A method and system for active disturbance rejection control suitable for biomimetic chest cavity motion simulation

Abstract

This invention relates to an active disturbance rejection control method and system suitable for biomimetic chest cavity motion simulation. The method includes: acquiring and preprocessing a nonlinear motion trajectory signal to generate an original target position command; processing the original target position command using a tracking differentiator to generate a smoothly transitioning desired position signal and desired velocity signal; receiving the actual position feedback signal of the controlled object and the control input of the previous control cycle through an extended state observer, and estimating the actual position state observation value, actual velocity state observation value, and total disturbance state observation value of the controlled object in real time; calculating the position tracking error and velocity tracking error based on the desired position signal, desired velocity signal, and the actual position state observation value and actual velocity state observation value of the controlled object, and calculating the initial state feedback control quantity; generating an actual control command and sending it to the motor driver to drive the biomimetic chest cavity motion simulation platform to reproduce the target respiratory motion trajectory.

Description

Technical Field

[0001] This invention relates to the field of medical device control technology, and more specifically to a self-disturbance rejection control method and system suitable for bionic chest cavity motion simulation. Background Technology

[0002] With the rapid development of flexible electronics technology in wearable medical devices, health monitoring, and other fields, reliable evaluation of its performance in dynamic human environments has become crucial. The biomimetic chest cavity motion simulation platform, as a key testing device, has the core task of faithfully reproducing the complex nonlinear motion trajectory of the human chest cavity during respiration.

[0003] In existing technologies, model-based control strategies (such as PID) are commonly used to control the various mechanical actuators in a bionic chest cavity motion simulation platform. The core principle is to design and tune the proportional, integral, and derivative parameters of the controller based on a precise mathematical model of the controlled object, and generate control signals through feedback errors, aiming to ensure that the system output stably and accurately follows the target command. Its effectiveness highly depends on the precise matching between the controller parameters and the mathematical model of the controlled object.

[0004] However, during operation, the biomimetic thoracic motion simulation platform suffers from unmodeled internal dynamics due to the inherent nonlinear friction and clearances in its mechanical mechanisms (such as joints and links). Simultaneously, the flexible electronic devices attached to it for testing generate time-varying external unknown load impedances due to stretching and deformation. The coupling effect of these two types of interference severely reduces the tracking accuracy of the platform's motion trajectory and the reliability of the performance test results for the flexible electronic devices.

[0005] Furthermore, traditional PID control strategies are unable to cope with the aforementioned combined internal and external disturbances, specifically exhibiting poor trajectory tracking accuracy and weak anti-interference capabilities. This directly limits the realism of the bionic chest cavity platform in simulating complex respiratory dynamics, thereby affecting the reliability of performance evaluation of flexible electronic devices in near-real-world scenarios. Summary of the Invention

[0006] The purpose of this invention is to provide an active disturbance rejection control method and system suitable for biomimetic chest cavity motion simulation. This method can estimate and compensate for the combined disturbances of unmodeled dynamics within the system and external time-varying load impedance in real time, achieving high-precision and robust tracking of a given nonlinear respiratory motion trajectory. It can significantly improve the reliability of performance evaluation of flexible electronic devices in near-real-world scenarios.

[0007] To achieve the above-mentioned technical objectives, the technical solution adopted by the present invention is as follows:

[0008] In a first aspect, the present invention provides an active disturbance rejection control method suitable for biomimetic chest cavity motion simulation, the method comprising:

[0009] S01, acquire the nonlinear motion trajectory signal simulating the rise and fall of the human chest cavity during breathing, preprocess the nonlinear motion trajectory signal, and generate the original target position command;

[0010] S02, the original target position command is processed using a tracking differentiator to generate a smoothly transitioned desired position signal v1 and desired velocity signal v2;

[0011] S03, the actual position feedback signal y of the controlled object and the control input u of the previous control cycle are received through the extended state observer, and the actual position state observation value z1, the actual velocity state observation value z2 and the total disturbance state observation value z3 of the controlled object are estimated in real time; the total disturbance state is composed of the coupling of the unmodeled dynamics inside the system and the time-varying unknown load impedance outside.

