Anti-interference safe deviation rectifying method and system for bridge crane

Abstract

The invention discloses an anti-interference safety deviation correction method and system for a bridge crane. The method comprises the steps that current state variable information of a cart system is obtained; estimating interference generated in the cart system by using an interference observer; designing a coupling controller based on a PD controller; designing upper and lower limits of the steering angle and the offset distance according to preset safety requirements; designing a corresponding state interlocking control barrier function; a QP method is adopted, and the coupling control law is used as a constraint condition of a nominal method and a state interlocking control obstacle function to carry out optimal solution of quadratic programming; replacing the interference in the constraint condition with the estimated value and the error upper bound in combination with the observation value of the interference observer; and solving the safety control law of the cart system at the moment, and inputting the safety control law into a PLC (Programmable Logic Controller) of an actual system to finish control. According to the anti-interference safety deviation correction method and system for the bridge crane, provided by the invention, the anti-interference safety control of track deviation correction is well realized through the interference observer and the state interlocking control obstacle function.

Description

Technical Field

[0001] This application relates to the field of lifting system transportation, and in particular to a method and system for anti-interference safety correction of a bridge crane. Background Technology

[0002] Currently, the trolley of a bridge crane may deviate from the lane line during operation, requiring corrective measures for control. Current correction control technologies primarily focus on accuracy but rarely consider environmental or trolley-related disturbances. They are sensitive to changes in external parameters and lack robust anti-disturbance performance. Furthermore, the safety of the trolley itself is not considered during correction. Given the trolley's dynamic state, external disturbances may lead to excessive steering angles or overly large reverse deviation corrections, potentially causing the trolley to veer far off the lane line and resulting in dangerous accidents.

[0003] Therefore, it is necessary to provide a method and system for anti-interference safety correction of bridge cranes to solve the above problems. Summary of the Invention

[0004] This application provides a method and system for anti-interference safety correction of bridge cranes. By using an interference observer and a state interlocking control obstacle function, anti-interference safety control of trajectory correction is well achieved.

[0005] In a first aspect, this application provides a method for anti-interference safety correction of a bridge crane, characterized by comprising the following steps:

[0006] Obtain the current state variable information of the large vehicle system;

[0007] The interference generated in the vehicle system is estimated using an interference observer.

[0008] Design a coupled controller based on a PD controller;

[0009] The upper and lower limits of the steering angle and offset distance are designed according to the preset safety requirements;

[0010] Design the corresponding state interlocking control barrier function;

[0011] The optimal solution for quadratic programming is obtained by using the QP method, where the coupled control law is used as the nominal method and the constraint condition of the state interlocking control obstacle function.

[0012] By combining the observations from the aforementioned interference observer, the interference in the constraints is replaced with the estimated value and the upper bound of the error.

[0013] The safety control law of the trolley system at this time is obtained and input into the PLC of the actual system to complete the control.

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

[0015]

[0016] Where q = [x, y, θ] are the state variables of the trolley system, representing the displacement and heading angle along the x and y axes, respectively, and d... x d y d L v represents the corresponding disturbances caused by the displacement of the x and y axes and the heading angle in the large vehicle system. r Let ω be the longitudinal velocity of the vehicle, and ω be the angular velocity of the vehicle's turn.

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

[0018]

[0019] Among them, L d Let z be the gain of the interference observer, and z be the reconstructed state. For the observations that cause interference, u is the angular velocity of the steering direction.

[0020] Preferably, the coupling controller is designed as follows:

[0021]

[0022] Among them, k p , k d , k ω For adjustable controllable gain, e x =xx r , It is the differential of the angle.

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

[0024]

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

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

[0027]

[0028] Among them, h i Design for the initial control barrier function, k s1 , k s2 γ is the parameter to be adjusted, which is related to the system state. i The relative order τ of the i-th control barrier function i , which is the number of times the control law directly displays after differentiating CBF.

