Flexible lower limb exoskeleton power-assisted control method based on multi-source information fusion

Abstract

The invention belongs to the technical field of lower limb exoskeleton control, and particularly relates to a flexible lower limb exoskeleton assistance control method based on multi-source information fusion. The method comprises the following steps: firstly, acquiring plantar pressure, hip joint acceleration and angular velocity and knee joint acceleration and angular velocity, and fusing the hip joint acceleration and angular velocity and the knee joint acceleration and angular velocity respectively to obtain hip and knee joint angles; performing normalization processing on hip and knee joint angles and plantar pressure to form a multi-source information matrix; inputting the multi-source information matrix into a gait phase recognition model for gait phase recognition to obtain a gait phase; then, according to the gait phase, the joint angle and the joint torque, a Bowden line playing a power assisting role is judged; calculating expected Bowden line change lengths under the assistance of hip and knee joints respectively; and finally, converting the expected change length of the Bowden cable under the assistance of the hip and knee joints into expected rotation angles of hip and knee joint motors, and controlling the hip and knee joint motors by adopting fuzzy PID control according to the rotation angles. The method solves the problems that in lower limb exoskeleton control, the gait phase recognition accuracy is low, and precise power-assisted control is difficult to carry out.

Description

Technical Field

[0001] The present invention belongs to the technical field of lower limb exoskeleton control, and in particular relates to a flexible lower limb exoskeleton power-assisted control method based on multi-source information fusion. Background Art

[0002] In recent years, assisted exoskeletons designed to enhance the user's coordination and athletic ability and reduce metabolic rate have attracted widespread attention and play an important role in rehabilitation medicine, military operations, emergency rescue, etc. Flexible exoskeletons are composed of mechanical systems and control systems. The control system obtains human-computer interaction information through sensors, monitors the user's posture, movement and force feedback, and thus controls the movement of the exoskeleton.

[0003] At present, most lower limb exoskeletons have poor human-machine interaction and insufficient gait intention perception, which affects the control system decision-making and cannot achieve the expected power-assisting effect. There are two main types of existing lower limb exoskeleton power-assisting control. One is to perform power-assisting control based on the interaction force between the human body and the exoskeleton, and the other is to perform power-assisting control based on the predetermined trajectory set according to the movement state (such as running, climbing stairs, walking on flat ground, etc.). Both of these power-assisting controls take the exoskeleton as the main body, without considering the gait phase of the person, and ignore the main role of the person in the human-machine interaction process, resulting in poor human-machine interaction and poor power-assisting effect.

[0004] In existing gait phase perception research, single modal information is mainly considered, resulting in inaccurate gait phase division. For example, based on plantar pressure, the gait phase of a single leg can be divided into the pre-stance phase, middle phase, late phase and swing phase. Since the plantar pressure in the swing phase is always zero, it cannot be subdivided. The gait phase division based on joint angles cannot determine whether the lower limbs touch the ground and enter the support phase, because the angles of each joint are different when different people touch the ground. Failure to accurately identify the gait phase will affect the precise control of the lower limb exoskeleton.

[0005] In addition, during the power-assisting process, the accurate modeling of the Bowden cable trajectory and the calculation of its changing length directly affect the movement of the joint motor. When the front and rear Bowden cable power-assisting switches, there is a step phenomenon in the movement of the joint motor, which further increases the difficulty of power-assisting control. Summary of the invention

[0006] In view of the deficiencies in the prior art, the technical problem that the present invention intends to solve is to provide a flexible lower limb exoskeleton power-assist control method based on multi-source information fusion, so as to solve the problem of low accuracy of human gait phase recognition in lower limb exoskeleton control, making it difficult to perform precise control and provide appropriate power assistance.

