Underactuated crane self-adaptive control method based on all-drive system method

Abstract

The invention relates to the technical field of robot intelligent control, in particular to an under-actuated crane self-adaptive control method based on an all-drive system method, the control method is based on an all-drive system theory, and an all-drive system model and a self-adaptive control strategy for an under-actuated crane are established. According to the control strategy, unknown parameters of the crane, disturbance data externally applied to the crane and error data of the crane are considered, so that external disturbance and unknown system parameters can be accurately estimated and compensated while linkage of horizontal displacement of the crane and lifting motion of the load can be realized through the strategy; and a load swing angle can be effectively inhibited and eliminated, so that the overall transportation efficiency is improved, and stable and efficient operation of the under-actuated crane system is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of robot intelligent control, and particularly to an adaptive control method for an under-actuated crane based on a full-drive system method. Background Art

[0002] An under-actuated system is a system in which the number of degrees of freedom to be controlled is more than the number of independent control inputs of the system. A crane is a typical under-actuated system. For example, the degrees of freedom of a tower crane are 5, namely trolley displacement, pitch, boom rotation, hoisting rope telescoping, and load swing angle, and the number of control inputs is 3, namely motors at three joints. That is, by inputting control inputs to the controller of the crane, the control of the degrees of freedom is achieved. The prior art directly uses the under-actuated model of the crane for controller design, and even assumes that one or more of several degrees of freedom are fixed, so as to assume that the number of degrees of freedom to be changed is equal to the number of control inputs, thereby achieving the control of the degrees of freedom through control inputs. However, in reality, when the crane is in complex working conditions, there are often no degrees of freedom that can remain fixed. Therefore, the control strategy directly designed using the under-actuated system model will reduce the operating stability of the crane in complex working conditions.

[0003] In summary, the prior art reduces the operating stability of the crane in complex working conditions.

[0004] Therefore, the prior art still needs to be improved and enhanced. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides an adaptive control method for an under-actuated crane based on a full-drive system method, which solves the problem that the prior art reduces the operating stability of the crane in complex working conditions.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] In the first aspect, the present invention provides an adaptive control method for an under-actuated crane based on a full-drive system method, which includes:

[0008] Obtain the system state data of the crane, where the crane is an under-actuated crane, and obtain the system state error data based on the system state data;

[0009] Obtain the reference input data of the controller of the crane, and determine the external disturbance data of the crane based on the reference input data;

[0010] Apply an adaptive control strategy to the system state error data and the external disturbance data to obtain the control data output by the controller, where the control data is used to control the operation of the crane, and the adaptive control strategy is a control method designed based on a fully actuated system model.

[0011] In one implementation, based on the system state data, obtain the system state error data, including:

[0012] Determine the load swing angle, the hoisting rope data, and the displacement data of the crane in the system state data, where the load is an object suspended on the crane by the hoisting rope;

[0013] Obtain the hoisting rope tracking trajectory and the crane tracking trajectory;

[0014] Based on the load swing angle, the hoisting rope data, the displacement data, the hoisting rope tracking trajectory, and the crane tracking trajectory, obtain the system state error data.

[0015] In one implementation, based on the reference input data, determine the external disturbance data of the crane, including:

[0016] Based on the load swing angle, the hoisting rope data, and the displacement data of the crane, obtain the coupled data of the displacement and swing angle information of the crane;

[0017] Based on the coupled data, the load swing angle, and the hoisting rope data, obtain a new system state vector;

[0018] Obtain a preset positive gain, and based on the reference input data, the new system state vector, and the positive gain, determine the external disturbance data of the crane.

[0019] In one implementation, based on the load swing angle, the hoisting rope data, and the displacement data of the crane, obtain the coupled data of the displacement and swing angle information of the crane, including:

[0020] Determine the length of the hoisting rope in the hoisting rope data;

[0021] Calculate the product of the length of the hoisting rope and the load swing angle, and then perform a weighted calculation on the product and the displacement data to obtain the coupled data of the displacement and swing angle information of the crane.