[0029] Preferably, the safety set formed by the state interlocking control barrier functions is represented as follows:

[0030]

[0031] in,

[0032]

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

[0034] When every state in the coupled state term is zero, that is, θ = y = 0;

[0035] When every state in the coupled state term is non-zero, there exists 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 is established. It always holds true within the safe set.

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

[0037]

[0038] st-2θω-2θd L ≥-α1h1

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

[0040] Where, ω n The coupling controller, This is an estimate of the interference. For the estimated variables, the compensation term Compensation

[0041] Secondly, this application also provides a bridge crane anti-interference safety correction system, comprising: a server, the server including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-described method.

[0042] This application offers the following advantages over existing technologies: It provides a method and system for anti-interference safety correction of a bridge crane. The method includes the following steps: acquiring the current state variable information of the trolley system; estimating the interference generated in the trolley system using an interference observer; designing a coupling controller based on a PD controller; designing upper and lower limits for the steering angle and offset distance according to preset safety requirements; designing corresponding state interlocking control obstacle functions; using the QP method, performing optimal solution for quadratic programming with the coupling control law as the nominal method and the constraints of the state interlocking control obstacle functions; again combining the observation values ​​of the interference observer, replacing the interference in the constraints with estimated values ​​and error upper bounds; 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. Through the interference observer and the state interlocking control obstacle function, anti-interference safety control for trajectory correction is effectively achieved. Attached Figure Description

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

[0044] Figure 1 This is a flowchart of the anti-interference safety correction method for bridge cranes in the embodiments of this application;

[0045] Figure 2 This is a simulation diagram of the running trajectory of the anti-interference safety correction method for bridge cranes in the embodiments of this application;

[0046] Figure 3 This is a simulation diagram of the angle constraint effect of the anti-interference safety correction method for bridge cranes in the embodiments of this application;

[0047] Figure 4 This is a simulation diagram showing the position constraint effect of the anti-interference safety correction method for bridge cranes in the embodiments of this application.

[0048] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0049] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0050] To address the aforementioned issues, the embodiments provided in this application offer a method and system for anti-interference safety correction of a bridge crane. By using an interference observer and a state interlocking control obstacle function, anti-interference safety control for trajectory correction is effectively achieved.

[0051] Figure 1 This is a flowchart of the anti-interference safety correction method for bridge cranes in the embodiments of this application; Figure 2 This is a simulation diagram of the running trajectory of the anti-interference safety correction method for bridge cranes in the embodiments of this application; Figure 3 This is a simulation diagram of the angle constraint effect of the anti-interference safety correction method for bridge cranes in the embodiments of this application; Figure 4 This is a simulation diagram showing the position constraint effect of the anti-interference safety correction method for a bridge crane in the embodiments of this application. Now refer to... Figures 1 to 4 This invention provides a method for anti-interference safety correction of a bridge crane, comprising the following steps:

[0052] Step S101: Obtain the current state variable information of the large vehicle system;

[0053] Step S102: Use an interference observer to estimate the interference generated in the trolley system;

[0054] Step S103: Design a coupled controller based on a PD controller;

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

[0056] Step S105: Design the corresponding state interlocking control obstacle function;

[0057] Step S106: Using the QP method, the coupled control law is used as the nominal method and the constraint condition of the state interlocking control obstacle function to perform the optimal solution of the quadratic programming;

[0058] Step S107: Combine the observations of the interference observer again, and replace the interference in the constraints with the estimated value and the upper bound of the error;

[0059] Step S108: Obtain the safety control law of the trolley system at this time, and input 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] Using Young's inequality, the following inequality relationship can be obtained: Solving the differential inequality yields the following error bound:

[0072]

[0073] It can be seen that the estimation error converges asymptotically, and the upper bound of eventual convergence can be expressed as: In the formula The parameter to be adjusted can be the upper bound of the error, which can be adjusted by adjusting the gain of the disturbance observer. For each disturbance, the upper bound of its estimation error can be denoted as... When the error in the system is a constant disturbance, the estimation error can asymptotically converge to zero, thereby providing a good error estimation and compensation for the trolley system.

