Adaptive fuzzy control method and system for tower crane based on state constraints

Abstract

The present invention proposes an adaptive fuzzy control method and system for a tower crane based on state constraints, which belongs to the technical field of tower crane control. The scheme includes: constructing a tower crane system dynamics model, obtaining a control target according to the tower crane system dynamics model; constructing an adaptive fuzzy controller according to the control target; the adaptive fuzzy controller includes load mass estimation and constraint state items; and using the controller to realize the positioning of the tower crane and eliminate the swing angle. A closed-loop control method is designed for a variable rope length tower crane. A fuzzy adaptive mechanism is introduced to improve the control performance. Considering safety and efficiency, an auxiliary item is established, which theoretically limits the cantilever, trolley and rope within a safe range to ensure that the load maintains a safe distance from surrounding obstacles and equipment.

Description

Technical Field

[0001] The invention belongs to the technical field of tower crane control, and in particular relates to a state-constrained tower crane adaptive fuzzy control method and system. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] As an indispensable transportation equipment, tower crane plays a key role in industrial applications. The main moving parts of tower crane include cantilever, trolley and rope, which work together to transport the load to the target location. The key control goal is to ensure accurate positioning and eliminate swing angle. However, as a typical under-driven system, tower crane uses fewer drives to control more degrees of freedom. Due to their strong nonlinearity and state coupling, it is difficult to achieve control goals.

[0004] At present, the control methods of tower cranes are generally divided into two categories: open-loop control and closed-loop control. For open-loop control, under the influence of external disturbances and dynamic uncertainties, the open-loop control method may suffer significant performance degradation and lead to instability. Closed-loop control can effectively solve the above problems. At present, adaptive control is more commonly used in closed-loop control, but most of the existing adaptive control methods are suitable for tower cranes with fixed rope lengths. In the process of using tower cranes to transport loads, the rope length will inevitably change. In addition, in order to ensure the safety of tower cranes and improve transportation performance, optimal trajectory planning and model predictive control (MPC) are often used to constrain state variables within a suitable range, but optimal trajectory planning and MPC rely on complex algorithms, which makes the controller design process complicated. Summary of the invention

[0005] In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a tower crane adaptive fuzzy control method and system based on state constraints, which enhances robustness by combining fuzzy logic and introduces a fuzzy adaptive mechanism to solve dynamic uncertainty and external interference. In addition, in order to prevent collision, auxiliary items are designed to ensure that the driving state variables are limited to a suitable range during the entire transportation process. In particular, in order to ensure the accurate positioning of the rope, an adaptive law is also used to accurately estimate the load mass.

[0006] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0007] A first aspect of the present invention provides a state-constrained adaptive fuzzy control method for a tower crane;

[0008] An adaptive fuzzy control method for a tower crane based on state constraints comprises:

[0009] Constructing a tower crane system dynamics model, and obtaining an energy function of the tower crane according to the tower crane system dynamics model;

[0010] According to the energy function, a control input of the tower crane is obtained, a load mass estimation and a constraint state item are introduced to construct an adaptive fuzzy controller, and a control target of the adaptive fuzzy controller is obtained;

[0011] The control input of the tower crane is adjusted according to the control target of the adaptive fuzzy controller to eliminate external interference.

[0012] As a further technical solution, the tower crane system dynamics model is:

[0013] ;

[0014] In the formula, the state vector , control vector , the friction vector ; is the rotation angle of the cantilever; is the average displacement of the trolley; is the length of the rope; , is the load swing angle; To control the moment of the cantilever; , To control the force of the cart and rope; , , is the friction torque / force;

[0015] and denote the disturbance vector and gravity vector respectively; , is the mass of the trolley and the load; , is the air friction coefficient; g is the gravity constant; , denote the symmetric inertia matrix and the centripetal-Coriolis matrix respectively; the disturbance variable , and Includes system dynamics uncertainties and external disturbances.

[0016] As a further technical solution, the energy function of the tower crane is defined as follows:

[0017] .

[0018] As a further technical solution, the process of obtaining the control input of the tower crane according to the energy function is:

[0019] Combining properties , The energy function is derived using the tower crane system dynamics model:

[0020] ;

[0021] Define total disturbance , and , using fuzzy structure to approximate , and ,Right now

[0022] , , ;

[0023] in , and represents the optimal weight vector with bounds; , and represents the approximation error, and its upper limits are , and ; is the fuzzy input vector; the fuzzy basis function Defined as:

[0024] ;

[0025] In the formula and denote the number of fuzzy rules and the membership function of the fuzzy set respectively; and denote the centroid and width of the Gaussian function, respectively. is the number of input variables.

