Shore power winch cable tension control system and method based on fuzzy active disturbance rejection control

Abstract

The invention discloses a shore power winch cable tension control system and method based on fuzzy active-disturbance-rejection control. The system comprises a tension sensor module, a fuzzy active-disturbance-rejection control module, a real-time parameter updating module, a motor control signal output end and a motor driving and executing module. The tension sensor collects a cable tension signal in real time and transmits the cable tension signal to the fuzzy active-disturbance-rejection control module. The module integrates a tracking differentiator, an expansion state observer and nonlinear state error feedback combined with fuzzy logic, and accordingly, a control signal is generated. The real-time parameter updating module dynamically obtains rotational inertia and rolling diameter parameters and feeds back the rotational inertia and rolling diameter parameters to the controller; and finally, controlling the signal to drive the motor to execute cable winding and unwinding operation. Through the fuzzy active disturbance rejection control core, the rapid transition of the tracking differentiator and the real-time disturbance estimation of the expansion state observer are combined, the influence of the time-varying working condition and the strong disturbance is effectively overcome, and the optimal consideration of the response speed and the control precision is realized.

Description

Technical Field

[0001] This invention belongs to the field of shore power winch cable tension control technology, and particularly relates to a shore power winch cable tension control system and method based on fuzzy active disturbance rejection control. Background Technology

[0002] Shore power winches, as core equipment in port vessel power supply systems, are responsible for providing reliable power to vessels during berthing by retracting and extending cables. With the advancement of automation and green transformation in global ports, shore power systems are placing higher demands on the control precision of cable tension, dynamic response speed, and environmental adaptability. Currently, port operators are generally seeking highly reliable tension control systems capable of adapting to complex operating conditions to ensure power continuity, extend cable lifespan, and improve terminal operation efficiency.

[0003] Currently, shore power winch cable tension control mainly adopts a classic PID control architecture, using a linear combination of proportional, integral, and derivative components to adjust tension errors. To further improve dynamic performance, some schemes introduce a feedforward compensation mechanism, generating a feedforward signal by measuring external disturbances (such as ship displacement speed) or system states (such as drum radius), which is then superimposed with the PID output to form the control quantity. In addition, some research has attempted to use Active Disturbance Rejection Control (ADRC) technology, estimating the total system disturbance and performing dynamic compensation through an extended state observer; or applying fuzzy control alone, utilizing its rule base to achieve self-tuning of PID parameters to address system nonlinearity.

[0004] While existing technologies have improved control performance to some extent, their most significant drawback lies in the difficulty of balancing response speed and control accuracy under time-varying conditions and strong disturbances. PID and feedforward strategies heavily rely on accurate system models and have poor adaptability to unmodeled dynamics such as changes in cable diameter, mechanical wear, and sensor drift, easily leading to overshoot and slow recovery. ADRC involves complex parameter tuning for nonlinear systems, and the observer performance is affected by model errors. Fuzzy control lacks the ability to actively compensate for sudden disturbances, resulting in deterioration of control performance under drastic load changes. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a shore power winch cable tension control system and method based on fuzzy active disturbance rejection control, which can balance response speed and control accuracy under time-varying operating conditions and strong disturbance environments.

[0006] Technical solution: The shore power winch cable tension control system of the present invention includes:

[0007] Tension sensor module, used to acquire cable tension signals in real time;

[0008] The fuzzy active disturbance rejection control module receives the cable tension signal from the tension sensor module and generates a control signal through the internal tracking differentiator TD, extended state observer ESO, and fuzzy logic and nonlinear state error feedback NLSEF.

[0009] The real-time parameter update module is used to acquire and update the rotational inertia JL and roll diameter RL parameters in real time, and transmit the rotational inertia and roll diameter parameters to the fuzzy active disturbance rejection control module.

[0010] The motor control signal output terminal receives the control signal generated by the fuzzy active disturbance rejection control module and outputs the control signal to the motor drive and execution module;

[0011] The motor drive and execution module is used to drive the cable reel to perform cable winding and unwinding operations based on the control signal.

