Driving and control integrated control system for collaborative robot

Abstract

The invention relates to a collaborative robot-oriented driving and control integrated control system, which is characterized by comprising an integrated main controller, a central controller and a control system, the distributed driving units are connected with the main controller through a high-speed bus, and each driving unit comprises a power conversion circuit, a current loop closed-loop control module and a motor state detection interface; the security collaboration bus adopts a time sensitive network (TSN) protocol; and the dynamic collision detection module is integrated to the main controller and generates a safety response instruction based on fusion judgment of the joint current mutation rate and the position tracking error. According to the driving and control integrated control system for the collaborative robot, the comprehensive performance and the application value of the collaborative robot are remarkably improved through innovation and improvement in multiple aspects of system architecture, driving technology, safety performance, energy efficiency optimization, control algorithm and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of robots, and in particular to a drive-control integrated control system for collaborative robots. Background Art

[0002] Collaborative robots, capable of working directly with humans in the same workspace, have seen increasing adoption in recent years in industrial production, medical rehabilitation, and service industries. However, existing collaborative robot control systems still suffer from numerous shortcomings in performance, safety, and energy efficiency, making them unable to meet the growing demands of practical applications.

[0003] Traditional collaborative robots utilize a centralized control architecture, with the main controller and drive unit separated and connected via complex wiring. This architecture leads to the following system issues: First, significant signal transmission delays affect the robot's real-time control performance, making it difficult to achieve high-precision motion control and fast dynamic response. Second, extensive wiring increases system complexity, cost, and the risk of failure, reducing system reliability. Third, the centralized control architecture lacks scalability and flexibility, making it difficult to adapt to the diverse requirements of robot configurations and functions for different collaborative tasks.

[0004] In terms of drive technology, most existing collaborative robots utilize traditional silicon-based power devices in their drive circuits, resulting in low switching frequencies that limit motor control accuracy and the system's dynamic performance. Furthermore, the drive units often lack effective compensation mechanisms for nonlinear motor friction, leading to jitter and reduced control accuracy in the robot's joints during slow motion. Furthermore, existing drive systems offer relatively simple motor protection measures and are unable to dynamically adjust current output based on the motor's actual temperature rise. This poses a risk of motor overheating and damage, reducing the robot's service life and reliability.

[0005] Safety performance is a crucial technical indicator for collaborative robots. Currently, most collaborative robots use safety protection mechanisms based on simple torque monitoring or position deviation detection. These methods suffer from low detection accuracy, high false alarm rates, and slow response speeds. This makes it difficult to promptly and accurately trigger protective actions when the robot collides with humans or other objects, ensuring the safety of both personnel and equipment. For example, in complex collaborative scenarios, the robot may be subject to external interference or accidental contact by the operator. In these cases, traditional safety protection systems may be unable to quickly detect the collision and take effective countermeasures, potentially leading to safety incidents.

[0006] In terms of energy efficiency, existing collaborative robots often directly consume the regenerative energy generated during motor braking in braking resistors, resulting in significant energy waste and increasing the robot's operating costs and energy consumption. Furthermore, due to the lack of effective energy management strategies, the robot's energy consumption cannot be properly optimized during different operating phases, further reducing the energy efficiency of the entire system.

[0007] Furthermore, traditional collaborative robot control systems also have limitations in their control algorithms. Most motion planning algorithms can only generate simple straight or circular trajectories, making it difficult to meet the refined posture and trajectory requirements of the robot's end effector in complex collaborative tasks. The accuracy and real-time performance of the joint space trajectory generation module need to be improved, impacting the robot's overall motion accuracy and coordination. Existing safety monitoring protocols are relatively limited in functionality and cannot provide comprehensive, real-time safety risk assessment and prevention for robots.

[0008] In summary, existing collaborative robot drive and control systems have significant deficiencies in terms of system architecture, drive technology, safety performance, energy efficiency management, and control algorithms, limiting their widespread application in a wider range of fields. Therefore, there is an urgent need for an integrated drive and control system for collaborative robots to overcome the shortcomings of existing technologies, improve the performance, safety, and energy efficiency of collaborative robots, and meet the evolving needs of practical applications. Summary of the Invention

[0009] The present invention proposes a drive-control integrated control system for collaborative robots, which solves the above-mentioned problems existing in the use process of the prior art.

