Human-machine interaction force control method based on flexible driver

Abstract

The application discloses a human-computer interaction force control method based on a flexible driver. In view of the safety hidden trouble problem existing in the human-computer interaction process of a rigid driver, a human-computer interaction force control algorithm is designed based on a flexible driver dynamics model fusing human body motion information. A human body motion compensation controller and a friction compensation controller are used to enhance the robustness and stability of the designed human-computer interaction control algorithm. The human-computer interaction force control method based on the flexible driver comprises the following steps: a. constructing a flexible driver dynamics model fusing human body motion information; b. designing a human body motion compensation controller and a friction compensation controller; c. designing a human-computer interaction force controller based on the above steps; and d. proving the rationality and effectiveness of the human-computer interaction force controller through a platform experiment.

Description

Technical fields:

[0001] This invention belongs to the technical fields of mechatronics, rehabilitation robots, and assistive robots, and relates to a human-computer interaction force control method based on a flexible actuator. Background technology:

[0002] In recent years, the rapid development of mechatronics technology has driven the rise of various projects involving physical interaction with humans, including rehabilitation robots and exoskeleton robots. Rehabilitation robots are widely used to rehabilitate stroke and neurological injury patients, while wearable robots focus on assisting patients with physical disabilities. Human-machine interaction control is crucial for two basic operating modes: human-driven mode and machine-driven mode. In human-driven mode, the robot should be able to follow human movement with minimal interaction force with the subject's limbs; in machine-driven mode, the robot should be able to provide accurate torque to the subject's limbs as needed. Safety, i.e., the stability of human-machine interaction, has always been a critical issue in both control modes. Safety and personalization are among the key driving factors for the research of flexible actuators. Unlike rigid actuators, flexible actuators decouple the drive device and end effector by introducing elastic elements in series between the load and the geared motor. This allows the flexible actuator to isolate external shocks and vibrations, improving the safety of robot applications and the comfort of human-machine interaction. First, a dynamic model of the flexible actuator incorporating human motion information is established. Second, a human motion compensation controller and a friction compensation controller are designed to reduce the mechanical impedance of the flexible actuator by compensating for its inertia and friction. Furthermore, based on a human motion compensation controller and a friction compensation controller, a human-computer interaction force control method based on a flexible actuator is designed to address the stability issue during human-computer interaction. Finally, platform experiments are conducted to verify the stability of the human-computer interaction force control method and the accuracy of trajectory tracking. Summary of the Invention:

[0003] This invention discloses a human-computer interaction force control method based on a flexible actuator. Addressing the controller instability problem caused by the lack of human dynamics modeling in human-computer interaction control, this invention proposes a human-computer interaction force control method based on a flexible actuator. The technical solution of this invention is as follows:

[0004] A human-computer interaction force control method based on a flexible actuator, the specific method is as follows:

[0005] S1: Construct a dynamic model of a flexible actuator that integrates human motion information;

[0006] S2: Design a human motion compensation controller and a friction compensation controller;

[0007] S3: Simplify the dynamic model of the flexible actuator and design a human-machine interactive force controller;

[0008] S4: Demonstrate the rationality and effectiveness of the human-computer interaction controller through platform experiments.

[0009] The specific process of step S1 is as follows:

[0010] First, according to Newton-Euler's method, the dynamic model from the motor to the bevel gear is expressed as follows:

[0011]

[0012] Among them, J m Let be the moment of inertia of the motor. B is the rotational acceleration of the motor. m This is the elastic damping coefficient of the motor. τ is the rotational speed of the motor. bg k is the interaction torque between the bevel gear pairs. m Let i be the torque constant of the motor. c For the input current, τ m This refers to the motor torque.

