Modular reconfigurable teaching robot control method and system

Abstract

The application relates to the technical field of industrial robot control and discloses a modular reconfigurable teaching robot control method and system, which comprises the following steps: collecting pulse sequences of joint position sensors and phase current sampling values, performing noise reduction through a double-stage sliding window filter, converting the double-stage numerical differential into angular acceleration, and reconstructing torque time sequence data; injecting a time-interleaved and frequency 3.0-4.5 Hz sinusoidal disturbance voltage vector in a control cycle resting gap; inputting the angular acceleration and the torque time sequence data into a sliding mode current observer to solve joint axial mutual inertia coefficients; and adjusting a control Jacobian matrix and a feedforward controller parameter according to the joint axial mutual inertia coefficients to generate target control current. The application realizes online self-recovery of a topological architecture and precisely offsets multi-axis inertia coupling interference.

Description

Technical Field

[0001] This invention relates to a modular and reconfigurable teaching assistant robot control method and system, belonging to the field of industrial robot control technology. Background Technology

[0002] Current modular reconfigurable robots achieve dynamic transformations of their physical configuration by inserting and connecting degree-of-freedom modules to adapt to spatial transformations of varying kinematic topologies. However, in situations where these robots undergo frequent configuration reconfiguration, the torque transmission path of multi-axis drive chains changes due to fit tolerances and surface wear in the connecting mechanisms, as well as time-varying damping variations in the static friction torque of bearings during operation. Conventional control methods typically rely on reading hardware identification codes from connector pin levels to retrieve standard configuration files and load fixed joint parameters. This static lookup mechanism is based on the assumption of a rigid correspondence between the model and the configuration. When the physical dimensions of the drive chain accumulate tolerances due to manual assembly, the retrieved control parameters become mismatched, leading to feedforward model mismatch. During the transient process of sudden acceleration of a high-inertia link, transient overload torque is generated due to mass distribution prediction deviations, causing phase current saturation overflow in the servo loop, resulting in control loop divergence and joint self-resonance. Furthermore, it can even lead to the risk of gravity slippage during unknown topological transitions when modules are disconnected and spliced.

[0003] Linear improvement methods that increase the capacity of the preset configuration library are not only unable to cope with dynamic parameter drifts that exceed the table range, but also significantly increase system hardware costs and control chip computation time if external optical vision cameras or multi-axis torque sensors are introduced, resulting in high-frequency time delays. Existing control schemes that seek breakthroughs in control algorithms to break free from hardware-level recognition mechanisms also have shortcomings when facing physical reconstruction conditions. For example, Chinese invention patent application CN105196294A discloses a distributed control system and control method for a reconfigurable robotic arm using position measurement. The scheme establishes a nonlinear velocity and disturbance observation model to construct a distributed controller. The passive observation mechanism relies on the ideal preset that the system disturbance is continuous and bounded. In the discrete variable topology transition state where modules are frequently plugged in, unplugged, and reassembled, the rigid body inertia undergoes a sudden step jump. The discontinuous physical impact breaks the inherent continuous boundary, causing the estimated residual to diverge. The scheme lacks active excitation methods and cannot dynamically peel off time-varying fluctuations such as bearing static friction during rest intervals, making it difficult to cope with multi-axis inertial nonlinear coupling under variable heavy load configurations.

[0004] Therefore, the technical problem to be solved by this invention is how to get rid of the dependence on external explicit identification media and static tables, use the existing processor in the system to calculate the equivalent inertial linkage coefficient that reflects the torque transmission relationship between degrees of freedom online, provide continuous impedance control torque compensation before identification convergence, and limit transient overload torque to rewrite the current pulse width modulation reference, so as to maintain the pose stability and trajectory tracking rigidity of the multi-joint system under physical topology drift conditions. Summary of the Invention

[0005] To address the problems in the background art, the technical solution of the present invention is as follows: A modular and reconfigurable teaching assistant robot control method, comprising the following steps:

[0006] Step S1: Collect the original pulse sequence of each joint position sensor and the phase current sampling value of the inverter output terminal. Filter out the noise in the original pulse sequence and phase current sampling value through a two-stage sliding window filter. Convert the filtered pulse sequence into the real-time angular acceleration of the joint through second-order numerical differential calculation. Reconstruct the filtered phase current sampling value into the timing data of the joint axial driving torque through coordinate transformation.

[0007] Step S2: During the initial rest interval of the control cycle, inject time-interleaved sinusoidal disturbance voltage vectors with a frequency of 3.0Hz to 4.5Hz from the position loop output terminal of each joint servo driver.

[0008] Step S3: Input the real-time angular acceleration and axial driving torque timing data of the joint into the sliding mode current observer, construct the sliding mode switching hyperplane by combining the velocity estimation residual, capture the transaxial inertial coupling component and strip the joint static friction term within the excitation period of the sinusoidal disturbance voltage vector, and calculate the joint axial mutual inertia coefficient.

[0009] Step S4: Adjust the dynamic weighting factor of the control Jacobian matrix and the rigid body dynamics feedforward compensation parameters of the feedforward controller according to the joint axial mutual inertia coefficient, and superimpose the feedforward compensation current onto the output of the proportional-integral-derivative controller to generate the target control current.

[0010] Step S5: The target control current is converted into a three-phase drive voltage command by the space voltage vector modulation unit and output to each discrete joint drive module to drive the joint linkage mechanism to move along the corrected dynamic trajectory.

[0011] Preferably, in step S2, when injecting the sinusoidal perturbation voltage vector, the frequency of the sinusoidal perturbation voltage vector is stabilized in the range of 3.4Hz to 3.6Hz. Each discrete joint drive module generates axial oscillation in sequence according to the time-staggered arrangement. The sinusoidal perturbation voltage vector is fully decoupled in the spatial topology by utilizing the spatiotemporal isolation implemented between adjacent joints, so as to obtain the transient energy consumption parameters of each degree of freedom and block the mutual modulation interference of cross-axis phases.

[0012] Preferably, step S4 includes the following sub-steps: Step S41, the calculated joint axial mutual inertia coefficients are arranged into a dynamic mutual inertia characteristic matrix. When the linkage coefficient in the dynamic mutual inertia characteristic matrix increases monotonically, it is determined that the current physical topology is transformed into a cantilever heavy-load configuration; Step S42, the dynamic weighting factor of the control Jacobian matrix is ​​monotonically adjusted according to the numerical variation of the dynamic mutual inertia characteristic matrix. The dynamic weighting factor of the control Jacobian matrix is ​​rigidly aligned with the long link distribution parameters of the cantilever heavy-load configuration. At the same time, the rigid body dynamics feedforward compensation parameter in the feedforward controller is increased to generate the target control current.

[0013] Preferably, step S1 includes the following sub-steps: Step S11, a non-volatile dynamic data buffer is opened in the system's memory to store the feature vectors of the joint axial mutual inertia coefficients of the historical standard configuration; Step S12, when a disconnection and reassembly action is detected in the discrete joint drive module, the level transition feature at the moment of physical interface disconnection is extracted in real time, and the current system state is determined to be a transitional unknown state; Step S13, the nearest neighbor stable parameters are retrieved from the non-volatile dynamic data buffer to implement control flow compensation, providing continuous impedance matching control torque within the initial 30ms before the algorithm converges, and maintaining the stability of the joint pose in the variable topology transition state.

[0014] Preferably, step S4 further includes the following amplitude limiting sub-step: step S43, using the joint axial mutual inertia coefficient to calculate the transient overload torque of each link module, and comparing the transient overload torque with the fixed mechanical safety boundary limit value in real time; step S44, when the transient overload torque exceeds the mechanical safety boundary limit value, the current pulse width modulation reference of the underlying servo loop is directly rewritten through the saturation limiting operator to implement amplitude limiting, suppressing the transient saturation overflow of the current loop caused by the sudden acceleration of the cantilever heavy load configuration.