[0012] S04, perform nonlinear state error feedback calculation, based on the desired position signal v1, desired velocity signal v2, and the observed values ​​of the actual position and velocity states of the controlled object z1 and z2, calculate the position tracking error e. p With speed tracking error e d And based on the error, the initial state feedback control quantity u0 is calculated through nonlinear combination;

[0013] S05, Generate an equivalent disturbance compensation component z based on the total disturbance state. k The initial state feedback control quantity u0 is used to subtract the equivalent disturbance compensation component z. k , generate actual control command u;

[0014] S06, the actual control command u is sent to the motor driver to drive the bionic chest cavity motion simulation platform to reproduce the target respiratory motion trajectory.

[0015] As a preferred embodiment of the present invention, step S02, which involves processing the original target position command using a tracking differentiator, specifically comprises:

[0016] A discrete state update equation is established. Based on the deviation x1 between the original target position command and the position tracking signal, the velocity tracking signal x2, the preset filter factor r, and the solution step size h, the acceleration control quantity fh is solved using the optimal control synthesis function.

[0017] The position tracking signal and the velocity tracking signal are discretized and iteratively updated using the acceleration control quantity fh to generate and output the desired position signal v1 and the desired velocity signal v2 with smooth transition.

[0018] As a preferred embodiment of the present invention, the optimal control synthesis function is:

[0019]

[0020] Where x1 is the deviation between the original target position command and the position tracking signal; x2 is the velocity tracking signal; r is the preset filtering factor; and h is the calculation step size.

[0021] As a preferred embodiment of the present invention, in S04, the specific steps for calculating the initial state feedback control quantity u0 based on the error through nonlinear combination are as follows:

[0022]

[0023] in, For proportional gain; This is the differential gain; , These are the nonlinear factors that determine the degree of nonlinear transformation of the error; It is a piecewise nonlinear function.

[0024] As a preferred embodiment of the present invention, the piecewise nonlinear function is specifically:

[0025]

[0026] in, This is the threshold width of the linear interval.

[0027] Secondly, the present invention provides an active disturbance rejection control system suitable for biomimetic chest cavity motion simulation, the system comprising:

[0028] The host computer is used to generate and send nonlinear motion trajectory signals that simulate the rise and fall of the human chest cavity during breathing.

[0029] The instruction processing module is used to preprocess the received nonlinear motion trajectory signal to generate an original target position instruction; and to process the original target position instruction to generate a smooth transition desired position signal v1 and a desired velocity signal v2.

[0030] The state observation module is used to receive the actual position feedback signal y of the controlled object and the control input u of the previous control cycle, and to estimate the actual position state observation value z1, the actual velocity state observation value z2 and the total disturbance state observation value z3 of the controlled object in real time.

[0031] The error compensation module is used to calculate the position tracking error e based on the desired position signal v1, the desired velocity signal v2, the observed actual position state z1 of the controlled object, and the observed actual velocity state z2. p With speed tracking error e d The initial state feedback control quantity u0 is calculated based on the error through nonlinear combination.

[0032] The control command reprocessing module is used to generate an equivalent disturbance compensation component z based on the total disturbance state. k And control the initial state feedback control quantity u0 minus the equivalent disturbance compensation component z. k , generate actual control command u;

[0033] The execution and feedback module is used to receive the actual control command u and drive the bionic chest cavity motion simulation platform to reproduce the target breathing motion trajectory; it is also used to generate the actual position feedback signal y of the controlled object.

[0034] As a preferred embodiment of the present invention, the execution and feedback module includes a motor drive unit, a plurality of linear motors connected to the motor drive unit, and a plurality of displacement sensors for real-time detection of the actual displacement information of each mechanism of the bionic thoracic motion simulation platform.

