An electrically controlled bidirectional brake pay-off trolley control system

Abstract

This invention discloses an electrically controlled bidirectional braking cable laying pulley control system, belonging to the field of intelligent control technology for power construction equipment. The system includes a pulley body, a sensor module, a controller module, and an execution drive module. The controller module includes: a state recognition unit that constructs an operating state vector based on cable tension and pulley speed, and integrates an improved fusion tension estimation model, combining historical tension trends with current observations for weighted fusion to improve the stability and robustness of tension estimation; and a control optimization solution unit that establishes a joint control strategy based on performance objectives and safety constraints, switching control paths between normal and abnormal states according to the current state, generating acceleration control inputs to adjust the pulley state, and achieving closed-loop control. This invention possesses dynamic recognition, intelligent judgment, and adaptive control capabilities, and is suitable for cable laying conditions with significant tension fluctuations and high safety requirements, improving the control accuracy and system stability of the pulley operation.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology for power construction equipment, and particularly relates to an electronically controlled bidirectional braking cable laying pulley control system. Background Technology

[0002] In the tension laying of overhead power lines, the laying pulley is a crucial device for cable support and traction, and its operational stability directly affects the laying quality and construction safety. Traditional pulley systems mostly rely on mechanical braking or semi-automatic control, lacking the ability to dynamically perceive and intelligently regulate the pulley's operating status, which is particularly inadequate in complex terrain, variable load, and long-distance laying operations.

[0003] First, most existing systems rely on a coarse judgment method based on single-cycle tension values, which struggles to accurately perceive cable tension fluctuations and cannot respond quickly to sudden tension anomalies or drastic fluctuations, posing certain safety hazards. Second, traditional systems are mostly open-loop or weakly closed-loop structures, lacking real-time feedback mechanisms, resulting in a lag between control input and the actual operating state of the trolley. Furthermore, given significant tension signal noise and limited measurement accuracy, direct tension control strategies are insufficient in both accuracy and robustness, often leading to unstable system control performance. Especially in scenarios where the pulley requires bidirectional braking, how to accurately identify and predict cable tension and pulley speed, and then dynamically adjust braking behavior to ensure stable tension while achieving controllable and safe operation of the pulley, remains a key technical challenge. Existing technologies have unresolved issues in the following aspects: lack of in-depth processing and prediction mechanisms for tension signals, making it impossible to provide reliable estimates when signal fluctuations are severe; incomplete feedback paths in the control system, lacking a closed-loop control mechanism based on state vectors; lack of a joint control model for the dual objectives of tension and safety, making it impossible to achieve a dynamic balance between performance and safety; and a single control optimization method, lacking the ability to adapt strategies after identifying abnormal operating conditions.

[0004] Therefore, there is an urgent need for an electrically controlled trolley system with the ability to identify state, estimate tension, detect anomalies, and optimize control in a coordinated manner. This system should be able to achieve an effective balance between tension control accuracy, safety, and control efficiency through reasonable estimation models and multi-objective optimization methods, based on real-time perception. Summary of the Invention

[0005] To address the problems of inaccurate operating status identification, unreliable tension estimation, lag in control strategy response, and low system regulation efficiency in the aforementioned background technologies, this invention proposes an electronically controlled bidirectional braking cable release trolley control system. This system integrates an improved tension estimation method with an intelligent control strategy, constructing an integrated trolley control structure with closed-loop control, dynamic identification, and adaptive adjustment capabilities. The aim is to improve the operational stability, tension control accuracy, and operational safety of the trolley during cable release.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] An electronically controlled bidirectional braking cable-releasing trolley control system includes:

[0008] The main body of the trolley is used to slide along a preset route;

[0009] The sensor module is used to collect the operating status parameter information of the trolley sliding along the preset line. The operating status parameter information includes the cable tension value and the trolley speed.

[0010] A controller module, electrically connected to the sensor module and the actuator module, includes:

[0011] The state recognition unit is used to construct the current running state vector of the trolley based on the cable tension value and the trolley speed; the state recognition unit integrates an improved fusion tension estimation model, which is used to perform weighted fusion of historical tension trends and current observation data to generate cable tension estimates.

[0012] An abnormal state judgment unit is used to determine whether the operating state vector exceeds a preset tension threshold or speed threshold.

