A robust adaptive boundary control method for a mobile vehicle-mounted manipulator

Abstract

The present invention relates to the technical field of control of mobile vehicle-mounted manipulator systems, and in particular to a robust adaptive boundary control method for mobile vehicle-mounted manipulators. The method comprises the following steps: establishing a dynamic model of the mobile vehicle-mounted manipulator system; determining whether the mobile vehicle-mounted manipulator system has unknown external disturbances and uncertain system structural parameters; if so, designing a corresponding disturbance observer and structural parameter adaptive law, and constructing a robust adaptive boundary control method; constructing a target Lyapunov function, and analyzing system stability; and based on the stability analysis results, using Matlab to simulate and analyze the mobile vehicle-mounted manipulator system to which the robust adaptive boundary control method is applied, and determining whether the control performance meets a preset standard. If not, adjusting the structural parameter adaptive law and the disturbance observer. The present invention can improve the stability of the mobile vehicle-mounted manipulator system.

Description

Technical Field

[0001] The present invention relates to the technical field of control of a movable vehicle-mounted mechanical arm system, and in particular to a robust adaptive boundary control method for a movable vehicle-mounted mechanical arm. Background Art

[0002] Compared to rigid robotic arms, flexible robotic arms offer greater flexibility, a lighter structure, and lower energy consumption. These advantages broaden their application scenarios and are currently being widely adopted in diverse fields, including industrial production, resource exploration, expeditions, and disaster relief. Mobile flexible robotic arms typically consist of a movable base and a flexible arm. Their overall structure is more flexible, breaking through the operational limitations of traditional fixed robotic arms and meeting the practical needs of a wider range of industries, including logistics and handling.

[0003] However, the flexibility of the flexible manipulator structure can also cause vibrations, further affecting system performance. Furthermore, the mobility of the movable manipulator base and the strong coupling between the base and the manipulator pose numerous challenges to system control. Furthermore, in actual operation, various external environmental disturbances are often encountered, and even the structural parameters of the movable manipulator system are unknown or uncertain. Therefore, for a mobile vehicle-mounted flexible manipulator system with unknown external disturbances and uncertain system structural parameters, it is extremely necessary to develop a suitable robust adaptive boundary control method to achieve system stability. Summary of the Invention

[0004] To this end, the present invention provides a robust adaptive boundary control method for a movable vehicle-mounted robotic arm, which is used to overcome the problem in the prior art that unknown external disturbances and uncertain system structural parameters reduce system stability during the operation of the movable vehicle-mounted flexible robotic arm system.

[0005] To achieve the above objectives, the present invention provides a robust adaptive boundary control method for a movable vehicle-mounted robotic arm, comprising:

[0006] Step S1, establishing a dynamic model of the movable vehicle-mounted manipulator system based on the motion parameters of the movable vehicle-mounted manipulator system; the movable vehicle-mounted manipulator system includes a movable trolley, a vehicle-mounted manipulator arm, and a load at the end of the manipulator arm; the motion parameters include the motion position of the movable trolley, the mass of the movable trolley, the mass of the vehicle-mounted manipulator arm, the length of the vehicle-mounted manipulator arm, the mass of the load at the end of the manipulator arm, the bending stiffness of the vehicle-mounted manipulator arm, the rotation angle, the elastic deformation, the rotational inertia of the motor, the environmental disturbance at the movable trolley, the environmental disturbance at the wheel hub of the vehicle-mounted manipulator arm, the environmental disturbance at the load at the end of the vehicle-mounted manipulator arm, the control force applied by the movable trolley and the load at the end of the movable vehicle-mounted manipulator arm, and the control torque applied by the wheel hub of the movable vehicle-mounted manipulator arm;

[0007] Step S2, determining whether the movable vehicle-mounted manipulator system has unknown external disturbances and uncertain system structural parameters. If so, designing corresponding disturbance observers and structural parameter adaptation laws based on the unknown external disturbances and uncertain system structural parameters, and constructing a robust adaptive boundary control method;

[0008] Step S3, constructing a target Lyapunov function based on each of the unknown external disturbances and each of the uncertain system structural parameters, and analyzing the stability of the movable vehicle-mounted manipulator system according to the target Lyapunov function;

[0009] Step S4: Based on the stability analysis results, Matlab is used to perform simulation analysis on the movable vehicle-mounted robotic arm system to which the robust adaptive boundary control method is applied, and it is determined whether the control performance of the robust adaptive boundary control method meets the preset standard. If not, the structural parameter adaptive law and the disturbance observer are adjusted.