[0026] As a further technical solution, the process of introducing load quality estimation and constraint state terms to construct an adaptive fuzzy controller is:

[0027] ;

[0028] ;

[0029] ;

[0030] in , , , , , , , , , , , , , and is a positive gain; , and They are , and The estimated vector of ; , , The adaptive law of the vector is as follows:

[0031] , , ,

[0032] in , , indicates positive gain; , , Offset the influence of the corresponding approximation error and control the gain , , The following conditions are met:

[0033] , , ;

[0034] also, ,in , denote the initial and increased mass estimates, respectively; therefore, for:

[0035] ;

[0036] in , represents the control coefficient; in addition, , , Constraints , and Auxiliary terms within the safety range, the expression is:

[0037] ;

[0038] ;

[0039]

[0040] In the formula, , , is the positioning error variable; , and Respectively , and Target value of and express The upper and lower limits of and yes The upper and lower limits of and Corresponds to of the border.

[0041] As a further technical solution, the control objectives of the adaptive fuzzy controller are: obtaining the positioning error variable, adjusting the cantilever rotation angle , the average displacement of the trolley , the length of the rope Limiting to specific ranges and estimating accurate values ​​of load mass;

[0042] The positioning error variable is defined as follows:

[0043] , , ;

[0044] in, , and Respectively , and The positioning error and load swing angle can converge to zero asymptotically, that is,

[0045] ;

[0046] The cantilever rotation angle , the average displacement of the trolley , the length of the rope Restricted to a specific range, i.e.

[0047] , , ;

[0048] in and express The upper and lower limits of and yes The upper and lower limits of and Corresponds to the boundaries of;

[0049] The accurate value of the estimated load mass, i.e.

[0050] ;

[0051] in and Respectively The estimated value and estimation error.

[0052] As a further technical solution, a state-constrained tower crane adaptive fuzzy control method also includes performing stability analysis on the adaptive fuzzy controller.

[0053] A second aspect of the present invention provides an adaptive fuzzy control system for a tower crane based on state constraints.

[0054] An adaptive fuzzy control system for a tower crane based on state constraints comprises:

[0055] The energy function acquisition module is configured to: construct a tower crane system dynamics model, and acquire the energy function of the tower crane according to the tower crane system dynamics model;

[0056] An adaptive fuzzy controller construction module is configured to: obtain a control input of the tower crane according to the energy function, introduce a load mass estimation and a constraint state term to construct an adaptive fuzzy controller and obtain a control target of the adaptive fuzzy controller;

[0057] The control input adjustment module is configured to adjust the control input of the tower crane according to the control target of the adaptive fuzzy controller to eliminate external interference.

[0058] The third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the state-constrained tower crane adaptive fuzzy control method as described in the first aspect of the present invention.

[0059] The fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the state-constrained tower crane adaptive fuzzy control method are implemented as described in the first aspect of the present invention.

[0060] One or more of the above technical solutions have the following beneficial effects:

[0061] The present invention designs a closed-loop control method for a variable-rope tower crane. An adaptive fuzzy mechanism is introduced to improve control performance, which can simultaneously approximate unknown interference, uncertain dynamic characteristics, and uncertain friction. Considering safety and efficiency, auxiliary terms are established to theoretically limit the cantilever, trolley, and rope within a safe range, which is verified by experiments. Ensure that the load maintains a safe distance from surrounding obstacles and equipment.

[0062] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0064] Figure 1 This is a flow chart of the method of the first embodiment.

[0065] Figure 2 Graph showing comparative experimental results between the controller in the first embodiment and other controllers.

[0066] Figure 3 It is a system structure diagram of the second embodiment. DETAILED DESCRIPTION

[0067] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0068] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0069] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0070] The existing control methods for tower cranes are almost all aimed at tower cranes with fixed rope lengths. In industrial scenarios, the rope length will inevitably change during load transportation. Although many adaptive strategies for tower cranes have been proposed, the application of fuzzy adaptive methods in tower cranes is still rare. Although trajectory planning methods and MPC can guarantee state variable constraints, they usually rely on complex algorithms, which makes the controller design more complicated. Based on the above problems, the present invention proposes a tower crane adaptive fuzzy control method and system based on state constraints, which will be specifically analyzed in conjunction with the embodiments below.