[0022] In one implementation, based on the reference input data, the new system state vector, and the positive gain, determine the external disturbance data of the crane, including:

[0023] Based on the disturbance formula, calculate the external disturbance data;

[0024] The disturbance formula is:

[0025]

[0026] Wherein, is the external disturbance data, Z is the intermediate vector, is the derivative of Z, v v is the reference input data, q v is the new system state vector, is for q v derivative of, E1, E2, and E3 are all positive gains.

[0027] In one implementation, the adaptive control strategy is:

[0028]

[0029] Wherein, is the external disturbance data, v v ′ is the reference input data updated based on , U v is the control data output by the controller, is the system state error data, M v is the inertia matrix, q v is the new system state vector;

[0030]

[0031] Wherein, l is the length of the suspension rope, θ is the load swing angle, g is the acceleration due to gravity, m is the load mass, and M is the crane mass;

[0032] S 0～1 = [S0 S1] ∈ R 3×6 represents the gain matrix to be adjusted, l r represents the suspension rope tracking trajectory, is for l r second derivative of, ρ r represents the crane tracking trajectory, is for ρ r second derivative of, P2(S 0～1 ) ∈ R 6×3 represents a part of the solution P(S 0～1 ) of the Riccati equation formed by the gain matrix S 0～1 to be adjusted.

[0033] In one implementation, the Riccati equation is:

[0034] Φ T (S 0～1 )P(S 0～1 ) + P(S 0～1)Φ(S 0～1 )=-Q υ ≤0 6×6

[0035] Where, P(S 0～1 ) is one of the solutions of the unknown Riccati equation, Φ(S 0～1 ) and Q υ

[0036] is a known quantity, Q υ A positive definite diagonal matrix, Φ(S 0～1 )for:

[0037]

[0038] Solving the Riccati equation, we get P(S 0～1 ):

[0039] P(S 0～1 )=[P1(S 0～1 ) P2(S 0～1 )],P1,P2∈R 6×3

[0040] Where, P1(S 0～1 ) and P2(S 0～1 ) is the solution of the Riccati equation P(S 0～1 ) as follows:

[0041]

[0042] In a second aspect, an embodiment of the present invention further provides an adaptive control device for an underactuated crane based on an all-wheel drive system method, wherein the device includes the following components:

[0043] an error data calculation module, configured to obtain system state data of a crane, wherein the crane is an underactuated crane, and obtain system state error data based on the system state data;

[0044] a disturbance data calculation module, configured to obtain reference input data of a controller of the crane and determine external disturbance data of the crane based on the reference input data;

[0045] A control data calculation module is used to apply an adaptive control strategy to the system state error data and the external disturbance data to obtain control data output by the controller, wherein the control data is used to control the operation of the crane. The adaptive control strategy is a control method designed based on a full drive system model.

[0046] Thirdly, an embodiment of the present invention further provides a terminal device. The terminal device includes a memory, a processor, and an underactuated crane adaptive control program based on the full drive system method stored in the memory and operable on the processor. When the processor executes the underactuated crane adaptive control program based on the full drive system method, the steps of the above-mentioned underactuated crane adaptive control method based on the full drive system method are implemented.

[0047] Fourthly, an embodiment of the present invention further provides a computer-readable storage medium. An underactuated crane adaptive control program based on the full drive system method is stored on the computer-readable storage medium. When the underactuated crane adaptive control program based on the full drive system method is executed by a processor, the steps of the above-mentioned underactuated crane adaptive control method based on the full drive system method are implemented.