[0010] Furthermore, the step S1 includes:

[0011] Step S11, obtaining motion parameters of the movable vehicle-mounted manipulator system and performing dynamic analysis on the movable vehicle-mounted manipulator system;

[0012] Step S12: establishing a dynamic model of the movable vehicle-mounted robotic arm system based on the dynamic analysis results.

[0013] Furthermore, the step S2 includes:

[0014] Step S21: determining whether the movable vehicle-mounted manipulator system has unknown external disturbances and uncertain system structural parameters based on the dynamic model; if so, defining auxiliary variables based on the desired motion standard of the movable vehicle-mounted manipulator system and each of the motion parameters; the desired motion standard includes the desired rotation angle of the movable vehicle-mounted manipulator and the desired motion position of the movable vehicle;

[0015] Step S22, designing a corresponding disturbance observer and a structural parameter adaptive law based on the auxiliary variables;

[0016] Step S23: constructing the robust adaptive boundary control method based on the disturbance observer and the structural parameter adaptive law.

[0017] Furthermore, the step S3 includes:

[0018] Step S31, constructing an original Lyapunov function based on each of the motion parameters;

[0019] Step S32, constructing a key Lyapunov function based on each of the unknown external disturbances and each of the uncertain system structural parameters;

[0020] Step S33, constructing a target Lyapunov function based on the original Lyapunov function and the key Lyapunov function;

[0021] Step S34 , determining the positive definiteness of the target Lyapunov function based on the disturbance observer, the structural parameter adaptive law, and the robust adaptive boundary control method, and analyzing the stability of the movable vehicle-mounted manipulator system based on the positive definiteness determination result.

[0022] Furthermore, in step S4, a preset standard is determined according to the rotation angle of the movable vehicle-mounted robotic arm and the movement position of the movable vehicle.

[0023] Furthermore, the step S4 includes:

[0024] Step S41: Based on the analysis result that the movable vehicle-mounted robotic arm system has stability, using Matlab to perform simulation analysis on the movable vehicle-mounted robotic arm system to which the robust adaptive boundary control method is applied and the movable vehicle-mounted robotic arm system to which the robust adaptive boundary control method is not applied;

[0025] Step S42: determining whether the control performance of the robust adaptive boundary control method meets a preset standard based on the simulation analysis result; if not, adjusting the structural parameter adaptive law and the disturbance observer.

[0026] Furthermore, in the step S42, it includes:

[0027] The original operating characteristics of the movable vehicle-mounted robotic arm system without applying the robust adaptive boundary control method in the simulation analysis results are compared with the expected motion standards, and the key operating characteristics of the movable vehicle-mounted robotic arm system with applying the robust adaptive boundary control method in the simulation analysis results are compared with the expected motion standards, and based on the comparison results, it is determined whether the control performance of the robust adaptive boundary control method meets the preset standards.

[0028] Furthermore, in step S1, the dynamic model includes equations (1) to (7),

[0029] u(0,t)=0 (1)

[0030] u′(0,t)=0 (2)

[0031] u”(L,t)=0 (3)

[0032]

[0033] Among them, ρ, L, T e , EI, M p and J m are the mass per unit length, length, tension, bending stiffness, mass of the end load and the moment of inertia of the motor of the movable vehicle-mounted robotic arm; M v is the mass of the movable car, t is the time, x is the spatial position, and it must satisfy x∈[0,L], represents the first-order derivative of (·) with respect to t, represents the second-order derivative of (·) with respect to t, (·)′ represents the first-order derivative of (·) with respect to x, (·)” represents the second-order derivative of (·) with respect to x, (·)”′ represents the third-order derivative of (·) with respect to x, and (·)”” represents the fourth-order derivative of (·) with respect to x. γ(t) and u(x, t) are the rotation angle and elastic deformation of the movable vehicle-mounted manipulator, respectively, and ω(t) is the motion position of the car. ω (t), g γ (t) and g f (t) are the environmental disturbances on the movable trolley, the wheel hub of the movable vehicle-mounted manipulator, and the end load of the movable vehicle-mounted manipulator, respectively; κ ω (t) and κ f (t) are the control forces applied by the movable trolley and the end load of the movable vehicle-mounted manipulator, κ γ (t) is the control torque applied at the hub of the movable vehicle-mounted robotic arm.