[0012] This invention integrates fuzzy logic and active disturbance rejection control technology to construct a robust cable tension control system. It can significantly improve the dynamic response speed of the system under time-varying working conditions and strong disturbance environments, effectively suppress control deviations caused by external disturbances and changes in internal parameters, and adaptively adjust the control strategy by updating the moment of inertia and coil diameter parameters in real time to ensure that the system can maintain high-precision tension stability control under complex working conditions, thus achieving an organic unity of fast response and precise control.

[0013] Preferably, the tension sensor module includes a full-bridge strain gauge structure with an S-shaped stainless steel elastomer as its core. The tension sensor module is located near the cable reel outlet end to achieve hysteresis-free detection of cable tension and is connected to the fuzzy active disturbance rejection control module through a communication interface.

[0014] By adopting a full-bridge strain gauge structure with an S-shaped stainless steel elastomer as the core and installing it near the cable reel outlet, high-sensitivity and high-reliability direct measurement of cable tension is achieved. This design effectively avoids the signal lag and distortion caused by the transmission mechanism in traditional detection methods, providing real-time and accurate tension feedback for the subsequent control system, and ensuring the improvement of system response speed and control accuracy from the source.

[0015] Preferably, the tracking differentiator TD uses the fastest synthesis function fhan to perform second-order filtering on the tension setpoint to generate a smooth transition signal.

[0016] The tracking differentiator smooths the step-form tension setpoint through the fastest synthesis function, generating a transition signal that quickly and without overshoots the setpoint, while simultaneously providing its differential signal. This process effectively avoids the impact on the control system caused by sudden changes in the setpoint, fundamentally resolving the contradiction between system response speed and overshoot, and laying a stable and accurate tracking foundation for subsequent control links.

[0017] Preferably, the extended state observer (ESO) uses a nonlinear function fal() to estimate the total system disturbance, including changes in roll diameter, wave disturbance, and frictional changes.

[0018] By extending the nonlinear function in the state observer, the complex total disturbance in the system, which is coupled by factors such as the change in roll diameter, wave disturbance, and friction change, can be estimated and compensated in real time and accurately. This transforms the highly uncertain nonlinear system into a pure integral series structure that is easy to control, thereby significantly enhancing the control system's ability to suppress internal and external disturbances and providing a core guarantee for maintaining high-precision control under harsh conditions.

[0019] Preferably, the fuzzy logic achieves self-tuning of controller parameters based on system deviation; the nonlinear state error feedback is based on the nonlinear combination of the error between the state variable estimates of the tracking differentiator TD and the extended state observer ESO to generate a control signal.

[0020] By introducing fuzzy logic to achieve online self-tuning of controller parameters, the system can dynamically optimize control performance based on real-time deviations. At the same time, by combining nonlinear state error feedback to perform precise nonlinear combination of system state errors, the shortcomings of traditional linear combinations in balancing response speed and overshoot and suppressing steady-state errors are effectively overcome. This significantly enhances the adaptive capability, dynamic response quality and robust stability of the control system when facing complex working conditions.

[0021] Preferably, the extended state observer (ESO) internally includes an anti-saturation compensator, which is based on the compensation gain. Compensation will be provided, including It is the maximum output torque of the motor, representing the maximum electromagnetic torque that the permanent magnet synchronous motor can output under instantaneous overload or short-term peak conditions.

[0022] By introducing an anti-saturation compensator inside the extended state observer, the problem of decreased observer estimation performance and deterioration of system control quality caused by control saturation is effectively suppressed. This ensures that the estimation accuracy of system state and total disturbance remains at a high level under extreme conditions or large disturbances, thereby significantly improving the anti-saturation capability and robust stability of the control system under the physical constraints of the actual actuator.

[0023] Preferably, the real-time parameter update module includes a roll diameter encoder, a current sensor, and a coprocessor. The roll diameter encoder is used to measure the roll diameter RL in real time, and the current sensor is used to detect the motor phase current. The coprocessor is used to calculate the moment of inertia JL and the roll diameter RL parameters based on the signals from the roll diameter encoder and the current sensor using a recursive least squares algorithm. The coprocessor then transmits the moment of inertia JL and the roll diameter RL parameters to the fuzzy active disturbance rejection control module via a CAN bus.