[0048] Beneficial effects: The present invention constructs an adaptive control strategy based on the full drive system model, applies the adaptive control strategy to the system state error data and external disturbance data, obtains the control data output by the controller, and then controls the operation of the crane based on the control data. The present invention simultaneously considers external disturbances under complex working conditions and applies the improved adaptive control strategy of the present invention to external disturbances, thereby realizing precise control of the crane and improving the operation stability of the crane. Description of the Drawings

[0049] Figure 1 is the overall flowchart of the present invention;

[0050] Figure 2 is the working schematic diagram of the crane in the embodiment of the present invention;

[0051] Figure 3 is the simulation diagram of Experiment 1 in the embodiment of the present invention;

[0052] Figure 4 is the experimental diagram of Experiment 2 in the embodiment of the present invention;

[0053] Figure 5 is the structure diagram of the underactuated crane adaptive control device provided by the present invention based on the full drive system method;

[0054] Figure 6 is the internal structure principle block diagram of the terminal device provided by the embodiment of the present invention. Detailed Embodiments

[0055] The following describes the technical solutions in the present invention clearly and completely in conjunction with the embodiments and the accompanying drawings of the specification. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0056] It has been found through research that an underactuated system is a system in which the number of degrees of freedom to be controlled is more than the number of independent control inputs of the system. A crane is a typical underactuated system. For example, the degrees of freedom of a tower crane are 5, namely trolley displacement, pitching, boom slewing, hoisting rope telescoping, and load swing angle, and the number of control inputs is 3, namely the motors at three joints. That is, by inputting control inputs to the controller of the crane, the control of the degrees of freedom is realized. The prior art directly uses the underactuated model of the crane for controller design, and even assumes that one or more of several degrees of freedom are fixed, so as to assume that the degrees of freedom to be changed are equal to the number of control inputs, so as to realize the control of the degrees of freedom through the control inputs. However, in reality, when the crane is in complex working conditions, there are often no degrees of freedom that can remain fixed. Therefore, the control strategy directly designed using the underactuated system model will reduce the operating stability of the crane in complex working conditions.

[0057] To solve the above technical problems, the present invention provides an adaptive control method for an underactuated crane based on a fully actuated system method, which solves the problem that the prior art reduces the operating stability of the crane in complex working conditions.

[0058] Embodiment 1. The present invention in this embodiment provides an adaptive control method for an underactuated crane based on a fully actuated system method, which solves the problem that the prior art reduces the operating stability of the crane in complex working conditions and can be applied to a terminal device. The terminal device can be a terminal product with control functions, such as the controller of a crane, etc. In this embodiment, as Figure 1 shown, the adaptive control method for an underactuated crane based on the fully actuated system method specifically includes the following steps:

[0059] S100, obtain the system state data of the crane, the crane is an underactuated crane, and based on the system state data, obtain the system state error data;

[0060] S200, obtain the reference input data of the controller of the crane, and based on the reference input data, determine the external disturbance data of the crane;

[0061] S300, apply an adaptive control strategy to the system state error data and the external disturbance data to obtain the control data output by the controller, and the control data is used to control the operation of the crane. The adaptive control strategy is a control method designed based on a fully actuated system model.

[0062] The control data obtained through S100, S200, and S300 is used to control the trolley as Figure 2 shown, and the trolley is one of the operating mechanisms of the crane.

[0063] In this embodiment, step S100 includes the following specific steps S101, S102, and S103:

[0064] S101, determine the load swing angle θ, the sling data, and the displacement data ρ of the crane in the system state data, where the load is an object suspended by a sling on the crane.

[0065] The sling data is Figure 2 the sling length l in Figure 2 as shown, and the load swing angle θ is the angle by which the load deviates from the vertical direction.

[0066] S102, obtain the sling tracking trajectory l r and the crane tracking trajectory ρ r .

[0067] The sling tracking trajectory l r is the trajectory of the sling changing with time t, and the crane tracking trajectory ρ r is the trajectory of the crane changing with time t.

[0068] S103, based on the load swing angle θ, the sling data, the displacement data ρ, the sling tracking trajectory l r , the crane tracking trajectory ρ r , obtain the system state error data

[0069]

[0070] where e Θ = ρ + lθ - ρ r , is the derivative of e θ , is the derivative of e l , is the derivative of e Θ .