[0013] The control optimization solution unit is used to establish a joint control strategy of performance target and safety constraints based on the running state vector. When the state is normal, it generates control input according to the system performance index to guide the trolley state to converge toward the reference target. When the state is abnormal, it switches to a control path dominated by safety constraints and generates control input to limit risks.

[0014] The execution drive module is used to receive control input and drive the braking device corresponding to the current direction of movement of the trolley to adjust the running state of the trolley;

[0015] The control system is constructed as a closed-loop control structure, which completes state parameter acquisition, state identification, anomaly judgment and control input update in each control cycle to form operation feedback.

[0016] As a preferred embodiment of the present invention, the trolley body includes:

[0017] A pulley assembly, located at the bottom of the trolley body, is used to slide along the load-bearing cable in a preset route;

[0018] Shock-absorbing suspension components are used to mitigate the impact of tension fluctuations on the trolley structure.

[0019] As a preferred embodiment of the present invention, the sensor module includes:

[0020] The tension sensor unit is used to acquire observation data of the cable tension experienced by the trolley during the control cycle;

[0021] The speed sensing unit is used to acquire the sliding speed data of the trolley along the track direction;

[0022] The state extraction unit is used to fuse tension and velocity signals to construct an extended state vector that reflects the current operating state of the trolley.

[0023] In a preferred embodiment of the present invention, the fusion processing is based on an improved fusion tension estimation model, which includes the following steps:

[0024] Acquire cable tension observation data from multiple historical control periods, input them into the trend prediction model, and output the trend prediction tension value for the next period.

[0025] Collect the tension observation value F of the current cycle. raw (t);

[0026] The standard deviation of the tension change rate is calculated based on the tension change rate over multiple adjacent periods.

[0027] The fusion weight λ(t) is dynamically calculated according to the following formula:

[0028]

[0029] Where: k is the adjustment factor, and θ is the set fluctuation threshold;

[0030] Trend prediction tension value Compared with the current observed tension value F raw (t) is weighted and fused according to the fusion weight λ(t) to obtain the cable tension estimate F. tens (t), the expression is:

[0031]

[0032] The extended state vector output by the state extraction unit includes: the current period tension observation value, the tension change rate, the pulley speed, the pulley acceleration, the cable tension estimate, and the velocity sliding average value.

[0033] As a preferred embodiment of the present invention, the state recognition unit in the controller module is configured to:

[0034] Within each control cycle, based on the raw signals output by the tension sensor unit and the speed sensing unit, the following steps are performed to construct the current running state vector of the sled:

[0035] The tension and velocity signals are subjected to periodic differential processing to calculate the rate of change of cable tension and the rate of change of pulley speed.

[0036] The periodic difference processing uses a first-order difference algorithm with a fixed period to extract the dynamic change features within the current control period;

[0037] The original tension value, velocity value, and their derivative signals are smoothed using a filtering algorithm.

[0038] The filtering method is a sliding fit filtering algorithm, which achieves signal denoising and dynamic curve fitting based on a local multinomial regression model.

[0039] The processed six state information items, including tension, tension change rate, velocity, velocity change rate, tension estimate, and velocity sliding average, are combined to form a state vector, which is used as the input to the control optimization solution unit within this control cycle.

[0040] As a preferred embodiment of the present invention, the abnormal state judgment unit in the controller module is configured to:

[0041] Set the upper and lower limit threshold ranges for the tension parameters and the maximum threshold for the pulley speed;

[0042] Within each control cycle, the tension and velocity values ​​in the operating state vector are judged to be within a certain range.

[0043] If the tension exceeds the above-mentioned tension threshold range, or the speed exceeds the maximum speed threshold, it is determined that the control cycle has an abnormal state.

[0044] When the state vector continuously crosses the preset control cycle and detects an abnormal state, an abnormality trigger signal is output, and the controller module is instructed to switch the control path to the priority control strategy based on the control barrier function CBF in the next control cycle.