[0024] By integrating a roll diameter encoder, current sensor, and coprocessor, a high-precision real-time parameter identification system was built. This system can dynamically track changes in roll diameter online and accurately identify the system's moment of inertia, thereby feeding back key time-varying parameters to the controller in real time. This effectively overcomes the deterioration of control performance caused by drastic changes in mechanical parameters during cable winding and unwinding, ensuring that the system maintains excellent adaptability and control accuracy across the entire operating range.

[0025] Preferably, the motor drive and execution module includes an integrated structure of a permanent magnet synchronous motor, a vector frequency converter, and a speed reducer.

[0026] By adopting an integrated structure of permanent magnet synchronous motor, vector frequency converter and reducer, the power density, transmission efficiency and dynamic response characteristics of motor drive and execution module are significantly improved, enabling it to accurately execute complex instructions from the controller. This ensures the speed, stability and high precision of cable tension control under time-varying load and strong disturbance conditions of shore power winch cable winding and unwinding.

[0027] Preferably, the system further includes an FPGA hardware module that supports a parallel pipeline architecture for processing fuzzy logic and uses the roll diameter change rate as a third input variable.

[0028] By introducing an FPGA hardware module based on a parallel pipeline architecture, the processing speed of complex control algorithms such as fuzzy logic is greatly accelerated. The rate of change of cable diameter is introduced as a key feedforward variable into the control system, thereby significantly improving the system's dynamic predictability and adaptive adjustment capability for time-varying working conditions. This ensures that the system can maintain extremely fast response speed and high control accuracy when facing rapid changes in cable diameter during cable winding and unwinding.

[0029] The shore power winch cable tension control method of the present invention includes the following steps:

[0030] S1. The cable tension signal is acquired in real time by the tension sensor module, and the cable tension signal is directly transmitted to the fuzzy active interference rejection control module through the shielded twisted pair cable.

[0031] S2. In the fuzzy active disturbance rejection control module, the tension setpoint is processed by the tracking differentiator TD to generate a transition signal, the total system disturbance is estimated by the extended state observer ESO, the controller parameters are self-tuned by fuzzy logic, and the control signal is generated by the nonlinear state error feedback NLSEF.

[0032] S3. The control signal is transmitted to the motor drive and execution module through the motor control signal output terminal; the motor drive and execution module drives the cable reel to perform the cable winding and unwinding operation;

[0033] S4. The rotational inertia JL and roll diameter RL parameters are acquired and updated in real time through the real-time parameter update module, and the rotational inertia JL and roll diameter RL parameters are transmitted to the fuzzy active disturbance rejection control module for dynamic correction of controller parameters.

[0034] Beneficial Effects: Compared with existing technologies, this invention has the following significant advantages: 1. The system, through a fuzzy active disturbance rejection control core, combines the rapid transition process of the tracking differentiator with the real-time disturbance estimation of the extended state observer, effectively overcoming the influence of time-varying operating conditions and strong disturbances on the system, thus achieving the best balance between response speed and control accuracy required by the invention; 2. By using the extended state observer to uniformly observe and compensate for the total disturbance of the system, and combining it with fuzzy logic online self-tuning of controller parameters, the system has excellent adaptive capability and robustness to complex environments and parameter changes such as continuous changes in roll diameter, wave impact, and friction fluctuations; 3. The adoption of an integrated hardware module design (such as the integration of permanent magnet synchronous motor and vector frequency converter) and standardized sensor configuration, combined with the solidification of the algorithm on the embedded platform, significantly reduces the hardware complexity of the system and the threshold for on-site debugging, making deployment and maintenance simpler and more efficient; 4. A millisecond-level high-speed closed loop from tension detection, parameter update to torque execution is constructed. Combined with short-distance wiring and real-time diagnostic communication mechanisms, the anti-interference capability and overall reliability of the system are significantly improved, ensuring long-term stable operation in harsh industrial environments. Attached Figure Description

[0035] Figure 1 This is a schematic diagram of the system structure of the present invention;

[0036] Figure 2 This is a flowchart of the method for real-time acquisition of tension signals according to the present invention;

[0037] Figure 3 This is a flowchart of the TD smoothing and ESO perturbation estimation method of the present invention;

[0038] Figure 4 This is a flowchart of the online tuning method for fuzzy logic according to the present invention;

[0039] Figure 5 This is a flowchart of the method for outputting torque commands using the NLSEF of the present invention;

[0040] Figure 6 This is a flowchart of the method for online updating of RL and JL and closing the loop according to the present invention. Detailed Implementation

[0041] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0042] This invention provides a shore power winch cable tension control system based on fuzzy active disturbance rejection control, comprising: a tension sensor module, a fuzzy active disturbance rejection controller (Fuzzy-ADRC), a real-time parameter update module, a motor control signal output terminal, and a motor drive and execution module.

