Train brake clamp error compensation control method based on double encoders

Abstract

The invention discloses a train braking clamp error compensation control method based on double encoders, and relates to the field of train braking. During system operation, a double-encoder sensing framework is constructed, the encoders are installed at the output ends of a torque motor and a transmission mechanism, a double detection channel is formed, and a hierarchical control strategy is implemented; an inner ring torque and speed control loop is constructed based on a motor side encoder, an outer ring position and inter-relay control loop is constructed based on an output side encoder, real-time error compensation is executed, back clearance and elastic deformation errors in a transmission chain are recognized by comparing measurement data of the two encoders, a compensation control instruction is generated, and the transmission chain is subjected to compensation control. A force sensor fusion mechanism is integrated, a force sensor is installed, data fusion is carried out on directly measured braking force and an inter-relay force estimated value based on displacement, fault diagnosis and safety fault tolerance are achieved, the data deviation of the double encoders is continuously monitored, and safety protection measures are triggered when the deviation exceeds a preset threshold value.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric caliper brake control, in particular to a train brake caliper error compensation control method based on double encoders. BACKGROUND

[0002] With the continuous improvement of the intelligent level of rail transit equipment, the control accuracy and safety reliability of the train brake system have become the core elements to ensure the operation safety. As the key component of the brake actuator, the accuracy of the brake force output of the electric caliper directly affects the train deceleration performance and parking stability. The current mainstream electric caliper adopts a motor-driven screw and a star-shaped reduction mechanism to push the brake pad to realize braking, and relies on a spring energy storage mechanism to trigger safety braking in fault conditions such as power failure. Although this structure has basic functions, its control strategy has limitations in the context of increasing demand for high-precision control.

[0003] Among them, the position feedback scheme based on a single encoder has long dominated the design of existing systems, that is, only a single encoder is installed on the motor output shaft, and the displacement of the caliper output end and the corresponding brake force are calculated through the transmission ratio. However, this method cannot effectively sense the nonlinear errors introduced by factors such as screw backlash, reduction mechanism gap, shaft system elastic deformation, and component wear in the transmission chain, resulting in significant deviation between the actual brake force and the command value. Especially in the long-term service process, mechanical wear and temperature drift further exacerbate model mismatch, making control hysteresis phenomenon obvious, and it is difficult to meet the needs of high dynamic and high precision braking scenes.

[0004] Although existing improvements attempt to introduce laser ranging or rotary angle sensors at the output end of the caliper to assist in correction, they are still limited to single-dimensional state observation and lack closed-loop sensing capability for the entire transmission process, making it impossible to achieve true force closed-loop control. Although the double-encoder architecture has been verified in the precision servo field of robot joints and other precision servo fields for its advantages in error compensation and state monitoring, in the application scenario of train electric caliper with strong safety constraints and high environmental disturbances, how to build a double-encoder collaborative control system that takes into account real-time, robustness, and safety fault tolerance capability is still lacking a systematic technical path. Therefore, there is an urgent need for a train brake caliper error compensation control method that can deeply integrate double-end position sensing, dynamically compensate transmission errors, and support force control closed loop. SUMMARY

[0005] In view of the deficiencies of the prior art, the present application provides a train brake caliper error compensation control method based on double encoders, which solves the problems mentioned in the background art.

[0006] To achieve the above purpose, the present application is implemented by the following technical scheme: a train brake caliper error compensation control method based on double encoders, comprising the following specific steps: Step 1: Construct a dual encoder sensing architecture. Install the first encoder on the output shaft of the torque motor as the motor-side encoder, and install the second encoder on the output end of the transmission mechanism as the output-side encoder to form a dual detection channel. Step 2: Implement a hierarchical control strategy, constructing an inner loop torque and speed control loop based on the motor-side encoder, and an outer loop position and indirect force control loop based on the output-side encoder; Step 3: Perform real-time error compensation. By comparing the measurement data of the motor-side encoder and the output-side encoder, identify the backlash and elastic deformation errors in the transmission chain and generate compensation control commands. Step 4: Integrate a force sensor fusion mechanism. Install a force sensor near the contact surface between the brake pad and the brake disc to fuse the directly measured braking force with the indirect force estimation value based on displacement. Step 5: Implement fault diagnosis and safety tolerance, continuously monitor the data deviation of the dual encoders, and trigger safety protection measures when the deviation exceeds the preset threshold.

