A prosthetic hand system and method based on force / position hybrid fuzzy control

Abstract

The present invention provides a prosthetic hand system and method based on force / position hybrid fuzzy control. The system comprises a prosthetic hand body, a force feedback module for obtaining the contact pressure of corresponding fingertips, a position feedback module for obtaining motion information of each joint, and calculating the flexion / extension angles and adduction / abduction angles of the corresponding joint based on this motion information to determine the position information of each joint. A control module for dynamically adjusting the force applied by the prosthetic hand during grasping using an impedance model based on the contact pressure of each fingertip, converting force control commands into position control commands, and combining these position control commands with the position information of each joint to generate a final control signal, which is then sent to the corresponding drive module. The present invention can ensure high-precision control while reducing the design complexity of the controller.

Description

Technical Field

[0001] The present invention belongs to the field of prosthetic limb control, and in particular relates to a prosthetic hand system and method based on force / position hybrid fuzzy control. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] Human hand grasping is an essential ability in daily life and work. Its complexity and dexterity have long been a research focus in prosthetic hand design and robotic control. The human hand can perform a wide range of grasping movements, from delicate pinching to strong gripping, all of which rely on the body's diverse feedback mechanisms. Force / position hybrid feedback control, a control strategy that combines force and position feedback, is widely used in robotic control systems. Compared to simple force or position feedback, force / position hybrid feedback enables more comprehensive perception and adaptation to complex environments, enhancing the robot's ability to interact in uncertain environments. Force feedback alone is prone to position errors, especially when grasping fragile objects. Without position feedback support, this can lead to damage. Position feedback alone can ignore changes in external forces, resulting in grasp failure or unstable control. Force / position hybrid feedback integrates information from both, enabling the system to find a balance between force and position, ensuring stable grasping.

[0004] Impedance control, a commonly used hybrid force / position control strategy for robots, adjusts the force-displacement relationship between the robot and its environment to control the robot's dynamic response characteristics under external forces, thereby achieving compliant interactive behavior. This control method does not directly control the applied force or precise position of the robot's end-point. Instead, it adjusts the system's mechanical impedance (i.e., stiffness, damping, and inertia) to ensure that the robot exhibits appropriate compliance and stability when interacting with its environment, enabling safe and effective operation in diverse tasks and environments. Fuzzy control is a control method based on fuzzy logic that addresses uncertainties and nonlinear systems. It achieves system control by converting expert experience and knowledge into fuzzy rules. While it is difficult to establish a specific mathematical model for the relationship between the system's impedance parameters and the tissue model during the dynamic interaction between a prosthetic hand and an object, its changes exhibit corresponding trends. Therefore, fuzzy control algorithms can be used to estimate the correlation coefficients in the impedance model.

[0005] Because impedance control achieves hybrid position and force control by adjusting the relationship between the applied force and the robot's end-point trajectory, the controller's accuracy requirements are very high. Accurate impedance control requires not only real-time monitoring and feedback of the system's status, but also the ability of the controller to quickly respond and adjust in complex operating environments. However, as control accuracy increases, controller design becomes increasingly complex, often requiring more sensors, higher computing power, and more complex algorithms. Reducing controller design complexity, reducing the system's hardware dependency, and reducing the computational burden while maintaining high-precision control remains a major challenge. Summary of the Invention

[0006] In order to solve the above problems, the present invention proposes a prosthetic hand system and method based on force / position hybrid fuzzy control. In terms of control, the present invention has a dual-loop structure, including an external force control loop and an internal position control loop. The external force control loop is implemented using an impedance model based on the inverse kinematics of the prosthetic hand, and a fuzzy control algorithm is used to calculate the damping and stiffness of the impedance model, so that the damping and stiffness can be self-adjusted in real time according to different contact environments. The internal position control loop is implemented using a model-free control (MFC) controller based on an ultra-local model (ULM), which integrates all the characteristics and uncertainties of the system into one unknown term, greatly reducing the complexity of the controller design. In addition, a time-lag estimator is used to estimate the value of the unknown term. The introduction of the time-lag estimator also reduces the requirements for accurate model parameters.