[0071] In this embodiment, step S200 includes the following specific steps S201, S202, S203, and S204:

[0072] S201, determine the sling length l in the sling data;

[0073] S202, calculate the product lθ of the sling length l and the load swing angle θ, and then perform a weighted calculation on this product and the displacement data ρ to obtain the coupling data Θ(t) of the displacement and swing angle information of the crane:

[0074]

[0075] In this embodiment, by constructing the coupled data Θ(t), the full-drive dynamic model is improved for real-time estimation and compensation of external disturbances, so that the improved full-drive dynamic model better conforms to the disturbed environment where the crane is located, thereby improving the operating stability of the crane.

[0076] S203. Obtain a new system state vector q based on the coupled data Θ(t), the load swing angle θ, and the sling data (the sling data is the sling length l). v :

[0077]

[0078] In the formula, is the second derivative of Θ(t).

[0079] S204. Obtain preset positive gains (the positive gains include E1, E2, and E3). Determine the external disturbance data of the crane based on the reference input data v v , the new system state vector q v , and the positive gains.

[0080]

[0081] is the derivative of q v , and diag() is a diagonal matrix. The external disturbance data in this embodiment is an estimated value.

[0082] In this embodiment, the adaptive control strategy in step S3 is a model for an underactuated system crane improved based on the existing full-drive model, that is, converting the dynamics of an underactuated crane system with external disturbances and complex working conditions such as load lifting and lowering into a full-drive system model. Based on the full-drive system model, unknown system parameters and their related non-linear terms can be separated, and then the working conditions with unknown system parameters can be considered, thereby ensuring the operating stability of the crane.

[0083] The adaptive control strategy in step S300 is as follows:

[0084]

[0085] In this embodiment, applying the adaptive control strategy to the system state error data and the external disturbance data to obtain the control data U output by the controller v , that is, substituting into formula (1) to obtain Uv , M v is the inertia matrix, and q v is the new system state vector.

[0086] Among them, M u (q f ) is a matrix composed of unknown parameters M (crane mass) and m (load mass).

[0087] The v in formula (1) v ′ is the reference input data updated based on ,

[0088]

[0089] In the formula, g is the acceleration due to gravity, m is the load mass, M is the crane mass, and P2(S 0～1 ) ∈ R 6×3 represents the solution of the Riccati equation formed by the gain matrix S to be adjusted. The Riccati equation is also the Riccati equation. 0～1

[0090] In formula (1) represents the estimated value of ξ v , that is, in this embodiment, the estimated value of ξ v is used to replace the real ξ , and the real ξ v is as shown in the above formula. Since ξ v is composed of non-constant M and m, if the control of the crane is directly implemented using ξ v , then M and m need to be measured one by one for each control, and sometimes M and m are difficult to measure. Therefore, in this embodiment, the estimated value v is used to replace the real ξ , thereby reducing the difficulty of the measurement work. v

[0091] In this embodiment, the Riccati equation is:

[0092] Φ T (S 0～1 )P(S 0～1 ) + P(S 0～1 )Φ(S 0～1 ) = -Q υ ≤ 0 6×6

[0093] In the formula, P(S 0～1 ) is one of the solutions of the unknown Riccati equation, Φ(S 0～1 ) and Q υ are known quantities, and Q​​υ is a positive definite diagonal matrix, Φ(S 0～1 ) is as follows:

[0094]

[0095] Solve the Riccati equation to obtain P(S 0～1 ):

[0096] P(S 0～1 ) = [P1(S 0～1 ) P2(S 0～1 ), P1, P2 ∈ R 6×3

[0097]

[0098] Example 2 provides a method for deriving formula (1), including the following specific steps S01, S02, S03, and S04:

[0099] S01, based on the fully actuated system method, convert the dynamic model of the underactuated crane system with external disturbances and accompanied by load lifting motion into a fully actuated system model.