[0007] Preferably, in step 1, the motor-side encoder is a high-resolution absolute encoder with a resolution of not less than 20 bits, which is directly installed on the rear shaft of the torque motor to accurately measure the angular position and rotational speed of the motor rotor; the output-side encoder is a robust and durable magnetic encoder, which is installed on the lead screw nut or equivalent push rod mechanism to directly measure the linear displacement of the actuator, with a measurement accuracy of ±5 micrometers.

[0008] Preferably, in step 2, the control cycle of the inner loop control circuit is set to less than 1 millisecond, and a proportional-integral-derivative controller is used to realize the rapid adjustment of motor torque and speed; the control cycle of the outer loop control circuit is set to about 10 milliseconds, and position closed-loop control is realized based on the displacement measurement value of the output side encoder, and indirect force control is realized through the mapping relationship between displacement and braking force.

[0009] Preferably, the error compensation algorithm in step 3 adopts a combination of feedforward compensation and feedback correction. Feedforward compensation predicts backlash and elastic deformation based on the mathematical model of the transmission chain, while feedback correction dynamically adjusts the error based on the real-time comparison of the dual encoder data deviation, with a compensation response time of less than 5 milliseconds.

[0010] Preferably, in step 4, the force sensor is a strain gauge or piezoelectric force sensor with a measurement range of 0 to 50 kN and an accuracy of ±0.5% of the full scale. The data fusion algorithm uses a Kalman filter to optimally fuse the direct measurement value of the force sensor with the indirect force estimation value based on the spring stiffness and the output encoder displacement. The fused force control accuracy is improved to within ±1%.

[0011] Preferably, in step 5, the fault diagnosis module sets the preset threshold for the data deviation of the dual encoders to ±2% of the displacement. When the deviation exceeds this threshold for three consecutive control cycles, the system determines that the transmission chain is abnormal or the encoder is faulty, and immediately triggers the safety braking command and sends an alarm signal to the monitoring system.

[0012] The method also includes an adaptive compensation mechanism. The system automatically identifies changes in mechanical characteristics caused by brake pad wear, temperature drift, etc. by monitoring the trend changes of the dual encoder data deviation over a long period of time, and dynamically adjusts the control parameters to ensure long-term control stability. The adaptive adjustment cycle is 24 hours.

[0013] Preferably, in the hierarchical control strategy, the inner loop torque control adopts field-oriented control technology, which decomposes the three-phase current into torque and excitation components through Clarke and Park transformations to achieve precise torque output of the motor; the outer loop position control adopts a fuzzy proportional-integral-derivative algorithm to dynamically adjust the position command based on the feedback from the output encoder.

[0014] During the real-time error compensation process, the system establishes a transmission chain error database, records backlash and elastic deformation data under different working conditions, and uses a neural network algorithm for error prediction. The input of the prediction model includes motor torque, operating speed and ambient temperature, and the output is the prediction error. The prediction accuracy is greater than 95%.

[0015] In the force sensor fusion mechanism, when the force sensor fails or the data is abnormal, the system automatically switches to pure displacement control mode. Based on the displacement measurement value of the output encoder and the preset displacement-force characteristic curve, the braking force control is maintained to ensure the redundancy and reliability of the system.

[0016] The method is applied to a train passive electric clamp system. The transmission mechanism includes a lead screw and a star-shaped reduction mechanism, which converts the rotational motion of the motor into linear motion to drive the brake pads. The dual encoder detection unit and the controller are connected through a high-speed serial communication interface with a communication rate of not less than 10 megabits per second.