[0043] The tension sensor module is fixed inside the bearing housing of the guide roller at the cable exit end of the reel, only 0.3–0.5 m away from the center line of the reel, ensuring that the cable is detected immediately after passing the guide roller and the tension signal is fed back to the controller without lag.

[0044] The Fuzzy Active Disturbance Rejection Controller (Fuzzy-ADRC) is installed inside the winch electrical control cabinet and is directly connected to the tension sensor via shielded twisted pair cable (analog signal) or RS-485 bus (digital signal). The wiring distance is less than 2 m to reduce noise. The electrical control cabinet is located between the reel and the motor, which facilitates wiring with subsequent modules.

[0045] The real-time parameter update module includes a winding diameter encoder and a current sensor. The winding diameter encoder is coaxially mounted at the end of the winding reel spindle and rigidly connected to the reel, measuring RL in real time. The current sensor is located inside the electrical control cabinet and is fitted onto the inverter output cable to detect the motor phase current. Both the encoder and the current sensor are connected to the controller's CAN-FD interface via shielded cables no more than 1m long, ensuring signal integrity within a 0.1s refresh cycle.

[0046] The motor control signal output terminal receives the control signal generated by the fuzzy active disturbance rejection control module and outputs the control signal to the motor drive and execution module.

[0047] The motor drive and execution module consists of a permanent magnet synchronous motor coaxially connected to the reel via a reducer, and is mounted on a frame below the reel. The vector frequency converter is fixed inside the electrical control cabinet, and the length of the motor power cable is less than 1.5 m to reduce line voltage drop and interference.

[0048] Furthermore, the tension sensor module uses an "S"-shaped stainless steel elastomer as its core, with four 350Ω full-bridge strain gauges attached to its surface. This converts the cable tension into a micro-strain signal, which is then amplified by an INA826 instrumentation amplifier, digitized by a 24-bit Σ-Δ ADC, and output in isolation, forming a dual-channel signal of 0–5 V analog and RS-485 digital. Two 2-m shielded twisted-pair cables directly connect the analog signal to the A / D port of the fuzzy active interference rejection controller (AADC), providing "zero-delay" tension feedback for the TD (Digital Transmission Control). Simultaneously, the RS-485 line serves as a redundant channel connected to the CAN bus gateway of the real-time parameter update module, enabling data synchronization with the roll diameter encoder and current sensor. The amplified analog signal, after NLSEF calculation within the controller, directly drives the vector inverter of the motor drive module via the PWM interface, seamlessly integrating the "force-electricity-data" link with the entire Fuzzy-ADRC closed-loop system, ensuring high precision and robustness of tension control.

[0049] Furthermore, the fuzzy active disturbance rejection controller module uses a TI TMS320F28388D DSP+ARM dual-core board as its core carrier, integrating a four-level algorithm pipeline of TD, ESO, fuzzy logic, and NLSEF. It receives the 0–5 V analog signal from the tension sensor via a 2 m shielded twisted-pair cable as the input variable for ESO. Simultaneously, it reads the roll diameter RL and moment of inertia JL from the real-time parameter update module every 0.1 seconds via the CAN-FD port for dynamically correcting the observer gain. After the TD smoothly transitions the tension setpoint, it sends the error and its rate of change to a 7×7 Gaussian fuzzy rule table, outputs it online, and updates it immediately, achieving adaptive operation without manual tuning. The NLSEF integrates the state errors of TD and ESO, and outputs them via 20 kHz PWM or ±10... The analog V interface is directly connected to the vector inverter of the motor drive module. At the same time, the operating status and fault codes are transmitted back to the HMI and real-time parameter update module via the same CAN-FD, forming a closed-loop integrated link of data flow and power flow of "sensor → controller → driver → reel". This ensures that the entire system maintains high-performance operation with ≤1.5% overshoot and ≤3s stability under high disturbance and time-varying reel diameter conditions.