[0013] Secondly, according to Newton-Euler's method, the dynamic model of the transformation from a bevel gear to a spur gear is expressed as follows:

[0014]

[0015] Among them, J bg Let the moment of inertia of the driven bevel gear be the rotational inertia of the bevel gear. B is the rotational acceleration of the driven bevel gear. bg The elastic damping coefficient of the driven bevel gear is... Let τ be the rotational speed of the driven bevel gear. mf This refers to the interaction torque between the spur gear and the rack. Where r is the radius of the spur gear, m0 is the module of the spur gear, and N is the number of teeth of the spur gear.

[0016] Again, based on Newton's and Euler's methods, the dynamic model from the rack to the human upper limb is expressed as follows:

[0017]

[0018] Among them, M r The equivalent mass of the rack is... B is the acceleration of the rack. r is the damping coefficient of the rack. X is the speed of the rack's movement. r X represents the displacement of the rack. ea This refers to the displacement of the human upper limb. F mf This refers to the interaction force between the spur gear and the rack.

[0019] By substituting formula (1) into formula (2), we can obtain

[0020]

[0021] Due to θ m =iθ gear ,and Where i is the transmission ratio between the bevel gear pairs, therefore substituting formula (4) into formula (3) yields

[0022]

[0023] Where A = m0N 2 M r +4(iJ m +J bg ) and B = m0N 2 B r +4(iB m +B bg f mg The nonlinear frictional force during the motion of the flexible actuator is specifically expressed as:

[0024]

[0025] Where z1 is the viscous damping coefficient, z2 is the Coulomb friction coefficient, z3 is the Stribek friction coefficient, and X Stribeck Let be the characteristic velocity of the Stribek friction. According to Hooke's law, the output force of the flexible actuator is expressed as...

[0026] F = K(X) rank -X ea (7)

[0027] Where F is the output force of the flexible actuator, and K is the spring stiffness coefficient. Substituting equation (7) into equation (6), the dynamic model of the flexible actuator is:

[0028]

[0029] in, The speed at which the flexible actuator outputs force. The acceleration of the output force of the flexible actuator. X represents the speed of movement of the human upper limbs. ea This refers to the acceleration of movement in the upper limbs of the human body.

[0030] The specific process of step S2 is as follows:

[0031] The dynamic model of the flexible actuator shows that the influence of human motion is reflected in... By designing a controller τ hbThis reduces the initial resistance experienced by the upper limbs at the start of exercise.

[0032]

[0033] Where, τ hb This is a human motion compensation controller that makes the movement of the human upper limbs smoother and more natural, thereby reducing additional resistance and discomfort.

[0034] Before designing the human-machine interface force controller, it was recognized that friction could negatively impact the performance of flexible actuator systems. Specifically, friction increases the output impedance of the flexible actuator, thereby reducing its flexibility and response speed. Therefore, a friction compensation controller was designed to...

[0035]

[0036] Where τ f For friction compensation controller, controller τ f Effectively reduce the impact of friction on the flexible actuator system to ensure that the flexible actuator can respond quickly and flexibly to the interaction force generated with the subject's upper limb.

[0037] The specific process of step S3 is as follows:

[0038] In step S2, the controller τ is designed respectively. hb and τ f To mitigate the drag and discomfort associated with human movement and compensate for the effects of friction on the flexible actuator system, it is necessary to consider various factors. However, due to the inherent complexity and uncertainties in practical applications, completely eliminating the adverse effects of human movement and friction on the performance of the flexible actuator system remains challenging. To improve the accuracy of the control system, factors that cannot be predicted or accurately compensated in the system are collectively referred to as a disturbance term d. Therefore, the dynamic model of the flexible actuator is simplified to...

[0039]

[0040] Where, τ fb For feedback controllers, τ d This is a disturbance compensator. To achieve human-machine interactive force feedback control, the error function e = F is defined. d -F1. Substituting the error function into formula (10) yields...

[0041]

[0042] Among them, F d For the expected force trajectory, For the velocity trajectory of the desired force, For the desired force acceleration trajectory, The first derivative of the error function. Let be the second derivative of the error function, and let... get

[0043]

[0044] in, For the estimated disturbance, k c Let γ be the control gain in the control system, and let γ be a constant. Matrix P needs to satisfy...