[0017] Preferably, the controller adopts a multi-core processor architecture, with one core dedicated to the inner loop control algorithm and another core handling the outer loop control and error compensation, and is equipped with a high-speed counting module, with the encoder signal acquisition frequency reaching 1 MHz.

[0018] Preferably, the fault diagnosis and safety fault tolerance mechanism also includes monitoring the force sensor data. When the force sensor data deviates from the braking force estimated by the dual encoder for a continuous period of more than 15%, the system marks the force sensor as abnormal and prioritizes the use of the dual encoder data for control.

[0019] Preferably, the method calculates braking energy in real time during braking, obtains braking energy consumption based on the integral of motor torque and speed, and predicts the thermal state of the braking system by combining temperature sensor data. When the predicted temperature exceeds the safety threshold, the braking force distribution is adjusted in advance.

[0020] This invention provides a train brake caliper error compensation control method based on dual encoders, which has the following beneficial effects: (1) When the system is running, by constructing a dual encoder sensing architecture, the first encoder and the second encoder are installed at the output end of the torque motor and the transmission mechanism as the motor-side encoder and the output-side encoder, forming a dual detection channel and implementing a hierarchical control strategy. The inner loop torque and speed control loop is constructed based on the motor-side encoder, and the outer loop position and indirect force control loop is constructed based on the output-side encoder. Real-time error compensation is performed. By comparing the measurement data of the motor-side encoder and the output-side encoder, the backlash and elastic deformation error in the transmission chain are identified, and compensation control commands are generated. The force sensor fusion mechanism is integrated. Force sensors are installed near the contact surface between the brake pad and the brake disc. The directly measured braking force and the estimated value of indirect force based on displacement are fused to achieve fault diagnosis and safety fault tolerance. The dual encoder data deviation is continuously monitored. When the deviation exceeds the preset threshold, safety protection measures are triggered.

[0021] (2) By configuring a high-resolution absolute encoder and a high-precision magnetic encoder on the torque motor side and the actuator output side respectively, a dual-feedback measurement structure is formed, enabling the system to simultaneously acquire the motion state of the motor end and the transmission end. This dual-encoder structure can effectively identify and separate backlash, elastic deformation, and mechanical coupling errors in the transmission chain, making the braking position and indirect force estimation more realistic and accurate. Combining the hierarchical strategy of inner-loop high-speed torque-speed control and outer-loop position-indirect force control, the system's control bandwidth and dynamic following performance are significantly improved, making braking force establishment faster and force output fluctuation smaller, thereby improving the overall control accuracy and response quality of the brake caliper.

[0022] (3) This method uses a real-time error compensation mechanism that combines feedforward and feedback, enabling the system to dynamically correct transmission chain errors within milliseconds, significantly reducing the problems of displacement force estimation distortion and compensation lag. Simultaneously, the fusion of direct measurement data from force sensors and indirect force values ​​from displacement estimation using Kalman filtering effectively suppresses noise interference and improves the stability and anti-disturbance capability of braking force estimation. Furthermore, through an adaptive modeling mechanism, the system can automatically update transmission chain model parameters and spring stiffness based on the long-term dual encoder deviation trend, maintaining high consistency and accuracy over a long period, and significantly improving the adaptability of the braking system under temperature changes and wear accumulation conditions.

[0023] (4) This invention introduces a dual encoder data deviation monitoring mechanism and sets a continuous periodic deviation threshold to promptly identify transmission chain abnormalities, encoder failures, or push rod mechanism operational imbalances. When the deviation exceeds the set range, a safety protection strategy can be automatically triggered to avoid insufficient clamping or excessive braking caused by transmission abnormalities. In addition, the system supports redundancy between the force sensor and displacement estimation. When the force sensor malfunctions or data drifts, it can automatically switch to pure displacement control mode to maintain the reliability of braking force output. The multi-dimensional fault diagnosis and fault tolerance mechanism significantly enhances the safety of the braking system, effectively reducing the probability of brake failure, false alarms, and unnecessary degradation protection, thereby ensuring that the train can maintain stable operation under various complex working conditions. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the brake cylinder of the electric clamp dual encoder braking force control system of the present invention. Figure 2 This is a schematic diagram of the clamp assembly of the electric clamp dual encoder braking force control system of the present invention; Figure 3 This is a flowchart of the dual encoder data fusion and braking force control of the present invention. Detailed Implementation