[0034] Furthermore, in step S2, the auxiliary variables include equations (8) to (10), the disturbance observer includes equation (11), the structural parameter adaptive law includes equations (12) to (13), and the robust adaptive boundary control method includes equations (14) to (16).

[0035]

[0036]

[0037] Among them, eta1, eta2, eta3, eta4, θ1, θ2, These are all normal control parameters. are the uncertain system structure parameters J m ,M v The estimated quantity, g γm ,g ωm are the external disturbances g acting on the hub of the movable vehicle-mounted manipulator and the movable trolley respectively. γ (t), g ω The upper bound of (t), is the external disturbance g on the load at the end of the movable vehicle-mounted manipulator f The estimate of (t), e γ =γ-γ d , e ω =ω-ω d , γ d and ω d are the expected rotation angle of the movable vehicle-mounted robotic arm and the expected movement position of the movable car, respectively.

[0038] Furthermore, in step S3, the target Lyapunov function includes equation (17), the original Lyapunov function includes equations (18) to (21), and the key Lyapunov function includes equation (22).

[0039] F(t)=F o (t)+F k (t)(17)

[0040] F o (t) = F a (t)+F b (t)+F c (t)(18)

[0041]

[0042] in, are the uncertain system structure parameters J m ,M vand the unknown external disturbance g at the end load of the movable vehicle-mounted manipulator f The estimated error of (t) is

[0043] Compared with the prior art, the beneficial effect of the present invention is that, by establishing a dynamic model of a movable vehicle-mounted manipulator system, the present invention can improve the accuracy of motion planning and ensure the reliability of the system during operation. By designing a disturbance observer, it is possible to estimate and compensate for unknown external disturbances of the system during operation, thereby improving the robustness and stability of the system. By designing a structural parameter adaptive law, it is possible to reduce the control error caused by uncertain system structural parameters and improve the accuracy of control. The robust adaptive boundary control method constructed in this way is used to control the movable vehicle-mounted manipulator system, thereby improving the stability of the system and effectively suppressing the vibration of the movable vehicle-mounted manipulator. By constructing a corresponding target Lyapunov function, the system stability analysis is performed, which has wide applicability and can improve the efficiency of stability analysis. The system is digitally simulated using Matlab, and it is determined whether the control performance of the robust adaptive boundary control method meets the preset standard. In this way, it is determined whether the structural parameter adaptive law and the disturbance observer are adjusted, which can ensure the accuracy of the constructed robust adaptive boundary control method, thereby achieving high-precision control of the system.

[0044] Furthermore, the present invention constructs a dynamic model of the movable vehicle-mounted robotic arm system by performing a dynamic analysis on the movable vehicle-mounted robotic arm system, which can improve the dynamic response performance and control performance of the system and provide theoretical support for subsequent analysis and adjustment.

[0045] Furthermore, the present invention defines auxiliary variables based on the expected motion standard and various motion parameters of the movable vehicle-mounted robotic arm system, thereby designing a disturbance observer and a structural parameter adaptation law, and constructing a robust adaptive boundary control method, which can more accurately capture and control the motion parameters of the movable vehicle-mounted robotic arm, optimize system performance, reduce control errors caused by unknown external disturbances and uncertain system structural parameters, and reduce system jitter.

[0046] Furthermore, the present invention can improve the accuracy and efficiency of system stability analysis by constructing various Lyapunov functions to perform system stability analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a flow chart of a robust adaptive boundary control method for a movable vehicle-mounted robotic arm according to an embodiment of the present invention;

[0048] Figure 2 Schematic diagram of the operation of the movable vehicle-mounted robotic arm according to an embodiment of the present invention;

[0049] Figure 3 is a vibration displacement diagram of the movable vehicle-mounted robotic arm without control in an embodiment of the present invention;

[0050] Figure 4 This is a diagram of the vehicle displacement of the movable vehicle-mounted robotic arm without control in an embodiment of the present invention;

[0051] Figure 5 FIG1 is a rotation angle diagram of the movable vehicle-mounted mechanical arm without control in an embodiment of the present invention;

[0052] Figure 6 A vibration displacement diagram of a movable vehicle-mounted robotic arm under control according to an embodiment of the present invention;

[0053] Figure 7 A diagram showing the displacement of a movable vehicle-mounted robotic arm controlled by an embodiment of the present invention;

[0054] Figure 8 A diagram illustrating the rotation angles of a movable vehicle-mounted robotic arm controlled according to an embodiment of the present invention. DETAILED DESCRIPTION

