Control method, control system and medium based on integrated controller

Abstract

The application discloses a control method and system based on a drive-control integrated controller and a medium thereof. The method comprises the following steps: acquiring system load energy and motion state characteristics of an actuator during operation of the drive-control integrated controller, analyzing response quality of a control instruction, and obtaining a control response quality analysis result; performing segmented processing on a control period signal corresponding to the control instruction according to the control response quality analysis result, performing directional drive control analysis on a local control period with unqualified response quality, and obtaining corresponding directional control parameters; adjusting drive output intensity of the local control period according to the directional control parameters, generating and sending control process parameters of the local control period to the actuator according to the adjusted drive output intensity, so that the actuator adjusts a drive control process of the local control period according to the control process parameters. The application improves the control precision and adaptive performance of the drive-control integrated controller under complex working conditions.

Description

Technical Field

[0001] This application relates to the field of industrial drive control technology, and in particular to a control method, control system and medium based on an integrated drive and control controller. Background Technology

[0002] As the demand for control of precision actuators in the field of intelligent manufacturing evolves towards higher precision, higher dynamics, and higher reliability, special application scenarios such as precision assembly of industrial robots and micro-operation of surgical robots pose differentiated challenges to the comprehensive performance of integrated drive and control controllers, and impose strict constraints on key performance indicators, such as response latency of <1ms.

[0003] While most integrated drive and control controllers on the market can support basic control needs in general scenarios, their design logic is largely anchored to standardized tasks, limiting their adaptability to specific scenarios. Specifically, system self-testing mechanisms typically revolve around general dimensions such as hardware connectivity verification and software functional module integrity checks. However, they lack targeted, in-depth testing processes for specific performance parameters that directly impact control performance in particular scenarios, such as micro-vibration suppression capabilities and high-frequency dynamic response bandwidth. This universal design of testing dimensions makes it difficult for the controller to accurately identify implicit adaptation gaps between the target execution unit and specific task requirements during startup. For example, high-precision assembly tasks may require execution units with vibration suppression capabilities as low as 5μm, while conventional self-tests can only confirm that the vibration value is within the "fault-free" range, failing to determine whether the task's precise requirements are met. This ambiguity in adaptability assessment objectively restricts the precise formulation of subsequent control strategies.

[0004] Therefore, how to improve the control accuracy and adaptability of the integrated drive and control controller under complex working conditions has become an urgent technical problem to be solved. Summary of the Invention

[0005] To improve the control accuracy and adaptability of the integrated drive and control controller under complex working conditions, this application provides a control method, control system and medium based on the integrated drive and control controller.

[0006] Firstly, the objective of this invention is achieved through the following technical solution: Control methods based on integrated drive and control controllers include: Acquire the system load energy and motion state characteristics of the actuator during the operation of the integrated drive and control controller; Based on the system load energy and the motion state characteristics, the response quality of the control command is analyzed to obtain the control response quality analysis results; Based on the control response quality analysis results, the control cycle signal corresponding to the control command is segmented, and directional drive control analysis is performed on the local control time period with unqualified response quality to obtain the corresponding directional control parameters. The drive output intensity of the local control period is adjusted according to the directional control parameters, and control procedure parameters for the local control period are generated and sent to the actuator according to the adjusted drive output intensity, so that the actuator adjusts the drive control process of the local control period according to the control procedure parameters.

[0007] By adopting the above technical solution, the system load energy reflects the energy consumption and load distribution of the drive and control system; the motion state characteristics reflect dynamic behaviors such as position, velocity, acceleration, and vibration, facilitating multi-dimensional feature analysis of the control command response quality. This overcomes the limitations of traditional controllers that rely solely on position error or velocity deviation for overall judgment. Furthermore, by performing segmented and refined analysis of the response quality during local control periods, the system accurately identifies periods of "unqualified response quality" and performs targeted drive control analysis for these periods, achieving refined spatiotemporal processing of the control process. This effectively solves the problem of local response distortion caused by instantaneous load changes and inertial disturbances in highly dynamic tasks, improving control accuracy to the micrometer level and stably controlling the response delay within 1ms. This is beneficial for meeting the stringent requirements of complex application scenarios such as precision assembly and surgical robots. Further, to enhance the system's adaptability to complex working conditions, this application can dynamically identify weak links in the control process and adjust the drive output strength and control procedure parameters accordingly, avoiding resource waste and stability degradation caused by global parameter adjustments. This invention enhances the proactive adaptability of the integrated drive and control controller to special tasks, filling the implicit adaptation gap in existing self-checking mechanisms. Through joint analysis of "system load energy + motion state characteristics," it essentially constructs a dynamic capability assessment mechanism during operation, continuously identifying deviation trends of the actuator in key performance dimensions during control execution. For example, in high-precision assembly tasks, the system can determine whether the current drive unit has sufficient vibration suppression capability based on trajectory jitter characteristics during local control periods and compensate through directional control parameters. Compared to traditional unified control modes, this method can dynamically adjust resource allocation according to the actual needs of different control periods, avoiding continuous high-power operation or wasted communication bandwidth. Especially in complex tasks such as multi-axis collaboration and high-frequency switching, it can significantly reduce overall system energy consumption and enable refined control and scheduling of control process parameters and drive control flow, achieving high-efficiency control of the drive and control system.

[0008] In a preferred embodiment, this application analyzes the response quality of control commands based on the system load energy and the motion state characteristics to obtain control response quality analysis results, specifically including: Based on the system load energy and the motion state characteristics, the dynamic load ratio of the control command in each control cycle is calculated; Based on the dynamic load ratio, calculate the proportion of drive current corresponding to each control timing point; Based on the driving current ratio, analyze the degree of deviation between the current motion trajectory and the preset standard trajectory to obtain the single-point trajectory deviation value at the current time point; The comprehensive trajectory deviation value for the entire control cycle is estimated based on the single-point trajectory deviation value. The response process of the control command is then analyzed based on the comprehensive trajectory deviation value to obtain the control response quality analysis result.

[0009] By adopting the above technical solution, the proportion of driving current at each timing point is derived, and a quantitative mapping relationship from macroscopic load to microscopic current distribution is constructed. Based on this, by analyzing single-point trajectory deviation values ​​and estimating the comprehensive trajectory deviation value, a multi-level, fine-grained evaluation of control response quality is achieved. This application can not only identify overall trajectory deviation but also pinpoint specific moments of runaway and their causes, such as overload and inertial shock.

[0010] In a preferred embodiment, this application further includes: Based on the system load energy, calculate the load energy difference between adjacent control periods; The current change and system impedance value corresponding to the proportion of the driving current are obtained based on the load energy difference; Based on the current change and the system impedance value, construct the drive response curve corresponding to the load energy difference; Based on the drive response curve, the response attenuation trend of the actuator is analyzed to obtain the drive response attenuation value under the current working condition. When the actual drive current reaches the drive response attenuation value, drive gain compensation information is output to the drive control unit of the actuator.

[0011] By adopting the above technical solution, a drive response curve is constructed, realizing the forward-looking modeling and prediction of the response decay trend of the actuator. When the actual drive current is close to the response decay value, the drive gain compensation information is actively output to intervene in the control process in advance, avoiding tracking failure or oscillation caused by response lag, and effectively enhancing the robustness of the system under non-steady-state conditions such as variable load and high acceleration and deceleration.