[0007] According to some embodiments, the present invention adopts the following technical solutions:

[0008] A prosthetic hand system based on force / position hybrid fuzzy control, comprising:

[0009] The prosthetic hand body includes a palm platform, on which are disposed a plurality of finger structures, each of which has a plurality of joints, and each joint is equipped with a drive module;

[0010] The force feedback modules include a plurality of modules, each of which is provided at the fingertip portion of the finger structure and is used to obtain the contact pressure of the corresponding fingertip;

[0011] Position feedback modules, including multiple ones, are respectively provided at each joint, for obtaining motion information of each joint, and calculating the flexion / extension angle and adduction / abduction angle of the corresponding joint based on the motion information, thereby determining the position information of each joint;

[0012] The control module is connected to the force feedback module and the position feedback module, and is used to dynamically adjust the force applied by the prosthetic hand during grasping based on the contact pressure of each fingertip using the impedance model, and convert the force control command into a position control command. The final control signal is obtained by combining the position control command with the position information of each joint and sent to the corresponding drive module;

[0013] The drive module is used to control and drive the motor of the prosthetic hand. It receives signals from the control module and converts them into specific motor drive instructions to ensure that the various parts of the prosthetic hand can operate in a coordinated manner.

[0014] As an optional implementation, the force feedback module includes an FSR sensor, which is connected to a voltage conversion module. The voltage conversion module is used to perform linear voltage-resistance conversion of the FSR sensor using a resistance voltage divider to achieve contact pressure measurement.

[0015] As an optional implementation, the position feedback module includes an ADXL345 module, the ADXL345 module is connected to an analog switch module, and the analog switch module is used to control the selection of each ADXL345 module.

[0016] As an optional embodiment, each joint is driven by a motor, and the drive module is connected to the motor to convert the final control signal generated by the control module into a corresponding motor action to adjust the movement of the corresponding joint of the prosthetic hand to complete the specified task.

[0017] As an optional embodiment, the control module can be implemented using an ATMega2560 chip, including an external force control loop, which is configured to: compare the contact pressure of each fingertip with a preset or obtained expected force, use a fuzzy controller to calculate the ideal impedance characteristics, use an impedance model to generate a position change instruction corresponding to the target interaction force based on the impedance characteristics, combine the position information of the grasped object, and use the inverse kinematics model of the prosthetic hand to convert the position change instruction into bending angle information of each joint of the prosthetic hand.

[0018] As an optional embodiment, the fuzzy controller is configured to use fuzzy control theory to calculate the desired force f d and the actual contact force f e Fuzzy is performed to obtain the damping B of the prosthetic hand system. f , stiffness K f , the damping of the grasping object B i and stiffness K i and the expected force f d and the actual contact force f e The change relationship is the fuzzy inference rule, and the fuzzy control input variable (f d, f e ) and system control quantity (B f , K f 、B i and K i ) to defuzzify the system control quantity.

[0019] As an optional embodiment, the control module includes an internal position control loop, which is configured to: take the position information of each joint of the prosthetic hand body and the bending angle information of the external force control loop as input, use the MFC controller to obtain the final control signal, and use the time lag estimator to calculate the value of the unknown item in the MFC controller, and output the final control signal to the prosthetic hand body to achieve closed-loop control.

[0020] A prosthetic hand control method based on force / position hybrid fuzzy control comprises the following steps:

[0021] Obtaining the contact pressure of each fingertip;

[0022] Acquire motion information of each joint, and calculate the flexion / extension angle and adduction / abduction angle of the corresponding joint based on the motion information to determine the position information of each joint;

[0023] According to the contact pressure of each fingertip, the impedance model is used to dynamically adjust the force applied by the prosthetic hand during grasping, and the force control command is converted into a position control command. The position control command is combined with the position information of each joint to obtain the final control signal.