[0100] S02, based on the obtained fully actuated system model, separate the unknown system parameters and their related non-linear terms, and then consider the working conditions with unknown system parameters to obtain a fully actuated dynamic model that is easy to control and observe.

[0101] S03, construct a coupling variable containing the trolley displacement and swing angle information, improve the above fully actuated dynamic model, and obtain a linear closed-loop system. Then design a finite-time asymptotically convergent disturbance observer for real-time estimation and compensation of external disturbances and internal model differences.

[0102] S04, for the improved fully actuated dynamic model, introduce the system tracking error vector, and combine with the designed disturbance observer to propose an adaptive tracking control strategy based on the fully actuated system method to perform real-time estimation of unknown system parameters and external disturbances, and then achieve stable control of the underactuated crane system.

[0103] In this example, the dynamic model of the underactuated crane system in step S01 is a dynamic model constructed based on the Euler-Lagrange equation:

[0104]

[0105] Wherein, M(q) represents the inertia matrix of the crane system, C(q, q) represents the centripetal-Coriolis force matrix, q represents the state vector of the crane, G(q) represents the gravitational potential energy vector of the crane, U represents the control vector of the crane (the control vector is used to characterize the data output by the controller of the crane for controlling the operation of the crane), and D represents the disturbance vector (the disturbance vector is used to characterize the external disturbance suffered by the crane).

[0106]

[0107] G(q) = [0 -mgcosθ mglsinθ] T

[0108] U = [F ρ F l 0] T

[0109] D = [d[[ID=D19]] ρ d l 0] T

[0110] q = [ρ(t) l(t) θ(t)] T

[0111] Wherein, F ρ 、F l respectively represent the control input of the trolley-direction motor and the control input of the hoisting-rope-direction motor, d ρ and d l represent uncertainties. Since M(q), C(q, q), G(q), U involved in formula (2) are related to θ(t), and θ(t) is related to the height of the load, the height of the load is controlled by the crane for lifting the load, and the disturbance vector D is also involved in formula (2), the dynamic model of formula (2) comprehensively considers the external disturbance and the load lifting movement, making the dynamic model more in line with the actual working environment of the crane.

[0112] The output variable y(t) ∈ R 6 is:

[0113]

[0114] The control objective of the underactuated crane system is to drive the trolley to accurately reach the target position, manipulate the hoisting rope to quickly reach the specified position, and at the same time suppress and eliminate the swing of the load, that is:

[0115]

[0116] Based on the dynamic model of formula (2), analyze the coupling relationship between the drivable variable ρ(t) and the underactuated variable θ(t) in this model as follows:

[0117]

[0118] Based on the full - drive system method, the full - drive system model of an under - actuated crane with load hoisting motion and external disturbances can be derived:

[0119]

[0120] Subsequently, the dynamic model of formula (2) is reconstructed into the following full - drive model:

[0121]

[0122] where and q f , G f (q f ), U f , D f ∈ R 2×2 , and their specific representations are as follows:

[0123]

[0124] In this embodiment, step S200 defines a new disturbance vector D u :

[0125]

[0126] Based on substituting formula (4) into formula (3) and using M u (q f ) to replace M f (q f ) in formula (3), then formula (3) can be rewritten as the full - drive dynamic model shown in formula (5):

[0127]

[0128] In the formula, M u (q f ) is a matrix composed of unknown parameters M (crane mass) and m (load mass), represents a vector composed of unknown parameters M and m.

[0129] The full - drive dynamic model of formula (5) simultaneously considers unknown parameters (including M and m) and external disturbances (i.e., the disturbance vector D u ).