[0045]

[0046] Where P is a positive definite matrix.

[0047] Design the auxiliary variable Z as follows:

[0048]

[0049] Among them, variables According to formulas (10), (11) and (15), the derivative of the auxiliary variable Z is:

[0050]

[0051] in, Therefore, the human-computer interaction force controller is designed as

[0052]

[0053] The specific process of step S4 is as follows:

[0054] (1) Interactive torque stability test experiment

[0055] The purpose of interactive torque stability testing is to analyze the interaction of joint torques during human movement and to evaluate the stability of an upper limb exoskeleton robot system driven by flexible actuators. In the interactive torque and stability test scenario, the joint movements of the upper limb exoskeleton robot driven by flexible actuators are stimulated by hand. The stability and adaptability of the system are evaluated by measuring the interaction forces at different frequencies, thereby obtaining the response characteristics of the upper limb exoskeleton robot system driven by flexible actuators at different motion frequencies.

[0056] (2) Human-initiated experiments

[0057] To verify the performance of the human-computer interaction control algorithm, subjects wearing flexible actuator-driven upper limb exoskeleton robots performed natural elbow flexion and extension movements to measure zero-force control. The human control mode allows subjects to perceive that the flexible actuator-driven upper limb exoskeleton robot generates no resistance during autonomous movement, thus enabling the robot to assist the human body on demand.

[0058] (3) Machine-driven experiments

[0059] The robot-responsible mode indicates that the robot provides torque tracking control to mimic human movement, enabling subjects to naturally follow the movements of an upper limb exoskeleton robot driven by flexible actuators. Attached Figure Description

[0060] Figure 1 A dynamic model diagram of a flexible actuator that incorporates human motion information;

[0061] Figure 2 Platform experimental diagram;

[0062] Figure 3 Figure 1 shows an experimental diagram of the stability test of an upper limb exoskeleton robot driven by a flexible actuator at different frequencies.

[0063] Figure 4 Figure 1 shows the performance of an upper limb exoskeleton robot driven by a flexible actuator in human active driving.

[0064] Figure 5 Figure showing the tracking performance of an upper limb exoskeleton robot driven by a flexible actuator in machine-driven operation. Detailed Implementation

[0065] The present invention will be further described below with reference to the embodiments and the accompanying drawings:

[0066] S1: Constructing a dynamic model of a flexible actuator that integrates human motion information

[0067] Figure 1 This is a diagram of the dynamic model of the flexible actuator that integrates human motion information according to the present invention. Figure 1 As shown, according to Newton-Euler's method, the dynamic model from the motor to the bevel gear is expressed as follows:

[0068]

[0069] Among them, J m Let be the moment of inertia of the motor. B is the rotational acceleration of the motor. m This is the elastic damping coefficient of the motor. τ is the rotational speed of the motor. bg k is the interaction torque between the bevel gear pairs.m Let i be the torque constant of the motor. c For the input current, τ m This refers to the motor torque.

[0070] like Figure 1 As shown, the dynamic model of the transition from a bevel gear to a spur gear is expressed as follows:

[0071]

[0072] Among them, J bg Let the moment of inertia of the driven bevel gear be the rotational inertia of the bevel gear. B is the rotational acceleration of the driven bevel gear. bg The elastic damping coefficient of the driven bevel gear is... Let τ be the rotational speed of the driven bevel gear. mf This is the interaction torque between the spur gear and the rack.

[0073] like Figure 1 As shown, the dynamic model from the rack to the human upper limb is represented as follows:

[0074]

[0075] Among them, M r The equivalent mass of the rack is... B is the acceleration of the rack. r is the damping coefficient of the rack. X is the speed of the rack's movement. r X represents the displacement of the rack. ea This refers to the displacement of the human upper limb. F mf This refers to the interaction force between the spur gear and the rack. Where r is the radius of the spur gear, m0 is the module of the spur gear, and N is the number of teeth of the spur gear.