[0074] In practical implementation, in order to better control the steering during the large vehicle's correction process, a coupled controller based on the PD controller is designed:

[0075]

[0076] In the formula e x =xx r The correction error is defined to track the desired straight line x. r =0,k p ,k d ,k ω For adjustable controllable gain, The controller, by using angle differentiation, ensures the system's tracking performance and incorporates an error compensation term to achieve effective anti-interference control. In practical implementation, safety is a crucial concern during the correction process. Due to structural limitations and safety considerations, the steering angle and offset of the large vehicle have upper and lower limits during correction and steering. Therefore, a control barrier function (CBF) is used to constrain the state, achieving strict safety control.

[0077] Firstly, regarding the constraint on the offset angle, the safety constraint objective is to ensure that the steering angle during the steering process is not too large. The range of the safe steering angle is defined as ±θ0, and the classic control obstacle function is constructed as follows:

[0078]

[0079] Among them, ψ 0,1 To define the obstacle function that limits the steering angle, we classify and discuss the coefficients of the control input. When θ = 0, the inequality holds true. If the condition is always true, then the CBF constraint requirement is met. Note that the CBF mainly serves as a safety constraint here. The nominal control input is still derived from the GVF method. When the steering angle exceeds the specified boundary, the CBF will correct the input to meet the safety constraint requirements.

[0080] Secondly, regarding the constraint on offset, the goal is to ensure that the lateral deviation of the vehicle during travel is not excessive. This requires the design of a novel CBF (Continuous Braking Factor) to ensure system safety.

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

[0082]

[0083] Where, k s1 ,k s2 γ is the parameter to be adjusted, which is related to the system state. i The relative order τ of the i-th CBF i ,Right now

[0084]

[0085] Based on this, a new state-interlocked control barrier function (SICBF) is proposed. The design can be as follows:

[0086]

[0087] Among them, h i Design for the initial control barrier function, k s1 ,k s2 γ is the parameter to be adjusted, which is related to the system state. i The relative order τ of the i-th control barrier function i The safety set formed by SICBFs, which is the number of times the control law directly displays after differentiating the CBF, can be expressed as: Considering the two safety requirements, a safety boundary is defined for the angle and deviation, denoted as ±θ0 and ±y0 respectively. The initial CBF for the angle and deviation is designed as: h1 = θ0 2 -θ 2 , It is known that in SICBF, H1 = h1, and the relative order of the CBF with respect to angle is 1. Therefore, the proof of SICBF below will only focus on the offset. The safety set for safety control is defined as:

[0088]

[0089] in,

[0090]

[0091] With the help of the state interlocking term Γ2, the relative order of the initial CBF with respect to the offset is reduced to first order, greatly simplifying the calculation process and explicitly showing the control quantity in the first derivative with respect to the SIBF. However, the case where the coefficients before the control law are 0 may still occur. To solve this problem, subsequent theorems and proofs will be given below. First, the expansion of the SIBF is given:

[0092]

[0093] Definition 1. (State Interlocking Control Barrier Function): For a trolley 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] In the formula k s1 ,k s2 α are both positive adjustment parameters for any state (y, θ) ∈ C H ,have

[0096]

[0097] In the formula R M =θ m (v r +M y All of them are composed of known parameters.

[0098] Theorem 1. For a trolley system, given a SICBF H2 designed for the system offset state and a disturbance observer capable of estimating the system error, then for the control set...

[0099]

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

[0101] The above theorem states that if the control law is in the control set... If the coefficients are selected from the set, then the system's security can be guaranteed, regardless of whether the coefficients before the control law are zero. The detailed proof is as follows. Considering the case where the coefficients before the control law are zero, i.e., k... hu =0, and k s2 It is a positive parameter to be adjusted, from which we can obtain: By classifying and discussing, we can obtain:

[0102] 1) When every state in the coupled state term is zero, i.e., θ = y = 0, substituting this into the inequality, the left side of the inequality becomes α²y₀. 2 Since the inequality is clearly greater than zero, the inequality always holds true.