[0015] Preferably, step S1 further includes the following sub-steps: step S14, obtaining the position differential sequence fed back by the joint absolute encoder as an auxiliary information source, and using the position differential sequence to calculate the joint dead zone damping value; step S15, removing the joint dead zone damping value from the joint axial driving torque time series data, and separating the nonlinear torque term caused by the joint dead zone and transmission damping to eliminate the transmission damping fluctuation caused by the temperature rise of the reducer.

[0016] Preferably, step S3 further includes the following sub-steps: step S31, using the phase-locked loop filter unit to calculate in real time the cross-correlation coefficient between the phase current sampling value at the inverter output and the injected sinusoidal disturbance voltage vector; step S32, comparing the cross-correlation coefficient with a fixed environmental discrimination limit value of 0.75 in real time; step S33, when the cross-correlation coefficient is lower than the environmental discrimination limit value of 0.75, determining that the current disturbance originates from the interactive collision interference of the external environment, and locking the rigid body dynamics feedforward compensation parameters in the feedforward controller.

[0017] Preferably, step S5 includes the following sub-steps: step S51, adjusting the conduction timing of the three-phase inverter bridge arms through the space voltage vector modulation unit to map the target control current in situ to the duty cycle instruction of the underlying register; step S52, controlling the three-phase stator winding voltage of the joint motor to generate a corresponding electromotive force according to the duty cycle instruction of the underlying register to offset the flexible transmission error caused by the elasticity of the transmission chain.

[0018] Preferably, step S1 further includes the following sub-steps: step S16, setting the sliding window time width of the dual-stage sliding window filter to a fixed length of 12.5ms; step S17, performing first-order forward differential weighted smoothing on the phase current sampled values ​​within the sliding window to filter out harmonic glitches, which are high-frequency interferences, introduced by the inverter switching, and inputting the filtered phase current sampled values ​​and the reconstructed joint axial driving torque timing data to the sliding mode current observer.

[0019] A modular and reconfigurable teaching assistant robot control system, comprising a method for implementing modular and reconfigurable teaching assistant robot control, including:

[0020] The data acquisition and reconstruction module is used to periodically acquire the original pulse sequences of the position sensors of each joint of the robot and the phase current sampling values ​​of the inverter output. It filters out noise in the original pulse sequences and phase current sampling values ​​through a two-stage sliding window filter, and uses second-order numerical differential calculation to convert the filtered pulse sequences into real-time angular acceleration of the joints. At the same time, it reconstructs the filtered phase current sampling values ​​into joint axial driving torque timing data through coordinate transformation.

[0021] The self-decoupling excitation injection module is used to address the sudden change in rigid body inertia and multi-axis inertial nonlinear coupling interference caused by configuration reconstruction. During the initial rest interval of the robot motion control cycle, it injects a multi-frequency sinusoidal disturbance voltage vector with staggered time arrangement and a frequency range of 3.0Hz to 4.5Hz from the position loop output terminal of each joint servo driver.

[0022] The joint state observation module includes a sliding mode current observer, which is used to input the real-time angular acceleration and axial driving torque timing data of the joint into the sliding mode current observer, and construct the sliding mode switching hyperplane by combining the velocity estimation residual. It captures the transaxial inertial coupling component and strips the static friction term of the joint within the excitation cycle of the multi-frequency sinusoidal disturbance voltage vector, and calculates the dynamic mutual inertia coefficient between the joints that characterizes the torque transmission relationship between the degrees of freedom.

[0023] The matrix parameter adaptive recombination module is used to adjust the dynamic weighting factor of the control Jacobian matrix and the rigid body dynamics feedforward compensation parameters in the feedforward controller according to the dynamic mutual inertia coefficient between joints. The generated feedforward compensation current is directly superimposed on the output of the proportional-integral-derivative controller to generate the target control current for correcting configuration abrupt mismatch. The control output module is used to convert the target control current into a three-phase drive voltage command through the space voltage vector modulation unit and output it to the discrete joint drive module to drive the joint linkage mechanism to move along the corrected dynamic trajectory.

[0024] Compared with the prior art, the beneficial effects of the present invention are:

[0025] 1. In the control of modular and reconfigurable teaching assistant robots, the existing processor in the system is used to collect the pulse sequence of the position encoder and the characteristic value of the phase current sampling. During the operation interval, the staggered sinusoidal disturbance voltage vector is input to enable adjacent joints to independently capture the mechanical energy dissipation characteristics. With the help of a first-order sliding mode observer to isolate the velocity residual and remove the bearing static friction term, the equivalent inertial linkage coefficient reflecting the torque transmission relationship between degrees of freedom is calculated online. A dynamic self-healing perception channel is established to avoid recognition failure caused by the deformation of the connector contact points.

[0026] 2. This method uses the equivalent inertial linkage coefficients obtained through online calculation to form a dynamic mutual inertial characteristic matrix, directly corrects the kinematic transformation operator, and monotonically adjusts the spatial transformation weights in the control Jacobian matrix based on the numerical variation of the above characteristic matrix. At the same time, it reconstructs the mass distribution prediction operator in the dynamic feedforward controller and directly superimposes the generated feedforward compensation current to the output of the main controller to achieve adaptive reorganization of the control matrix, avoiding servo loop divergence and motion trajectory distortion caused by unknown physical configuration switching.

[0027] 3. This method opens a non-volatile dynamic data buffer area inside the control unit to store the equivalent inertial linkage coefficient feature vector of the historical standard configuration. When the disconnection state transition signal is captured and it is determined that the transition state is in place, the nearest neighbor stable parameter is directly retrieved from the above buffer area to implement control flow compensation. This forms a time-series complement between steady-state calibration and dynamic buffering with the online adaptive reassembly step. It provides continuous impedance matching control torque in the initial 30ms when the identification algorithm has not converged, maintaining the joint pose stability in the variable topology transition state. Attached Figure Description

[0028] Figure 1 This is a flowchart of the multi-axis inertial coupling interference countermeasure control method for the teaching assistant robot of the present invention;

[0029] Figure 2 This is a structural diagram of the central control and discrete execution layer of the teaching assistant robot of the present invention.

[0030] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0031] The technical solutions of the embodiments of this application will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this application are within the scope of protection of this application.

[0032] A modular and reconfigurable teaching assistant robot control method includes the following steps:

[0033] Step S1: Collect the original pulse sequence of each joint position sensor and the phase current sampling value of the inverter output terminal. Filter out the noise in the original pulse sequence and phase current sampling value through a two-stage sliding window filter. Convert the filtered pulse sequence into the real-time angular acceleration of the joint through second-order numerical differential calculation. Reconstruct the filtered phase current sampling value into the timing data of the joint axial driving torque through coordinate transformation.

[0034] Step S2: During the initial rest interval of the control cycle, inject time-interleaved sinusoidal disturbance voltage vectors with a frequency of 3.0Hz to 4.5Hz from the position loop output terminal of each joint servo driver.

[0035] Step S3: Input the real-time angular acceleration and axial driving torque timing data of the joint into the sliding mode current observer, construct the sliding mode switching hyperplane by combining the velocity estimation residual, capture the transaxial inertial coupling component and strip the joint static friction term within the excitation period of the sinusoidal disturbance voltage vector, and calculate the joint axial mutual inertia coefficient.

[0036] Step S4: Adjust the dynamic weighting factor of the control Jacobian matrix and the rigid body dynamics feedforward compensation parameters of the feedforward controller according to the joint axial mutual inertia coefficient, and superimpose the feedforward compensation current onto the output of the proportional-integral-derivative controller to generate the target control current.

[0037] Step S5: The target control current is converted into a three-phase drive voltage command by the space voltage vector modulation unit and output to each discrete joint drive module to drive the joint linkage mechanism to move along the corrected dynamic trajectory.

[0038] Preferably, in step S2, when injecting the sinusoidal perturbation voltage vector, the frequency of the sinusoidal perturbation voltage vector is stabilized in the range of 3.4Hz to 3.6Hz. Each discrete joint drive module generates axial oscillation in sequence according to the time-staggered arrangement. The sinusoidal perturbation voltage vector is fully decoupled in the spatial topology by utilizing the spatiotemporal isolation implemented between adjacent joints, so as to obtain the transient energy consumption parameters of each degree of freedom and block the mutual modulation interference of cross-axis phases.

