An anti-interference safety correction method and system for a bridge crane

Abstract

The application discloses a kind of bridge crane anti-interference safety deviation correction method and system, method includes: obtaining current state variable information of trolley system;Interference observer is estimated in trolley system generated disturbance;Design the coupling controller based on PD controller;According to the upper and lower limits of the preset safety requirement design out steering angle and offset distance;Design corresponding state interlocking control barrier function;Using QP method, coupling control law is regarded as the optimal solution of quadratic programming by constraint condition of state interlocking control barrier function;Again, the estimated value and error upper limit are replaced into disturbance in constraint condition by combining the observation value of interference observer;The safety control law of trolley system at this time is obtained, and it is input into the PLC of actual system to complete control.The application provides a kind of bridge crane anti-interference safety deviation correction method and system, and the anti-interference safety control of trajectory deviation correction is well realized by interference observer and state interlocking control barrier function.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of hoisting system transportation, in particular to a bridge crane anti-interference safe deviation correction method and system. BACKGROUND

[0002] At present, the trolley part of the bridge crane may deviate from the lane line during driving, at which time certain deviation correction means need to be controlled. The current deviation correction control technology mainly aims at the accuracy of deviation correction, but rarely considers the interference generated by the environment or the trolley itself, is sensitive to changes in external parameters, and has poor anti-interference performance. At the same time, in the process of deviation correction, the safety state of the trolley itself is not taken into account, and considering the state variables when the trolley is driving, the trolley may generate a too large steering angle, a too large reverse deviation correction, and other phenomena when subjected to external interference, thereby causing the trolley to deviate far out of the lane line range and other dangerous accidents.

[0003] Therefore, it is necessary to provide a bridge crane anti-interference safe deviation correction method and system to solve the above problems. SUMMARY

[0004] The present application provides a bridge crane anti-interference safe deviation correction method and system, which realizes anti-interference safe control of trajectory deviation correction through an interference observer and a state interlocking control barrier function.

[0005] In a first aspect, the present application provides a bridge crane anti-interference safe deviation correction method, characterized in that it comprises the following steps:

[0006] Obtaining current state variable information of the trolley system;

[0007] Estimating the interference generated in the trolley system using an interference observer;

[0008] Designing a coupling controller based on a PD controller;

[0009] Designing upper and lower limits of the steering angle and deviation distance according to preset safety requirements;

[0010] Designing a corresponding state interlocking control barrier function;

[0011] Using the QP method to perform optimal solution of quadratic programming of the coupling control law as a nominal method and the constraint condition of the state interlocking control barrier function;

[0012] Again combining the observation value of the interference observer, replacing the interference in the constraint condition with the estimated value and the error upper limit;

[0013] Obtaining the safety control law of the trolley system at this time, and inputting it into the PLC of the actual system to complete control.

[0014] Preferably, the current state variable information of the cart system is obtained by establishing the following kinematic model:

[0015]

[0016] where q = [x, y, θ] is the state variable of the cart system, x and y are the displacement of x and y axes respectively, and θ is the heading angle, d x , d y , d L are the corresponding disturbances caused by the displacement of x and y axes and the heading angle of the cart system, v r is the longitudinal velocity of the cart, and ω is the steering angle velocity of the cart.

[0017] Preferably, the disturbance observer is designed as follows:

[0018]

[0019] where L d is the gain of the disturbance observer, z is the reconstructed state, is the observed value of the disturbance, and u is the steering angle velocity. Preferably, the coupling controller is designed as follows:

[0020]

[0021]

[0022] where k p , k d , k ω are adjustable control gains, e x = x - x r , is the angle differential.

[0023] Preferably, the initial control barrier function of the steering angle and the offset distance is:

[0024]

[0025] where h1 is the initial control barrier function of the steering angle, h2 is the initial control barrier function of the offset distance, θ0 is the initial value of the steering angle, θ is the current value of the steering angle, y0 is the initial value of the offset distance, and y1 is the current value of the offset distance.