[0035] Thirdly, the present invention provides an electronic device, including a processor and a memory;

[0036] The processor is connected to the memory;

[0037] Memory, used to store executable program code;

[0038] The processor reads the executable program code stored in the memory and runs the program corresponding to the executable program code to perform the steps of the above-described active disturbance rejection control method applicable to biomimetic chest cavity motion simulation.

[0039] In summary, the present invention has the following beneficial effects:

[0040] 1. This invention introduces an extended state observer to unify the unmodeled dynamics within the biomimetic thoracic motion simulation platform system with the external time-varying unknown load impedance (such as the deformation of flexible electronic devices) into a single total disturbance state variable for real-time observation and estimation. Based on this, feedforward compensation is performed, achieving active suppression of the composite disturbances experienced by the platform. This method significantly reduces the dependence on a precise mathematical model of the controlled object and enhances the applicability and robustness of the controller under complex and uncertain operating conditions.

[0041] 2. This invention employs an active disturbance rejection control architecture comprised of a tracking differentiator, an extended state observer, and nonlinear state error feedback. This architecture smoothly transitions nonlinear breathing trajectory commands and utilizes nonlinear feedback combinations and disturbance compensation to generate control commands, achieving high-precision, high-dynamic-response tracking of a given nonlinear breathing trajectory. Compared to traditional PID control, this method can more accurately reproduce complex breathing movements in scenarios with combined internal and external disturbances, thus providing a more realistic and reliable dynamic performance evaluation environment for flexible electronic devices attached to the platform. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart of the method of the present invention;

[0044] Figure 2 This is a system structure block diagram of the present invention;

[0045] Figure 3 This is a flowchart of the active disturbance rejection control algorithm in an embodiment of the present invention. Detailed Implementation

[0046] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed merely to enable those skilled in the art to better understand and implement the subject matter described herein, and are not intended to limit the scope, applicability, or examples set forth in the claims. The function and arrangement of the elements discussed may be changed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the various examples. For example, the described methods may be performed in a different order than described, and steps may be added, omitted, or combined. Furthermore, features described in some examples may be combined in other examples.

[0047] Example 1

[0048] like Figure 1 and Figure 3 As shown, this embodiment provides a specific implementation example of an active disturbance rejection control method suitable for biomimetic chest cavity motion simulation. The specific steps of the method include:

[0049] S01, acquire the nonlinear motion trajectory signal simulating the rise and fall of the human chest cavity during breathing, preprocess the nonlinear motion trajectory signal, and generate the original target position command;

[0050] S02, the original target position command is processed using a tracking differentiator to generate a smoothly transitioning desired position signal v1 and desired velocity signal v2.

[0051] First, the nonlinear motion trajectory signal simulating the human chest cavity breathing fluctuations is acquired from the host computer and preprocessed to generate the original target position command r. targetThe tracking differentiator (TD) processes the instruction, extracts the smooth differential signal, filters out high-frequency abrupt changes in the instruction, and generates a smooth transition of the desired position signal v1 and the desired velocity signal v2.

[0052] Specifically, when the tracking differentiator processes data, the first step is to set the core parameters, including the solution step size h (used to control period consistency) and the filter factor r. The filter factor r determines the system's tracking speed of the command; the larger the value of r, the faster the tracking, but overshoot must be avoided.

[0053] Next, state iteration and optimal control variable calculation are performed. In each control cycle k, the following discrete state update equation is executed:

[0054]

[0055]

[0056] The optimal control synthesis function is:

[0057]

[0058] Where x1 is the original target position instruction r target x1 is the deviation from the position tracking signal v1(k); x2 is the position tracking signal v2(k). The optimal control synthesis function is used to calculate the optimal acceleration control quantity fh that enables the system state to approach the target at the fastest speed without overshoot under the current deviation and speed.

