Disclosure of Invention
The invention aims to provide an active-passive hybrid driving flexible lower limb exoskeleton system and a coordination control method.
The technical scheme is that the active-passive hybrid driving flexible lower limb exoskeleton system and the coordination control method comprise the following steps:
(1) When the inertial sensor detects that the angular velocity of the hip joint reaches a threshold value, starting an active and passive hybrid driving flexible lower limb exoskeleton power-assisted system, and identifying the current motion state of the hip joint of the human body by a gait identification algorithm through acquiring the angle and angular velocity information detected by the inertial sensor;
(2) Under the condition that the movement state of the hip joint meets the requirement, planning a power-assisted track required by the current movement state, planning the power-assisted track required by the current movement stage by a power-assisted track planning algorithm, and outputting the power-assisted size required by rope driving;
(3) The force track tracking algorithm tracks a power-assisted track output by the power-assisted track planning algorithm, the output force Fd (theta) of the power-assisted track planning algorithm is different from the force Fc measured by the tension sensor, the output force Fe is calculated through the PID controller, the output force Fe is output to the motor rotation speed Vc through the admittance controller, and the motor rotates the pull rope to output power to the human body;
(4) The condition for judging the stop is when the hip joint angular velocity is less than a certain set threshold value approaching zero.
The active-passive hybrid driving flexible lower limb exoskeleton comprises an elastic energy storage element, a rope driving system, a flexible binding device, a sensing system and a control system, wherein the control system comprises a gait recognition algorithm, a power-assisted track planning algorithm and a force track tracking algorithm, the gait recognition algorithm detects the angle and angular velocity information of a hip joint in a sagittal plane through an inertial sensor, the movement state of the hip joint of a human body is detected in real time, the power-assisted track planning algorithm calculates the rope driving power-assisted size in real time according to the movement state of the hip joint, the force track tracking algorithm converts expected force into motor speed through an admittance controller, real-time correction is carried out through feedback information of the force sensor, tracking errors are reduced, and accurate track tracking of the expected force is realized.
The sensing system comprises inertial sensors respectively arranged at the front sides of the two thighs and a tension sensor arranged on the rope driving device.
In the above-described arrangement, the first and second embodiments,
Each sensor provides a stable and effective physical signal for the control system and is used for identifying the motion state of the human body.
And filtering the data measured by each sensor through a low-pass filter to remove abnormal data and obtain smooth data. The low pass filter formula is as follows:
Y(n)=αX(n)+(1-α)Y(n-1),
where α is a filter coefficient, X (n) is a current sample value, Y (n-1) is a last filtered output value, and Y (n) is a current filtered output value.
Further, the gait recognition algorithm obtains hip joint angle theta and angular velocity v information through data measured by an inertial sensor, and divides a gait cycle through a finite state machine to obtain a current state. The finite state machine states are as follows:
in a first state, gait cycles are 0 to 30%, and the force of the state is the assistance of the rope driving power, wherein the force comprises hip joint assistance and spring tension.
And in the second state, the gait cycle is 30-50%, and the spring stores energy by utilizing the gravitational potential energy of a human body.
And a third state, namely 50 to 87 percent of gait cycle, wherein the spring releases energy to assist a person.
And in a fourth state, the gait cycle is 87-100%, the force of the rope drives assistance, the force is hip joint assistance, and the spring stores energy by using the gravity of human legs.
Further, the power-assisted trajectory planning algorithm designs the power-assisted trajectory of each stage of the rope driving system:
the first stage is a gait cycle of 0 to 10%, and the rope driving assistance is as follows:
and a second stage, namely a gait cycle of 10-30%, wherein the size of the rope driving assistance is as follows:
And a third stage, namely a gait cycle is 30-87%, wherein the rope keeps an early warning state, and the force on the rope is as follows:
Fe,
And a fourth stage, namely 87% to 100% of gait cycle, wherein the rope driving assistance is as follows:
In the formula,Is the peak value of the expected joint assistance, Fp is the rope pre-warning force, Fs is the spring tension, θ is the angle of the hip joint to the vertical,Is the maximum angle of the hip joint,Is the hip joint angle at which peak force is reached.