[0012] In a preferred embodiment of this application, the step of segmenting the control cycle signal corresponding to the control command based on the control response quality analysis results, and performing directional drive control analysis on the local control periods with substandard response quality to obtain the corresponding directional control parameters specifically includes: The control commands are parsed to obtain the target motion trajectory, load type, and response time requirements, thus forming control requirement parameters; Based on the obtained controller self-test results, analyze the working status of each drive channel, the availability of sensor interfaces, and the quality of communication links to form a list of available functions; The control requirement parameters are matched with the list of available functions to determine the optimal combination of drive units and control algorithm configuration, and drive and control configuration parameters are generated. The control cycle signal is segmented according to the drive control configuration parameters to generate local control time periods that match different drive modes; Local control periods with response quality below a preset threshold are marked, and drive characteristics are modeled based on the drive control configuration parameters to obtain the drive characteristic parameters of the local control periods. Based on the driving characteristic parameters, the required driving output intensity and control channel scheduling strategy for the local control period are analyzed to generate directional control parameters.

[0013] By adopting the above technical solution, and through dual optimization of drive control configuration parameters and drive characteristic parameters, "on-demand control" is achieved for local time periods. Compared with a globally unified control strategy, this improves the efficiency of drive output intensity adjustment.

[0014] In a preferred embodiment, this application also includes: Based on the drive control configuration parameters, historical operating data under the corresponding drive mode is retrieved. The historical operating data includes position error sequence, current response curve and temperature change trend. Based on the historical operating data, time-frequency domain analysis and key feature extraction are performed to identify control deviation patterns under typical operating conditions and obtain condition-adaptive control parameters. The historical operating data is input into a preset deep learning model, which outputs the optimal combination of control gains for the current operating conditions. By combining the condition-adaptive control parameters with the optimal control gain, a first control strategy information is generated and used to optimize the directional control parameters.

[0015] By employing the aforementioned technical solution, historical operating data matching the drive and control configuration parameters is retrieved, and time-frequency domain analysis and key feature extraction are performed to identify control deviation patterns under typical operating conditions, thereby obtaining condition-adaptive control parameters based on empirical knowledge. Furthermore, the historical data is input into a pre-set deep learning model, which outputs the optimal control gain combination for the current operating condition, achieving a leap from "experience-driven" to "data-driven intelligence." The first control strategy information generated by combining these two types of parameters enhances the self-learning capability of the control system.

[0016] In a preferred embodiment of this application: the step of adjusting the drive output intensity of the local control period according to the directional control parameters, and generating and sending control procedure parameters for the local control period to the actuator based on the adjusted drive output intensity, includes: The first control strategy information is decomposed into an energy demand sequence and a communication resource scheduling plan to obtain a control resource allocation scheme. Analyze the matching degree between the control resource allocation scheme and the real-time energy consumption status of the system, calculate the control energy efficiency ratio, and obtain the first matching degree index; Analyze the dynamic consistency between the power demand sequence and the system energy consumption status information, evaluate the power supply stability, and obtain the second matching degree index; Based on the first matching degree index and the second matching degree index, a multi-objective optimization algorithm is used to generate the final dynamic control command; Based on the dynamic control instructions, control procedure parameters including drive timing, output voltage, current limiting and communication priority are generated and sent to the drive control unit of the actuator.

[0017] By adopting the above technical solution, the first control strategy information is decomposed into an energy demand sequence and a communication resource scheduling plan, forming a complete control resource allocation scheme. A first matching degree index (control energy efficiency ratio) and a second matching degree index (power supply stability) are introduced for dual evaluation, achieving multi-objective collaborative optimization of control resource scheduling. A multi-objective optimization algorithm is used to generate the final dynamic control command to achieve a balance between control performance, energy efficiency, and system stability.

[0018] In a preferred embodiment of this application, the method further includes: Acquire multi-dimensional environmental interference data of the integrated drive and control controller under the current operating conditions; Based on the multi-dimensional environmental interference data, the environmental adaptability level of the integrated drive and control controller is evaluated to obtain the controller's environmental adaptability compliance level. The operating parameters of each functional module inside the integrated drive and control controller are obtained, and the module-level stability evaluation of each module of the controller is performed based on the operating parameters to obtain the module-level stability evaluation results. Based on the module-level stability assessment results, the module adaptability level corresponding to each functional module is obtained; Based on the module adaptability level of each functional module and the controller's environmental adaptability compliance level, a comprehensive health level of the controller is generated.

[0019] By adopting the above technical solution, this application establishes a quantitative system for environmental tolerance in complex industrial environments by collecting multi-dimensional environmental interference data and evaluating the environmental adaptability level of the integrated drive and control controller to conduct multi-dimensional analysis of its environmental adaptability in actual service environments. Simultaneously, module-level stability evaluation is performed based on the operating parameters of each functional module to obtain the module adaptability level, achieving penetrating monitoring of health status from the system to the components, thereby improving the long-term reliability of the integrated drive and control controller.

[0020] Secondly, the objective of this invention is achieved through the following technical solution: A control system based on an integrated drive and control controller, applied to the control method based on an integrated drive and control controller as described above, the system comprising: The state perception module is used to acquire the system load energy and motion state characteristics of the actuators during the operation of the integrated drive and control controller; The response quality analysis module is used to analyze the response quality of control commands based on the system load energy and the motion state characteristics, and obtain the control response quality analysis results. The control signal segmentation module is used to segment the control cycle signal corresponding to the control command according to the control response quality analysis results, and identify local control periods with unqualified response quality. The directional control analysis module is used to perform directional drive control analysis on the local control period where the response quality is unqualified, and obtain the corresponding directional control parameters. The drive control scheduling module is used to adjust the drive output intensity of the local control period according to the directional control parameters, generate control procedure parameters for the local control period, and send the control procedure parameters to the actuator so that the actuator adjusts the drive control process of the local control period according to the control procedure parameters.

[0021] In a preferred embodiment, this application further includes an output port protection circuit, the output port protection circuit comprising: Optocoupler isolation unit, with its input terminal connected to the MCU control signal terminal; The power electronic switch unit has its input terminal connected to the output terminal of the optocoupler isolation unit and its output terminal connected to the power supply terminal of an external device. The power electronic switch unit is turned on based on a first-level signal received from the MCU control signal terminal and turned off based on a second-level signal received from the MCU control signal terminal. The first level signal is generated when the feedback current signal received by the MCU chip of the actuator is greater than a preset current threshold signal, or when the feedback voltage signal received by the MCU chip of the actuator is greater than a preset voltage threshold signal.

[0022] By adopting the above technical solution, the first level signal is the turn-on signal; the second level signal is the turn-off signal. By setting up an optocoupler isolation unit and a power electronic switch unit, an output port protection circuit is formed, effectively achieving electrical isolation between the MCU control signal and external high-power devices. When the feedback current signal or feedback voltage signal exceeds a preset threshold, the MCU chip automatically generates a turn-off signal (the second level signal) and outputs it to the optocoupler isolation unit through the MCU control signal terminal. The optocoupler isolation unit then outputs the second level signal to the power electronic switch unit, at which point the power electronic switch unit quickly cuts off the power output, achieving millisecond-level overcurrent and overvoltage protection.

[0023] Thirdly, the objective of this invention is achieved through the following technical solution: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the control method based on the integrated drive and control controller described above.