[0025] 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 embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1 This invention provides a train brake caliper error compensation control method based on dual encoders. Please refer to [link / reference]. Figure 1 , Figure 2 , Figure 3Currently, with the continuous improvement of the intelligence level of rail transit equipment, the control accuracy and safety reliability of train braking systems have become core elements for ensuring operational safety. As a key component of the braking actuator, the accuracy of the electric clamp's braking force output directly affects the train's deceleration performance and stopping stability. Existing systems mostly adopt a single encoder position feedback scheme, installing only a single encoder on the motor output shaft. The displacement of the clamp output end and the corresponding braking force are calculated through the transmission ratio. This cannot effectively detect nonlinear errors introduced by factors such as lead screw backlash, reduction mechanism clearance, shaft elastic deformation, and component wear in the transmission chain, resulting in a significant deviation between the actual braking force and the command value. Especially during long-term service, mechanical wear and temperature drift further exacerbate model mismatch, making control hysteresis obvious and difficult to meet the requirements of high-dynamic, high-precision braking scenarios. Although the dual-encoder architecture has proven its advantages in error compensation and state monitoring in precision servo fields such as robot joints, a systematic technical path is still lacking for constructing a dual-encoder collaborative control system that balances real-time performance, robustness, and safety fault tolerance in the application scenario of train electric clamps, which faces strong safety constraints and high environmental disturbances. To address the aforementioned technical problems, this invention proposes a train brake caliper error compensation control method based on dual encoders. By constructing a dual encoder sensing architecture, implementing a hierarchical control strategy, executing real-time error compensation, integrating a force sensor fusion mechanism, and achieving fault diagnosis and safety fault tolerance, the method can effectively solve the problems in the background technology and is applied to a train brake caliper error compensation control method based on dual encoders.

[0027] refer to Figure 1 The overall technical architecture of this invention includes a torque motor, a transmission mechanism, a dual encoder detection unit, a force sensor, a controller, and a safety braking actuator. The torque motor provides braking force, the transmission mechanism converts the motor's rotational motion into linear motion to actuate the brake pads, the dual encoder detection unit acquires real-time position information from the motor side and the output side, the force sensor directly measures the braking force, the controller executes all control logic and data processing, and the safety braking actuator ensures the train stops safely under fault conditions.

[0028] refer to Figure 3 The logical framework of the real-time error compensation and force sensor fusion mechanism in this invention includes an error identification module, a feedforward compensation module, a feedback correction module, a Kalman filter fusion module, and a fault switching module. Each module works together to complete the entire process from error detection to compensation execution and then to redundant control.

[0029] Example 2 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 1Specifically, in the above-mentioned train brake caliper error compensation control method based on dual encoders, step 1 involves constructing a dual encoder sensing architecture. A first encoder is installed on the output shaft of the torque motor as a motor-side encoder, and a second encoder is installed at the output end of the transmission mechanism as an output-side encoder, forming a dual detection channel. Specifically, the motor-side encoder uses a high-resolution absolute encoder with a resolution of no less than 20 bits, directly mounted on the rear shaft of the torque motor, for accurately measuring the angular position and rotational speed of the motor rotor. Its signal output is transmitted to the controller via a high-speed serial communication interface with a communication rate of no less than 10 megabits per second. The output-side encoder uses a robust and durable magnetic encoder, mounted on a lead screw nut or equivalent push rod mechanism, to directly measure the linear displacement of the actuator, achieving a measurement accuracy of ±5 micrometers. Its installation position must ensure strict synchronization with the brake pad propulsion stroke to avoid introducing additional measurement noise due to installation eccentricity or looseness. The connection between the dual encoder detection unit and the controller uses shielded twisted-pair cable to suppress the impact of electromagnetic interference on the high-precision position signal. Simultaneously, the controller is equipped with a dedicated high-speed counting module, and the encoder signal acquisition frequency reaches 1 MHz, ensuring the capture of complete dynamic response data even during high-speed braking. This dual detection channel allows the system to simultaneously acquire status information from both the input and output ends of the drive train, providing a fundamental data source for subsequent error identification and compensation.