[0071] Embodiment 1

[0072] This embodiment discloses a state-constrained tower crane adaptive fuzzy control method, which is applied to a 5-DOF tower crane.

[0073] like Figure 1 As shown, a tower crane adaptive fuzzy control method based on state constraints includes:

[0074] Step S1, constructing a tower crane system dynamics model, and obtaining an energy function of the tower crane according to the tower crane system dynamics model;

[0075] Among them, the matrix-vector form of the developed dynamic model of the 5-DOF tower crane is as follows:

[0076] ; (1)

[0077] In the formula, the state vector , control vector , the friction vector ; is the rotation angle of the cantilever; is the average displacement of the trolley; is the length of the rope; , is the load swing angle; To control the moment of the cantilever; , To control the force of the cart and rope; , , is the friction torque / force;

[0078] and denote the disturbance vector and gravity vector respectively; , is the mass of the trolley and the load; , is the air friction coefficient; g is the gravity constant; , denote the symmetric inertia matrix and the centripetal-Coriolis matrix respectively; the disturbance variable , and Includes system dynamics uncertainties and external disturbances.

[0079] In this embodiment, the energy function of the tower crane is defined as follows:

[0080] (2)

[0081] Step S2, obtaining the control input of the tower crane according to the energy function, introducing the load mass estimation and the constraint state item to construct an adaptive fuzzy controller and obtain the control target of the adaptive fuzzy controller. The process of obtaining the control input of the tower crane according to the energy function is as follows:

[0082] Combining properties , The energy function is derived using the tower crane system dynamics model to obtain the control input of the tower crane, where:

[0083] (3)

[0084] Du Yu controls external disturbances and uncertainties in the input by defining the total disturbance , and , using fuzzy structure to approximate , and ,Right now

[0085] , , (4)

[0086] in , and represents the optimal weight vector with bounds; , and represents the approximation error, and its upper limits are , and ; is the fuzzy input vector; the fuzzy basis function Defined as:

[0087] (5)

[0088] In the formula and denote the number of fuzzy rules and the membership function of the fuzzy set respectively; and denote the centroid and width of the Gaussian function, respectively. is the number of input variables.

[0089] In this embodiment, the adaptive fuzzy controller is:

[0090] (6)

[0091] (7)

[0092] (8)

[0093] in , , , , , , , , , , , , , and is a positive gain; , and They are , and The estimated vector of ; , , The adaptive law of the vector is as follows:

[0094] , , (9)

[0095] in , , indicates positive gain; , , Offset the influence of the corresponding approximation error and control the gain , , The following conditions are met:

[0096] , , ;

[0097] also, ,in , denote the initial and increased mass estimates, respectively; therefore, for:

[0098] (10)

[0099] in , represents the control coefficient; in addition, , , Constraints , and Auxiliary terms within the safety range, the expression is:

[0100] (11)

[0101] (12)

[0102] (13)

[0103] In the formula, , , is the positioning error variable; , and Respectively , and Target value of and express The upper and lower limits of and yes The upper and lower limits of and Corresponds to of the border.

[0104] By introducing load mass estimation and constraint state terms to construct an adaptive fuzzy controller, the uncertain dynamic characteristics and external disturbances in the tower crane are effectively solved. In addition, the constraint state term is introduced as an auxiliary term to ensure the safety and efficiency of transportation. In this way, all the driving state variables of the tower crane are limited to a suitable range. At the same time, the adaptive law is also used to estimate the load mass, which further ensures the accurate positioning of the rope.

[0105] In this embodiment, the control objectives of the adaptive fuzzy controller include obtaining the positioning error variable, adjusting the cantilever rotation angle , the average displacement of the trolley , the length of the rope Equi-drive state variables are limited to a specific range and the accurate value of the estimated load mass;

[0106] The positioning error variable is defined as follows:

[0107] , , (14)

[0108] in, , and Respectively , and The positioning error and load swing angle can converge to zero asymptotically, that is,

[0109] (15)

[0110] The cantilever rotation angle , the average displacement of the trolley , the length of the rope Restricted to a specific range, i.e.

[0111] , ,

[0112] in and express The upper and lower limits of and yes The upper and lower limits of and Corresponds to the boundaries of;

[0113] The accurate value of the estimated load mass, i.e.

[0114] (16)

[0115] in and Respectively The estimated value and estimation error.

[0116] Step S3, adjusting the control input of the tower crane according to the control target of the adaptive fuzzy controller to eliminate external interference. After solving the uncertain dynamic characteristics and external interference in the tower crane through the adaptive fuzzy controller, the control input in the stable state is obtained, and the tower crane is adjusted according to the control input in the stable state to increase the stability of the tower crane.