[0010] The technical solution of the present invention is achieved as follows:

[0011] A drive-control integrated control system for collaborative robots, characterized by comprising:

[0012] An integrated main controller with a built-in real-time multi-tasking operating system for running the robot's motion planning algorithm, joint space trajectory generation module, and safety monitoring protocol;

[0013] Distributed drive units are connected to the main controller via a high-speed bus. Each drive unit includes a power conversion circuit, a current loop closed-loop control module, and a motor status detection interface.

[0014] The safety collaborative bus uses the time-sensitive network (TSN) protocol to transmit joint torque commands, position feedback, and safety emergency stop signals in real time.

[0015] The dynamic collision detection module is integrated into the main controller and generates safety response instructions based on the fusion judgment of joint current mutation rate and position tracking error.

[0016] The above solution demonstrates that the system utilizes an architecture combining an integrated master controller with distributed drive units, achieving deep integration of hardware separation and software fusion. This architecture retains the advantages of centralized control in overall coordinated management while leveraging the strengths of distributed drives in localized rapid response and flexible configuration, effectively reducing system complexity and failure risks while improving system reliability and maintainability. Furthermore, connecting the master controller and drive units via a high-speed bus significantly reduces signal transmission delays, enhancing the robot's real-time control performance and providing a solid foundation for high-precision motion control and rapid dynamic response, meeting the stringent real-time and synchronization requirements of complex collaborative tasks.

[0017] The main controller features a built-in real-time multitasking operating system, capable of efficiently running multiple key task modules, including the robot's motion planning algorithm, joint space trajectory generation module, and safety monitoring protocol. This real-time multitasking operating system ensures that each task module is scheduled and executed promptly within the specified timeframe, avoiding interference between tasks and improving the system's overall operational efficiency and stability. The motion planning algorithm generates smooth, continuous Cartesian space trajectories with continuous velocity and acceleration. The joint space trajectory generation module accurately converts these into joint commands based on inverse dynamics calculations, laying a solid foundation for precise robot motion. The safety monitoring protocol monitors the system's operating status in real time, promptly identifying and addressing potential safety hazards to ensure the safety of both the robot and personnel.

[0018] Preferably, the distributed drive unit further comprises:

[0019] Silicon carbide SiC-based three-phase inverter, switching frequency ≥ 100kHz;

[0020] Embedded microprocessor for executing the field-oriented control (FOC) algorithm and compensating for nonlinear friction in real time;

[0021] The temperature / current coupling protection circuit dynamically limits the peak current output according to the motor temperature rise.

[0022] The above solution demonstrates that the distributed drive unit is equipped with a silicon carbide (SiC)-based three-phase inverter with a switching frequency of ≥100kHz. Compared to traditional silicon-based inverters, this significantly improves power conversion efficiency and frequency response, providing higher-quality drive current to the motor and reducing motor torque ripple, thereby improving the smoothness and precision of the robot's motion. Furthermore, the high switching frequency enables the inverter to achieve current regulation in a shorter time, enhancing the system's dynamic performance, enabling rapid response to control commands and precise motor control, providing a strong guarantee for the robot's stable operation in complex motion trajectories and high-speed movements.

[0023] The embedded microprocessor executes a field-oriented control (FOC) algorithm and compensates for nonlinear friction in real time. By precisely calculating the direction of the motor's stator magnetic field and implementing vector control, it maximizes the motor's efficiency and output torque, ensuring efficient operation across the entire motor speed range. Furthermore, the nonlinear friction compensation mechanism effectively reduces motor jitter and reduced control accuracy at low speeds, improving the smoothness and precision of the robot's joint motion. This is crucial for collaborative scenarios requiring high-precision operations, such as precision assembly and medical surgical assistance, significantly enhancing the robot's application value and operational reliability in these areas.

[0024] The temperature-current coupled protection circuit dynamically limits peak current output based on the motor's temperature rise, effectively preventing motor damage from overheating, extending the motor's service life, and improving system reliability. This dynamic protection mechanism adjusts current output based on the motor's temperature rise under different operating conditions, ensuring the motor always operates within a safe temperature range while fully realizing its performance potential, thereby improving motor utilization and overall system energy efficiency.

[0025] Preferably, the safety collaborative bus realizes its function by time-division multiplexing transmission of control instructions and safety signals, with a safety signal transmission delay of ≤100μs, and a bus fault tolerance mechanism based on a dual-ring redundant architecture to maintain system operation under single-node failure.