[0024] In summary, this application includes at least one of the following beneficial technical effects: 1. The control cycle signal is segmented and processed to accurately identify the "local control period with unqualified response quality". Targeted drive control analysis and drive output intensity adjustment are carried out for this period, realizing the spatiotemporal fine management of the control process. This effectively solves the problem of local response distortion caused by instantaneous disturbances in high dynamic tasks and significantly improves control accuracy and dynamic response performance. 2. Based on the construction of the drive response curve according to the load energy difference, dynamic impedance compensation of the system is realized. By real-time matching of the current change and the impedance value, the problem of drive response decay hysteresis in traditional methods is solved. Attached Figure Description

[0025] Figure 1 This is a flowchart of a control method based on an integrated drive and control controller in one embodiment of this application; Figure 2 This is one of the circuit diagrams of the output port protection circuit in a control system based on an integrated drive and control controller according to an embodiment of this application; Figure 3 This is another implementation circuit diagram of the output port protection circuit in a control system based on an integrated drive and control controller, as described in one embodiment of this application. Detailed Implementation

[0026] The present application will be further described in detail below with reference to the accompanying drawings.

[0027] In one embodiment, such as Figure 1 As shown, this application discloses a control method based on an integrated drive and control controller, which specifically includes the following steps: S1: Obtain the system load energy and motion state characteristics of the actuator during the operation of the integrated drive and control controller.

[0028] In this embodiment, the system load energy is used to reflect the energy consumption and load distribution of the drive and control system per unit time, and to reflect the work done by the actuator to overcome mechanical resistance; the motion state characteristics describe the physical quantities of the dynamic behavior of the actuator, including position, velocity, acceleration, vibration, etc., and reflect the real-time motion state of the mechanical system.

[0029] Specifically, the three-phase current of the drive and control system is collected through a current sensor (ACS758LCB-150B). , , Combined with bus voltage The formula for calculating instantaneous power is: The effective value of the current The square root of the integral of the square of the current signal i(t) over the time period T is taken. For example, when the motor drives the load to accelerate, the instantaneous value of P(t) may reach 150% of the rated power. If the current waveform is square, with a peak value of 5A and a duty cycle of 50%, then... .

[0030] The position signal in the motion state characteristics is obtained by using a grating ruler or laser interferometer to obtain the absolute position. The vibration signal is acquired using a MEMS sensor (ADXL355) to collect acceleration signals, which are then converted into velocity and displacement using an integration algorithm. ,(in, For speed, For acceleration, (This refers to displacement, i.e., the cumulative change in the object's position over time.) Then, an FFT transformation is performed on the vibration signal to identify high-frequency vibration components above 200 Hz.

[0031] S2: Based on the system load energy and motion state characteristics, analyze the response quality of control commands to obtain the control response quality analysis results.

[0032] In this embodiment, step S2 includes: S21: Calculate the dynamic load ratio of the control command in each control cycle based on the system load energy and motion state characteristics.

[0033] In this embodiment, the dynamic load ratio is used to reflect the ratio of the current load energy to the rated load energy, thereby quantifying the degree of load fluctuation. Dynamic Load Ratio ,in Rated power, such as 200W; This refers to instantaneous power.

[0034] S22: Calculate the proportion of drive current corresponding to each control timing point based on the dynamic load ratio.

[0035] In this embodiment, the drive current ratio is the current allocation ratio of each drive channel, reflecting the rationality of power allocation. For example, in the XYZ+rotary axis collaborative control scenario of a four-axis palletizing robot, in order to analyze the positioning control accuracy and control quality of the drive control system, this application introduces a refined analysis of the drive current ratio and motor trajectory deviation value.

[0036] Specifically, based on the application scenario described above, it is necessary to assign weights to the currents of each axis (including the X-axis, Y-axis, Z-axis, and θ-axis) according to process requirements. For example, the X-axis represents the transport direction, and its corresponding weighting coefficient is... The Y-axis represents the horizontal adjustment direction, and the corresponding weighting coefficient is... The Z-axis represents the vertical rising and falling direction, and the corresponding weighting coefficient is... The θ-axis is the rotation axis, and the corresponding weighting coefficient is... .

[0037] When calculating current quotas, it is necessary to calculate the current quota for each axis. The calculation formula is as follows: ,in, Current quota for each axis refers to the current quota for a certain drive axis (such as X / Y / Z axis) at the current moment; dynamic load ratio. The current quota for each axis is determined; the larger the load, the higher the allowable current allocation. Rated current; The weighting coefficients for each axis reflect the priority of power allocation. For example, the X-axis current is 8A × 76.8% × 0.4 = 2.46A; the θ-axis current is 8A × 76.8% × 0.1 = 0.61A.

[0038] S23: Based on the proportion of driving current, analyze the degree of deviation between the current motion trajectory and the preset standard trajectory, and obtain the single-point trajectory deviation value of the current time point.

[0039] In this embodiment, the single-point trajectory deviation value is the instantaneous deviation between the actual trajectory and the theoretical trajectory at the current time point, which is used to locate microscopic defects in the control accuracy; trajectory deviation analysis is performed based on the end effector of the palletizing robot.

[0040] Specifically, the actual positions of the XYZ axes (in mm) are collected, and the θ axis is refined to one decimal place. The actual positions of the XYZ axes are respectively... , .

[0041] The formula for single-point trajectory deviation includes: , , , in, , , , These are the instantaneous deviations between the actual positions of the X-axis, Y-axis, Z-axis, and θ-axis and the theoretical trajectory at the current moment; , , The theoretical trajectory coordinates are preset and generated by a motion planning algorithm. An ideal motion path can be generated through AM software or offline programming.

[0042] The formula for calculating the deviation of a single point's trajectory from the overall trajectory is as follows: For example, when At that time, .

[0043] S24: Estimate the comprehensive trajectory deviation value of the entire control cycle based on the single-point trajectory deviation value, perform quality analysis on the response process of the control command based on the comprehensive trajectory deviation value, and obtain the control response quality analysis results.

[0044] In this embodiment, the comprehensive trajectory deviation value refers to the cumulative effect of trajectory deviation throughout the entire control cycle, and is used as a macroscopic indicator to measure control quality. For example, it is used to calculate the stacking accuracy of a palletizing robot.

[0045] Specifically, deviation values ​​are collected at N time points throughout the current control cycle. The overall trajectory deviation value is calculated using the root mean square deviation, and the formula is as follows: Where C is the root mean square deviation within the control period, and N is the number of sampling points within the control period, such as 1000 points every 20ms.

[0046] The quality judgment is based on a combination of threshold judgment rules: C≤0.5mm is qualified; 0.5mm≤C≤1.0mm requires local optimization; C>1.0mm requires global replanning. By responding to the quality classification results, the control response quality analysis results containing the comprehensive trajectory deviation value and quality judgment results throughout the entire control cycle are obtained.

[0047] S3: Based on the control response quality analysis results, the control cycle signal corresponding to the control command is segmented and processed. For the local control period with unqualified response quality, directional drive control analysis is performed to obtain the corresponding directional control parameters.

[0048] Specifically, step S3 includes: S31: Parse control commands, obtain target motion trajectory, load type and response time requirements, and form control requirement parameters.

[0049] In this embodiment, the control requirement parameters are a set of parameters describing the task objective, including the target trajectory, load type, and response time requirements. This is assumed to be a control scenario for a palletizing robot's workpiece grasping task.

[0050] Specifically, G-code instructions are parsed via an industrial bus (such as EtherCAT) to extract the geometric features of the target motion trajectory (such as straight lines, arcs, and spirals). The load type (such as cardboard boxes or metal parts) and response time requirements are obtained by reading PLC input signals, with the response time requirement being ≤20ms. In other words, the control requirements parameters include trajectory type, load mass, and cycle time. The trajectory type is then mapped to a motion mode, such as high-speed motion mode or precision motion mode.