Further, a current corresponding assisting stage of the rope driving system is obtained according to the gait recognition algorithm, and the expected assisting force Fd (theta) of the current rope driving system is calculated through the assisting track planning algorithm.
Further, the rope driving system expects the auxiliary force Fd (theta) to be differed from the force Fc measured by the tension sensor, the output force Fe is calculated by the PID controller, and the calculation formula of the PID algorithm is as follows:
Where Fe (T) is the output force of the PID controller, Kp is the proportional gain, Tt is the integral time constant, TD is the derivative time constant, and e (T) is the difference between the desired force and the measured force.
Further, the PID algorithm calculates the speed Vc required by the output force converted to the motor by the admittance controller, and the admittance control model is as follows:
where Md is the inertia coefficient, Bd is the damping coefficient, Kd is the stiffness coefficient, Xr is the reference position, X is the actual position, and Fe is the contact force.
The admittance equation is defined in the Laplace domain as:
Where Y is the virtual admittance, Vc is the rope speed of the feedback term, Mv is the virtual inertia, and Cv is the damping.
Further, the working process of the control system comprises the following steps:
At the hip maximum flexion point, the cord drive system begins to assist to 30% of the gait cycle stopping. And at the maximum buckling point of the hip joint, the elastic energy storage element starts to store energy, the elastic potential energy starts to be released after the elastic energy storage element finishes storing energy to the maximum extending point of the hip joint, and the elastic potential energy starts to be released after the elastic potential energy finishes releasing to the maximum buckling point of the next hip joint, and the operation is changed into the start of storing energy.
The gait recognition algorithm detects the current hip joint angle theta, the power-assisted track planning algorithm calculates the rope driving auxiliary force Fd corresponding to the current hip joint angle theta, the expected auxiliary force Fd is differed from the rope force Fc detected by the tension sensor and then outputs a force Fe (t) through the PID controller, the force track tracking algorithm converts the output force Fe (t) of the PID controller into the speed Vc required by the motor through the admittance controller, and the motor pull rope outputs the auxiliary force to apply work to a human body.
Further, when the hip joint angle or the angular speed exceeds a set threshold, the active and passive hybrid drive flexible lower limb exoskeleton stops working.
Further, when the rope driving system does not provide assistance, the rope keeps low pretightening force, so that the rope can keep a tensioning state at all times, the rope can keep close to the sliding groove, and meanwhile, the rope can quickly respond when the assistance is provided in the next period.
Further, the active and passive hybrid driving flexible lower limb exoskeleton system comprises a battery, a soft outer garment, a rope driving system, a sensing system, an elastic energy storage element, a flexible binding device, a Bowden wire driving device and a control system,
The battery is arranged in front of the chest and provides energy for the control system
The soft outer garment is worn on a human body, the battery, the rope driving system and the elastic energy storage element are arranged on the soft outer garment, the rope driving system is arranged on the back of the human body, and power is provided for the control system
The sensing system comprises inertial sensors respectively arranged at the front sides of two thighs and a tension sensor arranged on the rope driving device
The elastic energy storage element is arranged on the front side of thigh and stores energy when it is extended and releases energy when it is contracted
The flexible binding device is arranged on the upper side and the lower side of the knee joint and mainly used for fixing the connection points of the elastic energy storage element, the Bowden wire driving device and the human body and simultaneously enabling the human body to be uniformly stressed
The Bowden wire drive device is mounted on the rear side of thigh for providing assistance for hip joint
The control system collects human motion information through the sensing system, and the control rope driving system drives the Bowden wire driving device to provide assistance for lower limbs of a human body.
Compared with the prior art, the invention has the following beneficial effects:
1. The coordination control method of the active and passive hybrid driving flexible lower limb exoskeleton system comprises a gait recognition algorithm, a power-assisted track planning algorithm and a force track tracking algorithm, wherein the gait recognition algorithm detects the angle and angular velocity information of a hip joint in a sagittal plane through an inertial sensor, detects the movement state of the hip joint of a human body in real time, the power-assisted track planning algorithm calculates the rope driving power assistance according to the movement state of the hip joint in real time, the force track tracking algorithm converts expected force into motor speed through an admittance controller, a motor stay rope outputs force to the legs of the human body, real-time correction is performed through force sensor feedback information, tracking errors are reduced, and accurate expected force track tracking is realized.