[0024] As an optional embodiment, the process of dynamically adjusting the force applied by the prosthetic hand during grasping using an impedance model according to the contact pressure of each fingertip and converting the force control command into a position control command includes:

[0025] According to the contact pressure of each fingertip and the preset or obtained expected force, the ideal impedance characteristics are calculated using a fuzzy controller. The impedance model is used to generate position change instructions corresponding to the target interaction force based on the impedance characteristics. Combined with the position information of the grasped object, the position change instructions are converted into bending angle information of each joint of the prosthetic hand using the inverse kinematics model of the prosthetic hand.

[0026] As an optional implementation, the process of combining the position control instruction and the position information of each joint to obtain the final control signal includes:

[0027] The position information of each joint of the prosthetic hand and the bending angle information of the external force control loop are used as input. The MFC controller is used to obtain the final control signal, and the time-delay estimator is used to calculate the value of the unknown term in the MFC controller. The final control signal is output to the prosthetic hand to achieve closed-loop control.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The prosthetic hand system based on force / position hybrid fuzzy control proposed in this invention uses FSR sensors and ADXL345 modules to obtain fingertip force information and joint angle information of the prosthetic hand, which is easy to integrate;

[0030] 2. The number of ADXL345 modules in the prosthetic hand system based on force / position hybrid fuzzy control proposed by the present invention can be increased or decreased according to the number of drivers of the prosthetic hand, and has a wide range of applications;

[0031] 3. The proposed prosthetic hand controller based on force / position hybrid fuzzy control is designed according to the ULM, integrating all characteristics and uncertainties of the system into one unknown term, which greatly reduces the complexity of the controller design;

[0032] 4. The prosthetic hand controller based on force / position hybrid fuzzy control proposed in the present invention uses a time-delay estimator to estimate the value of unknown terms, which greatly reduces the requirements for accurate model parameters;

[0033] 5. The prosthetic hand controller based on force / position hybrid fuzzy control proposed in the present invention uses a fuzzy control algorithm to estimate the damping and stiffness of the impedance model, so that the two can self-adjust in real time according to different contact environments, thereby improving the flexibility and accuracy of the prosthetic hand and enhancing its ability to adapt to different object shapes and materials.

[0034] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, preferred embodiments are given below and described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0036] Figure 1 This is a schematic diagram of the installation positions of the FSR sensor and ADXL345 module on the prosthetic hand in the force / position hybrid fuzzy control system proposed in the present invention;

[0037] Figure 2 This is a schematic diagram of the force / position hybrid fuzzy control system proposed by the present invention and the communication between its various parts;

[0038] Figure 3 The overall framework of the force / position hybrid fuzzy controller proposed in this invention;

[0039] Figure 4 This is a possible way to establish a rectangular coordinate system for a prosthetic hand.

[0040] Among them, 1 is the prosthetic hand, 2 is the FSR sensor, 3 is the ADXL345 module, 4 is the first prosthetic finger joint, 5 is the second prosthetic finger joint, 6 is the third prosthetic finger joint, and 7 is the fourth prosthetic finger joint. DETAILED DESCRIPTION

[0041] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0042] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0043] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0044] In the absence of conflict, the embodiments and features in the embodiments of this application can be combined with each other.

[0045] Example 1

[0046] This embodiment provides a prosthetic hand system based on force / position hybrid fuzzy control, including:

[0047] The prosthetic hand body includes a palm platform, on which are disposed a plurality of finger structures, each finger structure having a plurality of joints, each joint being driven by an independent motor, which is driven by a drive module;

[0048] Force feedback module: This module is responsible for acquiring pressure signals when the fingertips contact an object. In this embodiment, this module uses a voltage conversion method based on resistor voltage division, using an FSR sensor to detect pressure changes at the prosthetic fingertips in real time. The FSR sensor effectively captures the contact pressure between the prosthetic fingertips and the surface of the object, ensuring that the prosthetic can adjust the applied force in real time during grasping tasks, preventing damage to the object caused by over-tightening or drop caused by over-loosening.

[0049] Position Feedback Module: This module is primarily responsible for motion detection and position signal acquisition for the prosthetic hand. In this embodiment, this module uses the ADXL345 chip, a high-precision three-axis accelerometer, to acquire motion information from each of the five finger joints. By monitoring acceleration data, the chip calculates the flexion / extension angles and adduction / abduction angles of each joint, providing real-time position information for the prosthetic hand.

