the error caused by parameter uncertainty by adjusting the state weight matrix, the range of the input weight matrix and limiting the upper bound of parameter uncertainty.

[0052] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0053] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, which can be embodied in any computer-readable medium for use by an instruction execution system, apparatus or device (such as a computer-based system, a system including a processing module or other system that can fetch instructions from an instruction execution system, apparatus or device and execute instructions), or used in combination with these instruction execution systems, apparatuses or devices.

[0054] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A robust motion control method based on state-dependent Riccati equations, applied to an arm-carrying unmanned submersible, the arm-carrying unmanned submersible comprising a hull and an underwater mechanical arm arranged on the hull, characterized in that: include: A dynamic model of the arm-carrying unmanned underwater vehicle is established based on the Lagrangian energy method; A fuzzy disturbance observer is designed based on the dynamic model, and a fuzzy logic system is used to compensate for the external disturbance force on the hull and the underwater mechanical arm of the arm-carrying unmanned underwater vehicle during movement; A robust controller based on the state-dependent Riccati equation is designed based on the fuzzy disturbance estimator, and the error caused by parameter uncertainty is effectively compensated by adjusting the state weight matrix, the range of the input weight matrix and limiting the upper bound of parameter uncertainty.

2. The robust motion control method based on the state-dependent Riccati equation according to claim 1, characterized in that: The dynamic model of the arm-carrying unmanned underwater vehicle established based on the Lagrangian energy method is expressed by the following formula: ； in, represents the position vector of the arm-carrying unmanned underwater vehicle, are the position vector and Euler angle vector of the hull; x , y , z They are the position components of the hull in the longitudinal, transverse and vertical directions respectively; They are the attitude components of the hull in the three directions of roll, pitch and pitch; is the angle vector of the two-DOF joint robot, and are the speed and acceleration of the arm-carrying unmanned submersible, is the inertia matrix; are the Coriolis and centripetal force matrices; is the water resistance matrix; is the resilience matrix; is the boatman coupling matrix; is the environmental disturbance force matrix; u is the driving force matrix.

3. The robust motion control method based on the state-dependent Riccati equation according to claim 1 is characterized in that: The fuzzy disturbance observer designed based on the dynamic model is expressed by the following formula: ； in, L >0, L is the observer gain matrix chosen by the designer, and is the estimate of the observer parameters, is an estimate of the external disturbance, is the basis function of the fuzzy logic system; Will is defined as the observation error of the fuzzy disturbance observer, and Defined as the parameter error of the fuzzy disturbance observer; According to the approximate discrete time linear model, it is expressed by the following formula: ； in, and Prepare the parameter matrix for implementing suboptimal control of the UUV with a boom, N is the weight vector matrix corresponding to the perturbation, is the collective disturbance estimate of the fuzzy disturbance observer; Therefore, the dynamic equation of the fuzzy disturbance observer error is expressed as follows: 。 4. The robust motion control method based on the state-dependent Riccati equation according to claim 1, characterized in that: The method of using a fuzzy logic system to compensate for the external interference force on the hull and the underwater mechanical arm of the arm-carrying unmanned underwater vehicle during movement includes: The observer parameters are adjusted by the adaptive law, which is expressed as follows: ； Among them, the adjustment parameters and is a positive constant matrix, and .

5. The robust motion control method based on the state-dependent Riccati equation according to claim 1, characterized in that: The equivalent state correlation coefficient form of the system compensated by the fuzzy disturbance observer is expressed by the following formula: ； in, u r is a nominal controller that does not consider external disturbances and parameter uncertainties, u s It is the input to handle external disturbances.

6. The robust motion control method based on the state-dependent Riccati equation according to claim 1, characterized in that: The robust controller based on the state-dependent Riccati equation designed based on the fuzzy disturbance estimator is expressed as follows: ； in, Q is the symmetric positive definite semidefinite weight matrix of the state, R is the symmetric positive definite weight matrix of the input; The control input of the reference system is set to: ； in, p ( x ) is a symmetric positive definite matrix, and its solution system is defined as: 。 7. The robust motion control method based on the state-dependent Riccati equation according to claim 1, characterized in that: The reference form of the established uncertainty system is expressed as follows: ； in, x r is the state vector, is the nominal input; According to the actual system and reference form, the closed-loop error can be expressed as follows: ； in, is defined as: .

8. The robust motion control method based on the state-dependent Riccati equation according to claim 7, characterized in that: The control input of the reference system is set to: ； in, p ( x ) is a symmetric positive definite matrix, and its solution system is defined as: 。 9. A robust motion control system based on state-dependent Riccati equations, applied to an arm-carrying unmanned submersible, the arm-carrying unmanned submersible comprising a hull and an underwater mechanical arm disposed on the hull, characterized in that: The system is applied to the robust motion control method based on the state-dependent Riccati equation as described in any one of claims 1-8.

10. A robust motion control system based on state-dependent Riccati equations, applied to an arm-carrying unmanned submersible, the arm-carrying unmanned submersible comprising a hull and an underwater mechanical arm disposed on the hull, characterized in that: include: The first unit is at least used to establish a dynamic model of the arm-carrying unmanned underwater vehicle based on the Lagrangian energy method; The second unit is at least used to design a fuzzy disturbance estimator based on the dynamic model, and use a fuzzy logic system to compensate for the external interference force on the hull and the underwater mechanical arm of the arm-carrying unmanned underwater vehicle during the movement; The third unit is at least used to design a robust controller based on the state-dependent Riccati equation based on the fuzzy disturbance estimator, and effectively compensate for the error caused by parameter uncertainty by adjusting the state weight matrix, the range of the input weight matrix and limiting the upper bound of parameter uncertainty.

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