[0051] S32: Based on the acquired controller self-test results, analyze the working status of each drive channel, the availability of sensor interfaces, and the quality of communication links to form a list of available functions.

[0052] In this embodiment, the list of available functions is a real-time status list of the controller hardware and communication links, including drive channel health, sensor availability, etc., which is used to define the boundary conditions of feasible control strategies.

[0053] Specifically, the self-test items in the controller self-test results include: firstly, the operating status of each drive channel, such as detecting the IGBT module temperature (threshold 85℃) and the motor encoder signal integrity (bit error rate <10⁻). 6 The second step is to check the communication status of the laser rangefinder (accuracy ±0.1mm) and pressure sensor (range 0-50kg) through the sensor interface. The communication link is used to verify the EtherCAT frame transmission delay (target <1μs). For example, if drive channel A is normal (temperature 72℃) and sensor B is offline with a communication interruption, the EtherCAT link delay is 0.8μs.

[0054] A binary status table can be generated from the available function list. For example, the status of drive channel X is "normal" and the availability flag is "√", the status of sensor Y is "fault" and the availability flag is "×", and the status of EtherCAT communication is "normal" and the availability flag is "√".

[0055] S33: Match the control requirement parameters with the list of available functions to determine the optimal combination of drive units and control algorithm configuration, and generate drive and control configuration parameters.

[0056] In this embodiment, the drive control configuration parameters are an optimized combination of drive unit combinations (such as SVPWM, DTC) and control algorithm parameters (such as PID gain). Information such as IGBT temperature and bus voltage anomaly alarms is obtained through the driver's built-in fault diagnosis interface (such as EtherCAT PDO messages). If the IGBT temperature of drive channel A exceeds 85°C, it is marked as unavailable. Sensor availability verification is performed through communication status and data integrity; for example, if the pressure sensor return value exceeds the range (>50kg) or the bit error rate is >10⁻, it will be checked. 5 If so, it is determined to be unusable.

[0057] Based on the load material (e.g., metal / plastic / glass) identified by the PLC input signal or vision system, the corresponding friction compensation algorithm is selected. The friction compensation algorithm is based on different rule bases associated with different load types. For example, if the load type is metal, a compatible algorithm of "SVPWM and friction compensation" is used, and DTC is disabled. If the load type is plastic, a compatible algorithm of "DTC + friction compensation" is used, and open-loop control is disabled. If the load type is glass, a "current loop priority control" algorithm is used, and high-frequency PWM modulation is prohibited.

[0058] Specifically, unusable modules are first excluded, such as control channels corresponding to faulty sensors. Compatible algorithms are then selected based on load type; for example, friction compensation algorithms must be enabled for metal parts. A multi-objective optimization matching algorithm is set up, with optimization scores based on motion accuracy (weight 0.4), response speed (weight 0.3), and energy efficiency (weight 0.3). The quantitative indicator for motion accuracy is the standard deviation (σ) of the trajectory tracking error, with the corresponding scoring rule being σ ≤ 0.5 mm for 100 points, and deducting 10 points for every 0.1 mm increase in σ. The quantitative indicator for response speed is the percentage of command responses completed within the control cycle; for example, 95% of commands completed within 5ms, achieving a 100% success rate, is 100 points, with a deduction of 10 points for every 5% decrease. The quantitative indicator for energy efficiency is unit energy consumption (Wh / kg·m), with the scoring rule: energy consumption ≤ 0.5 Wh / kg·m for 100 points, and deducting 10 points for every 0.1 Wh / kg·m increase.

[0059] S34: The control cycle signal is segmented according to the drive control configuration parameters to generate local control time periods that match different drive modes.

[0060] In this embodiment, the complete 20ms control cycle is divided into four 5ms sub-periods, or segmented by event. Segment boundaries are inserted at trajectory inflection points or load abrupt change points, with event-based segmentation being preferred. A mode is assigned to each sub-period, such as allocating the high-speed mode to the linear motion mechanism and the precision mode to the acceleration / deceleration periods. For example, the allocation results are: [0-5ms] linear acceleration (high-speed mode), [5-10ms] curve transition (precision mode), [10-15ms] linear deceleration (high-speed mode), [15-20ms] attitude adjustment (precision mode). A weighted score is calculated based on the combination of drive mode and control algorithm, and the highest-scoring scheme is selected according to the score ranking. If the precision mode has the highest score, it is selected first. If multiple schemes have similar scores, additional constraints are introduced, such as prioritizing the scheme with the lowest energy consumption.

[0061] S35: Mark the local control periods where the response quality is lower than the preset threshold, and model the drive characteristics based on the drive control configuration parameters to obtain the drive characteristic parameters of the local control periods.

[0062] In this embodiment, the driving characteristic parameters are dynamic characteristic parameters of the local control period, such as impedance value, vibration frequency, response delay, etc., which reflect the driving bottleneck of a specific period.

[0063] Specifically, the workpiece position deviation is monitored in real time using a laser rangefinder, and the single-point trajectory deviation value is calculated. If the deviation of three consecutive sampling points is greater than 0.5mm (position deviation threshold), then that time period is marked as a low-quality segment. For example, e(t) = 0.3mm in [5-6ms] is recorded as normal, while e(t) = 0.6mm in [6-7ms] needs to be marked as a low-quality segment.

[0064] Establish a transfer function model for low-quality periods: Through parameter identification methods in step response testing, the static gain K = 0.8, characterizing amplification capability, the time constant T = 0.1s, characterizing response speed, and the pure time delay τ = 0.02s, characterizing signal delay, are obtained. The time constant describes the speed of the system's response. The static gain K characterizes the system's ability to amplify the input signal, i.e., the ratio of the output steady-state value to the input steady-state value; for example, if the input is a 1V step signal, and the output stabilizes at 0.8V, then K = 0.8. The larger the time constant, the slower the system response and the longer the time required to reach steady state. The pure time delay τ represents the time delay from input to output, commonly seen in sensor sampling, communication transmission, or actuator action delay. Characterizing the inherent delays in signal transmission or physical processes, such as sensor response delays and network communication delays. Transfer function models for low-quality periods are used to compensate for these delays during the optimization and adjustment phase. This can be achieved by introducing delay compensation elements (such as Smith predictors) into controllers (e.g., PID controllers) to counteract the effects of pure time delays. Control parameter adjustments can optimize controller gain based on T and K, avoiding overshoot or oscillations.

[0065] For example, when a four-axis palletizing robot grasps slippery plastic parts, a vibration delay occurs in the Z-axis. Step response testing yields K=0.8, T=0.1s, and τ=0.02s. The diagnostic result is: the time delay τ=0.02s indicates a delay in the sensor or communication, requiring optimization of the data acquisition timing. The compensation method is to add a predictive algorithm to the controller to compensate for the 0.02s time delay in advance.

[0066] S36: Based on the drive characteristic parameters, analyze the drive output strength and control channel scheduling strategy required during the local control period, and generate directional control parameters.

[0067] In this embodiment, the directional control parameters are customized control commands for low-quality periods, including drive strength, current loop bandwidth, communication priority, and vibration suppression gain, to achieve localized fine-grained control. Drive output strength adjustment includes increasing the PWM duty cycle and improving the current loop bandwidth. Scheduling strategy optimization includes assigning higher communication priority to key sensors and dynamically adjusting the control timing.

[0068] Specifically, the driving intensity in the directional control parameters ranges from 50% to 100%, the current loop bandwidth ranges from 00Hz to 500Hz, the communication priority ranges from RT_CLASS_1 to 3, and priority marking is used to distinguish them; the vibration suppression gain ranges from 0% to 200%.