[0039] Preferably, step S4 includes the following sub-steps: Step S41, the calculated joint axial mutual inertia coefficients are arranged into a dynamic mutual inertia characteristic matrix. When the linkage coefficient in the dynamic mutual inertia characteristic matrix increases monotonically, it is determined that the current physical topology is transformed into a cantilever heavy-load configuration; Step S42, the dynamic weighting factor of the control Jacobian matrix is ​​monotonically adjusted according to the numerical variation of the dynamic mutual inertia characteristic matrix. The dynamic weighting factor of the control Jacobian matrix is ​​rigidly aligned with the long link distribution parameters of the cantilever heavy-load configuration. At the same time, the rigid body dynamics feedforward compensation parameter in the feedforward controller is increased to generate the target control current.

[0040] Preferably, step S1 includes the following sub-steps: Step S11, a non-volatile dynamic data buffer is opened in the system's memory to store the feature vectors of the joint axial mutual inertia coefficients of the historical standard configuration; Step S12, when a disconnection and reassembly action is detected in the discrete joint drive module, the level transition feature at the moment of physical interface disconnection is extracted in real time, and the current system state is determined to be a transitional unknown state; Step S13, the nearest neighbor stable parameters are retrieved from the non-volatile dynamic data buffer to implement control flow compensation, providing continuous impedance matching control torque within the initial 30ms before the algorithm converges, and maintaining the stability of the joint pose in the variable topology transition state.

[0041] Preferably, step S4 further includes the following amplitude limiting sub-step: step S43, using the joint axial mutual inertia coefficient to calculate the transient overload torque of each link module, and comparing the transient overload torque with the fixed mechanical safety boundary limit value in real time; step S44, when the transient overload torque exceeds the mechanical safety boundary limit value, the current pulse width modulation reference of the underlying servo loop is directly rewritten through the saturation limiting operator to implement amplitude limiting, suppressing the transient saturation overflow of the current loop caused by the sudden acceleration of the cantilever heavy load configuration.

[0042] Preferably, step S1 further includes the following sub-steps: step S14, obtaining the position differential sequence fed back by the joint absolute encoder as an auxiliary information source, and using the position differential sequence to calculate the joint dead zone damping value; step S15, removing the joint dead zone damping value from the joint axial driving torque time series data, and separating the nonlinear torque term caused by the joint dead zone and transmission damping to eliminate the transmission damping fluctuation caused by the temperature rise of the reducer.

[0043] Preferably, step S3 further includes the following sub-steps: step S31, using the phase-locked loop filter unit to calculate in real time the cross-correlation coefficient between the phase current sampling value at the inverter output and the injected sinusoidal disturbance voltage vector; step S32, comparing the cross-correlation coefficient with a fixed environmental discrimination limit value of 0.75 in real time; step S33, when the cross-correlation coefficient is lower than the environmental discrimination limit value of 0.75, determining that the current disturbance originates from the interactive collision interference of the external environment, and locking the rigid body dynamics feedforward compensation parameters in the feedforward controller.

[0044] Preferably, step S5 includes the following sub-steps: step S51, adjusting the conduction timing of the three-phase inverter bridge arms through the space voltage vector modulation unit to map the target control current in situ to the duty cycle instruction of the underlying register; step S52, controlling the three-phase stator winding voltage of the joint motor to generate a corresponding electromotive force according to the duty cycle instruction of the underlying register to offset the flexible transmission error caused by the elasticity of the transmission chain.

[0045] Preferably, step S1 further includes the following sub-steps: step S16, setting the sliding window time width of the dual-stage sliding window filter to a fixed length of 12.5ms; step S17, performing first-order forward differential weighted smoothing on the phase current sampled values ​​within the sliding window to filter out harmonic glitches, which are high-frequency interferences, introduced by the inverter switching, and inputting the filtered phase current sampled values ​​and the reconstructed joint axial driving torque timing data to the sliding mode current observer.

[0046] A modular and reconfigurable teaching assistant robot control system, comprising a method for implementing modular and reconfigurable teaching assistant robot control, including:

[0047] The data acquisition and reconstruction module is used to periodically acquire the original pulse sequences of the position sensors of each joint of the robot and the phase current sampling values ​​of the inverter output. It filters out noise in the original pulse sequences and phase current sampling values ​​through a two-stage sliding window filter, and uses second-order numerical differential calculation to convert the filtered pulse sequences into real-time angular acceleration of the joints. At the same time, it reconstructs the filtered phase current sampling values ​​into joint axial driving torque timing data through coordinate transformation.

[0048] The self-decoupling excitation injection module is used to address the sudden change in rigid body inertia and multi-axis inertial nonlinear coupling interference caused by configuration reconstruction. During the initial rest interval of the robot motion control cycle, it injects a multi-frequency sinusoidal disturbance voltage vector with staggered time arrangement and a frequency range of 3.0Hz to 4.5Hz from the position loop output terminal of each joint servo driver.

[0049] The joint state observation module includes a sliding mode current observer, which is used to input the real-time angular acceleration and axial driving torque timing data of the joint into the sliding mode current observer, and construct the sliding mode switching hyperplane by combining the velocity estimation residual. It captures the transaxial inertial coupling component and strips the static friction term of the joint within the excitation cycle of the multi-frequency sinusoidal disturbance voltage vector, and calculates the dynamic mutual inertia coefficient between the joints that characterizes the torque transmission relationship between the degrees of freedom.

[0050] The matrix parameter adaptive recombination module is used to adjust the dynamic weighting factor of the control Jacobian matrix and the rigid body dynamics feedforward compensation parameters in the feedforward controller according to the dynamic mutual inertia coefficient between joints. The generated feedforward compensation current is directly superimposed on the output of the proportional-integral-derivative controller to generate the target control current for correcting configuration abrupt mismatch. The control output module is used to convert the target control current into a three-phase drive voltage command through the space voltage vector modulation unit and output it to the discrete joint drive module to drive the joint linkage mechanism to move along the corrected dynamic trajectory.

[0051] Embodiment 1: When a combined constraint is generated between industrial multi-modal dynamic working conditions and a restricted interaction field, the modular reconfigurable robot completes different operation tasks by changing the joint and link structures. This dynamic reorganization of the physical configuration causes changes in the mass distribution, moment of inertia, and spatial torque transmission path of the transmission chain. The traditional control method relies on setting electrical contact pins or magnetic induction components at the physical interface to read the hardware identification code, and then retrieves a fixed file in the static parameter library inside the control unit to establish the kinematic and dynamic models. However, frequent mechanical plugging and unplugging cause physical deformation or oxide layers on the surface of the electrical contacts, resulting in misjudgment or loss of the hardware identification code. At the same time, the cumulative tolerances introduced by assembly manufacturing, the inherent non-linear backlash of the reduction gearbox inside the transmission chain, and the variation of the grease viscosity caused by the increase in operating temperature all lead to non-linear drift of the actual force parameters of the robot beyond the coverage range of the preset parameter library, resulting in mismatch of the feed-forward control model. Especially in the dynamic transition stage when the heavy-duty link suddenly accelerates or decelerates, due to the prediction deviation of the mass distribution, transient overload torque is generated in the multi-axis transmission chain, causing saturation overflow of the phase current inside the servo loop. Subsequently, when multiple joints are linked and coordinated, it causes divergence of the control loop, distortion of the motion trajectory, and self-excited resonance of the transmission chain. Even in the unknown variable topology transition state where the module is disconnected and spliced, there is a hidden danger of joint gravity slipping. To eliminate the above control mismatch phenomenon, the control unit abandons the method of relying on external communication pins or surface markings, translates the geometric configuration identification problem into a dynamic impedance topology estimation and information processing problem inside the control structure, and directly uses the position encoders and phase current sensors built into the robot system to construct a data flow loop. The control unit periodically collects the original pulse sequences of the joint position encoders and the phase current sampling values at the output end of the inverter through the data bus at a fixed frequency, and inputs these original data streams into a two-stage orthogonal sliding window filter with a fixed length of 12.5 ms. The high-frequency harmonic spikes introduced by the switching of the inverter power transistors are filtered out through the first-order forward difference weighted smoothing algorithm. Specifically, the engineering design basis for setting the fixed length time width of the two-stage sliding window filter to 12.5 milliseconds is that the switching frequency of the system's underlying inverter power transistors is 10 kHz, and the corresponding pulse width modulation waveform period is 0.1 ms. The high-frequency harmonic spikes introduced by it are mainly concentrated in the 10 kHz and its multiple high-frequency bands. At the same time, the execution period of the existing processor control loop in the system is 1 ms.