[0130] The full-drive dynamics model of Equation (5) does not consider the information related to the crane displacement data ρ, which does not conform to the actual operation of the crane, resulting in difficulty in ensuring that the trolley accurately reaches the target position according to the full-drive dynamics model of Equation (5). Therefore, step S300 of this embodiment constructs a coupling variable Θ(t) based on the displacement data ρ and the load swing angle θ:

[0131]

[0132] The second derivative of the coupling variable Θ(t) with respect to time is

[0133]

[0134] When considering the coupling variable Θ(t), the improved full-drive dynamics model as shown in Equation (6) can be obtained based on the full-drive dynamics model of Equation (5).

[0135]

[0136] In the formula, Among them, represents the linear closed-loop system:

[0137] Step S400 of this embodiment introduces the system tracking error vector e T (t) based on the improved full-drive dynamics model of Equation (6):

[0138]

[0139] where e θ = θ, e l = l - l r , e Θ = ρ + lθ - ρ r .

[0140] The linear closed-loop system is reconstructed into the state-space equation:

[0141]

[0142] Among them, B = [0 3×3 I3] T ∈R 6×3 ,

[0143] Based on the state-space equation, combined with the LQR control or other control strategies, the reference input vector v v can be designed in the following form:

[0144]

[0145] Among them, represents the system state tracking error vector; S 0～1 = [S0 S1] ∈ R 3×6 represents the gain matrix to be adjusted, and its specific expression is:

[0146]

[0147] Among them, k μν (μ = θ, l, ρ; ν = p, d) represents the control gain to be adjusted.

[0148] Combining the above reference input vector, disturbance observer, and adaptive control theory, the adaptive control strategy of formula (1) can be obtained.

[0149] Next, the effectiveness of the adaptive control strategy of formula (1) of the present invention is demonstrated through experiments:

[0150] Experiment 1: In this experiment, the actual mass M of the crane used is 11 kg, the actual mass m of the load is 3 kg, and the nominal value of the crane (the nominal value is a reference value artificially assigned when performing operations or designing because it is impossible to obtain an accurate true value) is 8 kg, and the nominal value of the load mass is 1 kg.

[0151] During the period of 0 - 5 s and 10 - 12 s when the crane lifts the load, a sinusoidal disturbance signal with an amplitude of 5 deg is applied. As Figure 3 shown, the calculated by using the method of the present invention v coincides with the actually added sinusoidal disturbance signal, thereby proving that the present invention can accurately estimate the external disturbance signal. The control data U v is calculated for the crane by using formula (1), and then the crane operation is controlled by this control data U Figure 3 . As v shown, this U

[0152] can make the maximum value of the load swing angle θ be 2.39 deg, and the crane and the suspension rope are accurately positioned, and the lifting time is 6.4 s. This shows that the proposed adaptive tracking control strategy has good performance in dealing with various complex working conditions. Figure 4 shown, from Figure 4It can be learned that the positioning error of the crane is 0.5 mm, the final positioning of the suspension rope is accurate, and the hoisting time is 4.47 s. It should be noted that the final estimated value of the load mass is 0.2203 kg. Therefore, the proposed adaptive tracking control scheme still has good performance in the experiment, and its effectiveness has also been verified.

[0153] In summary, based on the full-drive system method, the dynamics of the underactuated crane system under complex working conditions such as external disturbances and load lifting movements is converted into a full-drive system model; based on this full-drive system model, the unknown parameters of the system and their related non-linear terms are separated, and then the working conditions with unknown system parameters are considered, and a full-drive dynamics model that is easy to control and observe is obtained; furthermore, a coupling variable containing the trolley displacement and swing angle information is constructed to improve the above full-drive dynamics model, and a linear closed-loop system is obtained, and then a finite-time asymptotically convergent disturbance observer is designed to estimate and compensate the external disturbance in real time; for the above full-drive dynamics model, a system error vector is introduced, and combined with the designed disturbance observer, an adaptive tracking control strategy based on the full-drive system method is proposed to estimate the unknown system parameters and external disturbances in real time. The invention can realize the linkage of the horizontal displacement of the crane and the load lifting movement, accurately estimate and compensate the external disturbance and unknown system parameters, effectively suppress and eliminate the load swing angle, thereby improving the overall transportation efficiency and ensuring the stable and efficient operation of the underactuated crane system.