[0026] Preferably, the dynamic collision detection module performs contact force estimation based on a deep learning model, with inputs being current ripple spectrum characteristics and position deviation derivatives, and a hierarchical response strategy: the first-level response triggers joint compliance control, and the second-level response cuts off the driver power supply.

[0027] Preferably, it also includes an energy feedback unit, which includes a parallel architecture of a braking resistor and a supercapacitor for capturing regenerated electric energy of the motor and an adaptive energy scheduling algorithm based on the DC bus voltage ripple.

[0028] Preferably, the embedded microprocessor operates a parameter self-tuning PID controller to identify the motor electrical parameters online and update the control gain, as well as a resonance suppressor to eliminate mechanical resonance in a specific frequency range.

[0029] Preferably, the integrated main controller deploys an adaptive impedance control algorithm based on Lyapunov stability and a dynamic load inertia observer to update motion control parameters in real time.

[0030] The following steps are involved:

[0031] Receive mission instructions and generate Cartesian space trajectories;

[0032] Output joint torque instruction set through inverse dynamics calculation;

[0033] Synchronously monitor the safety bus status and switch to zero-force mode if collision protection is triggered;

[0034] Acquire motor phase currents and rotor position and perform sensorless speed estimation.

[0035] The control method based on this system includes receiving task instructions and generating Cartesian space trajectories, outputting joint torque instruction sets through inverse dynamics calculations, synchronously monitoring the safety bus status, and collecting motor phase currents and rotor positions to perform sensorless speed estimation. This control method enables efficient and precise control of collaborative robots, simplifies the control process, reduces unnecessary calculation steps, and improves control efficiency. Furthermore, through technologies such as inverse dynamics calculations and sensorless speed estimation, control accuracy and system real-time performance are further improved, ensuring that the robot can quickly and accurately complete various complex task instructions and meet the requirements for efficient and precise robot operation in industrial production. Furthermore, clear collision protection trigger conditions enable timely and accurate triggering of corresponding protection actions when a collision occurs, ensuring the safety of the robot and operator, avoiding safety incidents caused by misjudgments or delayed triggering, and improving the reliability and safety of the entire control system.

[0036] In summary, the beneficial effects of the present invention are:

[0037] The present invention discloses an integrated drive and control system for collaborative robots: DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0039] Example

[0040] This embodiment discloses an integrated drive and control control system for collaborative robots, which is characterized by including: an integrated main controller with a built-in real-time multi-tasking operating system for running the robot motion planning algorithm, the joint space trajectory generation module and the safety monitoring protocol; distributed drive units, which are connected to the main controller via a high-speed bus, and each drive unit includes a power conversion circuit, a current loop closed-loop control module and a motor status detection interface; a safety collaborative bus, which adopts the time-sensitive network TSN protocol for real-time transmission of joint torque instructions, position feedback and safety emergency stop signals; a dynamic collision detection module, which is integrated in the main controller and generates safety response instructions based on the fusion judgment of the joint current mutation rate and the position tracking error.

[0041] The integrated main controller utilizes a high-performance industrial-grade multi-core processor and a built-in real-time multitasking operating system. This operating system features multi-priority task scheduling, real-time interrupt handling, and multi-tasking parallel computing capabilities, enabling efficient simultaneous execution of the robot's motion planning algorithm, joint space trajectory generation module, and safety monitoring protocol. The robot's motion planning algorithm utilizes advanced interpolation algorithms to generate smooth, continuous Cartesian space trajectories with consistent velocity and acceleration. The joint space trajectory generation module, based on the principles of inverse kinematics, accurately converts Cartesian space trajectories into motion commands for each joint. The safety monitoring protocol provides real-time monitoring of system operating status, including key parameters such as joint torque, position deviation, and motor temperature.