[0052] To eliminate pulse width modulation (PWM) switching noise and parasitic electromagnetic interference (EMI) at the 50 Hz to 60 Hz power frequency introduced by connector wear in the frequency domain, and to keep the control chain phase time lag caused by the filter within the 15 ms safety margin allowed by the system stability margin, the number of sampling points in the sliding window was precisely set to 125 data frames, corresponding to a physical time width of 12.5 ms. This width forms a core notch cutoff point at 80 Hz in the frequency domain transfer function, which can effectively filter out high-frequency inverter glitches and mechanical micro-vibration noise in the drive chain, while ensuring that the filtered phase current and position signals are resistant to active injection disturbances at 3.4 Hz to 3.6 Hz. The pressure signal maintains an extremely high data dynamic tracking response speed. The filtered position pulse sequence is converted into the real-time angular velocity and real-time angular acceleration of the joint through second-order numerical differentiation. At the same time, the filtered phase current sample value is converted into a discrete active torque time-series vector that directly represents the output torque of the joint motor through Clark and Park coordinate transformation. In actual operation, in order to suppress the exponential amplification of high-frequency discrete noise by second-order numerical differentiation and offset its inherent phase lag, a pre-filter with a fixed length of 12.5 milliseconds and a two-stage orthogonal sliding window performs a symmetrical moving weighted average on the original pulse sequence, pre-filtering out the quantization step spikes caused by the encoder line count limitation in the time domain.

[0053] The second-order numerical differentiation employs a three-point backward differential operator based on a 10 kHz high-frequency sampling period. Although the output angular acceleration signal has a specific forward phase delay, the sliding mode current observer incorporates a reaching law damping adjustment factor in its sliding mode switching hyperplane design. Furthermore, the saturation boundary layer thickness of the sliding mode switching term is rigidly normalized to the time constant of the two-stage orthogonal sliding window filter on a time scale. This utilizes the integral filtering effect within the sliding hyperplane to suppress the penetration of high-frequency noise in the differentiation process. The feedback gain of the state prediction deviation compensates for the phase lag, achieving stable convergence of the sliding mode state under the dual constraints of noise amplification and phase delay. During the initial rest interval of the motion control cycle, the control unit injects a set of time-interleaved sinusoidal disturbance voltage vectors with a stable frequency in the range of 3.4 Hz to 3.6 Hz from the position loop output of each joint servo driver. This causes each joint drive module to generate axial micro-oscillations sequentially according to a preset time-interleaved sequence. The spatial isolation between adjacent degrees of freedom fully decouples the disturbance voltage vectors in the spatial topology. This allows adjacent joints to independently capture the mechanical energy dissipation characteristics generated by each joint during independent oscillation, blocking the mutual modulation interference of cross-axis phases. Furthermore, the specific mechanism of full decoupling in spatial topology lies in the fact that, due to the complex nonlinear interweaving of the joint links of a multi-degree-of-freedom robot in rigid body dynamics, if multi-axis excitation is applied simultaneously, the cross terms of their inertia matrix will cause phase amplitude modulation interference that cannot be discretized and stripped. This invention, through time-staggered arrangement, enables the single independent joint currently being tested to generate micro-oscillations within a set 20-millisecond disturbance time window, while the remaining non-test joints are maintained in a high-stiffness position-locked state by the underlying servo position loop, acting as temporary rigid mechanical boundaries. This freezes the dynamic geometric coupling constraints of the multi-axis transmission chain into a known static spatial coordinate mapping, allowing the observer channel adjacent to the currently tested joint to exclusively capture the rotational inertia and mechanical energy dissipation damping characteristics of that specific joint. The multi-axis spatial dynamic equations are decoupled and discretized on the time axis, blocking the mutual modulation interference of cross-axis transient phases caused by the simultaneous movement of multiple joints.

[0054] The control unit inputs the obtained real-time joint angular acceleration and discrete active torque time-series vector as input parameters into a pre-set first-order sliding mode dynamic current observer. This observer uses the velocity estimation residual between the measured motion state and the nominal motion model to construct the sliding mode switching hyperplane. During the excitation period of the sinusoidal disturbance voltage vector, the observer captures the transaxial inertial coupling component between adjacent mechanical degrees of freedom through integration. After separating the static friction term of the joint bearing and the nonlinear torque disturbance caused by transmission damping, it calculates the equivalent constant parameter reflecting the true torque transmission relationship between degrees of freedom. This parameter is defined as the joint axial mutual inertia coefficient. Specifically, the sliding mode current observer integrates the electro-drive dynamic state equation containing nominal electromagnetic parameters and rigid body rotation parameters. When the measured real-time joint angular acceleration and discrete active torque time-series vector are input, an electrical difference is generated between the nominal estimated current and the actual phase current output by the observer. This electrical difference is mapped by high-frequency switching gain to form a value used to maintain the sliding mode. The equivalent input term for continuous control of the hyperplane stability is physically mapped directly to a comprehensive unmodeled dynamic term including joint mutual inertial torque and frictional drag torque. Within a specific continuous excitation period of the sinusoidal disturbance voltage vector, the equivalent input term is low-pass filtered and time-integrated to remove the quasi-static frictional drag torque that is constant in the rotational direction. Then, the residual dynamic torque component, which exhibits a linear proportionality with angular acceleration, is subjected to online least-squares multivariate linear regression with the input real-time angular acceleration. This allows for in-situ discretization calculation of the joint axial mutual inertia coefficient, which characterizes the mutual conduction of mechanical rotational inertia, at the electrical observation level. The aforementioned two-stage orthogonal sliding window filter provides characteristic data input for the rapid convergence of the sliding mode switching hyperplane by high-frequency noise reduction of the original signal. In turn, the sliding mode observer's separation calculation of the cross-axis inertial coupling component enhances the dynamic response bandwidth of the feedforward compensation. This causal feedback loop between features drives the motion trajectory tracking error to decrease monotonically.

[0055] The control unit arranges the calculated joint axial mutual inertia coefficients into a dynamic mutual inertia characteristic matrix. When the linkage coefficients in this matrix monotonically increase, the control unit determines that the current physical topology has transformed into a cantilever heavy-load configuration, and monotonically adjusts the spatial transformation weights in the control Jacobian matrix accordingly. It rigidly aligns the dynamic weighting factors of the Jacobian matrix with the spatial distribution parameters of the long connecting rod, while simultaneously increasing the rigid body dynamics feedforward compensation parameters in the feedforward controller. The resulting feedforward compensation current is directly superimposed on the output of the proportional-integral-derivative controller. This eliminates the technical conflict between large-inertia feedforward tracking and servo divergence during topology changes within a single control architecture. Specifically, the control Jacobian matrix contains geometric image elements representing the transformation of each degree of freedom of motion from joint space to end-effector Cartesian space. The execution path of rigid alignment is as follows: When the linkage coefficient in the dynamic mutual inertia feature matrix increases monotonically due to configuration variation, the control unit extracts the ratio of the current linkage coefficient to the pre-stored reference configuration inertia constant in the memory, and uses this ratio as a linear amplification factor to monotonically act on the row vector geometric elements in the control Jacobian matrix corresponding to the degree of freedom of the long link, thereby proportionally and equivalently amplifying the sensitivity of the end displacement to the angle of the joint rotation at the algorithm level; at the same time, the rigid body dynamics feedforward compensation parameter in the feedforward controller increases nonlinearly and monotonically with the square of this ratio, so as to accurately offset the centripetal force and inertial overload moment caused by the increase of the rotation radius under the heavy load configuration of the cantilever, and ensure that the spatial weight distribution of the control matrix and the actual physical size change of the transmission chain achieve real-time numerical matching.