[0069] S4: Adjust the drive output intensity of the local control period according to the directional control parameters, generate and send the control procedure parameters of the local control period to the actuator according to the adjusted drive output intensity, so that the actuator can adjust the drive control process of the local control period according to the control procedure parameters.

[0070] Specifically, step S4 includes: S41: Decompose the first control strategy information into an energy demand sequence and a communication resource scheduling plan to obtain a control resource allocation scheme.

[0071] In this embodiment, the power demand sequence is the power demand sequence of each drive channel within the control period, such as the time-series values ​​of voltage, current, and power. The communication resource scheduling plan is a priority allocation scheme for the communication between sensor data and control commands, such as real-time requirements and bandwidth occupancy ratios. This is exemplified in applications such as drive control for industrial robots performing precision mounting tasks.

[0072] Specifically, the electricity demand sequence is decomposed into For example, the placement head descent phase requires 48V, 3.5A, and 168W of power. The communication resource scheduling plan, such as the laser rangefinder in the sensor data, allocates a priority of RT_CLASS_3; the control command is an EtherCAT periodic task with a period of 20ms. Then, power is allocated according to the drive channel capacity: for example, the X / Y axes share a 48V power supply with a maximum current of 15A; the Z-axis has independent power supply with a maximum current of 8A.

[0073] S42: Analyze the matching degree between the control resource allocation scheme and the real-time energy consumption status of the system, calculate the control energy efficiency ratio, and obtain the first matching degree index.

[0074] In this embodiment, the control energy efficiency ratio is the ratio of control effectiveness to energy consumption. That is, energy consumption is monitored at the placement station performing the placement task. Control effectiveness indicators measure placement accuracy and cycle time. Cycle time is the time from gripping to placement completion, and placement accuracy is measured using a laser interferometer. Energy consumption indicators are the measured actual power consumption.

[0075] Specifically, controlling the energy efficiency ratio The calculation formula is: .

[0076] S43: Analyze the dynamic consistency between the power demand sequence and the system energy consumption status information, evaluate the power supply stability, and obtain the second matching degree index.

[0077] In this embodiment, power supply stability is evaluated by measuring the power supply driving the robot joints. Power quality monitoring calculates stability indicators using the collected bus voltage waveform and phase current harmonic components.

[0078] Specifically, stability metrics include voltage ripple coefficient and total harmonic distortion (THD) of current, where voltage ripple coefficient... ,in The maximum peak value of the voltage waveform, in V; This represents the minimum valley value of the voltage waveform; This represents the average value of the voltage waveform. ,in This is the effective value of the fundamental current; The effective value of the h-th harmonic current; the threshold for the pass rate of total harmonic distortion of current is 8%.

[0079] S44: Based on the first and second matching degree indices, a multi-objective optimization algorithm is used to generate the final dynamic control command.

[0080] In this embodiment, the first matching degree index is the control energy efficiency ratio, and the second matching degree index is the power supply stability; the dynamic control command is a real-time control command set including parameters such as timing, voltage, current, and communication priority. The optimization objective of the multi-objective optimization algorithm (such as using the NSGA-II algorithm) includes minimizing energy consumption. And maximize control energy efficiency ratio (max) The constraint condition is the voltage ripple coefficient. ≤2% and current harmonics THD <8%.

[0081] Specifically, a weighted addition model is used to fuse and optimize the objective. : Among them, the weighting coefficient The values ​​were successively set to 0.4, 0.3, 0.2, and 0.1. A Pareto front was searched using a genetic algorithm, and the control instruction sequence with the highest overall score was selected.

[0082] S45: Based on the dynamic control command, generate control procedure parameters including drive timing, output voltage, current limiting and communication priority, and send them to the drive control unit of the actuator.

[0083] In this embodiment, the drive control unit refers to the core control module of the actuator, responsible for receiving control commands and converting them into specific drive actions, such as servo drivers, current and voltage sensors, and communication interfaces. It possesses software drive control functions including "current loop control, voltage compensation, and communication scheduling." Drive timing is achieved by allocating timing weights based on segmentation results, such as a 75% duty cycle for the acceleration segment and 60% for the constant speed segment. Output voltage control is achieved by dynamically adjusting the PWM duty cycle. Current limiting is achieved by setting hard limiting values ​​(e.g., ±5A) and soft limiting values ​​(e.g., ±4.5A). Communication priorities are distinguished by priority tags, for example, critical sensor data (e.g., pressure sensor data) is tagged as RT_CLASS_3. Control process parameters are generated into CANopen protocol messages and sent to the driver via EtherCAT real-time Ethernet.

[0084] In this embodiment, the control method based on the integrated drive and control controller further includes: S101: Calculate the load energy difference between adjacent control periods based on the system load energy.

[0085] In this embodiment, the load energy difference is the change in system load energy between adjacent control periods, used to reflect the magnitude of load fluctuations. For example, when a palletizing robot grasps goods of different weights, the current distribution needs to be dynamically adjusted. For instance, assume the three-phase current and bus voltage data from the current sensor are as follows: For example, the effective value of the current calculated from a 48V bus voltage at timestamp 0.001. When the current is 4.2A, the calculated instantaneous power P(t) = 201.6W. When calculating the load energy difference, with a control period set to 20ms, the calculated load energy difference ΔE between adjacent periods is: , of which For phase current, Take the instantaneous value of the three-phase current of the drive motor; E(t) is the cumulative energy value at the end of the previous control cycle; E(t+ ) represents the current control period (t~t+) The cumulative energy value at the end of the cycle. For example, if the power integral value of the current cycle is 3.06J and the previous cycle is 2.89J, then the corresponding load energy difference is +0.17J.

[0086] S102: Obtain the current change and system impedance value corresponding to the proportion of drive current based on the load energy difference.

[0087] In this embodiment, the system impedance value is measured by a frequency sweep test method to measure the impedance characteristics of the drive system. That is, a sinusoidal current excitation (frequency range 10Hz~1kHz) is applied, the voltage response is recorded, and the impedance amplitude is calculated. and phase angle The calibration results are as follows: low-frequency impedance. =0.5Ω, high-frequency impedance =2.3Ω. The change in current is calculated based on the load energy difference ΔE: Where η is the energy conversion efficiency, such as 0.85.

[0088] S103: Construct the drive response curve corresponding to the load energy difference based on the current change and the system impedance value.

[0089] In this embodiment, the drive response curve is a functional relationship curve between the current change and the system impedance, describing the dynamic characteristics of the drive system. The functional relationship between the current change and the system impedance is: For example, when ΔI = 0.003A, Z = 0.5 + (0.003)^2 ×(2.3−0.5)≈0.500017Ω.

[0090] Specifically, retrieve the load energy difference sequence from the past 100 control cycles. Furthermore, a sampling index screening model was used to model the response capability and fit the changing trend: in, This is the responsiveness index for the nth period; The initial response capability is 100% of the nominal value; k is the attenuation coefficient (obtained by least squares fitting).

[0091] S104: Based on the drive response curve, analyze the response decay trend of the actuator to obtain the drive response decay value under the current operating condition.

[0092] In this embodiment, the drive response attenuation value is the critical value at which the system response capability begins to decrease significantly when the actual drive current reaches a certain threshold. It is used to predict the performance limit of the equipment and trigger the compensation mechanism in advance. For example, to facilitate observation of the drive response changes of the end effector of the four-axis palletizing robot (such as the micron-level placement accuracy control of the placement head).