[0030] Example 3 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 3 Specifically, in the above-mentioned train brake caliper error compensation control method based on dual encoders, step 2 implements a hierarchical control strategy. An inner-loop torque and speed control loop is constructed based on the motor-side encoder, and an outer-loop position and indirect force control loop is constructed based on the output-side encoder. Specifically, the control cycle of the inner-loop control loop is set to less than 1 millisecond. A proportional-integral-derivative (PID) controller is used to achieve rapid adjustment of motor torque and speed. Its control algorithm runs on a dedicated kernel of the controller, independent of other tasks, ensuring the real-time performance and determinism of control commands. The inner-loop torque control uses field-oriented control technology, decomposing the three-phase current into torque and excitation components through Clarke and Park transformations to achieve precise torque output from the motor. The Clarke transformation matrix is ​​a standard 3 / 2 transformation, and the Park transformation angle is calculated from the rotor position provided in real-time by the motor-side encoder. The current loop sampling frequency is 10 kHz, and the current sensor accuracy is ±0.1% of full scale.

[0031] The control cycle of the outer loop control is set to approximately 10 milliseconds. Position closed-loop control is achieved based on displacement measurements from the output encoder, and indirect force control is achieved through a mapping relationship between displacement and braking force. This mapping relationship is pre-calibrated based on the spring stiffness characteristic curve, with a spring stiffness coefficient k of 800 N / mm. The outer loop position control employs a fuzzy proportional-integral-derivative (FID) algorithm. The fuzzy rule base contains 9 rules. The input variables are the position error and its rate of change, while the output variables are the proportional gain, integral time, and derivative time corrections. Fuzzification uses triangular membership functions, and defuzzification uses the centroid method. The inner and outer loops exchange data through shared memory, forming a tightly coupled cascaded control structure.

[0032] Example 4 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 3 Specifically, in the above method, step 3 involves performing real-time error compensation by comparing the measurement data from the motor-side encoder and the output-side encoder to identify transmission chain errors and generate compensation control commands. The system will determine the angular position θ of the motor-side encoder. m The transmission ratio N is converted into the theoretical output displacement. , where N is the lead of the lead screw and the total transmission ratio of the star reducer, with a typical value of 1200 radians / mm.

[0033] Subsequently, the displacement was measured using the output encoder. The error was obtained by comparison. This error includes a combination of deviations caused by backlash, gear clearance, bearing clearance, and elastic deformation.

[0034] Error compensation includes feedforward compensation and feedback correction: Feedforward compensation predicts backlash and elastic deformation based on a nonlinear spring-damping model, with a typical dead zone width δ of 15 micrometers and a typical elastic coefficient K of 500 N / micrometer; Feedback correction uses an adaptive sliding mode observer to estimate unknown disturbances and generate a compensation torque command ΔT. comp The response time is less than 5 milliseconds.

[0035] In addition, the system establishes a transmission chain error database, recording motor torque T, operating speed v, and ambient temperature T. env The error data is collected and a three-layer feedforward neural network is used for error prediction. There are 3 input nodes, 20 hidden layers, and 1 output. The prediction accuracy is greater than 95%. The compensation control command is finally superimposed on the output of the outer loop position controller to form the corrected target displacement command.

