CN114700948B - Lower limb exoskeleton robot control system based on divergent motion component - Google Patents

Lower limb exoskeleton robot control system based on divergent motion component Download PDF

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CN114700948B
CN114700948B CN202210417881.1A CN202210417881A CN114700948B CN 114700948 B CN114700948 B CN 114700948B CN 202210417881 A CN202210417881 A CN 202210417881A CN 114700948 B CN114700948 B CN 114700948B
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planner
centroid
motion component
divergent motion
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CN114700948A (en
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李智军
郝旭
李国欣
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University of Science and Technology of China USTC
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/0006—Exoskeletons, i.e. resembling a human figure
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1602—Programme controls characterised by the control system, structure, architecture
    • B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
    • B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
    • B25J9/00—Programme-controlled manipulators
    • B25J9/16—Programme controls
    • B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
    • B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

The invention provides a lower limb exoskeleton robot control system based on divergent motion components, which comprises a reference track planner and a linear quadratic Gaussian controller, wherein the reference track planner is used for planning the reference track; the reference trajectory planner obtains actual parameters of the lower limb exoskeleton robot through the step planner, then a zero momentum point planner is utilized to generate a zero momentum point trajectory according to steps, a centroid trajectory is calculated based on a linear inverted pendulum model, and finally divergent motion components in centroid motion are controlled based on divergent motion component theory, so that a reference trajectory of robot feedforward walking is obtained; the linear quadratic gaussian controller enables a robust tracking of the reference trajectory in the presence of uncertainty and robustness in the presence of process disturbances and measurement noise based on the divergent motion component. The reference trajectory planner based on the divergent motion component is introduced on the basis of the centroid planner, and the motion decoupling of the sagittal plane and the coronal plane simplifies the complexity of the step planning.