[0093] Specifically, setting an impedance threshold =2.0Ω, when the real-time impedance Compensation is triggered on time. The compensation amount is determined based on a mapping table between the impedance value Z component and the gain compensation coefficient G. The mapping table for the gain compensation amount is as follows: Impedance value Z (Ω) Gain compensation coefficient G 2.0 +5% 2.1 +8% 2.2 +12% For example, when Z = 2.05Ω, looking up the table, G = +6.5%. Driving parameter adjustments, such as modifying the driver's amplification factor, are... in, This refers to the original gain compensation coefficient; This is the increased gain compensation coefficient.

[0094] S105: When the actual drive current reaches the drive response attenuation value, output drive gain compensation information to the drive control unit of the actuator.

[0095] In this embodiment, the drive control unit is like a placement head that performs a 20μm high-precision placement task; the drive gain compensation information is compensation information that compensates for the decrease in response capability caused by load changes by adjusting the amplification factor of the driver.

[0096] For example, the standard deviation of the positional deviation σ before compensation was 8.2 μm, and after compensation it decreased to σ = 4.1 μm. The effectiveness of the compensation is determined by whether the deviation after compensation satisfies σ < 5 μm; otherwise, the impedance threshold is recalculated and the compensation is iterated.

[0097] In one embodiment, the control method based on the integrated drive and control controller further includes: S100: Based on the drive control configuration parameters, retrieve historical operating data for the corresponding drive mode. The historical operating data includes position error sequence, current response curve and temperature change trend.

[0098] In this embodiment, the position error sequence is the deviation between the actual position and the theoretical trajectory collected by the encoder, such as e(t), the current response curve is the time-domain waveform of the three-phase current, and the temperature change trend is the temperature curve of the driver IGBT module and the motor winding.

[0099] S200: Based on historical operating data, time-frequency domain analysis and key feature extraction are performed to identify control deviation patterns under typical operating conditions and obtain condition-adaptive control parameters.

[0100] In this embodiment, time-frequency domain analysis analyzes the characteristics of the signal in both the time and frequency dimensions; typical operating conditions such as sudden load changes and friction interference are also included. Specifically, time-domain analysis calculates the statistical characteristics of position errors, such as the mean of all position errors. Variance of positional error The maximum instantaneous deviation of the position error, i.e., the peak value of the error. Where i is the index of the data point; N is the number of sampling points within the control period. For example, if... =0.15mm, =0.05mm indicates the presence of a systematic deviation. Frequency domain analysis uses Short-Time Fourier Transform (STFT) to analyze the spectral characteristics of the current signal. Different frequency bands reflect load inertia changes: low frequency (0-10Hz); mid frequency (10-100Hz); and high frequency (100-1kHz). For example, a peak in the spectrum at 20Hz may correspond to periodic vibrations caused by belt slippage. Then, the error autocorrelation function (ACF) is used to reveal the periodicity of the error sequence.

[0101] S300: Input historical operating data into a preset deep learning model and output the optimal combination of control gains for the current operating conditions.

[0102] In this embodiment, historical operating data is categorized into different operating condition pairs: normal and abnormal. Optimal control parameters are labeled as monitoring signals. The optimal control gain combination refers to dynamic gain parameters such as PID parameters, current loop bandwidth, and friction compensation coefficient output by the deep learning model. The deep learning model employs a hybrid LSTM+Transformer model. The LSTM layer captures long-term dependencies in the time series, such as temperature change trends; the Transformer layer models the local correlations of time-frequency domain features, such as harmonic components. The input features are [position error sequence, current spectrum, temperature gradient, THD]. A loss function based on weighted coefficients for control parameter prediction and operating condition identification is constructed for model training. The training set is required to contain 5 typical operating conditions, and the validation set is required to contain 2 extreme operating conditions.

[0103] S400: Combines the operating condition adaptive control parameters with the optimal control gain combination to generate the first control strategy information, which is then used to optimize the directional control parameters.

[0104] In this embodiment, the adaptive control parameters are represented by PID control parameters; firstly, a mapping table (operation parameter mapping table) based on the operating condition type and the adaptive control parameters is constructed, and the table content is as follows: Specifically, by fusing the predicted values ​​output by the deep learning model with the mapping table parameters, the formula for determining the optimal gain combination is as follows: Among them, the weighting coefficient Used to balance the contributions of deep learning model predictions and historical mapping table parameters; The predicted gain combination output by the deep learning model includes PID parameters, current limiting value, and friction compensation coefficient. This is a preset working condition parameter mapping table. If the model's predicted values ​​conflict with the parameters in the mapping table (e.g., model suggestions...), the mapping will be updated accordingly. =1.8, but the mapping table has the largest limit. If the value is 1.5, then the weighted average will be truncated to a reasonable range.

[0105] In one embodiment, after step S4, the control method based on the integrated drive and control controller further includes: S51: Obtain multi-dimensional environmental interference data of the integrated drive and control controller under the current operating conditions.

[0106] In this embodiment, a health level assessment of the operating environment of the integrated drive and control controller is conducted in the industrial environment of the palletizing workshop where the palletizing robot operates. The multi-dimensional environmental interference data is a set of environmental parameters affecting the controller's performance, including temperature, humidity, vibration, and electromagnetic interference.

[0107] Specifically, the temperature is the temperature of the controller PCB board, the humidity is the humidity of the monitored workshop (working environment), the vibration sensor is used to detect mechanical vibration, and the electromagnetic interference is monitored by the electromagnetic interference sensor (frequency range 10kHz-1GHz) of the EMC test probe, and the high-frequency noise is evaluated by EMC noise.

[0108] S52: Based on multi-dimensional environmental interference data, the environmental adaptability level of the integrated drive and control controller is evaluated to obtain the controller's environmental adaptability compliance level.

[0109] In this embodiment, the environmental adaptability level is the rating of the controller's adaptability in a specific environment (e.g., A-excellent, B-medium, C-critical); the module-level stability assessment is the rating of the matching degree of each module under the current environment and load, such as high, medium, and low.

[0110] Specifically, the critical threshold for temperature in the multidimensional environmental interference data is >85°C, with health effects including PCB thermal deformation and capacitor failure; the critical threshold for humidity is >85%RH, with health effects including PCB leakage and metal corrosion; the critical threshold for vibration is >2.0g, with health effects including solder joint detachment and connector loosening; and the critical threshold for EMC noise is >60dBμV / m, with health effects including increased communication bit error rate and decreased ADC accuracy.

[0111] The environmental adaptability level assessment is conducted using a weighted scoring formula. First, the environmental adaptability score is calculated: ,in For variable indexing of multidimensional environmental disturbance data; This represents the actual interference value. The critical threshold is used as the basis for classifying environmental adaptability compliance levels based on the classification thresholds of 0.5 and 1.0. ≤0.5 is Grade A (Excellent), 0.5≤ ≤1.0 is grade B (good). >1.0 is grade C (critical).

[0112] S53: Obtain the operating parameters of each functional module inside the integrated drive and control controller, perform module-level stability evaluation on each module of the controller based on the operating parameters, and obtain the module-level stability evaluation results.

[0113] In this embodiment, each functional module includes a power supply module, a drive module, a communication module, and a sensor module, wherein the power supply module acquires the operating parameters of voltage ripple (…). The normal range is set to ≤2%. The driving module's operating parameters obtain the current harmonic distortion rate (...). The normal range is set as follows: The communication module's operating parameters acquire the EtherCAT frame delay, with a normal range of ≤2μs. The sensor module's operating parameters acquire the encoder resolution error, with a normal range of ≤0.02mm. These normal range threshold settings are merely example values; you can define your own.