[0045] As a preferred embodiment of the present invention, the control optimization solving unit in the controller module is constructed for:

[0046] Within each control cycle, based on the trolley running state vector provided by the state recognition unit, including the trolley's current position s car Sliding speed v car and cable tension value F tensA joint control optimization model is constructed, which includes the following:

[0047] (1) Construct the objective function for tension performance control, the formula is:

[0048]

[0049] Wherein: F ref This is the target value for cable tension, used to define performance control objectives;

[0050] (2) Construct multiple security control barrier functions, including:

[0051] Braking distance h1 constraint of the sled:

[0052]

[0053] Where: s limit Indicates the location of the end point of the route. For the maximum braking deceleration, d safe A preset braking safety redundancy distance is used to ensure that the sled can come to a safe stop within the remaining distance;

[0054] Cable tension lower limit h2 constraint:

[0055] h2=F tens -F min ;

[0056] Wherein: F min This is the lower limit threshold for cable tension, used to prevent cable slack or cable slippage caused by excessively low tension.

[0057] Cable tension limit h3 constraint:

[0058] h3 = F max -F tens ;

[0059] Wherein: F max This is the upper limit threshold for cable tension, used to prevent cable breakage or equipment overload.

[0060] (3) Construct the controller optimization objective function, minimizing the control input energy and relaxation term penalty, as shown in the formula:

[0061]

[0062] Where: a cmd The acceleration control input generated for the controller is ρ, the relaxation variable penalty factor, which is used to measure the performance trade-off cost in the objective function; ξ is the relaxation variable in the control objective, which is used to relax the performance objective when the constraints are tight; J is the optimization control objective function.

[0063] (4) By solving the convex quadratic programming problem composed of the above objective function and constraint function, the optimal acceleration control input for the current period is obtained. It is also used to drive the execution module to adjust the trolley state.

[0064] As a preferred embodiment of the present invention, the control optimization solution unit further includes:

[0065] When the trolley's operating state is stable or the tension fluctuation amplitude is below a set threshold, the penalty factor ρ of the relaxation variable in the objective function is dynamically adjusted by analyzing the changing trend of the state vector over multiple consecutive control cycles. This guides the controller to increase the acceleration control input a while meeting safety constraints. cmd The convergence efficiency and overall system control energy efficiency.

[0066] As a preferred embodiment of the present invention, the parameter setting method of the penalty factor ρ in the control optimization solution unit includes a preset interface setting mode: a fixed value is imported into the controller module through an external interface to form a static parameter control scheme;

[0067] Dynamic update mode: Combining historical data of trolley operation with the current load status, the embedded algorithm module automatically generates the penalty factor value to be used in the current cycle;

[0068] Response feedback verification module: used to verify the control input a after the penalty factor ρ is updated. cmd Feedback analysis is performed on the results of changes in the objective function; the parameter setting mode supports initial selection or dynamic switching during operation.

[0069] As a preferred embodiment of the present invention, the execution drive module includes: receiving the acceleration control input output by the control optimization solution unit, and adjusting the actual running state of the trolley according to the braking device action corresponding to the acceleration control input and the running direction of the trolley. The execution drive module and the controller module constitute a control execution path with synchronized response.

[0070] The beneficial effects of this invention are as follows: By setting up a trolley body and a sensor module for acquiring trolley operating state parameters, the system can continuously collect key parameters such as cable tension and trolley speed during the trolley's sliding operation along a preset line, providing a reliable basis for subsequent control decisions. The controller module forms an integrated information and execution architecture with the sensor module and the execution drive module through electrical connection, enabling state recognition and control output to have high timeliness and coordination. Inside the controller, the state recognition unit not only performs basic recognition processing on the tension and speed of the current cycle, but also integrates an improved fusion tension estimation model. This model effectively suppresses noise interference in the tension signal by performing weighted fusion calculations on historical tension change trends and current observation data, improving the robustness and reliability of the estimation results under fluctuating conditions, providing a more stable tension input basis for the generation of control inputs, and improving the accuracy and robustness of the overall system control. Furthermore, the system is equipped with an abnormal state judgment unit, which can compare preset tension and speed thresholds in real time based on the constructed operating state vector. When a potential operating abnormality is detected, a protection mechanism can be actively triggered, thereby enhancing the system's adaptability to sudden operating conditions and its risk management level. The control optimization solution unit constructs a dual-strategy adjustment mechanism between normal and abnormal states. When the state is good, it prioritizes achieving the performance target, guiding the trolley state to smoothly converge to the target value. When risks arise, it automatically switches to a control path dominated by safety constraints, promptly suppressing potential over-tension or over-speed risks. This demonstrates the significant improvement in safety and adaptive performance of the control strategy of this invention. The execution drive module maintains synchronous response with the control input, instantly adjusting the braking action in the trolley's running direction based on the control optimization solution results. This ensures that control commands are quickly and accurately transmitted to the actual execution stage, forming a complete closed-loop control path encompassing state, judgment, optimization, and execution. Attached Figure Description

[0071] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] in:

[0073] Figure 1 This is a schematic diagram of the overall structure of the system of the present invention. Detailed Implementation

[0074] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the described embodiments of the present invention are within the scope of protection of the present invention.