[0055] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0056] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0057] It should be noted that, in the description of the present invention, terms such as "up", "down", "left", "right", "inside", and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0058] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0059] See also Figures 1-8 As shown, Figure 1 This is a flow chart of a robust adaptive boundary control method for a movable vehicle-mounted robotic arm according to an embodiment of the present invention; Figure 2 Schematic diagram of the operation of the movable vehicle-mounted robotic arm according to an embodiment of the present invention; Figure 3 is a vibration displacement diagram of the movable vehicle-mounted robotic arm without control in an embodiment of the present invention; Figure 4 This is a diagram of the vehicle displacement of the movable vehicle-mounted robotic arm without control in an embodiment of the present invention; Figure 5 FIG1 is a rotation angle diagram of the movable vehicle-mounted mechanical arm without control in an embodiment of the present invention; Figure 6 A vibration displacement diagram of a movable vehicle-mounted robotic arm under control according to an embodiment of the present invention; Figure 7 A diagram showing the displacement of a movable vehicle-mounted robotic arm controlled by an embodiment of the present invention; Figure 8 A diagram illustrating the rotation angles of a movable vehicle-mounted robotic arm controlled according to an embodiment of the present invention.

[0060] An embodiment of the present invention provides a robust adaptive boundary control method for a movable vehicle-mounted robotic arm, comprising:

[0061] Step S1, establishing a dynamic model of the movable vehicle-mounted manipulator system based on the motion parameters of the movable vehicle-mounted manipulator system; the movable vehicle-mounted manipulator system includes a movable trolley, a vehicle-mounted manipulator arm, and a load at the end of the manipulator arm; the motion parameters include the motion position of the movable trolley, the mass of the movable trolley, the mass of the vehicle-mounted manipulator arm, the length of the vehicle-mounted manipulator arm, the mass of the load at the end of the manipulator arm, the bending stiffness of the vehicle-mounted manipulator arm, the rotation angle, the elastic deformation, the rotational inertia of the motor, the environmental disturbance at the movable trolley, the environmental disturbance at the wheel hub of the vehicle-mounted manipulator arm, the environmental disturbance at the load at the end of the vehicle-mounted manipulator arm, the control force applied by the movable trolley and the load at the end of the movable vehicle-mounted manipulator arm, and the control torque applied by the wheel hub of the movable vehicle-mounted manipulator arm;

[0062] Specifically, the step S1 includes:

[0063] Step S11, obtaining motion parameters of the movable vehicle-mounted manipulator system and performing dynamic analysis on the movable vehicle-mounted manipulator system;

[0064] Step S12: establishing a dynamic model of the movable vehicle-mounted robotic arm system based on the dynamic analysis results.

[0065] Specifically, in step S1, the dynamic model includes equations (1) to (7),

[0066] u(0,t)=0 (1)

[0067] u′(0,t)=0 (2)

[0068] u”(L,t)=0 (3)

[0069]

[0070]

[0071] Among them, ρ, L, T e , EI, M p and J m are the mass per unit length, length, tension, bending stiffness, mass of the end load and the moment of inertia of the motor of the movable vehicle-mounted robotic arm; M v is the mass of the movable car, t is the time, x is the spatial position, and it must satisfy x∈[0,L], represents the first-order derivative of (·) with respect to t, represents the second-order derivative of (·) with respect to t, (·)′ represents the first-order derivative of (·) with respect to x, (·)” represents the second-order derivative of (·) with respect to x, (·)”′ represents the third-order derivative of (·) with respect to x, and (·)”” represents the fourth-order derivative of (·) with respect to x. γ and u(x, t) are the rotation angle and elastic deformation of the movable vehicle-mounted manipulator, respectively, and ω is the motion position of the car. g ω (t), g γ (t) and g f (t) are the environmental disturbances on the movable trolley, the wheel hub of the movable vehicle-mounted manipulator, and the end load of the movable vehicle-mounted manipulator, respectively; κ ω (t) and κ f (t) are the control forces applied by the movable trolley and the end load of the movable vehicle-mounted manipulator, κ γ (t) is the control torque applied at the wheel hub of the movable vehicle-mounted manipulator, η1,η2,η3,η4,θ1,θ2, These are all normal control parameters. are the uncertain system structure parameters J m ,M v The estimated quantity, g γm ,g ωm are the external disturbances g acting on the hub of the movable vehicle-mounted manipulator and the movable trolley respectively. γ (t), g ω The upper bound of (t), is the external disturbance g on the load at the end of the movable vehicle-mounted manipulator f The estimate of (t), e γ =γ-γ d , e ω =ω-ω d , γ d and ω d are the expected rotation angle of the movable vehicle-mounted robotic arm and the expected movement position of the movable car, respectively.