[0026] Preferably, the state interlocking control barrier function is constructed as follows:

[0027]

[0028] where h i is the initial control barrier function design,​ k s1 , k s2 is a parameter to be regulated, related to the state of the system, γ i is the relative order of the i-th control barrier function, τ i i.e. the number of times the control law directly shows after derivation of the CBF.

[0029] Preferably, the safety set constituted by the state interlocking control barrier functions is expressed as:

[0030]

[0031] wherein,

[0032]

[0033] Preferably, k hu = 0, and k s2 is a positive parameter to be regulated, then

[0034] When each state in the coupling state term is zero, i.e. θ = y = 0;

[0035] When each state in the coupling state term is not zero, there is a state relationship θ = -k s1 y / k s2 , the upper bound of the angle is defined as θ m , then y0> -k s2 θ m / k s1 , k s1 / k s2 > θ m / y0 holds, which is always true in the safety set.

[0036] Preferably, the quadratic programming is solved based on the following controller:

[0037]

[0038] s.t. -2θω - 2θd L ≥ -α1h1

[0039] k hu ω + β hf + η hd ≥ -α2H2(SICBF)

[0040] wherein, ω n is the coupling controller, is an estimation of the disturbance, is an estimation variable, a compensation term a compensation term

[0041] In a second aspect, the present application also provides a bridge crane anti-interference safety correction system, comprising a server, the server comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above method when executing the program.

[0042] The present application has the following beneficial effects compared with the prior art: the bridge crane anti-interference safety correction method and system provided by the present application, the method comprising the following steps: obtaining current state variable information of the trolley system; using an interference observer to estimate the interference generated in the trolley system; designing a coupling controller based on a PD controller; designing upper and lower limits of the turning angle and the offset distance according to preset safety requirements; designing a corresponding state interlocking control barrier function; using the QP method, taking the coupling control law as a nominal method and the state interlocking control barrier function as a constraint condition for quadratic programming optimal solution; again combining the observation value of the interference observer, replacing the interference in the constraint condition with the estimated value and the error upper limit; obtaining the safety control law of the trolley system at this time, inputting it into the PLC of the actual system to complete control, and through the interference observer and the state interlocking control barrier function, the anti-interference safety control of trajectory correction is well realized. BRIEF DESCRIPTION OF DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and serve to explain the principles of the present application, together with the description.

[0044] Figure 1 A flowchart of the bridge crane anti-interference safety correction method in the embodiment of the present application;

[0045] Figure 2 A running trajectory simulation effect diagram of the bridge crane anti-interference safety correction method in the embodiment of the present application;

[0046] Figure 3 An angle constraint simulation effect diagram of the bridge crane anti-interference safety correction method in the embodiment of the present application;

[0047] Figure 4 A position constraint simulation effect diagram of the bridge crane anti-interference safety correction method in the embodiment of the present application.

[0048] Through the above drawings, the specific embodiments of the present application have been shown, and will be described in more detail hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application by any means, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. DETAILED DESCRIPTION

[0049] The exemplary embodiments will be described in detail herein with reference to the attached drawings. In the following description, like reference numerals refer to like elements, unless indicated otherwise. The following description of exemplary embodiments is not representative of all embodiments consistent with the present application. Rather, it is merely an example of apparatus and methods consistent with some aspects of the present application as detailed in the appended claims.

[0050] To solve the above problems, the embodiments provided by the present application provide a bridge crane anti-interference safety correction method and system, which realizes anti-interference safety control of trajectory correction through disturbance observer and state interlocking control barrier function.