2. According to the active and passive hybrid driving flexible lower limb exoskeleton system coordination control method provided by the invention, the gait recognition algorithm adopts the single inertial sensor arranged on the front side of the thigh to acquire hip joint angle and angular velocity information, the hip joint motion state is recognized through the finite state machine, the gait recognition algorithm has the characteristics of high precision and high instantaneity, and meanwhile, the single sensor acquisition is more friendly to the wearable exoskeleton, and has better generalization capability and adaptability.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be further described below.
In the drawings, like structural elements are referred to by like reference numerals and components having similar structure or function are referred to by like reference numerals. The dimensions and thickness of each component shown in the drawings are arbitrarily shown, and the present invention is not limited to the dimensions and thickness of each component. The thickness of the components is exaggerated in some places in the drawings for clarity of illustration.
The embodiment provides a coordination control method for an active-passive hybrid drive flexible lower limb exoskeleton system, which is used for controlling the active-passive hybrid drive flexible lower limb exoskeleton.
As shown in fig. 1, the active-passive hybrid driving flexible lower limb exoskeleton of the invention comprises a battery 1, a soft outer garment 2, a driving system 3, a sensing system 4, an elastic energy storage element 5, a binding device 6 and a bowden cable driving device 7, wherein the driving system 3 is driven by a motor and provides a power source for the exoskeleton, the sensing system 4 comprises an inertial sensor and a tension sensor, the inertial sensor detects hip joint angle and angular velocity information, and the tension sensor detects the upward tension of the bowden cable.
The active-passive hybrid driving flexible lower limb exoskeleton system coordination control method comprises a gait recognition algorithm, a power-assisted track planning algorithm and a force track tracking algorithm, wherein the gait recognition algorithm detects the angle and angular velocity information of a hip joint in a sagittal plane through an inertial sensor, detects the movement state of the hip joint of a human body in real time, the power-assisted track planning algorithm calculates the rope driving power-assisted size in real time according to the movement state of the hip joint, the force track tracking algorithm converts expected force into motor speed through an admittance controller, a motor stay rope outputs force to legs of the human body, real-time correction is carried out through force sensor feedback information, tracking errors are reduced, and accurate expected force track tracking is realized.
The specific implementation mode is as follows:
the inertial sensor collects angle and angular velocity information of hip joint motion on a sagittal plane, and the collected data is subjected to filtering processing through low-pass filtering, wherein the low-pass filtering formula is as follows:
Y(n)=aX(n)+(1-α)Y(n-1),
where α is a filter coefficient, X (n) is a current sample value, Y (n-1) is a last filtered output value, and Y (n) is a current filtered output value.
The gait recognition algorithm judges the current motion state through the filtered angle and angular velocity information, and the motion state is classified as follows:
in a first state, gait cycles are 0 to 30%, and the force of the state is the assistance of the rope driving power, wherein the force comprises hip joint assistance and spring tension.
And in the second state, the gait cycle is 30-50%, and the spring stores energy by utilizing the gravitational potential energy of a human body.
And a third state, namely 50 to 87 percent of gait cycle, wherein the spring releases energy to assist a person.
And in a fourth state, the gait cycle is 87-100%, the force of the rope drives assistance, the force is hip joint assistance, and the spring stores energy by using the gravity of human legs.
And the rope-driven power-assisted track planning algorithm plans the rope-driven power-assisted track according to the human motion information detected by the gait recognition algorithm. The power assisting track of each stage is as follows:
the first stage is a gait cycle of 0 to 10%, and the rope driving assistance is as follows:
and a second stage, namely a gait cycle of 10-30%, wherein the size of the rope driving assistance is as follows:
And a third stage, namely a gait cycle is 30-87%, wherein the rope keeps an early warning state, and the force on the rope is as follows:
Fp,
And a fourth stage, namely 87% to 100% of gait cycle, wherein the rope driving assistance is as follows:
In the formula,Is the peak value of the expected joint assistance, Fp is the rope pre-warning force, Fs is the spring tension, θ is the angle of the hip joint to the vertical,Is the maximum angle of the hip joint,Is the hip joint angle at which peak force is reached.