[0050] Furthermore, the real-time parameter update module consists of three parts: a roll diameter encoder, a Hall current sensor, and an ARM coprocessor. The encoder is coaxially mounted on the main spindle of the roll reel, outputting the roll diameter RL in real time with a resolution of 0.1 mm. The Hall sensor is fitted onto the output bus of the frequency converter, acquiring the motor phase current with an accuracy of ±200 A / 0.5%. The signals from both are synchronously fed into the ARM coprocessor via a 16-bit ADC, running a recursive least squares algorithm to calculate the moment of inertia JL every 0.1 s. The result is broadcast to the fuzzy active disturbance rejection controller via a 1 m shielded cable and a shared CAN-FD bus with the controller in PDO form, used to refresh the ESO and NLSEF parameters online. At the same time, the status word with an identification error of <5% is sent back to the HMI, realizing closed-loop synchronization of RL, JL, and data from the controller and driver, ensuring real-time model matching under all working conditions of roll diameter 0.4-1.2 m and load 0-10 t.

[0051] Furthermore, the motor drive and execution module is an integrated unit consisting of a permanent magnet synchronous motor, a vector frequency converter, and a reducer. The motor is rated at 15 kW and 95 N·m, with a 2500 ppr incremental encoder coaxially mounted at the tail. The vector frequency converter is located in the electrical control cabinet, with a power cable of <1.5 m and a control cable of <1 m. It directly receives 20 kHz PWM or ±10 V torque commands from the fuzzy active disturbance rejection controller. The frequency converter's three closed-loop current-speed-torque bandwidths are set to 1 kHz-100 Hz-10 Hz, respectively. The motor current, speed, and fault codes are transmitted back to the controller via the same CAN-FD in real time. After the torque is amplified by the i=40 reducer, the reel is driven, forming a millimeter-level rigid power chain of "torque command → current loop → motor → reducer → reel". This achieves high-performance constant tension winding and unwinding with a control accuracy of ≤±2 N·m and tension fluctuation of ≤±2.5% within a full reel diameter of 0.4-1.2 m and a load range of 0-10 t.

[0052] This invention provides a cable tension control system and method based on fuzzy active disturbance rejection control (Fuzzy-ADRC), the method of use includes the following steps:

[0053] Step 1: The cable tension is converted into a 0-5 V analog signal by a full-bridge strain gauge tension sensor (1kHz, 0.5%FS) installed at the cable exit end of the reel; the signal is directly connected to the controller A / D port via a 2 m shielded cable to complete "zero-delay" feedback sampling, which serves as the raw input for all subsequent calculations;

[0054] Step 2: The tracking differentiator TD uses the fastest synthesis function fhan to perform second-order filtering on the tension setpoint, generating smooth transition signals v1 and v2 to suppress the impact caused by the setpoint step; the extended state observer ESO uses the nonlinear function fal(·) to estimate the total system disturbance z3, including the changes in roll diameter, wave disturbance, and friction changes, in real time, and immediately performs feedforward compensation to simplify the system dynamics into the form of a "nominal integral chain";

[0055] Step 3: Input the tension error and its rate of change into the 7×7 Gaussian membership fuzzy rule table, output the ESO gain correction amount, and refresh in real time. It can adapt to all working conditions with a roll diameter of 0.4-1.2 m and a load of 0-10 t without manual parameter adjustment.

[0056] Step 4: Nonlinear State Error Feedback (NLSEF) calculates the motor electromagnetic torque command directly through nonlinear combination based on the difference between the TD and ESO states, outputting a 20 kHz PWM or ±10 V analog signal, which is then sent to the vector inverter current loop via a <1.5 m shielded cable.