[0042] Distributed drive units are installed at the joints of the collaborative robot, for example, six in total. Each drive unit is connected to the integrated main controller via a high-speed bus. This high-speed bus utilizes industrial Ethernet technology, offering high-bandwidth, low-latency data transmission, ensuring real-time information exchange between the main controller and the drive units. Each drive unit includes a power conversion circuit, a current closed-loop control module, and a motor status detection interface. The power conversion circuit utilizes a silicon carbide (SiC)-based three-phase inverter with a switching frequency of up to 120kHz. Compared to traditional silicon-based inverters, this significantly improves power conversion efficiency and frequency response, providing high-quality drive current for the motor. The current closed-loop control module uses high-precision current sensors to collect motor phase current in real time. Advanced control algorithms enable precise current regulation to ensure stable motor output torque. The motor status detection interface monitors motor parameters such as speed, position, and temperature, and provides real-time feedback to the main controller.

[0043] The safety collaboration bus, built on the Time-Sensitive Networking (TSN) protocol, uses time-division multiplexing to transmit various types of data, including joint torque commands, position feedback signals, and safety emergency stop signals. Through precise timestamps and time slot allocation mechanisms, the transmission delay of safety signals is strictly controlled to within 80μs, far below the requirement of ≤100μs. This enables highly real-time coordinated control of the robot's joint movements and rapid, safe response. Furthermore, the safety collaboration bus utilizes a dual-ring redundant architecture. If a node fails, the system automatically switches to the backup loop to continue normal operation, effectively improving the reliability and safety of the entire system.

[0044] The dynamic collision detection module, integrated into the main controller, operates by collecting joint current signals and motor position feedback signals in real time. By calculating two key characteristic quantities, the joint current mutation rate and position tracking error, and employing a fusion judgment algorithm based on a deep learning model, it quickly and accurately detects potential collisions. Once a collision risk is detected, a safety response command is immediately generated, driving a hierarchical response mechanism to ensure the safety of both the robot and the operator.

[0045] Further explanation of the modules involved:

[0046] (1) Integrated main controller

[0047] 1. Hardware Configuration: A high-performance, industrial-grade multi-core ARM processor with a main frequency of 1.5GHz and four cores is used, each dedicated to processing a different type of task. Two cores are dedicated to running the motion planning algorithm and the joint space trajectory generation module, one core is responsible for executing the safety monitoring protocol, and the other core serves as a backup core to handle sudden high-load tasks or expand system performance. The main controller is equipped with 4GB of high-speed DDR4 or DDR45 memory, ensuring that the system can quickly read and store large amounts of real-time data. It also has 32GB of built-in flash memory for storing important data such as system software, motion planning parameters, and security configuration files.

[0048] 2. Software System: The built-in real-time multitasking operating system utilizes the open-source RT-Linux system, deeply customized and optimized to meet the stringent real-time and multitasking requirements of collaborative robots. This operating system incorporates an improved task scheduling algorithm, employing the earliest deadline first (EDF) scheduling algorithm based on dynamic priorities, ensuring that critical tasks receive processor resources in a timely manner according to their urgency.

[0049] For the robot motion planning algorithm, a fifth-order polynomial interpolation method can be used to generate Cartesian space trajectories. This method can generate a smooth trajectory with continuous acceleration, effectively avoiding jitter and impact of the robot during movement. In terms of joint space trajectory generation, an inverse kinematics solution algorithm based on the Jacobian matrix is ​​used, combined with a numerical iteration method, to accurately calculate the angle, velocity, and acceleration instructions of each joint, ensuring that the robot's end effector can accurately track the predetermined Cartesian space trajectory. The safety monitoring protocol adopts a modular software architecture design to monitor more than 50 key parameters in real time, including but not limited to joint torque, position deviation, motor temperature, driver voltage, etc. Once an abnormal situation is found, it will be immediately handled according to the preset safety strategy, such as issuing a warning signal, reducing the robot's operating speed, or triggering an emergency stop.

[0050] (2) Distributed drive unit

[0051] 1. Silicon carbide (SiC)-based three-phase inverter: The SiC-based three-phase inverter used in this embodiment is supplied by a professional manufacturer. Its core power device uses advanced SiC MOSFET, which has excellent characteristics such as high temperature resistance, high switching frequency and low on-resistance. The switching frequency of the inverter is stable at 120kHz. Compared with traditional silicon-based inverters, the switching loss is reduced by more than 60%. At the same time, it can output a purer sinusoidal current, effectively reducing the torque pulsation of the motor. In terms of heat dissipation design, an integrated high-efficiency radiator and intelligent fan heat dissipation system are used to ensure that the inverter can operate stably at high power output and the temperature rise is controlled within 40°C.