[0051] Figure 1 The flow chart of the bridge crane anti-interference safety correction method in the embodiments of the present application; Figure 2 The running trajectory simulation effect diagram of the bridge crane anti-interference safety correction method in the embodiments of the present application; Figure 3 The angle constraint simulation effect diagram of the bridge crane anti-interference safety correction method in the embodiments of the present application; Figure 4 The position constraint simulation effect diagram of the bridge crane anti-interference safety correction method in the embodiments of the present application. Now referring to Figures 1 to 4 The present application provides a bridge crane anti-interference safety correction method, comprising the following steps:

[0052] Step S101: obtaining current state variable information of the trolley system;

[0053] Step S102: estimating the disturbance generated in the trolley system by using a disturbance observer;

[0054] Step S103: designing a coupling controller based on a PD controller;

[0055] Step S104: designing upper and lower limits of the turning angle and the offset distance according to preset safety requirements;

[0056] Step S105: designing a corresponding state interlocking control barrier function;

[0057] Step S106: using the QP method to perform quadratic programming optimal solution of the coupling control law as a nominal method and the constraint condition of the state interlocking control barrier function;

[0058] Step S107: combining the observation value of the disturbance observer again, and replacing the disturbance in the constraint condition with the estimated value and the error upper limit;

[0059] Step S108: obtaining the safety control law of the trolley system at this time, and inputting it into the PLC of the actual system to complete the control.

[0060] In practical implementation, for the large vehicle part, the following uninterrupted kinematic model based on two-wheel differential motion control is established:

[0061]

[0062] Where q = [x, y, θ] are the system's state variables, namely the displacement and heading angle along the x and y axes; d x ,d y ,d L For potential disturbances in the system, including uncertainties within the system itself and disturbances that may arise from the external environment, v r Let ω be the longitudinal velocity of the vehicle and ω be the steering angular velocity. As can be seen from the model, the system inputs and state variables are not one-to-one; therefore, the vehicle is an underactuated system. Furthermore, considering the limitations of the actual system, the velocity in the kinematic model cannot be directly used as the control input. Therefore, the only input to the vehicle system is the steering angular velocity, while its longitudinal velocity is manually input and can be set as a constant in the algorithm.

[0063] In practical implementation, interference is unavoidable during the actual correction process of the large vehicle. Interference may stem from environmental uncertainties, such as uneven road surfaces, differences in motor performance on both sides, and tire pressure, which can lead to inconsistent front and rear wheel speeds, thus causing control errors. To address this, a method based on an interference observer is used to estimate and compensate for the interference. The system makes the following assumptions:

[0064] Assumption 1. During the operation of the large vehicle, the derivative of the system disturbance has a positive upper bound, which can be expressed as:

[0065] For a motion control system with disturbances, it can be written in nonlinear affine form as follows:

[0066]

[0067] In the formula The steering angular velocity is the control law u. The disturbance observer is designed as follows:

[0068]

[0069] In the formula L d Let z be the gain of the interference observer, and z be the reconstructed state. Let be the observed value causing the interference. The interference observation error is defined as... Define the associated Lyapunov function as follows: Differentiation yields:

[0070]

[0071] By the Young inequality, the inequality is: Solving the differential inequality, the error bound is:

[0072]

[0073] It can be seen that the estimation error is asymptotically convergent, and the upper bound of the final convergence can be expressed as where is the parameter to be adjusted, which can be adjusted by adjusting the gain of the disturbance observer to adjust the error upper bound. For each disturbance, the upper bound of the estimation error can be denoted as When the error in the system is a constant disturbance, the estimation error can be asymptotically convergent to zero, thereby providing good error estimation and compensation for the large car system.

[0074] In specific implementation, in order to better control the steering during the large car correction process, a coupling controller based on PD controller is designed:

[0075]

[0076] where e x = x - x r , defines the correction error, and tracks the expected straight line x r = 0, k p , k d , k ω is the adjustable control gain, is the angle differential This controller can guarantee the tracking performance of the system, and adds an error compensation term, which can satisfy the anti-interference control effect. In specific implementation, during the correction process, safety is one of the key issues that must be concerned. Due to the structural limitations of the large car itself and safety considerations, the steering angle has upper and lower limits, and the offset also has limitations. Based on this, the state is constrained by using the control barrier function (CBF) to achieve strict safety control.