[0056] To address external interference and electrical overload in industrial production environments, the control unit simultaneously activates a protection mechanism based on physical boundary limits when generating the target control current. It calculates the transient overload torque of each linkage module using the joint axial mutual inertia coefficient and compares this torque with a fixed mechanical safety boundary limit value in real time. If the transient overload torque exceeds the mechanical safety boundary limit value, the saturation limiting operator directly rewrites the current pulse width modulation reference of the servo loop to implement amplitude limiting, suppressing transient saturation overflow of the current loop caused by sudden acceleration in the cantilever heavy-load configuration. In actual operation, the internal software execution logic of the saturation limiting operator is as follows: the control unit compares the real-time calculated transient overload torque of the linkage module with the maximum value of the 25 Nm mechanical safety boundary limit value stored in memory.

[0057] When the overload torque exceeds 25 Nm, the operator activates the numerical truncation comparator, calculates a ratio coefficient less than 1 between the safety limit value and the current transient overload torque, and uses this ratio coefficient as a direct scaling factor to multiply and rewrite the current quadrature-axis target control current. This forces the current amplitude entering the space voltage vector modulation unit to be compressed to within the rated protection safety limit of 15 amps. By forcibly rewriting the maximum comparison value of the pulse width modulation duty cycle of the underlying timer counter in the microcontroller's register, the high-frequency drive pulse width that might otherwise saturate overflow is hard-chopped off, limiting the conduction time of the inverter power bridge from the source. This achieves servo hardware overload constraint protection that closely follows parameter updates, and the control unit is internally... A non-volatile dynamic data buffer is allocated within the internal memory to store the characteristic vectors of the joint axial mutual inertia coefficients for historical standard configurations. When a disconnection and reconnection of the discrete joint drive module is detected, and the level transition characteristics at the moment of physical interface disconnection are extracted in real time, the control unit determines the current state as a transitional unknown state. It then automatically retrieves nearby stable parameters from the non-volatile dynamic data buffer to implement control flow compensation. During the initial 30ms before the adaptive identification algorithm converges, it provides continuous impedance matching control torque to maintain joint pose stability during the topology transition. In actual operation, the physical interface of the discrete joint drive module is equipped with a dedicated hardware detection pin, which is connected to the central control layer bus chip via a... A 10kΩ physical resistor is connected to a 3.3V pull-up power rail, while the corresponding contact inside the joint module is directly connected to the system common ground. When the joint module is physically removed, the pin physically jumps from a low level to a 3.3V high level due to the instantaneous interruption of the ground loop. The edge trigger inside the bus chip monitors the level of this hardware pin in real time with a detection step of 1µs. When a low-to-high level transition edge is captured and the high level lasts for more than 5µs to eliminate connector contact jitter noise, the extraction algorithm generates a flag in the register, locking the current system state as a transitional unknown state, triggering subsequent cache data retrieval actions to offset non-operational activities. To mitigate external environmental interference, the phase-locked loop (PLL) filter unit calculates the cross-correlation coefficient between the sampled phase current value at the inverter output and the injected sinusoidal disturbance voltage vector in real time. This coefficient is then compared in real-time with a fixed environmental discrimination limit of 0.75. When the cross-correlation coefficient is lower than this limit, the control unit determines that the current disturbance originates from external environmental interference and locks the rigid body dynamics feedforward compensation parameters in the feedforward controller without modification. This establishes an overall anti-interference barrier for industrial operation. In practice, the environmental discrimination limit of 0.75 is established through mathematical statistics and engineering boundary determination of the coherence of the internal excitation sources. The cross-correlation coefficient reflects the actively injected 3.4 Hz to 3 Hz.The sampling sequence of phase current in the 6 Hz sinusoidal perturbation voltage vector feedback exhibits resonance similarity in both frequency and phase dimensions. When only internal physical configuration reconstruction occurs, the change in phase current is entirely dominated by the actively injected excitation, and both are in a highly phase-locked state. The measured cross-correlation coefficient remains stable in the high-range interval of 0.90 to 0.98.

[0058] When the robot is subjected to non-operational external collisions, the external impact force manifests as a disordered, broadband random disturbance signal, causing a large number of incoherent stray frequencies to flood the feedback current, thus lowering the cross-correlation coefficient. Experiments conducted under step collision loads ranging from 10 to 100 Newtons showed that the cross-correlation coefficient monotonically converged to 0.75 under a critical external interference of 50 Newtons. This value constitutes the statistical lower limit for distinguishing between structural dynamic drift and accidental environmental collisions. Below this value, it is determined that the energy of the external disturbance has overwhelmed the internal excitation source. The target control current is converted into a three-phase drive voltage command by the space voltage vector modulation unit and output to each discrete joint drive module. The control unit adjusts the three-phase inverter... The timing of the bridge arm's conduction maps the target control current in situ to a register duty cycle instruction, driving the joint linkage mechanism to complete the expected motion along the corrected dynamic trajectory, thus offsetting the flexible transmission error caused by the elasticity of the transmission chain. Specifically, the physical closed-loop mechanism for offsetting the flexible transmission error is as follows: Due to the slight elastic torsional deformation of the reducer and flexible transmission chain under heavy-load acceleration conditions, a transmission error lag occurs between the spatial angle of the motor rotor and the actual mechanical angle of the connecting rod end. This invention embeds a transmission chain stiffness torsional elastic model in the feedforward compensation controller, which calculates the elastic torsional angle variable of the current transmission chain on the torque transmission path online based on the calculated joint axial mutual inertia coefficient and transient torque.

[0059] The torsion angle variable is directly introduced into the underlying servo control loop as a position advance compensation amount, and converted into a high-frequency dynamic pulse component superimposed on the target control current. This component is mapped in situ to the conduction duty cycle command of the inverter bridge power transistor through space voltage vector modulation. By changing the dynamic voltage vector applied to the three-phase stator winding, the motor generates a transient electromagnetic torque and electromotive force that leads the nominal trajectory, pulling the motor rotor to rotate an additional small angle to actively compensate for the elastic deformation torque of the transmission chain. This offsets the displacement deviation caused by the flexible chain at the output shaft end. Under this continuous data flow control state evolution, the robot's overall trajectory tracking error converges to below 0.4% within 45ms. The multi-axis joint servo divergence and self-excited resonance caused by physical topology variations and mechanical interface wear are suppressed. The multi-degree-of-freedom industrial robot structure maintains the set cooperative motion state without using external machine vision cameras or multi-axis torque sensor arrays.

[0060] Example 2: When the control unit needs to test and verify the dynamic force characteristics of an industrial multi-degree-of-freedom joint linkage transmission chain under variable configuration operation, a fully functional robot test bench provides the testing environment. The test bench includes three pluggable joint degree-of-freedom modules and high-rigidity aluminum alloy connecting rods. The integrated joint absolute position encoder has a 23-bit binary pulse resolution, and the phase current sensor at the output of the main inverter has a detection range of 0A to 20A, a resolution of 0.05A, and a sampling frequency of 10kHz. This provides the controller level with a data source reflecting the high dynamic spatial constraint interaction characteristics. In the noise reduction parameter settings inside the control unit, there is a technical parameter for balancing the real-time performance of data acquisition and the system's computational load. This parameter corresponds to the window time width of the dual-stage orthogonal sliding window filter. When the spectral bandwidth of the phase current sampling value is in the preset high-frequency band, in order to avoid signal aliasing and ensure the dynamic response speed of the controller, the window time width tends to the lower limit of its time tolerance range. The calculated window time width is determined to be 12.5ms. This determined parameter constitutes the benchmark for all subsequent noise reduction actions.