[0154] This embodiment also provides an adaptive control device for an underactuated crane based on the full-drive system method, as Figure 5 shown, the device includes the following components:

[0155] An error data calculation module 01, configured to obtain the system state data of the crane, where the crane is an underactuated crane, and obtain system state error data according to the system state data;

[0156] A disturbance data calculation module 02, configured to obtain the reference input data of the controller of the crane, and determine the external disturbance data of the crane according to the reference input data;

[0157] A control data calculation module 03, configured to apply an adaptive control strategy to the system state error data and the external disturbance data to obtain control data output by the controller, where the control data is used to control the operation of the crane, and the adaptive control strategy is a control method designed based on the full-drive system model.

[0158] Based on the above embodiments, the present invention also provides a terminal device, and its principle block diagram can be as Figure 6As shown in the figure. The terminal device includes a processor, a memory, a network interface, and a display screen connected through a system bus. Among them, the processor of the terminal device is used to provide computing and control capabilities. The memory of the terminal device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the terminal device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements an adaptive control method for an underactuated crane based on a fully actuated system method. The display screen of the terminal device can be a liquid crystal display screen or an electronic ink display screen.

[0159] Those skilled in the art can understand that Figure 6 The block diagram of the principle shown in the figure is only the block diagram of some structures related to the solution of the present invention, and does not constitute a limitation on the terminal device to which the solution of the present invention is applied. The specific terminal device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0160] In one embodiment, a terminal device is provided. The terminal device includes a memory, a processor, and an adaptive control program for an underactuated crane based on a fully actuated system method stored in the memory and operable on the processor. When the processor executes the adaptive control program for an underactuated crane based on a fully actuated system method, the following operation instructions are implemented:

[0161] Obtain the system state data of the crane, where the crane is an underactuated crane, and based on the system state data, obtain the system state error data;

[0162] Obtain the reference input data of the controller of the crane, and based on the reference input data, determine the external disturbance data of the crane;

[0163] Apply an adaptive control strategy to the system state error data and the external disturbance data to obtain the control data output by the controller. The control data is used to control the operation of the crane, and the adaptive control strategy is a control method designed based on a fully actuated system model.

[0164] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided by the present invention can include non-volatile and / or volatile memories. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0165] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features. However, 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 invention.

Claims

1. An adaptive control method for an underactuated crane based on a full drive system method, characterized in that, Including: Obtain the system state data of the crane, where the crane is an underactuated crane, and based on the system state data, obtain the system state error data; Obtain the reference input data of the controller of the crane, and based on the reference input data, determine the external disturbance data of the crane; Apply an adaptive control strategy to the system state error data and the external disturbance data to obtain the control data output by the controller, where the control data is used to control the operation of the crane, and the adaptive control strategy is a control method designed based on a fully actuated system model.

2. The underactuated crane adaptive control method based on the all-wheel drive system method according to claim 1, characterized in that, Based on the system state data, obtaining the system state error data includes: Determine the load swing angle, the sling data, and the displacement data of the crane in the system state data, where the load is an object suspended by the sling on the crane; Obtain the sling tracking trajectory and the crane tracking trajectory; Based on the load swing angle, the sling data, the displacement data, the sling tracking trajectory, and the crane tracking trajectory, obtain the system state error data.

3. The underactuated crane adaptive control method based on the all-drive system method according to claim 1, characterized in that, Based on the reference input data, determining the external disturbance data of the crane includes: Based on the load swing angle, the sling data, and the displacement data of the crane, obtain the coupled data of the displacement and swing angle information of the crane; Based on the coupled data, the load swing angle, and the sling data, obtain a new system state vector; Obtain a preset positive gain, and based on the reference input data, the new system state vector, and the positive gain, determine the external disturbance data of the crane.