[0050] The ADXL345 chip's high sensitivity and low power consumption enable it to maintain high-precision data acquisition even under complex motion conditions. This module provides reliable position signal input for the prosthetic hand, enabling the entire system to respond quickly and accurately when performing different tasks.

[0051] Control module: This module is implemented using an ATMega2560 chip and is used to collect, process, calculate, and transmit all input data, as well as generate appropriate control signals. This module processes data from the force feedback module and the position feedback module to analyze the state of the prosthetic hand in real time and generate corresponding control strategies based on the MFC algorithm based on the ULM. The application of the MFC algorithm eliminates the need for the module to rely on precise system models and enables it to implement complex control functions through real-time learning and optimization. The control module, with its built-in computing power, is able to make intelligent decisions in different scenarios and adjust the movements of the prosthetic hand to suit specific task requirements, such as grasping, moving, rotating, etc., thereby achieving complex motion control. In addition, this module can transmit data to a host computer for storage via wired or wireless means.

[0052] The driver module is primarily responsible for controlling and driving the prosthetic hand's motors. This module receives signals from the control module and converts them into specific motor drive commands, ensuring coordinated operation of all parts of the prosthetic hand. Precise motor control is key to efficient prosthetic hand operation. Through the driver module, the prosthetic hand's joints achieve flexible movement, meeting a variety of complex operational requirements. The driver module boasts high-speed response capabilities, executing control commands instantly to ensure the stability and accuracy of the prosthetic hand.

[0053] like Figure 1As shown, in the force / position hybrid control system proposed in this embodiment, FSR sensors 2 are installed on the fingertips of the five fingers of the prosthetic hand 1, and ADXL345 modules 3 are installed on each knuckle of the prosthetic hand. In order to ensure precise control of different prosthetic hands, the number of ADXL345 modules 3 can be adjusted accordingly according to the number of different prosthetic hand drivers. Generally speaking, the number of ADXL345 modules 3 is consistent with the number of prosthetic hand drivers. In terms of circuit implementation, in order to ensure the electrical safety and reliability of the system, a design method is adopted to separate the main board and the driver board. This design can effectively reduce interference and electromagnetic coupling in the circuit and ensure the stability of signal transmission between modules.

[0054] like Figure 2 As shown, the force feedback module mainly consists of an FSR sensor 2 and a voltage conversion module located on the mainboard. The voltage conversion module uses a resistor divider to achieve linear voltage-resistance conversion of the FSR sensor, thereby achieving pressure measurement. The position feedback module mainly consists of an ADXL345 module 3 and an analog switch module located on the mainboard. Since the ADXL345 module 3 is an I / O module with only one address 2 The analog switch module is primarily used to control the selection of multiple ADXL345 modules 3, ensuring normal data transmission. The control module, located on the mainboard, serves as the system's core computing unit. It is responsible for collecting, analyzing, processing, and transmitting signals from the force feedback module and position feedback module, and generating corresponding control commands to drive the prosthetic hand's movements.

[0055] The driving module of this embodiment is the driving board, which serves as the execution unit of the system and is responsible for converting the instructions generated by the control module into specific motor actions, adjusting the movement of each joint of the prosthetic hand to complete the specified task.

[0056] In summary, the fingertip pressure signals of the prosthetic hand in this embodiment are acquired via thin film pressure sensors (FSRs) installed on the fingertips of the five fingers. Position information for each joint of the prosthetic hand is acquired via triaxial ADXL345 accelerometer modules installed on each phalanx of the five fingers. The main board is responsible for collecting, processing, and calculating these two signals, communicating with the host computer, and generating control signals for the drive motors. The driver board is responsible for controlling and driving the motors of the prosthetic hand. The controller based on force / position hybrid fuzzy control has a dual-loop structure, consisting of an external force control loop and an internal position control loop. The external force control loop is implemented using an impedance model based on the inverse kinematics of the prosthetic hand. A fuzzy control algorithm is used to calculate the damping and stiffness of the impedance model, enabling real-time self-adjustment of the damping and stiffness based on varying contact environments. The internal position control loop is implemented using an MFC controller based on a ULM, integrating all system characteristics and uncertainties into a single unknown term, significantly reducing the complexity of the controller design. Furthermore, a time-delay estimator is used to estimate the value of the unknown term. The introduction of this time-delay estimator also reduces the requirement for precise model parameters.