[0103] 2) When every state in the coupled state term is non-zero, there exists a state relationship θ = -k. s1 y / k s2 Regarding the CBF of angles, its effect on angle constraints has been proven above. Within the safety set, angles have strict safety constraints, and the upper bound of the angle is defined as θ. m Then we have the following relationship: y0>-k s2 θ m / k s1 , i.e., k s1 / k s2 >θ m When / y0 is established, the final result is: This holds true within the safe set. Substituting the above relation into the first derivative inequality after differentiating SICBF, we know that to satisfy the requirement of the safe set, it must satisfy...

[0104]

[0105] Strictly true, by scaling the two parts of the above equation, we get:

[0106]

[0107] In the formula M y For interference d y Substituting the actual bound value of into the original inequality, we get:

[0108]

[0109] For the sign-preserving property H1≥0 of the safe set, the parameter range can be obtained by rearranging the formula:

[0110]

[0111] Note again the safety bound for the offset angle |θ|≤θ m The parameter relationship 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 scenarios above, it can be concluded that when the coefficient before the control law is zero, the SIBF inequality of the system can still hold true under appropriate parameter selection, thus ensuring the safety of the trolley system under any condition.

[0115] Based on the SIBF design, firstly, the relative degree of interlocking term evaluation is used to appropriately reduce the order of the CBF, effectively preventing the problem of zero coefficients of control inputs leading to violations of safety conditions in traditional CBFs. Secondly, within the safety set, the mutual constraints between the two CBFs ensure that even when the input coefficients are zero, the inequalities still generally hold by selecting appropriate parameters, thus maintaining the safety of the system.

[0116] In practical implementation, if the control law is selected from the control set, the system state will be strictly constrained within the safety boundary. Furthermore, the control law after SICBF correction does not require an explicit expression; it can be obtained through quadratic programming. The quadratic programming-based controller solution method is as follows:

[0117]

[0118] st-2θω-2θd L ≥-α1h1

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

[0120] In the formula ω n For the PD coupled controller mentioned above, as the nominal control method in quadratic programming, the optimal control law is solved using CBF and SICBF as constraints. However, disturbances still exist in the constraints. Therefore, the DOB estimate is used to robustly handle the constraints. Theorem 2. For the large vehicle system, combining SICBF and DOB, the QP programming method is used to solve the quadratic programming problem with SICBF as the safety constraint. If the control law is:

[0121]

[0122] Find the middle, where This is an estimate of the interference. For the estimated variables, the compensation term Then the robustness and security of the system will be strictly guaranteed.

[0123] Proof: The interference terms appearing in SICBF can be calculated as follows:

[0124]

[0125] If the original constraint condition is true, then it is necessary to have

[0126]

[0127] This holds true. Similarly, for the position constraint error term, we have:

[0128]

[0129] If If the condition is met, the original SICBF constraint will also be met. This ensures the robustness of the constraint conditions.

[0130] This invention addresses the problem of trolley deviation during the movement of a bridge crane. It designs a control method that ensures a safe and efficient correction process. First, a disturbance observer is used to estimate and compensate for disturbances generated in the system. Next, a position-velocity coupled controller is designed to ensure the efficiency of trolley correction and serves as the nominal controller for subsequent safety control, also undergoing disturbance compensation. Finally, a novel control barrier function is designed to effectively limit the trolley's deviation angle and magnitude during operation. A robust method is employed to eliminate the influence of disturbances in the constraints. Finally, an anti-interference safety control law is obtained through quadratic programming. This method effectively handles the influence of environmental disturbances while considering safety constraints, strictly limiting the trolley's steering angle and motion deviation during operation. Compared to other correction and tracking methods, it can ensure correction efficiency while resisting the influence of external disturbances and strictly guaranteeing driving safety.