[0052] 2. Embedded microprocessor: The drive unit has a built-in high-performance embedded microprocessor, which is responsible for executing the field-oriented control (FOC) algorithm and real-time compensation of nonlinear friction. The microprocessor adopts a 32-bit architecture, with a main frequency of 200MHz and a rich peripheral interface, including high-precision ADC, PWM output module and high-speed communication interface. The field-oriented control (FOC) algorithm accurately calculates the direction of the stator magnetic field of the motor by collecting the phase current and rotor position information of the motor in real time, thereby realizing efficient vector control of the motor, enabling the motor to maintain high efficiency and high torque output within the full speed range. In order to compensate for nonlinear friction in real time, a compensation algorithm based on the LuGre friction model is adopted. By modeling and compensating for different friction characteristics of the motor, such as static friction, Coulomb friction and viscous friction, the accuracy and stability of the robot's joint motion are effectively improved.

[0053] 3. Temperature / Current Coupling Protection Circuit: The temperature-current coupling protection circuit uses a high-precision temperature sensor to monitor the motor winding temperature in real time. Combined with the motor's current output, it dynamically adjusts the driver's peak current output limit using a pre-established temperature-current characteristic curve. When the motor temperature rises, the peak current output is appropriately reduced to prevent overheating and damage. When the motor temperature is lower, the peak current output is appropriately increased to fully realize the motor's performance potential. This protection circuit has a fast response capability, detecting abnormal changes in temperature or current within 10ms and promptly adjusting the driver output to ensure safe operation of the motor and drive unit.

[0054] (3) Secure Collaborative Bus

[0055] 1. Time-Division Multiplexing Transmission Mechanism: The safety collaboration bus utilizes time-division multiplexing (TDM) technology from the TSN protocol to precisely allocate bus transmission slots to different data types. Within each transmission cycle, 20% of the time slots are allocated for transmitting safety signals, including joint torque commands, position feedback, and safety emergency stop signals. The remaining 80% of the time slots are used to transmit control commands and other non-safety-related data. This time-division multiplexing ensures the highest priority and determinism for the transmission of safety signals, while fully utilizing bus bandwidth for the transmission of other data. Actual tests have shown that the transmission latency of safety signals is consistently within 80μs, fully meeting the system's real-time requirements.

[0056] 2. Dual-ring redundant architecture: The safety collaborative bus adopts a redundant architecture with dual physical links. The two links are wired using different physical paths to avoid paralysis of the entire bus system due to faults on the same physical path. Under normal operating conditions, the two links work simultaneously and back up each other. When a node failure occurs in a link, the bus fault tolerance mechanism is immediately activated, automatically switching all data transmission to another normal link, and ensuring that the system data transmission is not affected by the redundant bandwidth of the backup link. In this embodiment, by setting a dual-port switching chip and a redundant management module at each node, rapid fault detection and link switching are achieved, and the switching time is controlled within 50ms, ensuring that the robot can still operate stably and safely in the event of a single node failure.

[0057] (4) Dynamic collision detection module

[0058] 1. Deep Learning Model Training: The deep learning model in the dynamic collision detection module uses a convolutional neural network (CNN) structure, with input data consisting of joint current ripple spectrum characteristics and position deviation derivatives. During the model training phase, a large amount of joint current and position data from the collaborative robots during normal operation and collision conditions was collected. Through data preprocessing and feature extraction, a rich training sample set was generated. The CNN model was trained using the TensorFlow deep learning framework. After 5,000 training iterations, the model's collision detection accuracy reached over 98%, with a false alarm rate of less than 2%. In practical applications, this deep learning model can analyze the joint current ripple spectrum characteristics and position deviation derivatives in real time to accurately determine whether the robots have collided.

[0059] 2. Graded response strategy: The dynamic collision detection module adopts a graded response strategy based on the severity of the collision. When a minor collision is detected (level one response), joint compliance control is immediately triggered, causing the robot joints to produce a certain degree of compliance displacement to cushion the impact of the collision. At the same time, the robot's operating speed is reduced, and an audible and visual alarm signal is issued to alert the operator. If the collision further intensifies (level two response), the driver power is quickly cut off, causing the robot to enter zero-force mode and completely stop movement to ensure the safety of the operator and equipment. The entire graded response process has a fast response speed and can complete the entire process from collision detection to triggering the corresponding safety action within 20ms.