[0072] It should be noted that the disturbance energy of the movable vehicle-mounted manipulator system of the embodiment of the present invention is limited, so g ω (t), g γ (t) and g f (t) are bounded, and the disturbance changes very little over time, satisfying g ω (t), g γ (t) and g f The first-order derivative of (t) with respect to time is 0.

[0073] The present invention constructs a dynamic model of the movable vehicle-mounted robotic arm system by performing a dynamic analysis on the movable vehicle-mounted robotic arm system, which can improve the dynamic response performance and control performance of the system and provide theoretical support for subsequent analysis and adjustment.

[0074] Step S2, determining whether the movable vehicle-mounted manipulator system has unknown external disturbances and uncertain system structural parameters. If so, designing corresponding disturbance observers and structural parameter adaptation laws based on the unknown external disturbances and uncertain system structural parameters, and constructing a robust adaptive boundary control method;

[0075] Specifically, the step S2 includes:

[0076] Step S21: determining whether the movable vehicle-mounted manipulator system has unknown external disturbances and uncertain system structural parameters based on the dynamic model; if so, defining auxiliary variables based on the desired motion standard of the movable vehicle-mounted manipulator system and each of the motion parameters; the desired motion standard includes the desired rotation angle of the movable vehicle-mounted manipulator and the desired motion position of the movable vehicle;

[0077] Step S22, designing a corresponding disturbance observer and a structural parameter adaptive law based on the auxiliary variables;

[0078] Step S23: constructing the robust adaptive boundary control method based on the disturbance observer and the structural parameter adaptive law.

[0079] Specifically, in step S2, the auxiliary variables include equations (8) to (10), the disturbance observer includes equation (11), the structural parameter adaptive law includes equations (12) to (13), and the robust adaptive boundary control method includes equations (14) to (16).

[0080]

[0081] Among them, eta1, eta2, eta3, eta4, θ1, θ2, These are all normal control parameters. are the uncertain system structure parameters J m ,M v The estimated quantity, g γm ,g ωm are the external disturbances g acting on the hub of the movable vehicle-mounted manipulator and the movable trolley respectively. γ (t), g ω The upper bound of (t), is the external disturbance g on the load at the end of the movable vehicle-mounted manipulator f The estimate of (t), e γ (t) = γ(t) - γ d , e ω (t)=ω(t)-ω d , γ d and ω d are the expected rotation angle of the movable vehicle-mounted robotic arm and the expected movement position of the movable car, respectively.

[0082] The present invention defines auxiliary variables based on the expected motion standard and various motion parameters of the movable vehicle-mounted robotic arm system, designs a disturbance observer and a structural parameter adaptation law, and constructs a robust adaptive boundary control method. This can more accurately capture and control the motion parameters of the movable vehicle-mounted robotic arm, optimize system performance, reduce control errors caused by unknown external disturbances and uncertain system structural parameters, and reduce system chattering.

[0083] Step S3, constructing a target Lyapunov function based on each of the unknown external disturbances and each of the uncertain system structural parameters, and analyzing the stability of the movable vehicle-mounted manipulator system according to the target Lyapunov function;

[0084] Specifically, the step S3 includes:

[0085] Step S31, constructing an original Lyapunov function based on each of the motion parameters;

[0086] Step S32, constructing a key Lyapunov function based on each of the unknown external disturbances and each of the uncertain system structural parameters;

[0087] Step S33, constructing a target Lyapunov function based on the original Lyapunov function and the key Lyapunov function;

[0088] Step S34 , determining the positive definiteness of the target Lyapunov function based on the disturbance observer, the structural parameter adaptive law, and the robust adaptive boundary control method, and analyzing the stability of the movable vehicle-mounted manipulator system based on the positive definiteness determination result.

[0089] Specifically, in step S3, the target Lyapunov function F(t) includes formula (17), the original Lyapunov function F o (t) includes equations (18) to (21), the key Lyapunov function F k (t) includes formula (22),

[0090] F(t)=F o (t)+F k (t)(17)

[0091] F o (t) = F a (t)+F b (t)+F c (t)(18)

[0092]

[0093]

[0094] in, are the uncertain system structure parameters J m ,M v and the unknown external disturbance g at the end load of the movable vehicle-mounted manipulator f The estimated error of (t) is

[0095] In a specific embodiment, to prove that the system is stable, we first need to prove the positive definiteness of F(t), as shown in Equations (23) and (24).