[0057] Example 2

[0058] This embodiment provides a prosthetic hand control method based on force / position hybrid fuzzy control, including:

[0059] The external force control loop (also known as the external force control loop) is implemented using an impedance model based on the inverse kinematics of the prosthetic hand. This model simulates the mechanical properties required for the prosthetic hand to interact with external objects, dynamically adjusts the force applied by the prosthetic hand during grasping, and converts force control into position control, integrating with the internal position control loop. The damping and stiffness of the impedance model are calculated using a fuzzy control algorithm.

[0060] Inner position control loop (also called inner position control loop): It is implemented using a ULM-based MFC controller, integrating all the characteristics and uncertainties of the system into one unknown term, and using a time-delay estimator to estimate the value of the unknown term.

[0061] like Figure 3 As shown in Figure 1, the external force control loop dynamically adjusts the force distribution of the prosthetic hand by continuously monitoring the contact state between the prosthetic hand and the object. First, the FSR sensors located at the prosthetic fingertips collect real-time force feedback data.

[0062] Using this data, along with the desired force set manually or obtained through other means, a fuzzy control algorithm calculates the ideal impedance characteristics and inputs them into the impedance model. The impedance model then generates position change instructions corresponding to the target interaction force. Finally, combined with the position information of the grasped object, this information is converted into the bending angle information of each joint of the prosthetic hand through the inverse kinematic model of the prosthetic hand. This method successfully converts force control into position control, ensuring that the prosthetic hand interacts with objects in a compliant and stable manner.

[0063] The internal position control loop monitors and adjusts the position of the prosthetic hand joints in real time, working in conjunction with the external force control loop to achieve precise control of the prosthetic hand. First, an ADXL345 module installed on the prosthetic hand collects position information of each joint in real time. Position feedback data from the prosthetic hand and angle information derived through inverse kinematics from the internal force control loop are then input into a ULM-based MFC controller. A time-delay estimator is used to calculate the values ​​of unknown terms in the MFC controller. Finally, the MFC controller outputs the results to the prosthetic hand, achieving closed-loop control of the prosthetic hand.

[0064] A possible way to establish a rectangular coordinate system for a prosthetic finger is as follows Figure 4 The external force control loop is used to track the interaction force f between the five fingertips of the prosthetic hand and the grasped object. d Assuming that the fingertips of the five fingers as the end effectors are flexible, the output force f of the prosthetic hand on the grasped object is e It can be expressed as an impedance model:

[0065]

[0066] Among them, B f and K f represent the damping and stiffness of the prosthetic hand system, Z f represents the compression displacement of the five fingertips. If the Kelvin-Voigt model is used, the five fingertips and the grasped object f i The force between them can be expressed as:

[0067]

[0068] Among them, B i and K i are the damping and stiffness of the grasped object, respectively. The reference trajectory along the z-axis of the prosthetic hand is expressed as follows:

[0069] Z r =Z d +Z f (3)

[0070]

[0071] Among them, Z d is the desired trajectory along the z-axis.

[0072] Next, using fuzzy control theory, the expected force f d and the actual contact force f e Perform fuzzification and establish a dual-input four-output fuzzy controller.

[0073] First, the variables are fuzzified. Let the expected force f d The value range is [f dmin , f dmax ], define f d The fuzzy set is {NB, NS, ZO, PS, PB}; the actual contact force f e The value range is [f emin , f emax ], define f e The fuzzy set is {NB, NS, ZO, PS, PB}; the control variable U of the system has a basic definition domain [-6, 6], and the fuzzy set is {NB, NS, ZO, PS, PB}.