[0117] In this embodiment, in order to verify whether the constructed adaptive fuzzy controller meets the control target, a stability analysis is performed on the adaptive fuzzy controller. Specifically:

[0118] Assume that the adaptive fuzzy controller satisfies the control objective, that is,

[0119] (1) The state variables can converge to zero, that is

[0120] (17)

[0121] (18)

[0122] (2) The accurate load mass can be estimated, that is .

[0123] (3) , and be constrained within an appropriate range.

[0124] In the process of stability proof, Lyapunov stability theory is first used to verify the state variable constraints.

[0125] The first step is to design state constraint auxiliary items , , ,as follows:

[0126] (19)

[0127] (20)

[0128] (twenty one)

[0129] , , The first-order derivative of is:

[0130] (twenty two)

[0131] (twenty three)

[0132] (twenty four)

[0133] Construct Lyapunov The function is:

[0134] (25)

[0135] in ;and , , They are defined as , , The weight error vector is then taken The time derivative of , , In the expression of , we can get the following results:

[0136] (26)

[0137] Calculate the 4 in the above formula Related items, and insert , the calculation results are as follows:

[0138] (27)

[0139] It is obvious from (27) that

[0140] (28)

[0141] Next, we first verify the control objective. The initial state is defined as , , , then we can deduce from (19)-(21), (25), (28)

[0142] (29)

[0143] Then, assuming that the state variable will exceed its constraints ,Right now ,or , then combined with the Lyapunov function and the constraint state auxiliary term , we can get the result This result is contrary to formula (29), so

[0144] , , (30)

[0145] Likewise, and The corresponding analysis was carried out. Therefore, if the initial conditions are satisfied: , , then the following results hold:

[0146] , , , (31)

[0147] , , (32)

[0148] Next, the integral of formula (26) is derived as

[0149] (33)

[0150] Secondly, Barbara's lemma is applied to complete the proof of state variable convergence and estimated load quality.

[0151] Step 2: In this step, Barbara’s lemma is introduced to further analyze the system stability.

[0152] Based on the dynamic model of the tower crane, the second-order derivative expression of the state variable is as follows:

[0153] (34)

[0154] (35)

[0155] (36)

[0156] (37)

[0157] (38)

[0158] Combining equations (28), (33) and (34)-(38), it is clear that

[0159] (39)

[0160] Based on (33) and (39), and using Barbara's lemma, we can get

[0161] (40)

[0162] In addition, (6)-(8), (40) and , , Substituting into (34)-(38), we can obtain the following results

[0163] (41)

[0164] Then, substituting (40)-(41) into the tower crane dynamics model, we obtain

[0165] , (42)

[0166] Then, substituting (6), (40)-(42) into the tower crane dynamics model, and substituting (12), (40)-(42) into the tower crane dynamics model, the following results can be obtained.

[0167] , (43)

[0168] Next, substituting (8), (10) and (40)-(43) into the tower crane dynamics model, we can obtain the following results:

[0169] (44)

[0170] As defined in (10), we can conclude that

[0171] (45)

[0172] in is a constant. If , then (45) does not hold (the left side is time-varying, while the other side is constant); therefore, based on (25) and (28), we can get the following result

[0173] (46)

[0174] Will Substituting (46) into (44), we obtain

[0175] (47)

[0176] Therefore, from (40)-(47), we can derive the following results:

[0177] , , , , ,

[0178] , , , , ,

[0179] In this embodiment, a series of comparative experiments are also carried out to verify the control performance of the adaptive fuzzy controller under various adverse conditions. An improved non-singular fast terminal sliding mode controller (INFTSMC), an adaptive anti-sway (ASR) controller and a linear quadratic (LQR) controller are selected as comparative methods. Figure 3From the results in , we can clearly conclude that: compared with other controllers, the adaptive fuzzy controller in the present invention has strong robustness. The controller parameters are shown in Table 1.

[0180] Table 1 Control system parameter configuration during the comparative experiment

[0181]

[0182] like Figure 3 As shown in the figure, all controllers achieve accurate positioning. In about 6 seconds, all controllers make the cantilever reach the target position. The INFTSMC and ASR controllers control the trolley to reach the target position slightly slower, 4 seconds and 5 seconds respectively, while the other controllers reach the trolley target position in about 3 seconds. The rope of the INFTSMC controller reaches the target position in 7 seconds, and the other controllers complete the rope positioning in 6 seconds. It is obvious from the enlarged sub-figure that the controller in this paper achieves accurate positioning, while the other controllers slightly overshoot.