[0060] (5) Energy feedback unit

[0061] 1. Braking resistor and supercapacitor parallel architecture: The energy feedback unit adopts an energy storage architecture that connects a braking resistor and a supercapacitor in parallel. The braking resistor uses a high-power alloy resistor with a rated power of 10kW, which can quickly consume most of the regenerative energy generated by the motor during braking and prevent the DC bus voltage from being too high. The supercapacitor uses a high-capacity, long-life double-layer capacitor with a rated capacity of 500F. It can store and release electrical energy in a short period of time and buffer and regulate the regenerative energy. During the motor's regenerative power generation process, the controller intelligently adjusts the working status of the braking resistor and supercapacitor according to the real-time situation of the DC bus voltage to achieve reasonable energy distribution and recycling. After actual testing, this energy feedback unit can increase the recovery rate of the motor's regenerative energy to more than 35%, effectively reducing the robot's energy consumption.

[0062] 2. Adaptive Energy Scheduling Algorithm: The energy feedback unit uses an adaptive energy scheduling algorithm based on the DC bus voltage ripple. By performing real-time monitoring of the DC bus voltage and performing Fast Fourier Transform (FFT) analysis, it extracts the frequency and amplitude characteristics of the voltage ripple. Based on these characteristics, the algorithm can determine the magnitude and changing trend of the motor's regenerative energy in real time and dynamically adjust the input power of the braking resistor and the charge and discharge current of the supercapacitor. For example, when the DC bus voltage ripple frequency is high and the amplitude is large, it indicates that the motor's regenerative energy is large and changes rapidly. In this case, the algorithm will increase the input power of the braking resistor and increase the charging current of the supercapacitor to quickly absorb the regenerative energy. Conversely, the algorithm will appropriately reduce the braking resistor power and supercapacitor charging current to avoid excessive energy consumption. This adaptive energy scheduling algorithm ensures that the energy feedback unit can operate efficiently and stably under different operating conditions, improving the energy efficiency of the entire system.

[0063] 3. System Operation Process

[0064] (1) System initialization

[0065] 1. After the collaborative robot is powered on, the integrated main controller first performs a hardware self-test, including memory testing, processor core status checks, and communication interface tests, to ensure proper operation of the main controller hardware. Simultaneously, each distributed drive unit also performs its own hardware initialization, checking and configuring parameters for the power conversion circuit, current loop closed-loop control module, and motor status detection interface.

[0066] 2. The main controller loads and starts the real-time multitasking operating system, completing the initialization and scheduling configuration of each task. It then reads pre-stored system configuration data, including the robot's kinematic parameters, dynamic parameters, and safety profile, from memory and performs parameter verification and updates.

[0067] 3. The master controller sends initialization commands to each distributed drive unit via the high-speed bus. Upon receiving the commands, the drive unit excites the motor, detects its position and status, and sends a motor-ready signal back to the master controller. Simultaneously, the secure collaborative bus discovers the network topology and configures nodes, establishing communication links between nodes and completing the initialization of the bus system.

[0068] 4. The dynamic collision detection module loads the deep learning model parameters and completes model initialization and calibration. The energy regenerative unit checks the status of the braking resistor and supercapacitor to ensure they are functioning properly and enters standby mode, ready to receive control commands from the main controller.

[0069] (2) Normal operation stage

[0070] 1. After the main controller receives the task command from the host computer, the motion planning algorithm module generates a Cartesian space trajectory based on the task requirements and the current state of the robot. The joint space trajectory generation module converts the Cartesian space trajectory into angle, velocity, and acceleration instructions for each joint and sends them to the corresponding distributed drive units via a high-speed bus.

[0071] 2. After receiving the joint commands, the distributed drive unit's embedded microprocessor runs the field-oriented control (FOC) algorithm to calculate the three-phase current commands required to drive the motor. The power conversion circuit then outputs the corresponding drive current, ensuring the motor operates according to the commands. Simultaneously, the current closed-loop control module collects motor phase currents in real time and precisely regulates them to ensure the stability of the motor's output torque. The motor status detection interface monitors motor parameters such as speed, position, and temperature in real time and feeds these parameters back to the main controller.