[0074] Among them, NB, NS, ZO, PS, and PB represent negative large, negative small, zero, positive small, and positive large, respectively, and represent the membership relationship in fuzzy control.

[0075] Depending on the magnitude of the forces, you can choose different membership functions for the system. For example, you can choose a triangular membership function for the smallest and largest forces, and a trapezoidal membership function for medium forces.

[0076] Secondly, create a fuzzy rule control table. f , K f , B i , K i With the parameter (f d , f e ) is transformed into fuzzy inference rules, and the deterministic relationship between the fuzzy control input variables and the system control quantity U is obtained, as shown in the following formulas (5)-(8).

[0077] Finally, defuzzification is performed and the output variable (B f , K f , B i , K i )'s actual adjustment range ([B fmin , B fmax ],[K fmin , K fmax ],[B imin , B imax ],[K imin , K imax ]):

[0078]

[0079] X d is the desired trajectory of the finger along the x-axis, and the inner position control loop is used to track the reference trajectory (Z r , X d ), thereby achieving mixed control of force and position. First, in order to reduce the difficulty of the controller, the dynamic equation of the prosthetic hand is converted into an ultra-local model ULM:

[0080]

[0081] Where α is a constant, x represents the rotation angle of each joint of the prosthetic hand, u represents the torque of each motor, and F is an unknown term, including all dynamic characteristics and uncertainties of the system. Define tracking error:

[0082] e=xx d (10)

[0083] In actual control tasks, system parameters are often unknown or inaccurate, which will bring great difficulties to the value of F. Therefore, it is necessary to design an estimator to estimate it:

[0084]

[0085] in, is the estimated value of F, and Δ represents a small delay. The delay estimator estimates the value of F at time t using the value of F at time t-Δ. If Δ is small enough, the delay estimator can estimate F with very small error.

[0086] Example 3

[0087] A prosthetic hand system based on force / position hybrid fuzzy control, comprising:

[0088] The prosthetic hand body includes a palm platform, on which are disposed a plurality of finger structures, each of which has a plurality of joints, and each joint is equipped with a drive module;

[0089] The force feedback modules include a plurality of modules, each of which is provided at the fingertip portion of the finger structure and is used to obtain the contact pressure of the corresponding fingertip;

[0090] Position feedback modules, including multiple ones, are respectively provided at each joint, for obtaining motion information of each joint, and calculating the flexion / extension angle and adduction / abduction angle of the corresponding joint based on the motion information, thereby determining the position information of each joint;

[0091] The control module is connected to the force feedback module and the position feedback module. It is used to dynamically adjust the force applied by the prosthetic hand during the grasping process according to the contact pressure of each fingertip using the impedance model, and convert the force control instruction into a position control instruction. The final control signal is obtained by combining the position control instruction with the position information of each joint and sent to the corresponding drive module.

[0092] In some embodiments, the control module is configured to execute the steps of the control method provided in the second embodiment.

[0093] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0094] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0095] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0096] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0097] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made by those skilled in the art that fall within the spirit and principles of the present invention and do not require creative effort are intended to be within the scope of protection of the present invention.