[0057] Step 5: The roll diameter encoder (0.1 mm) and Hall current sensor (±200 A, 0.5%) sample every 0.1 s. The ARM coprocessor runs the recursive least squares (RLS) algorithm to estimate the roll diameter RL and moment of inertia JL online. The results are broadcast to Fuzzy-ADRC via CAN-FD bus with an identification error of <5%. The controller immediately refreshes ESO and NLSEF with the new parameters and then returns to step 1, forming a millisecond-level closed loop of "sampling-estimation-compensation-update". This ensures that under sudden speed changes, sudden load changes, continuous changes in roll diameter, and 50 Hz sea wave disturbances, the system overshoot is ≤1.5%, the settling time is ≤3 s, and the recovery time is ≤2.1 s.

[0058] 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.

[0059] like Figure 1 As shown, this invention provides a cable tension control system based on fuzzy active disturbance rejection control. The constant tension control system for shore power winch cables operates according to a five-closed-loop process: "signal acquisition → disturbance estimation → parameter self-tuning → torque execution → online update". The specific implementation method is as follows:

[0060] In terms of mechanical installation and wiring, the S-shaped stainless steel tension sensor is embedded inside the guide roller bearing seat at the cable exit end of the reel, 0.35 m from the center of the reel and maintaining a cable wrap angle of 90°±2°. Its 0–5 V analog signal is directly connected to the A / D port of the DSP / ARM dual-core board in the electrical control cabinet via a 2 m double-shielded cable, while the absolute value rotary encoder is coaxially fixed to the reel spindle, and the 0.1 mm resolution data is connected to the ARM coprocessor SPI interface via a 1 m shielded cable. The electrical control cabinet, frequency converter, and DC 24 V power supply are centrally arranged, with power cables shorter than 1.5 m. The CAN-FD bus uses a 120 Ω terminating resistor to ensure lossless communication of 1 Mbit / s, forming a compact and low-noise wiring topology. In terms of hardware and software configuration, the TI TMS320F28388D has a main frequency of 200 MHz and an operating cycle of 200µs. The TD, ESO, fuzzy logic, and NLSEF algorithms are all embedded in Flash using C language and support online upgrades. The IP54 vector inverter has a current loop of 1kHz, a speed loop of 100 Hz, and a torque loop of 10 Hz, and can receive 20 kHz PWM or ±10 V commands. The 15 kW permanent magnet synchronous motor has a rated capacity of 95 N·m and directly drives the reel via an i=40 reducer with an efficiency of 92%, realizing an integrated electromechanical closed loop.

[0061] like Figure 2 As shown, a method for real-time acquisition of tension signals includes the following steps:

[0062] Step 1: Tension Acquisition; A full-bridge strain gauge tension sensor outputs an analog voltage at a sampling rate of 1 kHz. , The voltage is directly fed into the ADC channel of the DSP via a 2-meter double-shielded wire, completing delay-free sampling.

[0063] like Figure 3 As shown, a method for TD smoothing and ESO perturbation estimation is presented, with the following specific steps:

[0064] Step 2: TD transition arrangement, , Where r = 200, h = 0.001 s, and fhan(·) is the fastest synthesis function used to suppress set step shocks. Controller pins P7, P8, and P9 are configured as AD inputs; ESO extended state observation...

[0065]

[0066] Where β1=100, β2=300, β3=1000, b0=1.5, α=0.5, δ=0.01. z3 is the "total disturbance" estimate, used for feedforward compensation.

[0067] like Figure 4 As shown, a method for online tuning of fuzzy logic includes the following steps:

[0068] Step 3: Calculate the error and error change rate Input a 7×7 Gaussian membership rule table Real-time update of ESO gain after outputting Δβ The rule table is stored in the DSP Flash, and the table lookup time is less than 2µs.

[0069] like Figure 5 As shown, the method for outputting torque commands using NLSEF involves the following specific steps:

[0070] Step 4: NLSEF matrix generation instructions

[0071] in , , , The current u is sent to the vector inverter via 20 kHz PWM or ±10 V analog signal to directly drive the q-axis current of the motor.

[0072] like Figure 6 As shown, the method for online updating of RL and JL and closing the loop using RLS involves the following steps:

[0073] Step 5: Roll diameter calculation ,in For encoder angle increment, This refers to the cable diameter.