[0076] By substituting formula (18) into formula (19), we can obtain

[0077]

[0078] Due to θ m =iθ gear ,and Where i is the transmission ratio between the bevel gear pairs, therefore substituting formula (21) into formula (20) yields

[0079]

[0080] Where A = m0N 2 M r +4(iJ m +J bg ) and B = m0N2 B r +4(iB m +B bg f mg The nonlinear frictional force during the motion of the flexible actuator is specifically expressed as:

[0081]

[0082] Where z1 is the viscous damping coefficient, z2 is the Coulomb friction coefficient, z3 is the Stribek friction coefficient, and X Stribeck Let be the characteristic velocity of the Stribek friction. According to Hooke's law, the output force of the flexible actuator is expressed as...

[0083] F = K(X) rank -X ea ) (twenty four)

[0084] Where F is the output force of the flexible actuator, and K is the spring stiffness coefficient. Substituting equation (24) into equation (23), the dynamic model of the flexible actuator is:

[0085]

[0086] in, The speed at which the flexible actuator outputs force. The acceleration of the output force of the flexible actuator. X represents the speed of movement of the human upper limbs. ea This refers to the acceleration of movement in the upper limbs of the human body.

[0087] S2: Design a human motion compensation controller and a friction compensation controller.

[0088] like Figure 2 (b) is a human-computer interaction control system. From the figure, we can see that τ hb and τ f These are a human motion compensation controller and a friction compensation controller, respectively.

[0089] The dynamic model of the flexible actuator shows that the influence of human motion is reflected in...

[0090] By designing a controller τ hb This reduces the initial resistance experienced by the upper limbs at the start of exercise.

[0091]

[0092] Where, τ hb This is a human motion compensation controller that makes the movement of the human upper limbs smoother and more natural, thereby reducing additional resistance and discomfort.

[0093] Before designing the human-machine interface force controller, it was recognized that friction could negatively impact the performance of flexible actuator systems. Specifically, friction increases the output impedance of the flexible actuator, thereby reducing its flexibility and response speed. Therefore, a friction compensation controller was designed to...

[0094]

[0095] Where τ f Therefore, for the friction compensation controller, the controller τ f Effectively reduce the impact of friction on the flexible actuator system to ensure that the flexible actuator can respond quickly and flexibly to the interaction force generated with the subject's upper limb.

[0096] S3: Simplify the dynamic model of the flexible actuator and design a human-machine interactive force controller;

[0097] like Figure 2 (b) is a human-computer interaction control system. From the figure, we can see that τ fb and τ d These are a feedback controller and a disturbance compensator, respectively.

[0098] from Figure 2 (b) shows that the controller τ hb and τ f The design aims to reduce drag and discomfort associated with human movement and compensate for the effects of friction on the flexible actuator system. However, due to the inherent complexity and uncertainties in practical applications, completely eliminating the adverse effects of human movement and friction on the performance of the flexible actuator system remains challenging. To improve the accuracy of the control system, factors that cannot be predicted or accurately compensated in the system are collectively referred to as a disturbance term d. Therefore, the dynamic model of the flexible actuator is simplified to...

[0099]

[0100] Where, τ fb For feedback controllers, τ d This is a disturbance compensator. To achieve human-machine interactive force feedback control, the error function e = F is defined. d -F1. Substituting the error function into formula (28) yields...

[0101]

[0102] Among them, F d For the expected force trajectory, For the velocity trajectory of the desired force, For the desired force acceleration trajectory, The first derivative of the error function. Let be the second derivative of the error function, and let... get

[0103]

[0104] in, For the estimated disturbance, k c Let γ be the control gain in the control system, and let γ be a constant. Matrix P needs to satisfy...

[0105]

[0106] Where P is a positive definite matrix.