Claims

1. A prosthetic hand system based on force / position hybrid fuzzy control, characterized by: include: The prosthetic hand body includes a palm platform, on which are disposed a plurality of finger structures, each of which has a plurality of joints, and each joint is equipped with a drive module; The force feedback modules include a plurality of modules, each of which is provided at the fingertip portion of the finger structure and is used to obtain the contact pressure of the corresponding fingertip; Position feedback modules, including multiple ones, are respectively provided at each joint, for obtaining motion information of each joint, and calculating the flexion / extension angle and adduction / abduction angle of the corresponding joint based on the motion information, thereby determining the position information of each joint; The control module is connected to the force feedback module and the position feedback module, and is used to dynamically adjust the force applied by the prosthetic hand during the grasping process according to the contact pressure of each fingertip and the impedance model, and convert the force control instruction into a position control instruction. The final control signal is obtained by combining the position control instruction with the position information of each joint and sent to the corresponding drive module; the control module includes an external force control loop, which is configured to: compare the contact pressure of each fingertip with the preset or obtained expected force, calculate the ideal impedance characteristic by using a fuzzy controller, generate a position change instruction corresponding to the target interaction force based on the impedance characteristic by using the impedance model, and convert the position change instruction into bending angle information of each joint of the prosthetic hand by using the inverse kinematics model of the prosthetic hand in combination with the position information of the grasped object; The fuzzy controller is configured to use fuzzy control theory to calculate the desired force f d and the actual contact force f e Fuzzy is performed to obtain the damping B of the prosthetic hand system. f , stiffness K f , the damping of the grasping object B i and stiffness K i and the expected force f d and the actual contact force f e The change relationship is the fuzzy inference rule, and the fuzzy control input variable f is obtained. d , f e and system control quantity B f , K f 、B i and K i The deterministic relationship between them is used to defuzzify the system control quantity; The control module includes an internal position control loop configured to: use position information of each joint of the prosthetic hand body and bending angle information of the external force control loop as input, use a model-free controller to obtain a final control signal, use a time-delay estimator to calculate the value of an unknown term in the model-free controller, and output the final control signal to the prosthetic hand body to achieve closed-loop control; The drive module is used to control and drive the motor of the prosthetic hand. It receives signals from the control module and converts them into specific motor drive instructions to ensure that the various parts of the prosthetic hand can operate in a coordinated manner.

2. The prosthetic hand system based on force / position hybrid fuzzy control as claimed in claim 1, characterized in that: The force feedback module includes a thin film pressure sensor, which is connected to a voltage conversion module. The voltage conversion module is used to perform linear voltage-resistance conversion of the thin film pressure sensor using a resistance voltage divider to achieve contact pressure measurement.

3. The prosthetic hand system based on force / position hybrid fuzzy control as claimed in claim 1, characterized in that: The position feedback module includes a three-axis acceleration sensor module, which is connected to an analog switch module. The analog switch module is used to control the selection of each three-axis acceleration sensor module.

4. The prosthetic hand system based on force / position hybrid fuzzy control as claimed in claim 1, characterized in that: Each joint is driven by a motor. The drive module is connected to the motor and is used to convert the final control signal generated by the control module into corresponding motor actions to adjust the movement of the corresponding joints of the prosthetic hand to complete the specified task.

5. A prosthetic hand control method based on force / position hybrid fuzzy control, characterized in that: The prosthetic hand system based on force / position hybrid fuzzy control as claimed in claim 1 comprises the following steps: Obtaining the contact pressure of each fingertip; Acquire motion information of each joint, and calculate the flexion / extension angle and adduction / abduction angle of the corresponding joint based on the motion information to determine the position information of each joint; According to the contact pressure of each fingertip, the impedance model is used to dynamically adjust the force applied by the prosthetic hand during grasping, and the force control command is converted into a position control command. The position control command is combined with the position information of each joint to obtain the final control signal.

6. The prosthetic hand control method based on force / position hybrid fuzzy control as claimed in claim 5, characterized in that: The process of dynamically adjusting the force applied by the prosthetic hand during grasping using the impedance model based on the contact pressure of each fingertip and converting the force control command into a position control command includes: According to the contact pressure of each fingertip and the preset or obtained expected force, the ideal impedance characteristics are calculated using a fuzzy controller. The impedance model is used to generate position change instructions corresponding to the target interaction force based on the impedance characteristics. Combined with the position information of the grasped object, the position change instructions are converted into bending angle information of each joint of the prosthetic hand using the inverse kinematics model of the prosthetic hand.

7. The prosthetic hand control method based on force / position hybrid fuzzy control as claimed in claim 5, characterized in that: The process of combining the position control instructions and the position information of each joint to obtain the final control signal includes: Taking the position information of each joint of the prosthetic hand and the bending angle information of the external force control loop as input, a model-free controller is used to obtain the final control signal. A time-delay estimator is used to calculate the value of the unknown term in the model-free controller, and the final control signal is output to the prosthetic hand to achieve closed-loop control.

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