[0007] The present invention solves the technical problem by adopting the following technical solution: A flexible lower limb exoskeleton power-assisted control method based on multi-source information fusion, characterized in that the method comprises the following steps: Step S1: collecting plantar pressure, hip joint acceleration and angular velocity, knee joint acceleration and angular velocity, respectively fusing the hip joint acceleration and angular velocity, knee joint acceleration and angular velocity to obtain hip and knee joint angles; normalizing the hip and knee joint angles and plantar pressure to form a multi-source information matrix; inputting the multi-source information matrix into a gait phase recognition model to perform gait phase recognition to obtain a gait phase; Step S2: Determine the Bowden line that assists the gait according to the gait phase, joint angle and joint torque; perform kinematic modeling on the flexible lower limb exoskeleton to obtain the length change of the Bowden line without assistance. ; Calculate the tensile deformation of the Bowden cable under power assistance according to the following formula; (2) In the formula, is the tension of joint k, when hour, is the hip joint tension; hour, It is the tension of knee joint; The tensile deformation of the Bowden cable under the assistance of joint k; Calculate the expected joint assist torque according to the following formula: (3) In the formula, is the expected assist torque of joint k, is the angle of joint k, u is the coefficient, and d is the constant term; According to the following formula, the expected change length of the Bowden line under the assistance of hip and knee joints is calculated respectively; (5) In the formula, The expected change in length of the Bowden cable under the assistance of joint k, is the power lever arm of joint k; Step S3: Convert the expected change lengths of the Bowden cables under the assistance of the hip and knee joints into the expected rotation angles of the hip and knee joint motors respectively, and control the hip and knee joint motors by using fuzzy PID control according to the rotation angles.

[0008] Compared with the prior art, the present invention has the following beneficial effects: The present invention fully considers the gait phase of a person during walking, and performs gait phase identification based on multimodal data (plantar pressure, hip joint angle and knee joint angle), which is conducive to accurately determining the Bowden line that plays a power-assisting role and realizing accurate power-assisting control. After determining the Bowden line that plays a power-assisting role, the expected change length of the Bowden line under power-assisting is calculated, and the expected change length of the Bowden line under power-assisting is converted into the expected rotation angle of the joint motor, and then the joint motor is controlled. Since there is a step phenomenon in the movement of the joint motor when the front and rear Bowden lines are switched, fuzzy PID control is used to control the joint motor to alleviate the step phenomenon. This method realizes the precise power-assisting control of the flexible lower limb exoskeleton and improves the human-machine collaborative control capability of the entire exoskeleton system, so that the exoskeleton can accurately provide power assistance. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 It is the overall flow chart of the present invention; Figure 2 is a flow chart of the perception layer of the present invention; Figure 3 is the gait phase division diagram; Figure 4 is a flow chart of the conversion layer of the present invention; Figure 5 It is a flow chart of the control layer of the present invention. DETAILED DESCRIPTION

[0010] Specific embodiments are given below in conjunction with the accompanying drawings. The specific embodiments are only used to introduce the technical solutions of the present invention in detail and are not intended to limit the protection scope of the present application.

[0011] The present invention provides a flexible lower limb exoskeleton power-assisted control method based on multi-source information fusion (hereinafter referred to as method, see Figures 1 to 5 ), including the following steps: Step S1: The sensing layer collects plantar pressure, hip joint acceleration and angular velocity, knee joint acceleration and angular velocity, and fuses the hip joint acceleration and angular velocity, knee joint acceleration and angular velocity respectively to obtain the hip and knee joint angles; the multi-source information matrix is ​​input into the gait phase recognition model to perform gait phase recognition and realize gait phase perception; S11. The plantar pressure is collected in real time by means of a pressure sensor, the acceleration and angular velocity of the hip joint are collected by means of an accelerometer and a gyroscope located at the hip joint, and the acceleration and angular velocity of the knee joint are collected by means of an accelerometer and a gyroscope located at the knee joint. The vibration caused by muscle movement and heel contact during walking may lead to inaccurate sensor measurements, and the joint angle solved by a single acceleration is easily affected by high-frequency noise, which may cause error accumulation and lead to inaccurate solution results. Therefore, an extended Kalman filter is used to fuse the acceleration and angular velocity of the hip joint and the acceleration and angular velocity of the knee joint, respectively, to obtain the hip joint angle and the knee joint angle. S12, normalizing the plantar pressure and joint angle respectively, scaling the data to a uniform range to eliminate the dimensional differences or deviations between different features, and the normalization calculation formula is as shown in formula (1); the normalized plantar pressure, hip joint angle and knee joint angle form a multi-source information matrix; (1) In the formula, , They are the data before and after normalization, , Normalize the maximum and minimum values ​​in the data respectively; S13. Use the kernel principal component analysis method to reduce the dimension of the multi-source information matrix to obtain the multi-source information matrix after dimension reduction, retain the main structural features of the data, and help improve the efficiency of the gait phase recognition model; select the least squares support vector machine as the gait phase recognition model, input the multi-source information matrix after dimension reduction into the gait phase recognition model, and identify the gait phase; during the recognition process, use the particle swarm optimization algorithm to optimize the regularization parameter and kernel width of the least squares support vector machine.