[0061] The experimental design constructs a multi-dimensional control system comprising four independent test groups. These include a traditional control group using connector level identification and fixed parameter lookup; a control group with a specific component of the static friction term not separated within the observer; a control group with an injection frequency set below the lower limit of 1.5Hz; a control group with an injection frequency set above the upper limit of 5.5Hz; and a test group using the method of this invention, demonstrating testing at the lower limit of 3.0Hz, the median of 3.5Hz, and the upper limit of 4.5Hz. During the initial rest interval of each test group's action cycle, the control unit initiates a micro-amplitude excitation injection. The frequency selection of the sinusoidal perturbation voltage vector has a performance inflection point effect on the final topology identification performance, accompanied by phase changes. Data shows that when the injection frequency is below the lower limit of 1.5Hz, due to the shaft... The dynamic boundary of the lubricating film has not yet been opened, and the static friction resistance inside the joint absorbs the energy of the micro-oscillation, causing the velocity estimation residual of the sliding mode switching hyperplane to remain in a non-convergent state. When the injection frequency is increased to the upper limit of 5.5Hz, the high-frequency component of the injection voltage directly excites the self-excited resonance of the back clearance of the gearbox inside the transmission chain, and the torque residual sequence diverges, causing the servo drive to generate saturation protection action due to transient overload current. Only when the disturbance voltage frequency is limited to the working window of 3.0Hz to 4.5Hz, especially when it is stable in the decoupling range of 3.4Hz to 3.6Hz, the spatial topology decoupling between adjacent degrees of freedom is completed through spatiotemporal isolation. The adjacent joints directly capture the mechanical energy dissipation characteristics generated by each joint during independent oscillation, blocking the mutual modulation interference of cross-axis phases.

[0062] In actual operation testing, manually plugging and unplugging the mechanical interface replaced the No. 2 connecting rod with a cantilevered long-length heavy-duty connecting rod, thereby applying an inertial abrupt disturbance to the transmission chain in situ. At this time, the joint sensors output raw pulses and initial current data streams containing assembly tolerance noise. For the traditional control group using static lookup table matching, due to the lack of means to perceive local parameter drift caused by backlash and wear, the feedforward quality prediction suffers monotonically mismatch, and its joint trajectory tracking error monotonically increases from the initial 4.5% to 12.5%, causing the motion loop to diverge. However, in the test group using the method of this invention, a two-stage orthogonal sliding window filter smooths the raw signal, and a second-order numerical differentiator outputs time-delay-free real-time joint angular velocity and real-time angular acceleration. At the same time, the Clark and Park coordinate transformation module outputs the active torque timing vector. When these intermediate physical data after filtering and transformation are input into the first-order sliding mode dynamic current observer, the hyperplane is switched to estimate the residual across the axis based on the velocity. The inertial coupling component is calculated by continuous integration, and the joint axial mutual inertia coefficient is separated in real time. The joint axial mutual inertia coefficient, as intermediate data, exhibits a definite mathematical convergence law. Test data shows that when the injection frequency is 3.5Hz, the joint axial mutual inertia coefficient tends to stabilize within 38ms. For the test groups at the lower limit of 3.0Hz and the upper limit of 4.5Hz, the parameter convergence time is stable at 45ms and 42ms, respectively. The converged joint axial mutual inertia coefficient directly corrects the spatial transformation weight in the control Jacobian matrix, and the generated rigid body dynamics feedforward compensation current is directly superimposed on the control output of the proportional-integral-derivative controller. This makes the overall trajectory tracking error of the test group using the method of this invention monotonically decrease within 45ms and finally converge to a stable state of 0.38%. In the stress test of introducing non-ideal industrial environment conditions such as external collision and module disconnection and splicing, the adaptive protection structure of the control method of this invention shows operational stability.

[0063] When the interface is in a topology transition state due to mechanical disconnection, the control unit detects the level jump characteristic and determines the state as an unknown transition state. Then, within the initial 30ms time window before the adaptive identification parameters have converged, it automatically retrieves the inertial characteristic vector of the historical standard configuration from the non-volatile dynamic data buffer to calculate the target compensating torque. This controls the discrete joint drive module to generate an impedance matching control torque to maintain the joint's fixed posture, limiting the joint's gravity slip distance to within 0.15mm. When the drivetrain is subjected to an external non-operational impact of 50N, the phase current sensor at the inverter output collects the current fluctuation. The phase-locked loop filter unit then calculates the cross-correlation coefficient between the phase current sample value and the sinusoidal disturbance voltage vector in real time. Test monitoring data shows that because the external collision signal and the actively injected disturbance signal inside the system are in a disordered phase state, both... The cross-correlation coefficient decreased from 0.92 under normal conditions to 0.42, directly crossing and triggering the fixed environmental discrimination limit of 0.75, a logical decision boundary. Since the cross-correlation coefficient was lower than the environmental discrimination limit of 0.75, the criterion module of the control unit determined that this fluctuation was due to non-structural external environmental interference rather than a change in robot configuration. The control program locked the rigid body dynamics feedforward compensation parameters in the feedforward controller without rewriting them, thereby avoiding incorrect adjustment of the compensation current caused by external impact. This ensured that the trajectory tracking error of the system remained at a stable level of 0.41% after the collision. In addition, during the high-speed, high-inertia acceleration test, when the calculated transient overload torque exceeded the safety boundary, the saturation limiting operator limited the peak value of the inverter current to within the rated protection limit of 15A, blocking the risk of current overload damage to the servo loop. The quantitative test results of the above multi-dimensional comparison system are as follows.

[0064] The modular reconfigurable robot control method of this invention can construct a robust self-healing force characteristic sensing channel within the transmission chain through pure algorithm processing. By converting the original pulse sequence and phase current data chain into constant axial mutual inertia parameters, the control unit can complete the real-time reorganization of the control Jacobian matrix spatial transformation weights and feedforward dynamic tensors without adding external torque sensors or optical phases. The performance changes shown on the frequency response curve within a specific physical control window of 3.0Hz to 4.5Hz directly confirm the objective engineering optimization facts of the disturbance voltage injection range and numerical limit boundary, demonstrating the inevitable technical logic of synergistic enhancement between features to offset transmission losses. Therefore, the control scheme of this invention breaks away from the rigid dependence of the control model on the physical geometric configuration, providing a complete and reproducible engineering blueprint for offsetting assembly accumulation errors and ensuring the stability of the entire process of multi-degree-of-freedom industrial robot structures in complex interactive fields.

[0065] Example 3: This example combines Figures 1 to 2This document describes a control method and system for a modular and reconfigurable teaching assistant robot, such as... Figure 1 As shown, the original pulse sequences from the joint position sensors and the phase current sampling values ​​from the inverter output are periodically acquired. Noise in the original pulse sequences and phase current sampling values ​​is filtered out by a two-stage sliding window filter. The filtered pulse sequences are then converted into real-time joint angular acceleration through second-order numerical differentiation. The filtered phase current sampling values ​​are then reconstructed into joint axial driving torque timing data through coordinate transformation. Simultaneously, during the initial rest interval of the control cycle, staggered sinusoidal disturbance voltage vectors with frequencies ranging from 3.0Hz to 4.5Hz are injected from the position loop output of each joint servo driver. Finally, the real-time joint angular acceleration and joint axial driving torque timing data are input into the sliding mode current observer. By combining the velocity estimation residual to construct a sliding mode switching hyperplane, the transaxial inertial coupling component is captured within the excitation period of the sinusoidal disturbance voltage vector. The joint static friction term is stripped to output the core parameter: the joint axial mutual inertia coefficient. Then, based on the joint axial mutual inertia coefficient, the dynamic weighting factor of the control Jacobian matrix and the rigid body dynamics feedforward compensation parameter of the feedforward controller are dynamically adjusted. The generated feedforward compensation current is superimposed on the output of the proportional-integral-derivative controller to synthesize the target command and generate the target control current. Finally, the target control current is converted into a three-phase drive voltage command through the space voltage vector modulation unit and output to each discrete joint drive module to drive the joint linkage mechanism to move smoothly along the corrected dynamic trajectory.