[0059] Next, the position tracking signal v1(k) and velocity tracking signal v2(k) are discretized and iteratively updated using the acceleration control quantity fh to obtain v1(k+1) and v2(k+1); the updated two are used as the desired position signal v1 and desired velocity signal v2 for smooth transition.

[0060] By using the nonlinear design of the optimal control synthesis function fhan, the tracking differentiator can arrange a reasonable transient process for the system. This makes the desired position signal v1 and desired velocity signal v2 received by the subsequent controller smooth, fundamentally avoiding system overshoot and violent oscillation caused by sudden changes in the target command, and significantly expanding the adjustable range of the subsequent controller parameters, thus enhancing the system's adaptability.

[0061] S03 receives the actual position feedback signal y of the controlled object and the control input u of the previous control cycle through the extended state observer, and estimates the actual position state observation value z1, the actual velocity state observation value z2 and the total disturbance state observation value z3 of the controlled object in real time; the total disturbance state is composed of the coupling between the unmodeled dynamics inside the system and the time-varying unknown load impedance outside.

[0062] S04, perform nonlinear state error feedback (NLDEF) calculation. Based on the desired position signal v1, desired velocity signal v2, and the observed actual position and velocity states z1 and z2 of the controlled object, calculate the position tracking error e. p With speed tracking error e d The initial state feedback control quantity u0 is calculated based on the error through nonlinear combination.

[0063] The Extended State Observer (ESO) is the core of this invention to achieve self-disturbance rejection capability. It receives two inputs in parallel: one is the actual position feedback signal y of the controlled object obtained from the displacement sensor (e.g., a high-precision grating ruler), and the other is the control input u output from the previous control cycle. The goal of the Extended State Observer is to estimate the system state and total disturbance that cannot be directly measured in real time and accurately.

[0064] Specifically, the bionic thoracic motion system is regarded as a second-order system subjected to a total disturbance, which can be expanded into a new state variable x3, and together with the actual position and actual velocity, constitutes a third-order expanded state system.

[0065] In each control cycle, the extended state observer calculates the position observation error e1 = z1 - y, where z1 is the actual position state observation value estimated internally by the ESO; subsequently, the position observation error e1 is used to drive the following continuous-form observer equation (actually iterated in discrete form in the controller):

[0066]

[0067]

[0068]

[0069] in, , and b represents the three feedback gain coefficients of the extended state observer, which need to be tuned during actual operation; b is the system control gain.

[0070] To simplify debugging, the three gain coefficients are combined with one adjustable parameter—the observer bandwidth. Associations are tuned according to the following rules: , , Increase It can improve the speed of observation.

[0071] Through iteration, the extended state observer outputs a real-time estimate of the system state z. 1,z2, and z3, the total disturbance state observation formed by the coupling of unmodeled dynamics within the system (such as nonlinear friction and gaps) and external time-varying unknown load impedance (such as the deformation force of flexible electronic devices).

[0072] In this invention, the extended state observer does not require a precise mathematical model of the disturbance. It can estimate the total disturbance z3, which includes all internal and external uncertainties, in real time using only measurable input u and output y. This enables the controller to sense and respond to unpredictable disturbances, laying the foundation for active compensation and is the key to the high robustness of this solution.

[0073] S05, Generate equivalent disturbance compensation component z based on total disturbance state. k The initial state feedback control quantity u0 is used to subtract the equivalent disturbance compensation component z. k , generates actual control command u.

[0074] In this step, the tracking error is calculated first:

[0075] Position tracking error: ;

[0076] Speed ​​tracking error: ;

[0077] Based on position tracking error e p and velocity tracking error e d The specific steps for calculating the initial state feedback control quantity u0 through nonlinear combination are as follows:

[0078]

[0079] in, For proportional gain; This is the differential gain; , These are nonlinear factors that determine the degree of nonlinear transformation of the error and are used to adjust the nonlinear shape. It is a piecewise nonlinear function.

[0080] Piecewise nonlinear function Specifically:

[0081]

[0082] in, This is the threshold width of the linear interval.