[0074] Moment of inertia RLS , K(k) is the RLS gain matrix, with an update period of 0.1 s and an identification error of <5%. The updated RL and JL are broadcast back to the controller via the CAN-FD bus, immediately refreshing b0 in equation (3) to achieve millisecond-level closed-loop adaptation.

Claims

1. A shore power winch cable tension control system based on fuzzy active disturbance rejection control, characterized in that, The system comprises: a tension sensor module for real-time acquisition of cable tension signals; a fuzzy active disturbance rejection control module receiving the cable tension signals from the tension sensor module and generating control signals through an internal tracking differentiator TD, an extended state observer ESO and a fuzzy logic and nonlinear state error feedback NLSEF; a real-time parameter updating module for real-time acquisition and updating of the moment of inertia JL and the roll radius RL parameters and transmission of the parameters to the fuzzy active disturbance rejection control module; a motor control signal output end receiving the generated control signals from the fuzzy active disturbance rejection control module and outputting the control signals to a motor driving and executing module; the motor driving and executing module for driving the cable reel to perform cable winding and unwinding operations based on the control signals.

2. The shore power tractor cable tension control system of claim 1, wherein, The tension sensor module comprises a full-bridge strain gauge structure with an S-shaped stainless steel elastic body as the core, is arranged adjacent to the cable reel outlet end and is connected to the fuzzy active disturbance rejection control module through a communication interface for realizing non-lagging detection of the cable tension.

3. The shore power tractor cable tension control system of claim 1, wherein, The tracking differentiator TD adopts a fastest synthesis function fhan to perform second-order filtering on the tension set value to generate a smooth transition signal.

4. The shore power tractor cable tension control system of claim 1, wherein, The extended state observer ESO adopts a nonlinear function fal() to estimate the total disturbance of the system including the roll radius change, sea wave disturbance and friction change.

5. The shore power tractor cable tension control system of claim 1, wherein, The fuzzy logic realizes self-tuning of the controller parameters according to the system deviation; the nonlinear state error feedback performs nonlinear combination based on the error between the state variable estimations of the tracking differentiator TD and the extended state observer ESO to generate the control signals.

6. The shore power tractor cable tension control system of claim 1, wherein, The expansion state observer ESO internally contains an anti-windup compensator, which is based on a compensating gain compensating for the fact that is the maximum output torque of the motor, representing the maximum electromagnetic torque that the permanent magnet synchronous motor can output under instantaneous overload or short-term peak working conditions.

7. The shore power tractor cable tension control system of claim 1, wherein, The real-time parameter updating module comprises a roll radius encoder for real-time measurement of the roll radius RL, a current sensor for detection of the motor phase current and a coprocessor for calculation of the moment of inertia JL and the roll radius RL parameters through a recursive least squares algorithm according to the signals of the roll radius encoder and the current sensor and transmission of the parameters to the fuzzy active disturbance rejection control module through a CAN bus.

8. The shore power tractor cable tension control system of claim 1, wherein, The motor driving and executing module comprises a permanent magnet synchronous motor, a vector frequency converter and a reducer integrated structure.

9. The shore power tractor cable tension control system of claim 1, wherein, The system further comprises an FPGA hardware module supporting a parallel pipeline architecture for processing the fuzzy logic and taking the roll radius change rate as a third input variable.

10. A shore power winch cable tension control method based on fuzzy active disturbance rejection control, characterized in that, The system comprises the following steps: S1, real-time acquisition of cable tension signals by the tension sensor module and direct transmission of the cable tension signals to the fuzzy active disturbance rejection control module through a shielded twisted pair; S2, in the fuzzy active disturbance rejection control module, processing of the tension set value by the tracking differentiator TD to generate a transition signal, estimation of the total disturbance of the system by the extended state observer ESO, realization of self-tuning of the controller parameters by the fuzzy logic and generation of the control signals by the nonlinear state error feedback NLSEF; S3, the control signal is transmitted to the motor drive and execution module through the motor control signal output end; the cable reel is driven through the motor drive and execution module, and the cable winding and unwinding operation is performed; S4, the moment of inertia JL and the roll diameter RL parameters are acquired and updated in real time through the real-time parameter updating module, and are transmitted to the fuzzy active disturbance rejection control module for dynamic correction of the controller parameters.

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