The force track tracking algorithm tracks the force track output by the power-assisted track planning algorithm, the pull force Fc on the Bowden wire is detected through the pull force sensor, the expected auxiliary force Fd (theta) of the rope driving system is different from the force Fc measured by the pull force sensor, the output force Fe is calculated through the PID controller, and the calculation formula of the PID algorithm is as follows:
Where Fe (T) is the output force of the PID controller, Kp is the proportional gain, Tt is the integral time constant, TD is the derivative time constant, and e (T) is the difference between the desired force and the measured force.
The PID algorithm calculates the speed Vc required by the output force converted into the motor through the admittance controller, and the admittance control model is as follows:
Where Md is the inertia coefficient, Bd is the damping coefficient, Kd is the stiffness coefficient, Xr is the reference position, X is the actual position, and Fe is the contact force.
The admittance equation is defined in the Laplace domain as:
Where Y is the virtual admittance, Vc is the rope speed of the feedback term, Mv is the virtual inertia, and Cv is the damping.
As shown in fig. 3, the specific workflow of the active-passive hybrid driving flexible lower limb exoskeleton system coordination control method is as follows:
The working flow of the rope-driven active assisting is that when the inertial sensor in the sensing detection device 4 detects that the hip joint angular velocity reaches a threshold value, the active and passive hybrid driving flexible lower limb exoskeleton assisting system is started. The power-assisted trajectory planning algorithm outputs the power assistance required by the current motion state of the human body according to the angle and angular velocity information detected by the inertial sensor in the sensing detection device 4. The force track tracking algorithm controls the bowden wire driving device 7 to assist the lower limb of the human body in gait cycles of 0 to 30% and 87% to 100% through the rope driving system 3, the auxiliary force is firstly converted into the rotating speed required by the motor in the rope driving system 3 through the admittance controller, the rotating speed of the motor is controlled through the speed controller, the motor rotates to drive the actuator, namely the bowden wire driving device 7 to output the assistance to the human body, the Hall sensor in the motor feeds back the motor speed to the speed controller, the speed controller carries out real-time correction on the motor speed, the tension sensor in the sensing detection device 4 feeds back the tension in the bowden wire driving device 7, and the PID controller carries out real-time correction on the assistance required by the bowden wire driving device 7. And when the hip joint angle or the angular speed exceeds a set threshold value, the active and passive hybrid driving flexible lower limb exoskeleton stops working.
The passive power assisting work flow of the elastic energy storage element is that the elastic energy storage element 5 stores energy by utilizing gravitational potential energy in the human body movement process in the gait cycle of 30-50% and 87-100%, stores energy by utilizing energy provided by the Bowden wire driving device 7 in the gait cycle of 0-30%, and releases energy to provide passive power assistance for lower limbs in the gait cycle of 50-87%. The elastic energy storage element 5 is a passive driving device, the state of the elastic energy storage element is changed along with the change of the motion state of the lower limb of the human body, the elastic energy storage element is in the original length when the hip joint reaches the maximum buckling, the elastic energy storage element is lengthened in the hip joint stretching process, and the elastic energy storage element is shortened in the hip joint buckling process, and the control system is not required to provide control.
The coordination control method of the active and passive hybrid driving flexible lower limb exoskeleton system divides active and passive power-assisted phases in a gait cycle through a finite state machine, controls the rope driving system 3 to provide power assistance for human lower limbs in the gait cycle of 0 to 30% and 87% to 100%, and provides power assistance for human lower limbs in the gait cycle of 50% to 87% of the elastic energy storage elements 5.
The foregoing is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any person skilled in the art will make any equivalent substitution or modification to the technical solution and technical content disclosed in the invention without departing from the scope of the technical solution of the invention, and the technical solution of the invention is not departing from the scope of the invention.