[0083] When error When the error is large, a larger gain is provided to quickly eliminate the error; When smaller, As a linear feedback, it avoids flutter near the equilibrium point.

[0084] Finally, obtain the total disturbance state observation z3, divide z3 by the system control gain b, and obtain the equivalent disturbance compensation component z. k This component represents the control force required to counteract the current total disturbance. It is calculated by subtracting the equivalent disturbance compensation component z from the initial state feedback control quantity u0. k The final actual control command u is obtained:

[0085]

[0086] By using feedforward compensation, the estimated total disturbance z3 is directly canceled in the control command. This allows the controller to only handle the residual disturbance that is not observed by the ESO, thereby greatly reducing the burden on the feedback controller. This enables the system to exhibit the characteristics of an approximate integral series standard system, even under inaccurate models and strong disturbances, achieving high-precision and robust tracking control.

[0087] S06 sends the actual control command u to the motor driver to drive the bionic chest cavity motion simulation platform to reproduce the target respiratory motion trajectory.

[0088] Finally, the actual control command u is sent from the core controller to the motor driver (such as the COPLEY driver) via a real-time bus (such as EtherCAT); the motor driver drives the linear motor (such as the voice coil linear motor) to move, thereby driving the bionic chest cavity mechanism to accurately reproduce the target breathing trajectory; the high-precision grating ruler detects the actual displacement of the mechanism in real time, forming an actual position feedback signal y, and feeds it back to ESO, thereby completing the entire closed-loop control loop.

[0089] Example 2

[0090] like Figure 2 As shown, this embodiment provides an active disturbance rejection control system suitable for biomimetic chest cavity motion simulation. The system includes:

[0091] The host computer is used to generate and send nonlinear motion trajectory signals that simulate the rise and fall of the human chest cavity during breathing.

[0092] The instruction processing module is used to preprocess the received nonlinear motion trajectory signal to generate the original target position instruction; and to process the original target position instruction to generate the desired position signal v1 and desired velocity signal v2 with smooth transition.

[0093] The state observation module is used to receive the actual position feedback signal y of the controlled object and the control input u of the previous control cycle, and to estimate the actual position state observation value z1, the actual velocity state observation value z2 and the total disturbance state observation value z3 of the controlled object in real time.

[0094] The error compensation module is used to calculate the position tracking error e based on the desired position signal v1, the desired velocity signal v2, the observed actual position state z1 of the controlled object, and the observed actual velocity state z2. p With speed tracking error e d The initial state feedback control quantity u0 is calculated based on the error through nonlinear combination.

[0095] The control command reprocessing module is used to generate an equivalent disturbance compensation component z based on the total disturbance state. k And control the initial state feedback control quantity u0 minus the equivalent disturbance compensation component z. k , generate actual control command u;

[0096] The execution and feedback module is used to receive the actual control command u and drive the bionic chest cavity motion simulation platform to reproduce the target respiratory motion trajectory; it is also used to generate the actual position feedback signal y of the controlled object.

[0097] Several embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technological improvements to the embodiments in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A method for active disturbance rejection control suitable for biomimetic chest cavity motion simulation, characterized in that, The methods include: S01, acquire the nonlinear motion trajectory signal simulating the rise and fall of the human chest cavity during breathing, preprocess the nonlinear motion trajectory signal, and generate the original target position command; S02, the original target position command is processed using a tracking differentiator to generate a smooth transition desired position signal v1 and desired velocity signal v2; S03, the actual position feedback signal y of the controlled object and the control input u of the previous control cycle are received through the extended state observer, and the actual position state observation value z1, the actual velocity state observation value z2 and the total disturbance state observation value z3 of the controlled object are estimated in real time; the total disturbance state is composed of the coupling of the unmodeled dynamics inside the system and the time-varying unknown load impedance outside. S04, perform nonlinear state error feedback calculation, based on the desired position signal v1, desired velocity signal v2, and the observed values ​​of the actual position and velocity states of the controlled object z1 and z2, calculate the position tracking error e. p With speed tracking error e d And based on the error, the initial state feedback control quantity u0 is calculated through nonlinear combination; S05, Generate an equivalent disturbance compensation component z based on the total disturbance state. k The initial state feedback control quantity u0 is used to subtract the equivalent disturbance compensation component z. k , generate actual control command u; S06, the actual control command u is sent to the motor driver to drive the bionic chest cavity motion simulation platform to reproduce the target respiratory motion trajectory.