[0072] 3. The main controller receives real-time feedback from each joint's motor status via the safety collaborative bus, including parameters such as current, position, speed, and temperature. The safety monitoring protocol analyzes and determines these parameters in real time. If an anomaly is detected, such as an excessively high joint current mutation rate or position tracking error outside the allowable range, a warning signal is immediately issued and appropriate safety measures are taken based on the severity of the anomaly, such as reducing the robot's operating speed or triggering the collision protection mechanism.

[0073] 4. The dynamic collision detection module collects joint current ripple spectrum characteristics and position deviation derivatives in real time, inputting them into a deep learning model for collision detection analysis. Once the model determines a collision has occurred, it immediately generates a safety response command, driving a hierarchical response strategy that triggers joint compliance control or cuts off driver power to ensure the safety of the robot and operator.

[0074] 5. The energy feedback unit monitors the motor's operating status and DC bus voltage in real time. When the motor is regenerating, the adaptive energy scheduling algorithm rationally allocates the operating states of the braking resistor and supercapacitor, recycling the motor's regenerated energy and reducing the robot's energy consumption.

[0075] (3) Safety emergency stop and recovery

[0076] 1. When the system detects a serious safety malfunction, such as an operator pressing the emergency stop button or a violent collision causing a power outage in the drive, the master controller immediately issues a global emergency stop command. Upon receiving this command, each distributed drive unit quickly cuts off the motor drive current, instantly stopping the robot joints. The safety coordination bus then stops all data transmission and enters a safety lockout state, ensuring the system remains safe.

[0077] 2. In the safety emergency stop state, the main controller and each distributed drive unit perform fault diagnosis and status checks to determine the cause of the fault and whether the system is ready for resumption of operation. If the fault is temporary due to external factors, such as a minor collision caused by an operator's accidental touch, after troubleshooting, the operator can reset the system to its initialized state and resume operation. If the fault is an internal hardware or software failure, such as a drive failure or motor failure, professional maintenance personnel will be required to repair and replace the faulty component to ensure that the system has returned to normal before it can be put back into operation.

[0078] 3. After the system resumes operation, the main controller reloads the task instructions, gradually restores the motion control of each joint according to the safety initialization process, and closely monitors the system operation status to ensure that the robot can continue to perform the task safely and stably.

[0079] IV. System Performance Test and Results

[0080] (1) Motion accuracy test

[0081] 1. Test Method: A laser tracker and high-precision angle sensors are used to measure the motion accuracy of the collaborative robot's end effector at different postures and positions. Multiple test points are set in Cartesian space, and the robot's end effector moves to these test points in sequence. The laser tracker measures the end effector's actual position and posture, and the angle sensor measures the actual angle values ​​of each joint. Simultaneously, the theoretical command values ​​of the joint space trajectory generation module in the main controller are recorded. By comparing the actual measured values ​​with the theoretical command values, position and posture deviations are calculated.

[0082] 2. Test Results: After repeated measurement and statistical analysis of 100 test points, the collaborative robot's end-effector position deviation averaged 0.8 mm, and its posture deviation averaged 0.35°. These results demonstrate that the integrated drive and control system of this embodiment achieves high motion accuracy, meeting the precision requirements of industrial collaborative robots in applications such as precision assembly and welding.

[0083] (2) Safety collision detection performance test

[0084] 1. Test Method: Using a simulated collision device, collision tests of varying intensities and angles were conducted on the collaborative robot's joints. During the collision process, the detection signals from the dynamic collision detection module, the triggering time of the safety response command, and the motion response of the robot's joints were recorded in real time. Furthermore, by varying parameters such as the material of the colliding object, speed, and collision location, the adaptability and reliability of the collision detection module were comprehensively tested.

[0085] 2. Test Results: Across 50 collision tests under various conditions, the dynamic collision detection module achieved a detection accuracy of 99%, accurately detecting a collision within 15ms and triggering the appropriate safety response. For a Level 1 minor collision, the robot's joints were able to generate compliant displacement within 25ms, buffering the impact while reducing speed and issuing an alarm. For a Level 2 severe collision, the robot disconnected driver power within 20ms, entering zero-force mode and ensuring operator safety. Throughout the testing process, no false or missed alarms were observed, demonstrating the stable and reliable performance of the collision detection module, effectively ensuring the safety of both the robot and the operator.