[0096]

[0097] Among them, c1, c2, i1, i2 are all normal control parameters. Select appropriate parameters c1, c2, i1, i2, η1, η2, θ1, θ2 to satisfy the inequality Then we can get F o (t) positive definite, and because F k (t) is positive definite, so F(t) is a positive definite function.

[0098] In addition, by taking the derivative of F(t), we can get formula (25), and for F o (t) and combine equations (1) to (7) to obtain equation (26). k (t) Take the derivative and combine equations (11) to (13) to obtain equation (27). Combine equations (25) to (27) and substitute them into equations (14) to (16) to obtain equation (28).

[0099]

[0100] The negative definiteness of the time derivative of F(t) can be obtained, so the stability of the movable vehicle-mounted manipulator system can be proved, and the system signal e can be further obtained. γ (t), e ω (t) and u(L,t) are both bounded and stable towards zero.

[0101] The present invention performs system stability analysis by constructing various Lyapunov functions, thereby improving the accuracy and efficiency of system stability analysis.

[0102] Step S4: Based on the stability analysis results, Matlab is used to perform simulation analysis on the movable vehicle-mounted robotic arm system to which the robust adaptive boundary control method is applied, and it is determined whether the control performance of the robust adaptive boundary control method meets the preset standard. If not, the structural parameter adaptive law and the disturbance observer are adjusted.

[0103] Specifically, in step S4, a preset standard is determined according to the rotation angle of the movable vehicle-mounted robotic arm and the movement position of the movable vehicle.

[0104] Specifically, the step S4 includes:

[0105] Step S41: Based on the analysis result that the movable vehicle-mounted robotic arm system has stability, using Matlab to perform simulation analysis on the movable vehicle-mounted robotic arm system to which the robust adaptive boundary control method is applied and the movable vehicle-mounted robotic arm system to which the robust adaptive boundary control method is not applied;

[0106] Step S42: determining whether the control performance of the robust adaptive boundary control method meets a preset standard based on the simulation analysis result; if not, adjusting the structural parameter adaptive law and the disturbance observer.

[0107] Specifically, the step S42 includes:

[0108] The original operating characteristics of the movable vehicle-mounted robotic arm system without applying the robust adaptive boundary control method in the simulation analysis results are compared with the expected motion standards, and the key operating characteristics of the movable vehicle-mounted robotic arm system with applying the robust adaptive boundary control method in the simulation analysis results are compared with the expected motion standards, and based on the comparison results, it is determined whether the control performance of the robust adaptive boundary control method meets the preset standards.

[0109] In a specific embodiment, the preset standard is that the difference between the rotation angle of the movable vehicle-mounted robotic arm of the movable vehicle-mounted robotic arm system without applying the robust adaptive boundary control method and the expected rotation angle is not less than the preset difference and / or the deviation between the movement position of the movable vehicle and the expected movement position is not less than the preset deviation, and, the difference between the rotation angle of the movable vehicle-mounted robotic arm of the movable vehicle-mounted robotic arm system with applying the robust adaptive boundary control method and the expected rotation angle is less than the preset difference and the deviation between the movement position of the movable vehicle and the expected movement position is less than the preset deviation.

[0110] In implementation, the actual implementation personnel can set a preset difference based on the actual situation or the average deviation between the rotation angle of the movable vehicle-mounted manipulator system and the expected rotation angle based on the historical data. The actual implementation personnel can set a preset deviation based on the actual situation or the average deviation between the movement position of the movable car of the movable vehicle-mounted manipulator system and the expected movement position based on the historical data. Preferably, the preset difference value range is set to 0.05rad~0.1rad, and the preset deviation value range is set to 5mm~15mm. Figures 3 to 8 The response of the movable vehicle-mounted robotic arm system to the robust adaptive boundary control method proposed in an embodiment of the present invention is given. By comparison, it can be seen that when no control is applied, the elastic vibration of the robotic arm is large and the rotation angle of the robotic arm and the movement position of the cart deviate from the desired positions; the robust adaptive boundary control method proposed in the present invention can effectively suppress the vibration of the flexible arm and ensure that the angles of the mobile cart and the robotic arm are stable near the desired positions.