[0036] Example 5 This embodiment is an explanation based on Embodiment 1. Please refer to it. Figure 3Specifically, in the above method, step 4 integrates a force sensor fusion mechanism. A force sensor is installed near the contact surface between the brake pad and the brake disc, fusing the directly measured braking force with an indirect force estimation value based on displacement. Specifically, the force sensor is a strain gauge or piezoelectric force sensor with a measurement range covering 0 to 50 kN and an accuracy of ±0.5% of full scale. Its installation position is between the brake pad support arm and the clamp housing, ensuring direct bearing of the braking force load. The force sensor signal is acquired through a 24-bit Σ-Δ analog-to-digital converter with a sampling frequency of 1 kHz, and then filtered by a digital low-pass filter (cutoff frequency 200 Hz) to remove high-frequency noise. Indirect force estimation value. The calculations show that k is the spring stiffness coefficient, and x_0 is the initial displacement under no-load conditions, which is automatically updated by a zero-point calibration procedure before each braking cycle. The data fusion algorithm uses a Kalman filter to optimally fuse the direct measurement value F_meas from the force sensor with the indirect force estimate F_est. The fusion uses a Kalman filter, and the state vector [F...]... true ,dF / dt]^T.

[0037] The process noise covariance Q and observation noise covariance R are tuned offline based on sensor specifications and system dynamic characteristics. Q is set to diag([0.1,0.01]), and R is set to 0.25 (corresponding to the square of 0.5% accuracy). The fused force control accuracy is improved to within ±1%. When the force sensor fails or data is abnormal, the system automatically switches to pure displacement control mode, maintaining braking force control based on the displacement measurement value of the output encoder and the preset displacement-force characteristic curve. This characteristic curve is stored in the controller's non-volatile memory and supports online updates, ensuring the system's redundancy and reliability.

[0038] When the force sensor fails, the system switches to pure displacement control mode and maintains braking force output through preset displacement-force characteristics.

[0039] Step 5 enables fault diagnosis and safety tolerance: when the dual encoder deviation exceeds ±2% for three consecutive control cycles, it is determined to be a transmission chain abnormality or encoder failure, and safety braking is triggered; when the force sensor and dual encoder braking force deviation exceeds 15% for three consecutive cycles, the force sensor is marked as abnormal and the data source is switched. All fault records are entered into the event log.

[0040] In addition, there is an adaptive compensation mechanism: the system automatically collects the changes in back clearance δ and elastic coefficient K every morning. When the increment of δ is >5μm or the decrease of K is >10%, the model and neural network are updated, and the spring stiffness coefficient k is recalibrated to compensate for wear and temperature drift.

[0041] Real-time calculation of braking energy E during braking. brakeThe braking energy consumption is obtained based on the integral of motor torque and speed, and the thermal state of the braking system is predicted by combining temperature sensor data. When the predicted temperature exceeds the safety threshold, the braking force distribution is adjusted in advance. Where T(τ) is the motor torque and ω(τ) is the motor angular velocity, the integral is discretized using the trapezoidal rule, and the time step is 1 millisecond; the temperature sensor is installed on the back of the brake disc, the sampling frequency is 1 Hz, the thermal state prediction model adopts a first-order thermal inertial element, the time constant is calibrated to 120 seconds according to the brake disc material and heat dissipation conditions, and the safe temperature threshold is set to 350 degrees Celsius. When the predicted temperature exceeds this threshold, the system sends a request to the train network to coordinate other carriages to share the braking force and avoid local overheating.

[0042] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A train brake caliper error compensation control method based on dual encoders, characterized in that: The specific steps include the following: Step 1: Construct a dual encoder sensing architecture. Install the first encoder on the output shaft of the torque motor as the motor-side encoder, and install the second encoder on the output end of the transmission mechanism as the output-side encoder to form a dual detection channel. Step 2: Implement a hierarchical control strategy, constructing an inner loop torque and speed control loop based on the motor-side encoder, and an outer loop position and indirect force control loop based on the output-side encoder; Step 3: Perform real-time error compensation. By comparing the measurement data of the motor-side encoder and the output-side encoder, identify the backlash and elastic deformation errors in the transmission chain and generate compensation control commands. Step 4: Integrate a force sensor fusion mechanism. Install a force sensor near the contact surface between the brake pad and the brake disc to fuse the directly measured braking force with the indirect force estimation value based on displacement. Step 5: Implement fault diagnosis and safety tolerance, continuously monitor the data deviation of the dual encoders, and trigger safety protection measures when the deviation exceeds the preset threshold.