[0066] like Figure 2As shown, the non-volatile dynamic data buffer is fixed in the environment with a discrimination limit of 0.75. The physical unidirectional input is sent to the sliding mode current observer core for high-speed parameter estimation and calculation. The sliding mode current observer core for high-speed parameter estimation and calculation, along with the core digital signal control unit matrix adjustment / feedforward compensation / PID terminal, are integrated into the central control layer bus main control chip platform. The sliding mode current observer core for high-speed parameter estimation and calculation, and the core digital signal control unit matrix adjustment / feedforward compensation / PID terminal establish bidirectional signal interaction channels with the high-speed serial communication bus and the multi-discrete joint ring topology network, respectively. The three-phase drive voltage command / sinusoidal disturbance voltage vector is issued from the core digital signal control unit matrix adjustment / feedforward compensation / PID terminal and transmitted in situ via the high-speed serial communication bus and the multi-discrete joint ring topology network to the joint servo driver position ring output / injection terminal inside the discrete joint execution layer independent detachable joint module. A sinusoidal disturbance is injected into the joint servo driver position loop output terminal. The sinusoidal disturbance is unidirectionally output to the three-phase inverter power bridge drive output terminal, which is equipped with a phase current sampling resistor. The phase current sampling resistor at the three-phase inverter power bridge drive output terminal feeds back the collected phase current sampling value to the high-speed serial communication bus. The multi-discrete joint ring topology network is then fed back to the sliding mode current observer core for high-speed parameter estimation and solution. At the same time, the three-phase inverter power bridge drive output terminal is equipped with a phase current sampling resistor. The physical drive joint axial permanent magnet synchronous motor direct drive linkage mechanism generates axial drive torque. The axial drive torque generated by the joint axial permanent magnet synchronous motor direct drive linkage mechanism is transmitted to the joint position sensor via mechanical coaxial coupling. The joint position sensor receives the physical motion state and outputs the original pulse sequence to the high-speed serial communication bus multi-discrete joint ring topology network for final feedback to the sliding mode current observer core for high-speed parameter estimation and solution.

[0067] Example 4: When the spatial distribution parameters of the modular reconfigurable robot structure reach the upper limit critical value and the velocity estimation residual of the motion sliding mode switching hyperplane is in the nonlinear overload range, the inherent nonlinear backlash of the gearbox inside the transmission chain and the physical residual generated by the mechanical stress deformation of the link are superimposed, resulting in the generation of high-order transient disturbance components in the joint axial driving torque timing data that exceed the preset filtering frequency range. This causes the current loop of the servo driver to generate a transient overload of the current pulse width modulation reference, and the multi-axis joint linkage cooperative structure subsequently generates a safety risk of trajectory tracking instability.

[0068] The control unit constructs a high-frequency feedforward excitation field by injecting a sinusoidal disturbance voltage vector in situ at the output of the position loop. It also acquires the raw pulse sequence from the joint position encoder and the phase current sampling values ​​from the phase current sensor at a fixed frequency via a data bus. A noise reduction filter processes the raw signals to extract the real-time angular acceleration of the joint. Simultaneously, the phase current sampling values ​​are converted into a discrete active torque time-series vector through Park coordinate transformation. A sliding mode current observer, combined with velocity estimation residuals, tracks the transaxial inertial coupling components between adjacent mechanical degrees of freedom to calculate the joint axial mutual inertia coefficient. The control unit uses a discrete calibration action based on the monotonic change of the linkage residual to determine the adaptive adjustment critical point of the control Jacobian matrix spatial transformation weights. This is achieved by sequentially applying self-... Increment to And with For external physical impact loads with fixed step increments, the phase-locked loop filter unit synchronously calculates the cross-correlation coefficient between the sampled phase current values ​​and the sinusoidal disturbance voltage vector. This cross-correlation coefficient exhibits an inversely proportional monotonically decreasing trend as the intensity of the external physical impact load increases. When the external physical impact load reaches... At the critical operating condition, the cross-correlation coefficients monotonically converge to The constant value is fixed in the non-volatile dynamic data cache as an environmental discrimination limit.

[0069] When the cross-correlation coefficient generated by external interaction collisions is lower than the environmental discrimination limit value At this time, the control unit locks the rigid body dynamics feedforward compensation parameters in the feedforward controller without rewriting them, while the saturation limiting operator limits the peak value of the inverting phase current to within a certain range. Within the rated protection limit, the spatial transformation weights in the control Jacobian matrix and the spatial distribution parameters of the long link are kept rigidly aligned in real time under the adjustment of the dynamic weighting factor. The feedforward compensation current is directly superimposed on the output of the proportional-integral-derivative controller to correct the flexible transmission error caused by the elasticity of the transmission chain. Under this continuous data flow control state evolution, the overall trajectory tracking error of the robot is within... monotonically decreasing to The following describes how a multi-degree-of-freedom joint linkage cooperative structure maintains a preset cooperative motion state without using an external machine vision camera or multi-axis torque sensor array.

[0070] Example 5: When the system faces field deployment conditions involving deformation tolerances in different batches of connecting rods and zero-point level drift of the discrete joint drive module, the control unit establishes the initial zero-level boundary of the dynamic mutual inertia characteristic matrix through a field deployment pre-calibration program. With the joint connecting rod mechanism in a vertically downward, statically suspended reference pose, the control unit reads the initial pulse reading of the joint position encoder via the data bus. Simultaneously, the processor defines the current position pulse sequence as the zero physical level and controls the discrete joint drive module to apply a small-amplitude, slowly changing quasi-static test torque command. The phase current sensor continuously acquires the phase current sampling value at the inverter output terminal, and latches the discrete active torque timing vector at the critical shear point where the bearing overcomes static friction and undergoes a small rotation. The average value of this vector over three consecutive acquisition cycles is used to establish the initial static friction compensation constant reference value for each joint. The aforementioned initial zero-level boundary provides a pre-emptive physical constraint for zero-point drift offsetting for the first-order sliding mode dynamic current observer. By separating the parasitic force bias introduced by device manufacturing tolerances, the error accumulation propagation of the physical bias component to the sliding mode switching hyperplane is blocked.

[0071] In a clustered deployment scenario with multiple standard preset physical linkage configurations, the control unit calls the offline calibration and solidification dataset from the non-volatile dynamic data buffer to populate the underlying dynamic mutual inertia feature matrix. The offline calibration and solidification dataset is obtained by performing gradient oscillation experiments on typical topological configurations on a test bench. Under the lower limit of the short linkage configuration, the basic bias data of the joint axial mutual inertia coefficient under a 3.0Hz disturbance is measured to be 1.25 kg⋅cm, while under the upper limit of the long linkage heavy load configuration, the measured inertial coupling damping boundary data reaches 5.48 kg⋅cm. This set of discrete scalar data serves as the reference feature vector, forming the methodological foundation for the online adaptive logic of the control system. During the dynamic transition phase when the robot structure completes splicing and reassembly, the control unit uses real-time cross-correlation coefficients and fixed environmental discrimination limits. The logical judgment conclusion retrieves the feedforward controller dynamic compensation parameters that match the current working condition from the above offline dataset, so that the joint linkage mechanism always maintains the set multi-joint linkage posture response state under the parasitic wear interference of overload torque.