[0111] The present invention can improve the accuracy of motion planning and ensure the reliability of the system during operation by establishing a dynamic model of a movable vehicle-mounted manipulator system. By designing a disturbance observer, unknown external disturbances of the system during operation can be estimated and compensated, thereby improving the robustness and stability of the system. By designing a structural parameter adaptive law, the control error caused by uncertain system structural parameters can be reduced and the accuracy of control can be improved. The robust adaptive boundary control method constructed in this way is used to control the movable vehicle-mounted manipulator system, which can improve the stability of the system and effectively suppress the vibration of the movable vehicle-mounted manipulator. By constructing a corresponding target Lyapunov function, the system stability analysis is performed, which has wide applicability and can improve the efficiency of stability analysis. The system is digitally simulated using Matlab, and it is determined whether the control performance of the robust adaptive boundary control method meets the preset standard. In this way, it is determined whether the structural parameter adaptive law and the disturbance observer are adjusted, which can ensure the accuracy of the constructed robust adaptive boundary control method, thereby achieving high-precision control of the system.

[0112] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A robust adaptive boundary control method for a mobile vehicle-mounted robotic arm, characterized in that: include: Step S1, establishing a dynamic model of the movable vehicle-mounted manipulator system based on the motion parameters of the movable vehicle-mounted manipulator system; the movable vehicle-mounted manipulator system includes a movable trolley, a vehicle-mounted manipulator arm, and a load at the end of the manipulator arm; the motion parameters include the motion position of the movable trolley, the mass of the movable trolley, the mass of the vehicle-mounted manipulator arm, the length of the vehicle-mounted manipulator arm, the mass of the load at the end of the manipulator arm, the bending stiffness of the vehicle-mounted manipulator arm, the rotation angle, the elastic deformation, the rotational inertia of the motor, the environmental disturbance at the movable trolley, the environmental disturbance at the wheel hub of the vehicle-mounted manipulator arm, the environmental disturbance at the load at the end of the vehicle-mounted manipulator arm, the control force applied by the movable trolley and the load at the end of the movable vehicle-mounted manipulator arm, and the control torque applied by the wheel hub of the movable vehicle-mounted manipulator arm; Step S2, determining whether the movable vehicle-mounted manipulator system has unknown external disturbances and uncertain system structural parameters. If so, designing corresponding disturbance observers and structural parameter adaptation laws based on the unknown external disturbances and uncertain system structural parameters, and constructing a robust adaptive boundary control method; Step S3, constructing a target Lyapunov function based on each of the unknown external disturbances and each of the uncertain system structural parameters, and analyzing the stability of the movable vehicle-mounted manipulator system according to the target Lyapunov function; Step S4: Based on the stability analysis results, using Matlab to perform simulation analysis on the movable vehicle-mounted manipulator system to which the robust adaptive boundary control method is applied, and determining whether the control performance of the robust adaptive boundary control method meets a preset standard; if not, adjusting the structural parameter adaptive law and the disturbance observer; The step S2 includes: Step S21: determining whether the movable vehicle-mounted manipulator system has unknown external disturbances and uncertain system structural parameters based on the dynamic model; if so, defining auxiliary variables based on the desired motion standard of the movable vehicle-mounted manipulator system and each of the motion parameters; the desired motion standard includes the desired rotation angle of the movable vehicle-mounted manipulator and the desired motion position of the movable vehicle; Step S22, designing a corresponding disturbance observer and a structural parameter adaptive law based on the auxiliary variables; Step S23, constructing the robust adaptive boundary control method based on the disturbance observer and the structural parameter adaptive law; Wherein, the auxiliary variables include equations (8) to (10), the disturbance observer includes equation (11), the structural parameter adaptive law includes equations (12) to (13), and the robust adaptive boundary control method includes equations (14) to (16). Among them, L, T e , EI, M p and J m are the length, tension, bending stiffness, mass of the end load and the moment of inertia of the motor of the movable vehicle-mounted robotic arm per unit length; M v is the mass of the movable car, t is the time, x is the spatial position, and it must satisfy x∈[0,L], represents the first-order derivative of (·) with respect to t, represents the second-order derivative of (·) with respect to t, (·)′ represents the first-order derivative of (·) with respect to x, (·)” represents the second-order derivative of (·) with respect to x, (·)”′ represents the third-order derivative of (·) with respect to x, and (·)”” represents the fourth-order derivative of (·) with respect to x. γ is the rotation angle of the movable vehicle-mounted manipulator, and ω is the motion position of the car; g ω (t), g γ (t) and g f (t) are the environmental disturbances on the movable trolley, the wheel hub of the movable vehicle-mounted manipulator, and the end load of the movable vehicle-mounted manipulator, respectively; κ ω (t) and κ f (t) are the control forces applied by the movable trolley and the end load of the movable vehicle-mounted manipulator, κ γ (t) is the control torque applied at the wheel hub of the movable vehicle-mounted manipulator, η1,η2,η3,η4,θ1,θ2, These are all normal control parameters. are the uncertain system structure parameters J m ,M v The estimated quantity, g γm ,g ωm are the external disturbances g acting on the hub of the movable vehicle-mounted manipulator and the movable trolley respectively. γ (t), g ω The upper bound of (t), is the external disturbance g on the load at the end of the movable vehicle-mounted manipulator f The estimate of (t), e γ =γ-γ d , e ω =ω-ω d , γ d and ω d are the expected rotation angle of the movable vehicle-mounted robotic arm and the expected movement position of the movable car, respectively.