[0086] (3) Energy recovery efficiency test

[0087] 1. Test Method: While the collaborative robot performs cyclical motion tasks (such as handling, loading and unloading), energy monitoring instruments measure the motor input energy, braking resistor energy consumption, and supercapacitor energy storage. The time required to complete a motion cycle and the energy consumption at each stage are recorded, and the energy recovery efficiency is calculated. To ensure the accuracy of the test results, multiple repeated tests are performed, and the average value is used as the final test result.

[0088] 2. Test Results: After energy monitoring and calculation for 20 periodic motion tasks, the average motor input energy was 1200J / cycle, the average braking resistor energy consumption was 320J / cycle, and the average supercapacitor energy storage was 180J / cycle. The calculated energy recovery efficiency was 41.7%. This result demonstrates that the energy regeneration unit in this embodiment can effectively recover motor regenerated energy, improving the robot's energy efficiency and reducing operating costs.

[0089] This embodiment describes in detail a specific implementation scheme for an integrated drive and control control system for collaborative robots, covering aspects such as the overall system architecture, key module implementation, system operation process, and performance testing. Through actual hardware configuration, software design, and test verification, this patented technology fully demonstrates its significant advantages in improving the motion accuracy, safety performance, and energy efficiency of collaborative robots, and has high practical application value and market prospects. In actual applications, the parameters and configurations in this embodiment can be appropriately adjusted and optimized according to different collaborative robot models and application scenarios to further enhance the performance and adaptability of the system.

[0090] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A drive-control integrated control system for collaborative robots, characterized in that: include: An integrated main controller with a built-in real-time multi-tasking operating system for running the robot's motion planning algorithm, joint space trajectory generation module, and safety monitoring protocol; Distributed drive units are connected to the main controller via a high-speed bus. Each drive unit includes a power conversion circuit, a current loop closed-loop control module, and a motor status detection interface. The safety collaborative bus uses the time-sensitive network (TSN) protocol to transmit joint torque commands, position feedback, and safety emergency stop signals in real time. The dynamic collision detection module is integrated into the main controller and generates safety response instructions based on the fusion judgment of joint current mutation rate and position tracking error.

2. The integrated control system for collaborative robots according to claim 1, characterized in that: The distributed drive unit further comprises: Silicon carbide SiC-based three-phase inverter, switching frequency ≥ 100kHz; Embedded microprocessor for executing the field-oriented control (FOC) algorithm and compensating for nonlinear friction in real time; The temperature / current coupling protection circuit dynamically limits the peak current output according to the motor temperature rise.

3. The integrated control system for collaborative robots according to claim 1, characterized in that: The safety collaborative bus achieves its function by time-division multiplexing transmission of control instructions and safety signals, with a safety signal transmission delay of ≤100μs, and a bus fault tolerance mechanism based on a dual-ring redundant architecture, maintaining system operation under single-node failure.

4. The integrated control system for collaborative robots according to claim 1, characterized in that: The dynamic collision detection module performs contact force estimation based on a deep learning model, with inputs of current ripple spectrum characteristics and position deviation derivatives, as well as a hierarchical response strategy: the first-level response triggers joint compliance control, and the second-level response cuts off the driver power supply.

5. The integrated control system for collaborative robots according to claim 1, characterized in that: It also includes an energy feedback unit, which includes a parallel architecture of a braking resistor and a supercapacitor for capturing regenerated electric energy from the motor and an adaptive energy scheduling algorithm based on the DC bus voltage ripple.

6. The integrated drive and control system for collaborative robots according to claim 2, characterized in that: The embedded microprocessor operates a parameter self-tuning PID controller, identifies motor electrical parameters online and updates control gains, and a resonance suppressor to eliminate mechanical resonance in a specific frequency band.

7. The integrated control system for collaborative robots according to claim 1, characterized in that: The integrated main controller deploys an adaptive impedance control algorithm based on Lyapunov stability and a dynamic load inertia observer to update motion control parameters in real time.

8. A control method for the collaborative robot-oriented drive-control integrated control system according to any one of claims 1 to 7, characterized in that: The following steps are involved: Receive mission instructions and generate Cartesian space trajectories; Output joint torque instruction set through inverse dynamics calculation; Synchronously monitor the safety bus status and switch to zero-force mode if collision protection is triggered; Acquire motor phase currents and rotor position and perform sensorless speed estimation.

9. A collaborative robot, characterized in that: Integrate the drive and control integrated control system for collaborative robots as described in any one of claims 1 to 7.