[0075] like Figure 1 As shown, this is an embodiment of the present invention, which provides an electronically controlled bidirectional braking cable-releasing trolley control system, comprising:

[0076] (1) Main body of the trolley

[0077] Used for sliding along a preset route;

[0078] The main body of the trolley includes:

[0079] A pulley assembly, located at the bottom of the trolley body, is used to slide along the load-bearing cable in a preset route;

[0080] Shock-absorbing suspension components are used to mitigate the impact of tension fluctuations on the trolley structure.

[0081] (2) Sensor Module

[0082] This is used to collect operating status parameter information of the trolley as it slides along the preset line. The operating status parameter information includes cable tension value and trolley speed.

[0083] The sensor module includes:

[0084] The tension sensor unit is used to acquire observation data of the cable tension experienced by the trolley during the control cycle;

[0085] The speed sensing unit is used to acquire the sliding speed data of the trolley along the track direction;

[0086] The state extraction unit is used to fuse tension and velocity signals to construct an extended state vector that reflects the current operating state of the trolley.

[0087] (3) Controller Module

[0088] The controller module, electrically connected to the sensor module and the actuator module, includes:

[0089] The state recognition unit is used to construct the current running state vector of the trolley based on the cable tension value and the trolley speed; the state recognition unit integrates an improved fusion tension estimation model, which is used to perform weighted fusion of historical tension trends and current observation data to generate cable tension estimates.

[0090] An abnormal state judgment unit is used to determine whether the operating state vector exceeds a preset tension threshold or speed threshold.

[0091] The control optimization solution unit is used to establish a joint control strategy of performance target and safety constraints based on the running state vector. When the state is normal, it generates control input according to the system performance index to guide the trolley state to converge toward the reference target. When the state is abnormal, it switches to a control path dominated by safety constraints and generates control input to limit risks.

[0092] In a preferred embodiment of the present invention, the fusion processing is based on an improved fusion tension estimation model, which includes the following steps:

[0093] Acquire cable tension observation data from multiple historical control periods, input them into the trend prediction model, and output the trend prediction tension value for the next period.

[0094] Collect the tension observation value F of the current cycle. raw (t);

[0095] The standard deviation of the tension change rate is calculated based on the tension change rate over multiple adjacent periods.

[0096] The fusion weight λ(t) is dynamically calculated according to the following formula:

[0097]

[0098] Where: k is the adjustment factor, and θ is the set fluctuation threshold;

[0099] Trend prediction tension value Compared with the current observed tension value F raw (t) is weighted and fused according to the fusion weight λ(t) to obtain the cable tension estimate F. tens (t), the expression is:

[0100]

[0101] The extended state vector output by the state extraction unit includes: the current period tension observation value, the tension change rate, the pulley speed, the pulley acceleration, the cable tension estimate, and the velocity sliding average value.

[0102] In one embodiment, the state recognition unit in the controller module is configured to:

[0103] Within each control cycle, based on the raw signals output by the tension sensor unit and the speed sensing unit, the following steps are performed to construct the current running state vector of the sled:

[0104] The tension and velocity signals are subjected to periodic differential processing to calculate the rate of change of cable tension and the rate of change of pulley speed.

[0105] The periodic difference processing uses a first-order difference algorithm with a fixed period to extract the dynamic change features within the current control period;

[0106] The original tension value, velocity value, and their derivative signals are smoothed using a filtering algorithm.

[0107] The filtering method is a sliding fit filtering algorithm, which achieves signal denoising and dynamic curve fitting based on a local multinomial regression model.

[0108] The processed six state information items, including tension, tension change rate, velocity, velocity change rate, tension estimate, and velocity sliding average, are combined to form a state vector, which is used as the input to the control optimization solution unit within this control cycle.