[0107] Design the auxiliary variable Z as follows:

[0108]

[0109] Among them, variables According to formulas (28), (29) and (32), the derivative of the auxiliary variable Z is:

[0110]

[0111] in, Therefore, the human-computer interaction force controller is designed as

[0112]

[0113] S4: Demonstrate the rationality and effectiveness of the human-computer interaction controller through platform experiments.

[0114] This step verifies the effectiveness and feasibility of the human-computer interaction control algorithm through platform experiments.

[0115] like Figure 2 (a) is a data acquisition system that acquires muscle activation levels using angle sensors and a Biopac system, and obtains the elbow joint motion torque of a healthy person using the Hill muscle model. This torque is then used as the ideal input signal for the human-computer interaction force control system. Figure 2 (c) This is an upper limb exoskeleton robot system driven by a flexible actuator, mainly composed of a power module, a flexible actuator, a microcontroller, and an elbow joint module. The flexible actuator is fixed by a bracket and drives the elbow joint module via a Bowden cable, thereby assisting the subject's movement. The Coulomb friction coefficient z2 is estimated by detecting whether a given small input signal begins to generate force. Experimental observation shows that the flexible actuator generates an output force when the input current is 0.785A. Therefore, the Coulomb friction coefficient z2 is estimated to be 0.017 Nm. The characteristic velocity of Stribeck friction is... The value ranges from 0.00001 m / s to 0.1 m / s. In the human-machine interaction force controller, Q = diag{1000, 1} and γ = 1. The control gain k = [31.62271.0034]. In the auxiliary variable Z, the parameter L equals 1000.

[0116] (1) Interactive torque stability test experiment

[0117] The purpose of interactive torque stability testing is to analyze the interaction of joint torques during human movement and to evaluate the stability of the robotic system. By measuring the interaction forces at different frequencies, the stability and adaptability of the system are assessed, thereby understanding the robot's response characteristics at different motion frequencies. In the interactive torque and stability testing scenario, the joint movements of an upper limb exoskeleton robot driven by flexible actuators are stimulated by hand. Figure 3 The dashed and solid lines in the diagram represent the interaction torques and corresponding joint angles from low to high frequencies, respectively. Figure 3 As shown in (a), the peak value of the interaction torque is approximately 0.5 Nm at low frequencies. With increasing excitation frequency, the interaction torque continues to oscillate at a peak height below 1 Nm. Therefore, this experiment demonstrates that the proposed human-machine interface controller enhances the stability of the upper limb exoskeleton robot driven by flexible actuators.

[0118] (2) Human-initiated experiments

[0119] like Figure 4 This figure shows the performance experiment of an upper limb exoskeleton robot driven by flexible actuators in human active motion. As shown, three subjects wearing the upper limb exoskeleton robot, driven by flexible actuators, generated maximum interaction torques of 1.4 Nm, 1 Nm, and 0.9 Nm, respectively, during elbow flexion and extension movements. The critical point of motion reversal of the flexible actuators generates huge driving inertia, leading to a significant increase in the interaction torque between the subjects and the upper limb exoskeleton robot. Apart from the transition movement of the elbow joint from flexion to extension, the upper limb exoskeleton robot driven by flexible actuators has a negligible impact on the subjects' motor experience. Figure 4 As shown in (a), compared to the latter two subjects, the first subject had a larger range of elbow movement, resulting in a greater mutual torque between the subject and the robot during elbow movement. Even under these conditions, the controlled human-robot system remained stable. Therefore, the robustness and effectiveness of the proposed human-robot interaction controller were demonstrated through active human-robot experiments with three subjects.

[0120] (3) Machine-driven experiments

[0121] The joint torque of normal subjects during elbow flexion and extension movements was collected using the Hill muscle model and used as the desired torque trajectory for tracking by an upper limb exoskeleton robot driven by a flexible actuator. Figure 5(a) Torque tracking effect of upper limb exoskeleton robot assisting subject in elbow flexion and extension movements based on the proposed human-computer interaction controller. Figure 5 (b) represents the tracking error, where the maximum tracking error is less than 0.3 Nm. This indicates that the designed upper limb exoskeleton robot driven by flexible actuators can provide precise auxiliary torque to human joints, thus verifying the feasibility and effectiveness of the proposed human-machine interaction controller in a practical platform system.