[0012] like Figure 3As shown in the figure, the gait cycle of a single leg of the human body is divided into 6 phases, namely the early, middle and late stages of the support phase and the early, middle and late stages of the swing phase. The gait phase recognition model recognizes the gait phase according to the following basis: (a) Early support phase: from the beginning of the heel touching the ground to the sole of the foot touching the ground, the hip joint angle decreases, the knee joint angle increases, and both the hip and knee joints produce extension torque; (b) Middle support phase: during the stage of full sole landing, the heel pressure gradually decreases, the sole and toe pressure gradually increase, and the hip and knee joints change from extension torque to flexion torque; (c) Late support phase: from the heel leaving the ground to the toe leaving the ground, During the whole process, the plantar pressure is mainly distributed in the sole and toes area, and both the hip and knee joints generate flexion torque. When the toes leave the ground, the support phase ends and the swing phase begins. (d) Early swing phase: As soon as the toes leave the ground, the hip joint drives the knee joint to flex until the flexion angle of the knee and hip joints is maximum. The hip joint generates flexion torque and the knee joint generates extension torque. (e) Middle swing phase: The knee joint begins to extend from the maximum flexion state until the calf is in an upright position. (f) Late swing phase: Starting from the upright position of the calf until the heel touches the ground, the hip joint has an extension torque and the knee joint has a flexion torque in the middle and late swing phase.

[0013] Step S2: The conversion layer determines the Bowden line that assists the force according to the gait phase, joint angle, and joint torque obtained by the perception layer; calculates the expected change length of the Bowden line under hip joint assistance and the expected change length of the Bowden line under knee joint assistance, such as Figure 4 shown.

[0014] S21, determining a Bowden line that assists the gait according to the gait phase, joint angles, and joint torques; S22. Kinematic modeling of the flexible lower limb exoskeleton is performed. Based on the principle of non-interference between the front and rear Bowden lines and the change of joint angles, the length change of the Bowden line without assistance is obtained. ; S23, the wearer stands with his feet crossed, with the unassisted leg 50 cm behind the assisted leg, and sets the dynamometer at the end of the Bowden cable, and respectively stretches the front and rear Bowden cables by 30 mm with the hip joint motor and the knee joint motor, and records the position of the Bowden cable and the joint tension, and repeats multiple times; uses the least squares method to fit the tensile deformation of the assisted Bowden cable and the joint tension using a quadratic polynomial, determines the quadratic polynomial coefficients a, b and c, and then obtains the relationship between the joint tension and the tensile deformation of the assisted Bowden cable as shown in formula (2), and calculates the tensile deformation of the assisted Bowden cable based on this relationship; (2) In the formula, is the tension of joint k, when hour, is the hip joint tension; hour, It is the tension of knee joint; The tensile deformation of the Bowden cable under the assistance of joint k; S24, there is a linear relationship between the joint expected assist torque and the joint angle, so the joint expected assist torque and the joint angle are fitted to obtain a fitting equation; taking 50% of the joint torque in the current gait phase as a benchmark, and taking the minimum root mean square error and assist torque continuity as the goal, the coefficient u of the fitting equation is determined, and then the relationship between the joint expected assist torque and the joint angle as shown in formula (3) is obtained, and the joint expected assist torque is calculated according to this relationship; (3) (4) In the formula, is the expected assist torque of joint k, is the angle of joint k, RMSE is the root mean square error, is the expected assist torque of joint k at time m, is 50% of the torque of joint k at time m, and n is the number of data sampling times; S25, converting the expected assistance torque of the joint into the expected assistance magnitude, and calculating the expected change length of the Bowden line under the assistance of the hip joint and the knee joint respectively by formula (5); (5) In the formula, The expected change in length of the Bowden cable under the assistance of joint k, is the assist lever arm of joint k.

[0015] Step S3: The execution layer converts the expected change length of the Bowden cable under power assistance into the expected rotation angle of the joint motor. According to the expected rotation angle of the joint motor, the joint motor is controlled by fuzzy PID control to alleviate the step phenomenon of the joint motor motion trajectory caused by the switching of the front and rear Bowden cable power assistance, such as Figure 5 shown.