[0109] In one specific embodiment, the abnormal state judgment unit in the controller module is constructed for:

[0110] Set the upper and lower limit threshold ranges for the tension parameters and the maximum threshold for the pulley speed;

[0111] Within each control cycle, the tension and velocity values ​​in the operating state vector are judged to be within a certain range.

[0112] If the tension exceeds the above-mentioned tension threshold range, or the speed exceeds the maximum speed threshold, it is determined that the control cycle has an abnormal state.

[0113] When the state vector continuously crosses the preset control cycle and detects an abnormal state, an abnormality trigger signal is output, and the controller module is instructed to switch the control path to the priority control strategy based on the control barrier function CBF in the next control cycle.

[0114] In a preferred embodiment of the present invention, the control optimization solving unit in the controller module is configured to:

[0115] Within each control cycle, based on the trolley running state vector provided by the state recognition unit, including the trolley's current position s car Sliding speed v car and cable tension value F tens A joint control optimization model is constructed, which includes the following:

[0116] (1) Construct the objective function for tension performance control, the formula is:

[0117]

[0118] Wherein: F ref This is the target value for cable tension, used to define performance control objectives;

[0119] (2) Construct multiple security control barrier functions, including:

[0120] Braking distance h1 constraint of the sled:

[0121]

[0122] Where: s limit Indicates the location of the end point of the route. For the maximum braking deceleration, d safe A preset braking safety redundancy distance is used to ensure that the sled can come to a safe stop within the remaining distance;

[0123] Cable tension lower limit h2 constraint:

[0124] h2=F tens -F min ;

[0125] Wherein: F min This is the lower limit threshold for cable tension, used to prevent cable slack or cable slippage caused by excessively low tension.

[0126] Cable tension limit h3 constraint:

[0127] h3 = F max -F tens ;

[0128] Wherein: F max This is the upper limit threshold for cable tension, used to prevent cable breakage or equipment overload.

[0129] (3) Construct the controller optimization objective function, minimizing the control input energy and relaxation term penalty, as shown in the formula:

[0130]

[0131] Where: a cmd The acceleration control input generated for the controller is ρ, the relaxation variable penalty factor, which is used to measure the performance trade-off cost in the objective function; ξ is the relaxation variable in the control objective, which is used to relax the performance objective when the constraints are tight; J is the optimization control objective function.

[0132] (4) By solving the convex quadratic programming problem composed of the above objective function and constraint function, the optimal acceleration control input for the current period is obtained. It is also used to drive the execution module to adjust the trolley state.

[0133] In a specific implementation, the control optimization solution unit further includes:

[0134] When the trolley's operating state is stable or the tension fluctuation amplitude is below a set threshold, the penalty factor ρ of the relaxation variable in the objective function is dynamically adjusted by analyzing the changing trend of the state vector over multiple consecutive control cycles. This guides the controller to increase the acceleration control input a while meeting safety constraints. cmd The convergence efficiency and overall system control energy efficiency.

[0135] In a preferred embodiment, the parameter setting method for the penalty factor ρ in the control optimization solution unit includes:

[0136] Preset interface setting mode: Import fixed values ​​into the controller module through an external interface to form a static parameter control scheme;

[0137] Dynamic update mode: Combining historical data of trolley operation with the current load status, the embedded algorithm module automatically generates the penalty factor value to be used in the current cycle;

[0138] Response feedback verification module: used to verify the control input a after the penalty factor ρ is updated. cmd Feedback analysis is performed on the results of changes in the objective function; the parameter setting mode supports initial selection or dynamic switching during operation.

[0139] In summary, the controller module of this invention integrates three functional units—state recognition, anomaly detection, and optimization solution—to construct a closed-loop intelligent control path driven by tension estimation. Within each cycle, the controller completes the entire process from tension trend extraction, dynamic weight fusion, state parameter construction, to the generation of optimal control input, exhibiting significant real-time response and parameter adaptability. Compared to traditional cable-laying pulley control methods based on single-point measurement and fixed-rule judgment, this invention's system achieves predictive identification and fusion processing of cable tension change trends, effectively enhancing the robustness of control input to fluctuations in operating conditions. Simultaneously, its control mechanism based on multi-objective joint optimization solution ensures tension stability while also considering pulley braking distance and structural safety constraints, significantly improving the overall system's operational safety and control accuracy.