2. The train brake caliper error compensation control method based on dual encoders according to claim 1, characterized in that: The motor-side encoder is an absolute encoder with a resolution of no less than 20 bits, which is directly mounted on the rear shaft of the torque motor; the output-side encoder is a magnetic encoder, which is mounted on the lead screw nut or equivalent push rod mechanism and is used to directly measure the linear displacement of the actuator with a measurement accuracy of ±5μm.

3. The train brake caliper error compensation control method based on dual encoders according to claim 1, characterized in that: The control cycle of the inner loop torque and speed control loop is less than 1ms. It adopts a proportional-integral-derivative controller and combines field-oriented control technology. The three-phase current is decomposed into torque and excitation components through Clarke and Park transformations. The control cycle of the outer loop position and indirect force control loop is 10ms. The position closed loop is realized based on the displacement measurement value of the output encoder, and indirect force control is realized through the mapping relationship between displacement and braking force.

4. The train brake caliper error compensation control method based on dual encoders according to claim 1, characterized in that: Real-time error compensation adopts a combination of feedforward compensation and feedback correction. Feedforward compensation predicts backlash and elastic deformation based on the mathematical model of the transmission chain, while feedback correction dynamically adjusts the compensation command according to the data deviation of the dual encoders, with a compensation response time of less than 5ms.

5. The train brake caliper error compensation control method based on dual encoders according to claim 1, characterized in that: The force sensor is a strain gauge or piezoelectric force sensor with a measurement range of 0 to 50 kN and an accuracy of ±0.5% of full scale. The data fusion uses a Kalman filter to optimally fuse the direct measurements from the force sensor with the indirect force estimates calculated based on spring stiffness and output encoder displacement. The fused force control accuracy is better than ±1%.

6. The train brake caliper error compensation control method based on dual encoders according to claim 1, characterized in that: The fault diagnosis module sets the preset threshold for the data deviation of the dual encoders to ±2% of the displacement. When the deviation exceeds this threshold for three consecutive control cycles, the system determines that the transmission chain is abnormal or the encoder is faulty, and triggers a safety braking command and alarm signal.

7. The train brake caliper error compensation control method based on dual encoders according to claim 1, characterized in that: It also includes an adaptive compensation mechanism. The system identifies changes in mechanical characteristics caused by brake pad wear or temperature drift by monitoring the trend of data deviation between the two encoders over a long period of time, and automatically updates the transmission chain model parameters and spring stiffness coefficients every 24 hours.

8. The train brake caliper error compensation control method based on dual encoders according to claim 3, characterized in that: The outer loop position control adopts a fuzzy proportional-integral-derivative algorithm. The input variables are the position error and its rate of change, and the output variables are the proportional gain, integral time, and derivative time corrections. The fuzzy rule base contains 9 rules. Fuzzification is performed using triangular membership functions, and defuzzification is performed using the centroid method.

9. The train brake caliper error compensation control method based on dual encoders according to claim 4, characterized in that: The system establishes a transmission chain error database, records backlash and elastic deformation data of motor torque, operating speed and ambient temperature, and uses a three-layer feedforward neural network for error prediction. The input layer has 3 nodes, the hidden layer has 20 nodes, and the output layer has 1 node. The prediction accuracy is greater than 95%.

10. The train brake caliper error compensation control method based on dual encoders according to claim 5, characterized in that: When the force sensor malfunctions or the data is abnormal, the system automatically switches to pure displacement control mode, maintaining braking force control based on the displacement measurement value of the output encoder and the preset displacement-force characteristic curve. At the same time, when the deviation between the force sensor data and the braking force estimated by the dual encoder exceeds 15% for three consecutive control cycles, the system marks the force sensor as abnormal and prioritizes the use of dual encoder data for control.

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