[0114] Specifically, module-level stability assessment is conducted using a stability scoring formula: in, The actual parameter value is , The actual parameters are as follows; j is the variable index; For the corresponding nominal value; This is a tolerance factor, for example, voltage ripple δ=0.05, current harmonics δ=0.1.

[0115] Stability assessment was performed using preset stability score thresholds of 0.9 and 0.7, for example... A value ≥0.9 indicates a stable level; 0.7≤ A value ≤0.9 indicates a stability warning level. A score of ≤0.7 indicates a fault indication level.

[0116] S54: Based on the module-level stability assessment results, obtain the module adaptability level corresponding to each functional module.

[0117] Prioritizes the dynamic matching of module adaptability levels for each functional module: .

[0118] Alternatively, the matching threshold for the controller module and environmental conditions can be used to determine its suitability. For example, if the input voltage fluctuation of the power supply module is >±10%, the module's suitability level is Low; if the temperature of the drive module is greater than 70℃, the module's suitability level is Medium; if the EMC noise of the communication module is greater than 50dBμV / m, the module's suitability level is High; and if the humidity of the sensor module is greater than 80%RH, the module's suitability level is Low.

[0119] S55: Based on the module adaptability level of each functional module and the controller's environmental adaptability compliance level, a comprehensive health level of the controller is generated.

[0120] In this embodiment, the overall health level of the controller is the final rating of the overall health status of the controller, which, combined with environmental adaptability and module stability, determines whether a shutdown for maintenance or adjustment of the control strategy is required.

[0121] Specifically, the controller's overall health level is calculated using a weighted comprehensive method to determine the overall health index H, and then classified according to thresholds. The environmental adaptability score has a weight of 0.6, and the usability level of each functional module has a weight of 0.4. Where k1 is the variable index; The adaptability levels for each module are: High level with a score of 1, Medium level with a score of 0.5, and Low level with a score of 0. When H ≥ 0.8, the overall health level of the controller is healthy; when 0.6 ≤ H < 0.8, the overall health level of the controller is sub-healthy; and when H > 0.6, the overall health level of the controller is faulty.

[0122] For example, in a high-temperature and high-humidity workshop during summer—temperature 38°C, humidity 75%RH, vibration 0.5g, EMC noise 45dBμV / m—the environmental adaptability level is B. =0.65. Module status is as follows: Power module is Yellow ( =2.1%), the driver module is Green ( =5.8%), the communication module is High (delay 0.9μs), and the sensor module is Low (humidity exceeds the limit). Therefore, the controller's overall health level is sub-healthy, with H=0.72. Maintenance measures are needed, such as activating the backup power module, adjusting PID parameters to suppress temperature drift, and adding dehumidification equipment to the workshop.

[0123] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0124] In one embodiment, a control system based on an integrated drive and control controller is provided, which corresponds to the control method based on an integrated drive and control controller in the above embodiments.

[0125] The control system based on the integrated drive and control controller includes a state perception module, a response quality analysis module, a control signal segmentation module, a directional control analysis module, and a drive control scheduling module. Detailed descriptions of each functional module are as follows: The state perception module is used to acquire the system load energy and motion state characteristics of the actuators during the operation of the integrated drive and control controller; The response quality analysis module is used to analyze the response quality of control commands based on the system load energy and motion state characteristics, and obtain the control response quality analysis results. The control signal segmentation module is used to segment the control cycle signal corresponding to the control command based on the control response quality analysis results, and to identify local control periods with unqualified response quality. The directional control analysis module is used to perform directional drive control analysis on local control periods with substandard response quality, and obtain the corresponding directional control parameters. The drive control scheduling module is used to adjust the drive output intensity of the local control period according to the directional control parameters, generate the control procedure parameters of the local control period, and send the control procedure parameters to the actuator so that the actuator can adjust the drive control process of the local control period according to the control procedure parameters.

[0126] Optionally, the control system based on the integrated drive and control controller also includes an output port protection circuit, which comprises an optocoupler isolation unit and a power electronic switch unit. The input terminal of the optocoupler isolation unit is connected to the MCU control signal terminal. The input terminal of the power electronic switch unit is connected to the output terminal of the optocoupler isolation unit, and the output terminal is connected to the power supply terminal of the external device. The power electronic switch unit is turned on based on a first-level signal received from the MCU control signal terminal and turned off based on a second-level signal received from the MCU control signal terminal. The first-level signal is generated when the feedback current signal received by the MCU chip of the actuator is greater than a preset current threshold signal, or when the feedback voltage signal received by the MCU chip of the actuator is greater than a preset voltage threshold signal.

[0127] Specifically, such as Figure 2 As shown, with Figure 2 Taking the circuit diagram shown as an example, the optocoupler isolation unit is an optocoupler, the power electronic switch unit is a power electronic switch (ZXMS6004DT8), and the optocoupler is an Avago HCPL-0731 optocoupler; the MCU chip of the actuator outputs the control signal RBT_OUT1, and the control signal controls the power electronic switch M1 through optocoupler O33 isolation. When the electronic switch is turned on, it enables the external device and monitors the working voltage, current, and temperature in real time. If the chip design parameters are exceeded, it will automatically disconnect to protect the device from damage.

[0128] Furthermore, the power electronic switch (ZXMS6004DT8) integrates multiple protection functions, including over-temperature protection, over-voltage protection, over-current protection, and ESD protection. Over-temperature protection automatically cuts off the output when the junction temperature of the power electronic switch chip exceeds 150 degrees Celsius. Over-voltage protection allows the power electronic switch chip to withstand an input voltage of 60V; when the external voltage exceeds 36V, it automatically cuts off the output to prevent damage to external components. Over-current protection is based on the protection current of the power electronic switch chip, which is related to the control voltage. When the control voltage is 3V, the protection current is 1.7A; when the control voltage is 5V, the protection current is 2.2A. If the output current exceeds the protection current, the chip automatically cuts off the output to prevent damage to external components. ESD protection is achieved by integrating ESD protection devices within the power electronic switch chip, which can withstand a maximum electrostatic discharge voltage of 4000V from the human body. Compared to the traditional circuit structure that uses a field-effect transistor (WSP6946) and a 250mA self-resetting fuse for circuit output protection, this application uses a power electronic switch chip to eliminate the need for self-resetting fuses and ESD devices at the ports common in traditional circuits, thereby improving the circuit's protection capability.

[0129] In another embodiment, such as Figure 3 As shown, with Figure 3 Taking the circuit diagram shown as an example, the output port protection circuit also includes a current sampling unit and an isolation amplifier. The current sampling unit includes a resistor R7 and a voltage converter U4, and the isolation amplifier is... Figure 3 Specifically, the MCU chip of the actuator outputs a control signal RBT_OUT1, which, after being isolated by optocoupler O1, drives the control power electronic switch M1 to control the output port. When the power electronic switch M1 is turned on, it enables external devices and monitors the operating voltage, current, and temperature in real time. If these values ​​exceed the MCU chip's design parameters, it automatically disconnects to protect the equipment from damage. The voltage converter U4 collects the output port current through resistor R7, processes it after passing through the isolation amplifier U1A, and then transmits it to the MCU chip. The MCU chip receives the feedback current signal, compares it with the set parameters, and determines whether the output port operating current exceeds the set value, thus achieving closed-loop control of the output port. The MCU chip is the microprocessor of the integrated drive and control controller and can include various types of microprocessor chips. No chip model is limited here.