2. The active disturbance rejection control method for biomimetic chest cavity motion simulation according to claim 1, characterized in that, In step S02, the step of processing the original target position command through the tracking differentiator specifically includes: A discrete state update equation is established. Based on the deviation x1 between the original target position command and the position tracking signal, the velocity tracking signal x2, the preset filter factor r, and the solution step size h, the acceleration control quantity fh is solved using the optimal control synthesis function. The position tracking signal and the velocity tracking signal are discretized and iteratively updated using the acceleration control quantity fh to generate and output the desired position signal v1 and the desired velocity signal v2 with smooth transition.

3. The active disturbance rejection control method for biomimetic chest cavity motion simulation according to claim 2, characterized in that, The optimal control synthesis function is:

4. Among them, x1 is the deviation between the original target position command and the position tracking signal; x2 is the velocity tracking signal; r is the preset filtering factor; h is the calculation step size.

5. The active disturbance rejection control method for biomimetic chest cavity motion simulation according to claim 1, characterized in that, In S04, the specific steps for calculating the initial state feedback control quantity u0 based on the error through nonlinear combination are as follows:

6. Among them, For proportional gain; This is the differential gain; , These are the nonlinear factors that determine the degree of nonlinear transformation of the error; It is a piecewise nonlinear function.

7. The active disturbance rejection control method for biomimetic chest cavity motion simulation according to claim 4, characterized in that, The piecewise nonlinear function is specifically: ； in, This is the threshold width of the linear interval.

8. A self-disturbance rejection control system suitable for biomimetic chest cavity motion simulation, characterized in that, The system includes: The host computer is used to generate and send nonlinear motion trajectory signals that simulate the rise and fall of the human chest cavity during breathing. The instruction processing module is used to preprocess the received nonlinear motion trajectory signal to generate an original target position instruction; and to process the original target position instruction to generate a smooth transition desired position signal v1 and a desired velocity signal v2. The state observation module is used to receive the actual position feedback signal y of the controlled object and the control input u of the previous control cycle, and to estimate the actual position state observation value z1, the actual velocity state observation value z2 and the total disturbance state observation value z3 of the controlled object in real time. The error compensation module is used to calculate the position tracking error e based on the desired position signal v1, the desired velocity signal v2, the observed actual position state z1 of the controlled object, and the observed actual velocity state z2. p With speed tracking error e d The initial state feedback control quantity u0 is calculated based on the error through nonlinear combination. The control command reprocessing module is used to generate an equivalent disturbance compensation component z based on the total disturbance state. k And control the initial state feedback control quantity u0 minus the equivalent disturbance compensation component z. k , generate actual control command u; The execution and feedback module is used to receive the actual control command u and drive the bionic chest cavity motion simulation platform to reproduce the target breathing motion trajectory; it is also used to generate the actual position feedback signal y of the controlled object.

9. A self-disturbance rejection control system suitable for biomimetic chest cavity motion simulation according to claim 6, characterized in that, The execution and feedback module includes a motor drive unit, multiple linear motors connected to the motor drive unit, and multiple displacement sensors for real-time detection of the actual displacement information of each mechanism of the bionic thoracic motion simulation platform.

10. An electronic device, comprising a processor and a memory; characterized in that, The processor is connected to a memory; the memory is used to store executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the active disturbance rejection control method for bionic chest cavity motion simulation as described in any one of claims 1-5.

Images