[0077] First, for the constraint of the offset angle, the safety constraint target is to ensure that the steering angle during the steering process cannot be too large, and the range of the safe steering angle ± θ0 is defined. The classical control barrier function is constructed as:

[0078]

[0079] where ψ 0,1 is the barrier function that limits the steering angle, and the coefficients of the control input are classified and discussed. When θ = 0, the inequality If the inequality holds, the CBF constraint is satisfied. Note that the CBF here mainly plays the role of safety constraint, and the nominal control input is still derived by the GVF method, but when the steering angle exceeds the specified boundary, the CBF will modify the input to meet the safety constraint requirements.

[0080] Secondly, for the constraint of the offset, the goal is to ensure that the lateral offset of the trolley cannot be too large during the running process. For this, a new type of CBF needs to be designed to ensure the safety of the system.

[0081] First, define the system state interlocking term as:

[0082]

[0083] where k s1 ,k s2 are the parameters to be adjusted, which are related to the state of the system, and γ i is the relative order of the i-th CBF τ i , that is, the number of times the control law directly shows after taking the derivative of the CBF, the safety set composed of SICBF can be expressed as:

[0084]

[0085] On this basis, a new state interlocking control barrier function (SICBF) can be designed as follows:

[0086]

[0087] where h i is the initial control barrier function design, k s1 ,k s2 are the parameters to be adjusted, which are related to the state of the system, and γ i is the relative order of the i-th control barrier function τ i , that is, the number of times the control law directly shows after taking the derivative of the CBF, the safety set composed of SICBF can be expressed as: Considering the two safety requirements, a safety boundary is defined for the angle and deviation, represented as ±θ0 and ±y0, respectively. The initial CBF for the angle and deviation is designed as: h1 = θ0 2 - θ 2 , It can be seen that in SICBF H1 = h1, the relative order of the CBF for the angle is 1, so the proof process of SICBF in the following will only be described for the offset. The safety set of the safety control is defined as:

[0088]

[0089] where,

[0090]

[0091] With the help of the state interlocking term Γ2, the relative order of the initial CBF about the offset is reduced to one, which greatly simplifies the calculation process and displays the control quantity in the first-order derivative of the SICBF. However, the case where the coefficient of the control law is zero is still possible. To solve this problem, subsequent theorems and proofs will be given in the following text. First, the expansion of the SICBF is given:

[0092]

[0093] Definition 1. (State interlocking control barrier function): For a large vehicle system, the function H2(y, θ) is called a state interlocking barrier function of the system, and for the parameters in the expression, it satisfies the following form:

[0094]

[0095] where k s1 , k s2 , and α are positive adjustment parameters, and for any state (y, θ) ∈ C H , there are

[0096]

[0097] where R M = θ m (v r + M y ) are composed of known parameters.

[0098] Theorem 1. For a large vehicle system, given a SICBF H2 designed for the offset state of the system and a disturbance observer that can estimate the system error, then for the control set

[0099]

[0100] Any control law ω belonging to the control set will maintain the forward invariance of the safe set.

[0101] The above theorem shows that if the control law is selected in the control set , the safety of the system can be guaranteed, regardless of whether the coefficient of the control law is zero. The detailed proof process is as follows. Consider the case where the coefficient of the control law is zero, i.e., k hu = 0, and k s2 is a positive parameter to be adjusted, so we have: By classifying and discussing, we can get:

[0102] 1). When each state in the coupling state item is zero, i.e. θ = y = 0, so into the inequality, the left side of the inequality is α2y0 2 , which is obviously greater than zero, at this time the inequality is always true.