[0130] For specific limitations regarding the control system based on the integrated drive and control controller, please refer to the limitations of the control method based on the integrated drive and control controller mentioned above, which will not be repeated here. Each module in the above-mentioned control system based on the integrated drive and control controller can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in the processor of the computer device in hardware form or independent of the processor, or it can be stored in the memory of the computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0131] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements steps such as those of a control method based on an integrated drive and control controller.

[0132] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0133] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0134] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A control method based on an integrated drive and control controller, characterized in that, include: Acquire the system load energy and motion state characteristics of the actuator during the operation of the integrated drive and control controller; Based on the system load energy and the motion state characteristics, the response quality of the control command is analyzed to obtain the control response quality analysis results; Based on the control response quality analysis results, the control cycle signal corresponding to the control command is segmented, and directional drive control analysis is performed on the local control time period with unqualified response quality to obtain the corresponding directional control parameters. The drive output intensity of the local control period is adjusted according to the directional control parameters, and control procedure parameters for the local control period are generated and sent to the actuator according to the adjusted drive output intensity, so that the actuator adjusts the drive control process of the local control period according to the control procedure parameters.

2. The control method based on an integrated drive and control controller according to claim 1, characterized in that, Based on the system load energy and the motion state characteristics, the response quality of the control commands is analyzed to obtain the control response quality analysis results, specifically including: Based on the system load energy and the motion state characteristics, the dynamic load ratio of the control command in each control cycle is calculated; Based on the dynamic load ratio, calculate the proportion of drive current corresponding to each control timing point; Based on the driving current ratio, analyze the degree of deviation between the current motion trajectory and the preset standard trajectory to obtain the single-point trajectory deviation value at the current time point; The comprehensive trajectory deviation value for the entire control cycle is estimated based on the single-point trajectory deviation value. The response process of the control command is then analyzed based on the comprehensive trajectory deviation value to obtain the control response quality analysis result.

3. The control method based on an integrated drive and control controller according to claim 2, characterized in that, The method also includes: Based on the system load energy, calculate the load energy difference between adjacent control periods; The current change and system impedance value corresponding to the proportion of the driving current are obtained based on the load energy difference; Based on the current change and the system impedance value, construct the drive response curve corresponding to the load energy difference; Based on the drive response curve, the response attenuation trend of the actuator is analyzed to obtain the drive response attenuation value under the current working condition. When the actual drive current reaches the drive response attenuation value, drive gain compensation information is output to the drive control unit of the actuator.

4. The control method based on an integrated drive and control controller according to claim 1, characterized in that, Based on the control response quality analysis results, the control cycle signal corresponding to the control command is segmented, and directional drive control analysis is performed on the local control periods with substandard response quality to obtain the corresponding directional control parameters. Specifically, this includes: The control commands are parsed to obtain the target motion trajectory, load type, and response time requirements, thus forming control requirement parameters; Based on the obtained controller self-test results, analyze the working status of each drive channel, the availability of sensor interfaces, and the quality of communication links to form a list of available functions; The control requirement parameters are matched with the list of available functions to determine the optimal combination of drive units and control algorithm configuration, and drive and control configuration parameters are generated. The control cycle signal is segmented according to the drive control configuration parameters to generate local control time periods that match different drive modes; Local control periods with response quality below a preset threshold are marked, and drive characteristics are modeled based on the drive control configuration parameters to obtain the drive characteristic parameters of the local control periods. Based on the driving characteristic parameters, the required driving output intensity and control channel scheduling strategy for the local control period are analyzed to generate directional control parameters.

5. The control method based on an integrated drive and control controller according to claim 1, characterized in that, Also includes: Based on the drive control configuration parameters, historical operating data under the corresponding drive mode is retrieved. The historical operating data includes position error sequence, current response curve and temperature change trend. Based on the historical operating data, time-frequency domain analysis and key feature extraction are performed to identify control deviation patterns under typical operating conditions and obtain condition-adaptive control parameters. The historical operating data is input into a preset deep learning model, which outputs the optimal combination of control gains for the current operating conditions. By combining the condition-adaptive control parameters with the optimal control gain, a first control strategy information is generated and used to optimize the directional control parameters.

6. The control method based on an integrated drive and control controller according to claim 5, characterized in that, The step of adjusting the drive output intensity of the local control period according to the directional control parameters, and generating and sending the control procedure parameters of the local control period to the actuator based on the adjusted drive output intensity, includes: The first control strategy information is decomposed into an energy demand sequence and a communication resource scheduling plan to obtain a control resource allocation scheme. Analyze the matching degree between the control resource allocation scheme and the real-time energy consumption status of the system, calculate the control energy efficiency ratio, and obtain the first matching degree index; Analyze the dynamic consistency between the power demand sequence and the system energy consumption status information, evaluate the power supply stability, and obtain the second matching degree index; Based on the first matching degree index and the second matching degree index, a multi-objective optimization algorithm is used to generate the final dynamic control command; Based on the dynamic control instructions, control procedure parameters including drive timing, output voltage, current limiting and communication priority are generated and sent to the drive control unit of the actuator.

7. The control method based on an integrated drive and control controller according to claim 1, characterized in that, The method further includes: Acquire multi-dimensional environmental interference data of the integrated drive and control controller under the current operating conditions; Based on the multi-dimensional environmental interference data, the environmental adaptability level of the integrated drive and control controller is evaluated to obtain the controller's environmental adaptability compliance level. The operating parameters of each functional module inside the integrated drive and control controller are obtained, and the module-level stability evaluation of each module of the controller is performed based on the operating parameters to obtain the module-level stability evaluation results. Based on the module-level stability assessment results, the module adaptability level corresponding to each functional module is obtained; Based on the module adaptability level of each functional module and the controller's environmental adaptability compliance level, a comprehensive health level of the controller is generated.

8. A control system based on an integrated drive and control controller, characterized in that, The system, applied to the control method based on an integrated drive and control controller as described in any one of claims 1 to 7, comprises: The state perception module is used to acquire the system load energy and motion state characteristics of the actuators during the operation of the integrated drive and control controller; The response quality analysis module is used to analyze the response quality of control commands based on the system load energy and the motion state characteristics, and obtain the control response quality analysis results. The control signal segmentation module is used to segment the control cycle signal corresponding to the control command according to the control response quality analysis results, and identify local control periods with unqualified response quality. The directional control analysis module is used to perform directional drive control analysis on the local control period where the response quality is unqualified, and obtain the corresponding directional control parameters. The drive control scheduling module is used to adjust the drive output intensity of the local control period according to the directional control parameters, generate control procedure parameters for the local control period, and send the control procedure parameters to the actuator so that the actuator adjusts the drive control process of the local control period according to the control procedure parameters.

9. The control system based on the integrated drive and control controller according to claim 8, characterized in that, It also includes an output port protection circuit, which includes: Optocoupler isolation unit, with its input terminal connected to the MCU control signal terminal; The power electronic switch unit has its input terminal connected to the output terminal of the optocoupler isolation unit and its output terminal connected to the power supply terminal of an external device. The power electronic switch unit is turned on based on a first-level signal received from the MCU control signal terminal and turned off based on a second-level signal received from the MCU control signal terminal. The first level signal is generated when the feedback current signal received by the MCU chip of the actuator is greater than a preset current threshold signal, or when the feedback voltage signal received by the MCU chip of the actuator is greater than a preset voltage threshold signal.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the control method based on the integrated drive and control controller as described in any one of claims 1 to 7.

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