[0072] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A modular and reconfigurable control method for a teaching assistant robot, characterized in that, Includes the following steps: Step S1: Collect the original pulse sequence of each joint position sensor and the phase current sampling value of the inverter output terminal. Filter out the noise in the original pulse sequence and phase current sampling value through a two-stage sliding window filter. Convert the filtered pulse sequence into the real-time angular acceleration of the joint through second-order numerical differential calculation. Reconstruct the filtered phase current sampling value into the timing data of the joint axial driving torque through coordinate transformation. Step S2: During the initial rest interval of the control cycle, inject time-interleaved sinusoidal disturbance voltage vectors with a frequency of 3.0Hz to 4.5Hz from the position loop output terminal of each joint servo driver. Step S3: Input the real-time angular acceleration and axial driving torque timing data of the joint into the sliding mode current observer, construct the sliding mode switching hyperplane by combining the velocity estimation residual, capture the transaxial inertial coupling component and strip the joint static friction term within the excitation period of the sinusoidal disturbance voltage vector, and calculate the joint axial mutual inertia coefficient. Step S4: Adjust the dynamic weighting factor of the control Jacobian matrix and the rigid body dynamics feedforward compensation parameters of the feedforward controller according to the joint axial mutual inertia coefficient, and superimpose the feedforward compensation current onto the output of the proportional-integral-derivative controller to generate the target control current. Step S5: The target control current is converted into a three-phase drive voltage command by the space voltage vector modulation unit and output to each discrete joint drive module to drive the joint linkage mechanism to move along the corrected dynamic trajectory.

2. The modular and reconfigurable teaching assistant robot control method according to claim 1, characterized in that, In step S2, when injecting the sinusoidal perturbation voltage vector, the frequency of the sinusoidal perturbation voltage vector is stabilized in the range of 3.4Hz to 3.6Hz. Each discrete joint drive module generates axial oscillation in sequence according to the time-staggered arrangement. By utilizing the spatiotemporal isolation implemented between adjacent joints, the sinusoidal perturbation voltage vector is fully decoupled in the spatial topology, the transient energy dissipation parameters of each degree of freedom are obtained, and the mutual modulation interference of cross-axis phases is blocked.

3. The modular and reconfigurable teaching assistant robot control method according to claim 1, characterized in that, Step S4 includes the following sub-steps: Step S41, arrange the calculated joint axial mutual inertia coefficients into a dynamic mutual inertia characteristic matrix. When the linkage coefficient in the dynamic mutual inertia characteristic matrix increases monotonically, determine that the current physical topology has been transformed into a cantilever heavy-load configuration; Step S42, adjust the dynamic weighting factor of the control Jacobian matrix monotonically according to the numerical variation of the dynamic mutual inertia characteristic matrix, and rigidly align the dynamic weighting factor of the control Jacobian matrix with the long link distribution parameters of the cantilever heavy-load configuration. At the same time, increase the rigid body dynamics feedforward compensation parameter in the feedforward controller to generate the target control current.

4. The modular and reconfigurable teaching assistant robot control method according to claim 1, characterized in that, Step S1 includes the following sub-steps: Step S11, a non-volatile dynamic data buffer is opened in the system's memory to store the feature vectors of the joint axial mutual inertia coefficients of the historical standard configuration; Step S12, when a disconnection and reassembly action is detected in the discrete joint drive module, the level transition feature at the moment of physical interface disconnection is extracted in real time, and the current system state is determined to be a transitional unknown state; Step S13, the nearest neighbor stable parameters are retrieved from the non-volatile dynamic data buffer to implement control flow compensation, and continuous impedance matching control torque is provided in the initial 30ms before the algorithm converges to maintain the stability of the joint pose in the variable topology transition state.

5. The modular and reconfigurable teaching assistant robot control method according to claim 1, characterized in that, Step S4 also includes the following amplitude limiting sub-steps: Step S43, calculate the transient overload torque of each link module using the joint axial mutual inertia coefficient, and compare the transient overload torque with the fixed mechanical safety boundary limit value in real time; Step S44, when the transient overload torque exceeds the mechanical safety boundary limit value, directly rewrite the current pulse width modulation reference of the underlying servo loop through the saturation limiting operator to implement amplitude limiting, suppressing the transient saturation overflow of the current loop caused by the sudden acceleration of the cantilever heavy load configuration.

6. The modular and reconfigurable teaching assistant robot control method according to claim 1, characterized in that, Step S1 also includes the following sub-steps: Step S14, obtain the position differential sequence fed back by the joint absolute encoder as an auxiliary information source, and use the position differential sequence to calculate the joint dead zone damping value; Step S15: Remove the joint dead zone damping value from the joint axial driving torque time series data, and separate the nonlinear torque term caused by joint dead zone and transmission damping to eliminate transmission damping fluctuations caused by reducer temperature rise.

7. The modular and reconfigurable teaching assistant robot control method according to claim 1, characterized in that, Step S3 further includes the following sub-steps: Step S31, using the phase-locked loop filter unit to calculate the cross-correlation coefficient between the phase current sampling value at the inverter output and the injected sinusoidal disturbance voltage vector in real time; Step S32, comparing the cross-correlation coefficient with a fixed environmental discrimination limit value of 0.75 in real time; Step S33, when the cross-correlation coefficient is lower than the environmental discrimination limit value of 0.75, determining that the current disturbance originates from the interactive collision interference of the external environment, and locking the rigid body dynamics feedforward compensation parameters in the feedforward controller.

8. The modular and reconfigurable teaching assistant robot control method according to claim 1, characterized in that, Step S5 includes the following sub-steps: Step S51, adjust the conduction timing of the three-phase inverter bridge arms through the space voltage vector modulation unit, and map the target control current in place to the duty cycle instruction of the underlying register; Step S52, control the three-phase stator winding voltage of the joint motor to generate a corresponding electromotive force according to the duty cycle instruction of the underlying register, and offset the flexible transmission error caused by the elasticity of the transmission chain.

9. The modular and reconfigurable teaching assistant robot control method according to claim 1, characterized in that, Step S1 also includes the following sub-steps: Step S16, setting the sliding window time width of the dual-stage sliding window filter to a fixed length of 12.5ms; Step S17, performing first-order forward differential weighted smoothing on the phase current sampled values ​​within the sliding window to filter out harmonic glitches, which are high-frequency interferences, introduced by the inverter switching, and inputting the filtered phase current sampled values ​​and the reconstructed joint axial driving torque timing data into the sliding mode current observer.

10. A modular and reconfigurable teaching assistant robot control system, used to implement the modular and reconfigurable teaching assistant robot control method of claim 1, characterized in that, include: The data acquisition and reconstruction module is used to periodically acquire the original pulse sequences of the position sensors of each joint of the robot and the phase current sampling values ​​of the inverter output. It filters out noise in the original pulse sequences and phase current sampling values ​​through a two-stage sliding window filter, and uses second-order numerical differential calculation to convert the filtered pulse sequences into real-time angular acceleration of the joints. At the same time, it reconstructs the filtered phase current sampling values ​​into joint axial driving torque timing data through coordinate transformation. The self-decoupling excitation injection module is used to address the sudden change in rigid body inertia and multi-axis inertial nonlinear coupling interference caused by configuration reconstruction. During the initial rest interval of the robot motion control cycle, it injects a multi-frequency sinusoidal disturbance voltage vector with staggered time arrangement and a frequency range of 3.0Hz to 4.5Hz from the position loop output terminal of each joint servo driver. The joint state observation module includes a sliding mode current observer, which is used to input the real-time angular acceleration and axial driving torque timing data of the joint into the sliding mode current observer, and construct the sliding mode switching hyperplane by combining the velocity estimation residual. It captures the transaxial inertial coupling component and strips the static friction term of the joint within the excitation cycle of the multi-frequency sinusoidal disturbance voltage vector, and calculates the dynamic mutual inertia coefficient between the joints that characterizes the torque transmission relationship between the degrees of freedom. The matrix parameter adaptive recombination module is used to adjust the dynamic weighting factor of the control Jacobian matrix and the rigid body dynamics feedforward compensation parameters in the feedforward controller according to the dynamic mutual inertia coefficient between joints. The generated feedforward compensation current is directly superimposed on the output of the proportional-integral-derivative controller to generate the target control current for correcting configuration abrupt mismatch. The control output module is used to convert the target control current into a three-phase drive voltage command through the space voltage vector modulation unit and output it to the discrete joint drive module to drive the joint linkage mechanism to move along the corrected dynamic trajectory.

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