[0131] In summary, the present application provides a method and system for anti-interference safety correction of a bridge crane. The method includes the following steps: acquiring the current state variable information of the trolley system; estimating the interference generated in the trolley system using an interference observer; designing a coupled controller based on a PD controller; designing upper and lower limits for steering angle and offset distance according to preset safety requirements; designing corresponding state interlocking control obstacle functions; using the QP method, performing quadratic programming to find the optimal solution for the coupled control law as a nominal method and the constraint conditions of the state interlocking control obstacle functions; again combining the observation values ​​of the interference observer, replacing the interference in the constraint conditions with estimated values ​​and upper bounds of error; 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. Through the interference observer and the state interlocking control obstacle function, anti-interference safety control for trajectory correction is well achieved.

[0132] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the claims.

[0133] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for anti-interference and safety correction of a bridge crane, characterized in that, Includes the following steps: Obtain the current state variable information of the large vehicle system; The interference generated in the vehicle system is estimated using an interference observer. Design a coupled controller based on a PD controller; The upper and lower limits of the steering angle and offset distance are designed according to the preset safety requirements; Design the corresponding state interlocking control barrier function; The optimal solution for quadratic programming is obtained by using the QP method, where the coupled control law is used as the nominal method and the constraint condition of the state interlocking control obstacle function. By combining the observations from the aforementioned interference observer, the interference in the constraints is replaced with the estimated value and the upper bound of the error. The safety control law of the trolley system at this time is obtained and input into the PLC of the actual system to complete the control.

2. The anti-interference safety correction method for bridge cranes according to claim 1, characterized in that, The current state variable information of the large vehicle system is obtained by establishing the following kinematic model: Where q = [x, y, θ] are the state variables of the trolley system, representing the displacement and heading angle along the x and y axes, respectively, and d... x d y d L v represents the corresponding disturbances caused by the displacement of the x and y axes and the heading angle in the large vehicle system. r Let ω be the longitudinal velocity of the vehicle, and ω be the angular velocity of the vehicle's turn.

3. The anti-interference safety correction method for bridge cranes according to claim 2, characterized in that, The interference observer is designed as follows: Among them, L d Let z be the gain of the interference observer, and z be the reconstructed state. For the observations that cause interference, u is the angular velocity of the steering direction.

4. The anti-interference safety correction method for bridge cranes according to claim 1, characterized in that, The coupling controller is designed as follows: In the formula, k p k d k w For adjustable controllable gain, e x =xx r , It is the differential of the angle.

5. The anti-interference safety correction method for bridge cranes according to claim 1, characterized in that, The initial control obstacle function for the steering angle and offset distance is: Where h1 is the initial control obstacle function for steering angle, h2 is the initial control obstacle function for offset distance, θ0 is the initial value of steering angle, θ is the current value of steering angle, y0 is the initial value of offset distance, and y1 is the current value of offset distance.

6. The anti-interference safety correction method for bridge cranes according to claim 5, characterized in that, The state interlocking control barrier function is constructed as follows: Among them, h i Design for the initial control barrier function, k s1 k s2 γ is the parameter to be adjusted, which is related to the system state. i The relative order τ of the i-th control barrier function i , which is the number of times the control law directly displays after differentiating CBF.

7. The anti-interference safety correction method for bridge cranes according to claim 6, characterized in that, The safety set formed by the state interlocking control barrier functions is represented as follows: in, 8. The anti-interference safety correction method for bridge cranes according to claim 7, characterized in that, k hu =0, and k s2 If it is a positive parameter to be adjusted, then When every state in the coupled state term is zero, that is, θ = y = 0; When every state in the coupled state term is non-zero, there exists 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 is established. It always holds true within the safe set.

9. The anti-interference safety correction method for bridge cranes according to claim 4, characterized in that, The quadratic programming problem is solved based on the following controller: Where, ω n The coupling controller, This is an estimate of the interference. For the estimated variables, the compensation term Compensation 10. A bridge crane anti-interference safety correction system, characterized in that, include: The server includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the program, implements the method according to any one of claims 1-9.

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