4. The underactuated crane adaptive control method based on the all-wheel drive system method according to claim 3, characterized in that, Based on the load swing angle, the sling data, and the displacement data of the crane, obtaining the coupled data of the displacement and swing angle information of the crane includes: Determine the sling length in the sling data; Calculate the product of the sling length and the load swing angle, and then perform a weighted calculation on the product and the displacement data to obtain the coupled data of the displacement and swing angle information of the crane.

5. The underactuated crane adaptive control method based on the all-drive system method according to claim 3, characterized in that, Based on the reference input data, the new system state vector, and the positive gain, determining the external disturbance data of the crane includes: Calculate the external disturbance data according to the disturbance formula; The disturbance formula is: wherein, is the calculated external disturbance data, Z is the intermediate vector, is the derivative of Z, v v is the reference input data, q v is the new system state vector, is the derivative of q v , and E1, E2, and E3 are all positive gains.

6. The adaptive control method for an under-actuated crane based on the all-wheel drive system method according to claim 5, wherein The adaptive control strategy is: In the formula, is the external disturbance data, v v ′ is the reference input data updated based on , U v is the control data output by the controller, is the system state error data, M v is the inertia matrix, q v is the new system state vector; In the formula, l is the sling length, θ is the load swing angle, g is the acceleration due to gravity, m is the load mass, and M is the crane mass; S 0～1 = [S0 S1] ∈ R 3×6 represents the gain matrix to be adjusted, l r represents the hoisting rope tracking trajectory, is the second derivative of l r , ρ r represents the crane tracking trajectory, is the second derivative of ρ r , P2(S 0～1 ) ∈ R 6×3 represents a part of the solution P(S 0～1 ) of the Riccati equation formed by the gain matrix S to be adjusted 0～1 .

7. The adaptive control method for an underactuated crane based on the all-wheel drive system method according to claim 6, characterized in that, The Riccati equation is: Φ T (S 0～1 )P(S 0～1 )+P(S 0～1 )Φ(S 0～1 )=-Q υ ≤0 6×6 where, P(S 0～1 ) is one of the solutions of the unknown Riccati equation, Φ(S 0～1 ) and Q υ are known quantities, Q υ is a positive definite diagonal matrix, and Φ(S 0～1 ) is: Solve the Riccati equation to obtain P(S 0～1 ): P(S 0～1 ) = [P1(S 0～1 )P2(S 0～1 )], P1, P2 ∈ R 6×3 wherein, P1(S 0～1 ) and P2(S 0～1 ) are part of the solution P(S 0～1 ) of the said Riccati equation; 8. An underactuated crane adaptive control device based on a full drive system method, characterized in that The device includes the following components: An error data calculation module, configured to obtain the system state data of the crane, where the crane is an underactuated crane, and based on the system state data, obtain the system state error data; A disturbance data calculation module, configured to obtain the reference input data of the controller of the crane, and based on the reference input data, determine the external disturbance data of the crane; A control data calculation module, configured to apply an adaptive control strategy to the system state error data and the external disturbance data to obtain the control data output by the controller, where the control data is used to control the operation of the crane, and the adaptive control strategy is a control method designed based on a fully actuated system model.

9. A terminal device, characterized in that, The terminal device includes a memory, a processor, and an underactuated crane adaptive control program based on the full drive system method stored in the memory and executable on the processor. When the processor executes the underactuated crane adaptive control program based on the full drive system method, the steps of the underactuated crane adaptive control method based on the full drive system method as described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium, characterized in that, An underactuated crane adaptive control program based on the full drive system method is stored on the computer-readable storage medium. When the underactuated crane adaptive control program based on the full drive system method is executed by the processor, the steps of the underactuated crane adaptive control method based on the full drive system method as described in any one of claims 1-7 are implemented.

Images