Claims

1. A human-machine interaction force control method based on a flexible actuator, characterized in that, It includes the following steps: S1: Construct a dynamic model of a flexible actuator that integrates human motion information; S2: Design a human motion compensation controller and a friction compensation controller; S3: Simplify the dynamic model of the flexible actuator and design a human-machine interactive force controller; S4: Demonstrate the rationality and effectiveness of the human-computer interaction controller through platform experiments; In step S1, the process of constructing the dynamic model of the flexible actuator that integrates human motion information is as follows: First, according to Newton-Euler's method, the dynamic model from the motor to the bevel gear is expressed as follows: （1） in, Let be the moment of inertia of the motor. The rotational acceleration of the motor. This is the elastic damping coefficient of the motor. The rotational speed of the motor. The interaction torque between the bevel gear pairs. Let be the torque constant of the motor. For input current, This refers to the motor torque; Secondly, according to Newton-Euler's method, the dynamic model of the transformation from a bevel gear to a spur gear is expressed as follows: （2） in, Let the moment of inertia of the driven bevel gear be the rotational inertia of the bevel gear. The rotational acceleration of the driven bevel gear. The elastic damping coefficient of the driven bevel gear is... The rotational speed of the driven bevel gear. This refers to the interaction torque between the spur gear and the rack. ,in Let be the radius of the spur gear. This is the module of the spur gear. This represents the number of teeth on a spur gear. Again, based on Newton's and Euler's methods, the dynamic model from the rack to the human upper limb is expressed as follows: （3） in, The equivalent mass of the rack is... The acceleration of the rack's motion, is the damping coefficient of the rack. The speed of the rack movement, The displacement of the rack. This refers to the displacement of the human upper limbs. The interaction force between the spur gear and the rack; by substituting formula (1) into formula (2), we can obtain （4） because ,and ,in Given the transmission ratio between the bevel gear pairs, substituting formula (4) into formula (3) yields... （5） in, , , The nonlinear frictional force during the motion of the flexible actuator is specifically expressed as: （6） in The viscous damping coefficient is... The coefficient of friction is Coulomb. The coefficient of friction is Stribek. Let be the characteristic velocity of the Stribeck friction; according to Hooke's law, the output force of the flexible actuator is expressed as... （7） in, For the output force of the flexible actuator, Let be the spring stiffness coefficient; substituting formula (7) into formula (6), the dynamic model of the flexible actuator is: （8） in, The speed at which the flexible actuator outputs force. The acceleration of the output force of the flexible actuator. The speed of movement of the human upper limbs The acceleration of movement in the human upper limbs; In step S3, the specific process is as follows: Controllers were designed in step S2. and This is to reduce resistance and discomfort associated with human movement and to compensate for the effects of friction on the flexible actuator system; to improve the accuracy of the control system, factors that cannot be predicted or accurately compensated in the system are collectively referred to as a disturbance term. Therefore, the dynamic model of the flexible actuator simplifies to （9） in, For feedback controller, For disturbance compensators; to achieve human-machine interactive force feedback control, an error function is defined. Substituting the error function into formula (9) yields... （10） in, For the expected force trajectory, For the velocity trajectory of the desired force, For the desired force acceleration trajectory, The first derivative of the error function. Let be the second derivative of the error function, and let... ,get （11） in, , , , For the estimated disturbance, , For control gain in the control system, A constant, a matrix Need to meet （12） in, Design auxiliary variables for a positive definite matrix. for （13） Among them, variables According to formulas (9), (10) and (13), auxiliary variables The derivative is （14） Therefore, the human-computer interaction force controller is designed as （15） in, .

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