[0016] S31, will help the expected change in length of the Bowden cable Converted into the expected rotation angle of the joint motor, the expected rotation angle of the hip joint motor and the expected rotation angle of the knee joint motor are obtained. The calculation formula is as follows: (6) In the formula, is the desired rotation angle of the joint motor, is the radius of the turntable where the joint motor and the Bowden cable connect.

[0017] S32, respectively adopt fuzzy PID control to control the hip joint motor and the knee joint motor; taking the control of the hip joint motor as an example, the rotation angle error of the hip joint motor (the difference between the expected rotation angle and the actual rotation angle of the hip joint motor) and the error change rate are used as the input of the fuzzy controller, and the fuzzy controller adaptively adjusts the PID parameters according to the fuzzy rules to obtain the adjusted PID parameters, as shown in the following formula: (7) In the formula, For the manual setting value of PID parameters, is the PID parameter change, is the PID parameter after tuning; The adjusted PID parameters are applied to the PID controller, and the PID controller outputs a current signal for controlling the hip joint motor. The hip joint motor is controlled according to the current signal output by the PID controller; the output of the PID controller is expressed as: (8) Where u(t) is the current signal output by the PID controller, and e(t) is the rotation angle error of the hip joint motor; Similarly, the knee joint motor is controlled to complete the control of the flexible lower limb exoskeleton.

[0018] The DC motor control based on fuzzy PID has the characteristics of fast response speed, small overshoot and strong stability. Using fuzzy PID control to control the joint motor can effectively alleviate the step phenomenon of the joint motor motion trajectory caused by the switching of the front and rear Bowden cable power assistance, reduce the peak error and total error of the motor motion trajectory, reduce overshoot, and thus effectively provide more precise power assistance.

[0019] The present invention does not describe any prior art applications.

Claims

1. A flexible lower limb exoskeleton power-assisted control method based on multi-source information fusion, characterized in that: The method comprises the following steps: Step S1: collecting plantar pressure, hip joint acceleration and angular velocity, knee joint acceleration and angular velocity, respectively fusing the hip joint acceleration and angular velocity, knee joint acceleration and angular velocity to obtain hip and knee joint angles; normalizing the hip and knee joint angles and plantar pressure to form a multi-source information matrix; inputting the multi-source information matrix into a gait phase recognition model to perform gait phase recognition to obtain a gait phase; Step S2: Determine the Bowden line that assists the gait according to the gait phase, joint angle and joint torque; perform kinematic modeling on the flexible lower limb exoskeleton to obtain the length change of the Bowden line without assistance. ; Calculate the tensile deformation of the Bowden cable under power assistance according to the following formula; (2) In the formula, is the tension of joint k, when hour, is the hip joint tension; hour, It is the tension of knee joint; The tensile deformation of the Bowden cable under the assistance of joint k; Calculate the expected joint assist torque according to the following formula: (3) In the formula, is the expected assist torque of joint k, is the angle of joint k, u is the coefficient, and d is the constant term; According to the following formula, the expected change length of the Bowden line under the assistance of hip and knee joints is calculated respectively; (5) In the formula, The expected change in length of the Bowden cable under the assistance of joint k, is the power lever arm of joint k; Step S3: Convert the expected change lengths of the Bowden cables under the assistance of the hip and knee joints into the expected rotation angles of the hip and knee joint motors respectively, and control the hip and knee joint motors by using fuzzy PID control according to the rotation angles.

2. The flexible lower limb exoskeleton power-assisted control method based on multi-source information fusion according to claim 1 is characterized in that: The gait phase recognition model adopts a least squares support vector machine, and a particle swarm optimization algorithm is used to optimize the regularization parameter and kernel width of the least squares support vector machine.

3. The flexible lower limb exoskeleton power-assisted control method based on multi-source information fusion according to claim 1 or 2, characterized in that: In the process of fitting equation (3), the coefficient u is determined by the following formula: (4) Where RMSE is the root mean square error, is the expected assist torque of joint k at time m, is 50% of the torque of joint k at time m, and n is the number of data sampling times.

4. The flexible lower limb exoskeleton power-assisted control method based on multi-source information fusion according to claim 3 is characterized in that: The extended Kalman filter is used to fuse the acceleration and angular velocity of the hip joint, and the acceleration and angular velocity of the knee joint to obtain the angles of the hip and knee joints.

Images