[0140] The technical structure of this controller module is suitable for various stringing conditions. Especially in complex line construction environments with long distances, high elevation differences, multiple obstacles, or severe tension fluctuations, it can effectively mitigate safety risks such as tension exceeding limits and pulley derailment, and meet the engineering requirements for the integrated control objectives of "stability, accuracy, and speed" in power line tension stringing operations.

[0141] (4) Execution driver module

[0142] Used to receive control input and drive the braking device corresponding to the current direction of movement of the trolley to adjust the running state of the trolley;

[0143] The execution drive module includes: receiving the acceleration control input output by the control optimization solution unit, and adjusting the actual running state of the trolley according to the braking device action corresponding to the acceleration control input and the running direction of the trolley. The execution drive module and the controller module form a control execution path with synchronous response.

[0144] In summary, this invention overcomes key problems in traditional pulley control systems, such as delayed operational status recognition, insufficient tension prediction accuracy, and untimely control response. By introducing a tension estimation model that integrates historical trends and real-time observations, the robustness and predictability of tension signal processing are improved. A closed-loop control structure based on state vectors is constructed to achieve dynamic linkage between control input and pulley operational status. Furthermore, a performance-safety joint optimization strategy is established to achieve an efficient balance between tension stability and structural safety. The system is particularly suitable for power line laying construction scenarios with significant tension fluctuations and complex operating conditions, possessing excellent engineering deployability and control accuracy assurance capabilities, providing strong support for the development of intelligent power construction equipment and safe control technology for power line laying.

[0145] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of those different embodiments or examples.

[0146] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing a particular logical function or process. Furthermore, the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functionality involved.

[0147] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in this application, and these should all be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A control system for an electrically controlled bidirectional braking wire-releasing pulley, characterized in that, include: The main body of the trolley is used to slide along a preset route; The sensor module is used to collect the operating status parameter information of the trolley sliding along the preset line. The operating status parameter information includes the cable tension value and the trolley speed. A controller module, electrically connected to the sensor module and the actuator module, includes: The state recognition unit is used to construct the current running state vector of the trolley based on the cable tension value and the trolley speed; the state recognition unit integrates an improved fusion tension estimation model, which is used to perform weighted fusion of historical tension trends and current observation data to generate cable tension estimates. An abnormal state judgment unit is used to determine whether the operating state vector exceeds a preset tension threshold or speed threshold. The abnormal state judgment unit in the controller module is constructed for: Set the upper and lower limit threshold ranges for the tension parameters and the maximum threshold for the pulley speed; Within each control cycle, the tension and velocity values ​​in the operating state vector are judged to be within a certain range. If the tension exceeds the above-mentioned tension threshold range, or the speed exceeds the maximum speed threshold, it is determined that the control cycle has an abnormal state. When the state vector continuously crosses the preset control cycle and detects an abnormal state, an abnormality trigger signal is output, and the controller module is instructed to switch the control path to the priority control strategy based on the control barrier function CBF in the next control cycle. The control optimization solution unit is used to establish a joint control strategy of performance target and safety constraints based on the running state vector. When the state is normal, it generates control input according to the system performance index to guide the trolley state to converge toward the reference target. When the state is abnormal, it switches to a control path dominated by safety constraints and generates control input to limit risks. The control optimization solution unit in the controller module includes the following steps: Within each control cycle, based on the trolley's operating state vector provided by the state recognition unit, including the trolley's current position... Sliding speed and cable tension value A joint control optimization model is constructed, which includes the following: (1) Construct the objective function for tension performance control, the formula is: ； in: This is the target value for cable tension, used to define performance control objectives; (2) Construct multiple security control barrier functions, including: Puller braking distance constraint: ； in: Indicates the location of the end point of the route. For maximum braking deceleration, A preset braking safety redundancy distance is used to ensure that the sled can come to a safe stop within the remaining distance; lower limit of cable tension constraint: ； in: This is the lower limit threshold for cable tension, used to prevent cable slack or cable slippage caused by excessively low tension. upper limit of cable tension constraint: ； in: This is the upper limit threshold for cable tension, used to prevent cable breakage or equipment overload. (3) Construct the controller optimization objective function, minimizing the control input energy and relaxation term penalty, as shown in the formula: ； in: The acceleration control input generated for the controller, The slack variable penalty factor is used in the objective function to measure the cost of performance trade-offs. To control slack variables in the objective, used to relax performance targets when constraints are tight; To optimize the control objective function; (4) By solving the convex quadratic programming problem consisting of the above objective function and constraint function, the optimal acceleration control input for the current period is obtained. And it is used to drive the execution module to adjust the trolley state; The execution drive module is used to receive control input and drive the braking device corresponding to the current direction of movement of the trolley to adjust the running state of the trolley; The execution drive module includes: receiving the acceleration control input output by the control optimization solution unit, and adjusting the actual running state of the trolley according to the braking device action corresponding to the acceleration control input and the trolley running direction; the execution drive module and the controller module constitute a control execution path with synchronous response. The control system is constructed as a closed-loop control structure, which completes state parameter acquisition, state identification, anomaly judgment and control input update in each control cycle, forming operational feedback.