[0103] 2). When each state in the coupling state item is not zero, there is a state relationship θ = -k s1 y / k s2 , for the angle CBF, the effect of the angle constraint has been proved above, in the safety set, the angle has a strict safety constraint, define the upper bound of the angle as θ m , then there is the following relationship y0> -k s2 θ m / k s1 , i.e. k s1 / k s2 > θ m / y0, finally y0> 0 can be obtained always holds in the safety set. Substitute the above relationship into the first-order derivative inequality after the derivation of SICBF, and know that to meet the requirements of the safety set, it needs to meet

[0104]

[0105] strictly, put the two parts in the above formula separately, and can be obtained:

[0106]

[0107] In the formula, M y is the actual upper bound of the interference d y , and then substitute it into the original inequality, and can be obtained:

[0108]

[0109] For the non-negative property of the safety set H1≥0, the parameter range can be obtained by arranging the formula:

[0110]

[0111] Again, it is noted that the safety boundary of the offset angle | θ | ≤ θ m , and the parameter relationship formula can be obtained as follows:

[0112]

[0113] In the formula, R M = θ m (v r + M y ) is a constant defined by known parameters.

[0114] Combining the two cases, it can be concluded that the SICBF inequality of the system is still constant when the coefficient of the control law is zero, and the safety of the car system is guaranteed under any state by selecting appropriate parameters.

[0115] Based on the design of SICBF, first, the relative degree of interlocking terms is evaluated to appropriately reduce the order of CBF, effectively preventing the problem of zero coefficient of control input in traditional CBF leading to violation of safety conditions. Second, within the safety set, the mutual constraint between the two CBFs ensures that even when the input coefficient is zero, the inequality still universally holds by selecting appropriate parameters, thereby maintaining the safety of the system.

[0116] In specific implementation, if the control law is selected in the control set, the state of the system will be strictly limited within the safety boundary, and the control law after SICBF modification does not need to give an explicit expression, but can be obtained through the form of quadratic programming. The controller solving method based on quadratic programming is as follows:

[0117]

[0118] s.t.-2θω-2θd L ≥-α1h1

[0119] k hu ω+β hf +η hd ≥-α2H2(SICBF)

[0120] In the formula, ω n is the PD coupled controller in the above, as the nominal control method in quadratic programming, the CBF and SICBF are used as constraint conditions to solve the optimal control law. However, there is still the influence of disturbance in the constraint condition, for this, the estimated value of DOB is used to make robustness processing to the constraint condition. Theorem 2. For the car system, combining SICBF with DOB, the quadratic programming problem with SICBF as safety constraint condition is solved by using QP planning method, if the control law is obtained in:

[0121]

[0122] is the estimated value of disturbance, is the estimated variable, compensation term then the robust safety of the system is strictly guaranteed.

[0123] Proof: for the disturbance term appearing in SICBF, it can be calculated as:

[0124] ​

[0125] Then if the original constraint condition is satisfied, there must be

[0126]

[0127] is established. Similarly, for the position constraint error term, there is:

[0128]

[0129] Then if is established, the original SICBF constraint is naturally also established. In this way, the robustness of the constraint condition is guaranteed.

[0130] The present application aims at the problem of offset of the trolley part of the bridge crane during running, and designs a control method which can guarantee safe and efficient correction. The method first uses a disturbance observer to estimate and compensate the disturbance generated in the system, then designs a position and speed coupling controller to guarantee the efficiency of trolley correction, and at the same time, as a nominal controller for subsequent safety control, and disturbance compensation is added, finally, a new type of control barrier function is designed, which effectively limits the offset angle and offset amount of the trolley during running, and at the same time, a robust method is used to eliminate the influence of disturbance in the constraint condition, and finally, the anti-disturbance safety control law is obtained through quadratic programming. The method can well deal with the influence of environmental disturbance, and at the same time, safety constraints are considered, and the steering angle and movement offset of the trolley during running are strictly limited, compared with other correction and tracking methods, the efficiency of correction can be guaranteed while resisting the influence of external disturbance, and the running safety is strictly guaranteed.