[0183] In addition, the adaptive fuzzy controller of the present invention eliminates the swing significantly faster than other controllers. The adaptive fuzzy controller of the present invention stabilizes at 7 seconds and 9 seconds respectively. and , and the swing angle range is smaller than that of other controllers. All controllers eliminate the and Swing angle. Under the INFTSMC, ASR and LQR control methods, the swing elimination effect of ASR is relatively good ( The swing elimination time is 8 seconds. The residual swing of INFTSMC is relatively obvious. In addition, the adaptive fuzzy controller in the present invention accurately estimates the load quality within 6 seconds through online recognition (as shown in sub-figure shown).

[0184] In addition, the robustness of the adaptive fuzzy controller in the present invention is also verified. When the load mass changes, the adaptive fuzzy controller in the present invention can still accurately estimate its true mass. Compared with other controllers, the adaptive fuzzy controller in the present invention shows strong robustness. When the load mass is uncertain, the adaptive fuzzy controller in the present invention can accurately correct the current load mass and eliminate the swing angle quickly.

[0185] Embodiment 2

[0186] This embodiment discloses an adaptive fuzzy control system for a tower crane based on state constraints;

[0187] like Figure 2As shown, a tower crane adaptive fuzzy control system based on state constraints includes:

[0188] The control target acquisition module is configured to: construct a tower crane system dynamics model, and acquire a control target according to the tower crane system dynamics model;

[0189] An adaptive fuzzy controller construction module is configured to: construct an adaptive fuzzy controller according to the control target; the adaptive fuzzy controller includes load quality estimation and constraint state items;

[0190] The positioning and swing angle elimination module is configured to: use the adaptive fuzzy controller to achieve the positioning of the tower crane and eliminate the swing angle.

[0191] Embodiment 3

[0192] The purpose of this embodiment is to provide a computer-readable storage medium.

[0193] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in a state-constrained tower crane adaptive fuzzy control method as described in Example 1 of the present disclosure.

[0194] Embodiment 4

[0195] The purpose of this embodiment is to provide an electronic device.

[0196] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, the steps in a state-constrained tower crane adaptive fuzzy control method are implemented as described in Example 1 of the present disclosure.

[0197] The steps involved in the apparatuses of the above embodiments 2, 3 and 4 correspond to the method embodiment 1, and the specific implementation methods can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0198] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0199] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. An adaptive fuzzy control method for tower crane based on state constraints, characterized in that: include: Constructing a tower crane system dynamics model, and obtaining an energy function of the tower crane according to the tower crane system dynamics model; Among them, the tower crane system dynamics model is: ； In the formula, the state vector , control vector , the friction vector ; is the rotation angle of the cantilever; is the average displacement of the trolley; is the length of the rope; , is the load swing angle; To control the moment of the cantilever; , To control the force of the cart and rope; , , is the friction torque / force; and denote the disturbance vector and gravity vector respectively; , is the mass of the trolley and the load; , is the air friction coefficient; g is the gravity constant; , denote the symmetric inertia matrix and the centripetal-Coriolis matrix respectively; the disturbance variable , and Including system dynamics uncertainty and external disturbances; The process of obtaining the control input of the tower crane according to the energy function is: Combining properties , The energy function is derived using the tower crane system dynamics model: ； Define total disturbance , and , using fuzzy structure to approximate , and ,Right now ， ， ； in , and represents the optimal weight vector with bounds; , and represents the approximation error, and its upper limits are , and ; is the fuzzy input vector; the fuzzy basis function Defined as: ； In the formula and denote the number of fuzzy rules and the membership function of the fuzzy set respectively; and denote the centroid and width of the Gaussian function, respectively. is the number of input variables; The control input of the tower crane is obtained according to the energy function, and the load mass estimation and the constraint state item are introduced to construct an adaptive fuzzy controller and obtain the control target of the adaptive fuzzy controller; specifically, the process of introducing the load mass estimation and the constraint state item to construct the adaptive fuzzy controller is: ； ； ； in , , , , , , , , , , , , , and is a positive gain; , and They are , and The estimated vector of ; , , The adaptive law of the vector is as follows: ， ， ， in , , indicates positive gain; , , Offset the influence of the corresponding approximation error and control the gain , , The following conditions are met: ， ， ； also, ,in , denote the initial and increased mass estimates, respectively; therefore, for: ； in , represents the control coefficient; in addition, , , are the rotation angles of the constrained cantilever. , the average displacement of the trolley , length of rope Auxiliary terms within the safety range, the expression is: ； ； ； In the formula, , , is the positioning error variable; , and Respectively , and Target value of and express The upper and lower limits of and yes The upper and lower limits of and Corresponds to the boundaries of; The control input of the tower crane is adjusted according to the control target of the adaptive fuzzy controller to eliminate external interference.