2. The electronically controlled bidirectional braking cable-laying trolley control system according to claim 1, characterized in that, The main body of the trolley includes: A pulley assembly, located at the bottom of the trolley body, is used to slide along the load-bearing cable in a preset route; Shock-absorbing suspension components are used to mitigate the impact of tension fluctuations on the trolley structure.

3. The electronically controlled bidirectional braking cable-laying trolley control system according to claim 2, characterized in that, The sensor module includes: The tension sensor unit is used to acquire observation data of the cable tension experienced by the trolley during the control cycle; The speed sensing unit is used to acquire the sliding speed data of the trolley along the track direction; The state extraction unit is used to fuse tension and velocity signals to construct an extended state vector that reflects the current operating state of the trolley.

4. The electronically controlled bidirectional braking cable-laying trolley control system according to claim 3, characterized in that, The fusion process is based on an improved fusion tension estimation model, which includes the following steps: Acquire cable tension observation data from multiple historical control periods, input them into the trend prediction model, and output the trend prediction tension value for the next period. ; Collect tension observations for the current cycle. ; The standard deviation of the tension change rate is calculated based on the tension change rate over multiple adjacent periods. ; The fusion weight is dynamically calculated according to the following formula. : ; in: As a regulating factor, The set fluctuation threshold; Trend prediction tension value Compared with the current observed tension value According to fusion weight Weighted fusion is performed to obtain the cable tension estimate. The expression is: ； The extended state vector output by the state extraction unit includes: the current period tension observation value, the tension change rate, the pulley speed, the pulley acceleration, the cable tension estimate, and the velocity sliding average value.

5. The electronically controlled bidirectional braking cable-releasing trolley control system according to claim 4, characterized in that, The state recognition unit in the controller module is configured to: Within each control cycle, based on the raw signals output by the tension sensor unit and the speed sensing unit, the following steps are performed to construct the current running state vector of the sled: The tension and velocity signals are subjected to periodic differential processing to calculate the rate of change of cable tension and the rate of change of pulley speed. The periodic difference processing uses a first-order difference algorithm with a fixed period to extract the dynamic change features within the current control period; The original tension value, velocity value, and their derivative signals are smoothed using a filtering algorithm. The filtering algorithm is a sliding fit filtering algorithm, which achieves signal denoising and dynamic curve fitting based on a local multinomial regression model. The processed six state information items, including tension, tension change rate, velocity, velocity change rate, tension estimate, and velocity sliding average, are combined to form a state vector, which is used as the input to the control optimization solution unit within this control cycle.

6. The electronically controlled bidirectional braking cable-laying trolley control system according to claim 1, characterized in that, The control optimization solution unit further includes: When the trolley's operating state is stable or the tension fluctuation amplitude is below a set threshold, the penalty factor of the slack variable in the objective function is dynamically adjusted by analyzing the changing trend of the state vector over multiple consecutive control cycles. This guides the controller to increase the acceleration control input while meeting safety constraints. The convergence efficiency and overall system control energy efficiency.

7. The electronically controlled bidirectional braking cable-laying trolley control system according to claim 1, characterized in that, The penalty factor in the control optimization solution unit The parameter setting methods include: Preset interface setting mode: Import fixed values ​​into the controller module through an external interface to form a static parameter control scheme; Dynamic update mode: Combining historical data of trolley operation with the current load status, the embedded algorithm module automatically generates the penalty factor value to be used in the current cycle; Response feedback verification module: used for penalty factor After the update, the control input was adjusted. Feedback analysis is performed on the results of changes in the objective function; the parameter setting mode supports initial selection or dynamic switching during operation.

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