2. The robust adaptive boundary control method for a movable vehicle-mounted robotic arm according to claim 1, characterized in that: The step S1 includes: Step S11, obtaining motion parameters of the movable vehicle-mounted manipulator system and performing dynamic analysis on the movable vehicle-mounted manipulator system; Step S12: establishing a dynamic model of the movable vehicle-mounted robotic arm system based on the dynamic analysis results.

3. The robust adaptive boundary control method for a movable vehicle-mounted robotic arm according to claim 2, characterized in that: The step S3 includes: Step S31, constructing an original Lyapunov function based on each of the motion parameters; Step S32, constructing a key Lyapunov function based on each of the unknown external disturbances and each of the uncertain system structural parameters; Step S33, constructing a target Lyapunov function based on the original Lyapunov function and the key Lyapunov function; Step S34 , determining the positive definiteness of the target Lyapunov function based on the disturbance observer, the structural parameter adaptive law, and the robust adaptive boundary control method, and analyzing the stability of the movable vehicle-mounted manipulator system based on the positive definiteness determination result.

4. The robust adaptive boundary control method for a movable vehicle-mounted robotic arm according to claim 3, characterized in that: In step S4, a preset standard is determined according to the rotation angle of the movable vehicle-mounted robotic arm and the movement position of the movable vehicle.

5. The robust adaptive boundary control method for a movable vehicle-mounted robotic arm according to claim 4, characterized in that: The step S4 comprises: Step S41: Based on the analysis result that the movable vehicle-mounted robotic arm system has stability, using Matlab to perform simulation analysis on the movable vehicle-mounted robotic arm system to which the robust adaptive boundary control method is applied and the movable vehicle-mounted robotic arm system to which the robust adaptive boundary control method is not applied; Step S42: determining whether the control performance of the robust adaptive boundary control method meets a preset standard based on the simulation analysis result; if not, adjusting the structural parameter adaptive law and the disturbance observer.

6. The robust adaptive boundary control method for a movable vehicle-mounted robotic arm according to claim 5, characterized in that: In the step S42, it includes: The original operating characteristics of the movable vehicle-mounted robotic arm system without applying the robust adaptive boundary control method in the simulation analysis results are compared with the expected motion standards, and the key operating characteristics of the movable vehicle-mounted robotic arm system with applying the robust adaptive boundary control method in the simulation analysis results are compared with the expected motion standards, and based on the comparison results, it is determined whether the control performance of the robust adaptive boundary control method meets the preset standards.

7. The robust adaptive boundary control method for a movable vehicle-mounted robotic arm according to claim 6, characterized in that: In step S1, the dynamic model includes equations (1) to (7), u(0,t)=0 (1) u′(0,t)=0 (2) u”(L,t)=0 (3) Where ρ is the mass per unit length of the movable vehicle-mounted robotic arm.

8. The robust adaptive boundary control method for a movable vehicle-mounted robotic arm according to claim 7, characterized in that: In step S3, the target Lyapunov function includes equation (17), the original Lyapunov function includes equations (18) to (21), and the key Lyapunov function includes equation (22). F(t)=F o (t)+F k (t) (17) F o (t)=F a (t)+F b (t)+F c (t) (18) in, are the uncertain system structure parameters J m ,M v and the unknown external disturbance g at the end load of the movable vehicle-mounted manipulator f The estimated error of (t) is

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