2. The method for adaptive fuzzy control of a tower crane based on state constraints according to claim 1, characterized in that: The energy function of the tower crane is defined as follows: 。 3. The method for adaptive fuzzy control of a tower crane based on state constraints as claimed in claim 1, characterized in that: The control objective of the adaptive fuzzy controller is: Get the positioning error variable; the positioning error variable is defined as follows: ， ， ； in, , and Respectively , and The positioning error and load swing angle can converge to zero asymptotically, that is, ； The rotation angle of the cantilever , the average displacement of the trolley , the length of the rope Restricted to a specific range; the rotation angle of the cantilever , the average displacement of the trolley , the length of the rope Restricted to a specific range, i.e. ， ， ； in and express The upper and lower limits of and yes The upper and lower limits of and Corresponds to the boundaries of; Estimate the exact value of the load mass, i.e. ； in and Respectively The estimated value and estimation error.

4. The method for adaptive fuzzy control of a tower crane based on state constraints as claimed in claim 1, characterized in that: A state-constrained tower crane adaptive fuzzy control method also includes stability analysis of the adaptive fuzzy controller.

5. An adaptive fuzzy control system for tower crane based on state constraints, characterized in that: include: The energy function acquisition module is configured to: construct a tower crane system dynamics model, and acquire the energy function of the tower crane according to the tower crane system dynamics model; Among them, the tower crane system dynamics model is: ； In the formula, the state vector , control vector , the friction vector ; is the rotation angle of the cantilever; is the average displacement of the trolley; is the length of the rope; , is the load swing angle; To control the moment of the cantilever; , To control the force of the cart and rope; , , is the friction torque / force; and denote the disturbance vector and gravity vector respectively; , is the mass of the trolley and the load; , is the air friction coefficient; g is the gravity constant; , denote the symmetric inertia matrix and the centripetal-Coriolis matrix respectively; the disturbance variable , and Including system dynamics uncertainty and external disturbances; The process of obtaining the control input of the tower crane according to the energy function is: Combining properties , The energy function is derived using the tower crane system dynamics model: ； Define total disturbance , and , using fuzzy structure to approximate , and ,Right now ， ， ； in , and represents the optimal weight vector with bounds; , and represents the approximation error, and its upper limits are , and ; is the fuzzy input vector; the fuzzy basis function Defined as: ； In the formula and denote the number of fuzzy rules and the membership function of the fuzzy set respectively; and denote the centroid and width of the Gaussian function, respectively. is the number of input variables; The adaptive fuzzy controller construction module is configured to: obtain the control input of the tower crane according to the energy function, introduce the load mass estimation and the constraint state item to construct the adaptive fuzzy controller and obtain the control target of the adaptive fuzzy controller; wherein the process of introducing the load mass estimation and the constraint state item to construct the adaptive fuzzy controller is: ； ； ； in , , , , , , , , , , , , , and is a positive gain; , and They are , and The estimated vector of ; , , The adaptive law of the vector is as follows: ， ， ， in , , indicates positive gain; , , Offset the influence of the corresponding approximation error and control the gain , , The following conditions are met: ， ， ； also, ,in , denote the initial and increased mass estimates, respectively; therefore, for: ； in , represents the control coefficient; in addition, , , are the rotation angles of the constrained cantilever. , the average displacement of the trolley , length of rope Auxiliary terms within the safety range, the expression is: ； ； ； In the formula, , , is the positioning error variable; , and Respectively , and Target value of and express The upper and lower limits of and yes The upper and lower limits of and Corresponds to the boundaries of; The control input adjustment module is configured to adjust the control input of the tower crane according to the control target of the adaptive fuzzy controller to eliminate external interference.

6. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the state constraint-based adaptive fuzzy control method for a tower crane as described in any one of claims 1 to 4 are implemented.

7. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps in the state constraint-based adaptive fuzzy control method for a tower crane as described in any one of claims 1 to 4 are implemented.

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