[0131] In summary, the bridge crane anti-interference safety correction method and system provided by the embodiment of the present application, the method comprises the following steps: obtaining the current state variable information of the trolley system; using a disturbance observer to estimate the disturbance generated in the trolley system; designing a coupling controller based on a PD controller; designing the upper and lower limits of the steering angle and the offset distance according to the preset safety requirements; designing the corresponding state interlocking control barrier function; using the QP method to perform optimal solution of quadratic programming of the coupling control law as the nominal method and the constraint condition of the state interlocking control barrier function; again combining the observation value of the disturbance observer, replacing the disturbance in the constraint condition with the estimated value and the error upper limit; obtaining the safety control law of the trolley system at this time, inputting it into the PLC of the actual system to complete control, and through the disturbance observer and the state interlocking control barrier function, the anti-interference safety control of trajectory correction is well realized.

[0132] Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. It is intended that the specification and examples be considered as exemplary only, with the true scope and spirit of the application being indicated by the following claims.

[0133] It is to be understood that the application is not limited to the precise construction herein disclosed and shown in the drawings, and that various modifications and changes can be made by those skilled in the art without departing from the scope of the application. The scope of the application is limited only by the claims that follow.

Claims

1. A method for anti-jamming safety correction of a bridge crane, characterized in that, The method comprises the following steps: acquiring current state variable information of the cart system; estimating disturbance generated in the cart system by using a disturbance observer; designing a coupling controller based on a PD controller; designing upper and lower limits of a steering angle and a deviation distance according to preset safety requirements; designing a corresponding state interlocking control barrier function; adopting a QP method to perform optimal solution of quadratic programming of the coupling control law as a nominal method and constraint conditions of the state interlocking control barrier function; combining the observation value of the disturbance observer again, and replacing the disturbance in the constraint conditions with an estimated value and an error upper limit; obtaining a safety control law of the cart system at this time, and inputting the safety control law into a PLC of an actual system to complete control; the coupling controller is designed as follows: wherein is an adjustable control gain, , is the angular differential; an initial control barrier function of the steering angle and the deviation distance is: wherein, is an initial control barrier function for the steering angle, is an initial control barrier function for the offset distance, is an initial value for the steering angle, is a current value for the steering angle, is an initial value for the offset distance, is a current value for the offset distance; the state interlocking control barrier function is constructed as follows: wherein, is an initial control barrier function, is a parameter to be regulated, related to the state of the system, is the relative order of the control barrier function, i.e. the number of times the control law directly shows after derivation of the CBF; a safety set formed by the state interlocking control barrier function is represented as: wherein ; is a positive parameter to be adjusted, then ; When each of the states in the coupling state item is zero, i.e. ; When each state in the coupling state item is not zero, the state relationship is , the upper limit of the defined angle is , and always holds in the security set. the quadratic programming is solved based on the following controller: wherein, is the coupling controller, is an estimate of the disturbance, is an estimated variable, compensation term , compensation term .

2. The anti-jamming safety correction method of a bridge crane according to claim 1, characterized in that, the current state variable information of the cart system is acquired by establishing the following kinematic model: wherein, are the state variables of the cart system, the displacement of x, y axis and the heading angle, are the corresponding disturbances generated by the displacement of x, y axis and the heading angle of the cart system, is the longitudinal velocity of the cart, is the steering angular velocity of the cart.

3. The anti-jamming safety correction method of a bridge crane according to claim 2, characterized in that, the disturbance observer is designed as follows: wherein, is the gain of the disturbance observer, z is the reconstructed state, is the observed value of the disturbance, u is the steering angular velocity.

4. An anti-jamming safety correction system for a bridge crane, characterized in that, The method comprises the following steps: a server, the server comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the method of any one of claims 1-3 when executing the program.

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