Description

Lower limb exoskeleton robot control system based on divergent motion component
Technical Field
The invention relates to the field of medical instruments and rehabilitation auxiliary appliances, in particular to a lower limb exoskeleton robot control system based on divergent motion components.
Background
The walking performance of the lower limb exoskeleton robot is greatly improved, but still the walking performance of the lower limb exoskeleton robot is still far from being expected. While many lower extremity exoskeleton robots are capable of walking on flat terrain (or uneven terrain of known geometry), only a few robots are capable of compensating for severe interference or walking on rough terrain. Lower extremity exoskeleton robots have a unique feature of being similar to humans in kinematics and dynamics. According to this characteristic, they are able to adapt to our human daily living environment, and humans want to be able to walk out of the laboratory, complete daily living tasks, and cooperate with them precisely and safely. A key requirement for a lower extremity exoskeleton robot to work in a dynamic environment is the ability to react robustly to unknown disturbances while walking. At present, more and more researches are conducted in the aspect of developing a robust walking engine, and the research can be divided into two main categories: model-based and model-free. In the model-based approach, a physical model of system dynamics is considered and a walking system is designed based on the model. Model-free methods attempt to design a walking system by generating rhythmic motion for each limb without any kinetic model. The present invention is a model-based approach.
Although it is not impossible to consider the full rigid body dynamics of a lower extremity exoskeleton robot, the calculation amount is large and the solving time is long when solving highly nonlinear and large-sized problems due to the complex dynamics problem to be processed. Therefore, considering the whole body kinetic model is not affordable for real-time implementation. Conversely, to reduce the computational cost and complexity of the planning process, the overall dynamics are limited to centroids. Linear inverted pendulum models are known for their fast and efficient solution in real time, which approximate the dynamics of a lower extremity exoskeleton robot by considering only a single mass, which is constrained to move along a horizontally defined plane, and which is connected to the ground by a mass-free rod. Based on these assumptions, there is a simple solution to generate a reference centroid trace from a set of planned footprints.
Patent document CN110286679a discloses a robot gait planning method based on a linear inverted pendulum model, specifically, a zero moment track, a trunk centroid motion track and a traveling foot planning track are generated based on the linear inverted pendulum model, and each joint gait motion curve is resolved by inverse kinematics to seek to more conform to the actual mass distribution situation of a human robot, but precise control on divergent components in motion is still lacking, and expected effects are difficult to obtain in practical application.
Disclosure of Invention
In view of the drawbacks of the prior art, an object of the present invention is to provide a lower extremity exoskeleton robot control system based on divergent motion components, which according to the latest progress of humanoid robot movements is based on decoupling of centroid dynamics into divergent and convergent components, as long as the divergent components of the motion are controlled, can develop robust and stable walking.
The invention provides a lower limb exoskeleton robot control system based on divergent motion components, which comprises a reference track planner and a linear quadratic Gaussian controller, wherein the reference track planner is used for planning the reference track;
the reference track planner comprises a footstep planner, a zero momentum point planner, a centroid planner and a divergent motion component planner;
the reference trajectory planner obtains actual parameters of the lower limb exoskeleton robot through the step planner, then a zero momentum point planner is utilized to generate a zero momentum point trajectory according to steps, a centroid trajectory is calculated based on a linear inverted pendulum model, and finally divergent motion components in centroid motion are controlled based on divergent motion component theory, so that a reference trajectory of robot feedforward walking is obtained;
the linear quadratic gaussian controller enables robust tracking of the reference trajectory in the presence of uncertainty and robustness in the presence of process disturbances and measurement noise based on the divergent motion component.
Further, the linear quadratic gaussian controller uses the concept of divergent motion components, simplifying the complexity of the step planning by decoupling the motion of the sagittal and coronal planes.
Further, the linear quadratic Gaussian controller comprises a Kalman filter and an integrator;
the kalman filter is used for estimating the state of the system in the presence of measurement and process noise and based on the observability of the state error in each control period;
the integrator is used for eliminating steady-state errors and is used for estimating states and outputs according to the integrator.
Further, the foot planner generates a set of footprint locations from the entered step size information, wherein the step size information includes a step Size (SL), a Step Width (SW), a step Size Duration (SD), a Single Support Duration (SSD), and a Double Support Duration (DSD).
Further, the zero momentum point planner generates a zero momentum point trajectory based on the designated footprint and using the following formula:
where T represents time, reset at the end of each step, (t.gtoreq.T) ss +T ds )，T ss 、T ds The duration of the single support phase and the double support phase, respectively, fi= [ f i,x f i,y ]Is a set of planned foot positions (i e N) on the 2D plane.
Further, the dynamics model of the centroid planner is based on a linear inverted pendulum model, according to which the centroid is limited to move only along a predefined horizontal plane, thus the sagittal and coronal motions are decoupled, independent; the dynamics model represents the overall dynamics of the humanoid robot through a first-order stable dynamics equation, and is as follows:
where x represents the position of COM in the sagittal plane,is the natural frequency of pendulum, p x Is the location of a zero momentum point, which is a point on the ground plane where ground reaction forces act to compensate for gravity and inertia;
calculating a centroid trace, wherein the centroid trace can be obtained by solving the dynamic equation as a boundary value problem, and the boundary condition considered by solving the differential equation is the position of the centroid at the beginning and the end of the walking step; thus, the centroid trace is obtained using the following function:
wherein t is 0 、t f 、x 0 、x f The time and the corresponding position of the centroid at the beginning and end of the step, respectively.
Further, the dynamics of the divergent motion component are defined as follows:
in the formula, zeta,respectively representing the speed of the divergent motion component and the centroid, obtaining the corresponding derivative equation by taking the derivatives on both sides of the equation, and adding the equation +.>Substituting the derivative equation can obtain a state space expression of a dynamics equation of the linear inverted pendulum model, as follows:
from this state space matrix, the centroid always converges to the divergent motion component without control, and the divergent motion component reference trajectory can be obtained by substituting the generated centroid trajectory and its derivative into the equationTo be generated.
Further, the optimal state feedback control law tracked by the linear quadratic Gaussian controller is designed as follows:
wherein x, x des Representing the estimated state and the expected state, respectively. X is x i Is the integrator output, K represents the optimal gain matrix designed to minimize the following cost function, as follows:
wherein the method comprises the steps ofz T For the transposed matrix of z, Q is the performance index function of the linear quadratic function, R is the linear quadraticThe weights of the control quantities in the function, which are a compromise between tracking performance and control cost, can be adjusted by testing as needed.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention approximates the exoskeleton robot by using the linear inverted pendulum model, and represents the overall dynamics model of the robot as a state space system by using the divergent motion component concept, so that the model is not excessively complicated to cause excessive calculation, and the system is more stable.
2. The invention designs a reference track planner based on divergent motion components, and introduces the divergent motion component planner on the basis of a centroid planner, so that motion decoupling of sagittal plane and coronal plane simplifies the complexity of step planning.
3. The invention designs an optimal closed-loop controller based on linear quadratic Gaussian distribution, and utilizes a Kalman filtering and integrator combined mode to improve the stability of the system and generate robust and stable robot walking.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, given with reference to the accompanying drawings in which:
FIG. 1 is a flow chart of a lower extremity exoskeleton robot control system based on divergent motion components of the present invention;
FIG. 2 is a flow chart of a foot step planner of a lower extremity exoskeleton robot control system based on divergent motion components of the present invention;
fig. 3 is a flow chart of a linear quadratic gaussian controller of a lower extremity exoskeleton robot control system based on divergent motion components of the present invention.
Detailed Description
The present invention will be described in detail with reference to specific examples. The following examples will assist those skilled in the art in further understanding the present invention, but are not intended to limit the invention in any way. It should be noted that variations and modifications could be made by those skilled in the art without departing from the inventive concept. These are all within the scope of the present invention.
The invention provides a lower limb exoskeleton robot control system based on divergent motion components, which comprises a lower limb exoskeleton robot ginseng examination track planner and a linear quadratic Gaussian controller. The reference trajectory planner performs dynamics approximation on the lower limb exoskeleton robot by using a linear inverted pendulum model, and the overall dynamics model of the robot is represented as a state space system by using the concept of divergent motion components, so that the calculation of the model is simplified.
Calculating a reference track by using a reference track planner, wherein the reference track planner comprises a footstep planner, a zero momentum point planner, a centroid planner and a divergent motion component planner; the method comprises the steps of obtaining actual parameters of a lower limb exoskeleton robot through a step planner, generating a zero momentum point track according to steps by using a zero momentum point planner, calculating a centroid track based on a linear inverted pendulum model, and finally controlling divergent components in centroid movement based on a divergent motion component theory to realize feedforward walking of the robot.
Specifically, the reference trajectory planner includes four parts: a footstep planner, a zero momentum point planner, a centroid planner and a divergent motion component planner.
Specifically, the foot planner is to generate a set of footprint locations from input step size information, wherein the step size information includes a step Size (SL), a Step Width (SW), a step Size Duration (SD), a Single Support Duration (SSD), and a Double Support Duration (DSD).
Specifically, the zero momentum point planner generates a zero momentum point trajectory based on the designated footprint and using the following formula:
wherein SL represents the step size, T represents the time, reset at the end of each step, (t.gtoreq.T) ss +T ds )，T ss 、T ds The holding of the single support stage and the double support stage respectivelyTime duration, fi= [ f i,x f i,y ]Is a set of planned foot positions (i e N) on the 2D plane.
In particular, the dynamics model of the centroid planner is based on a linear inverted pendulum model according to which the centroid is limited to move along a predefined horizontal plane, and thus the sagittal and coronal plane motions are decoupled, independent. The model represents the overall dynamics of the humanoid robot through a first-order stable dynamics equation, and is shown as follows:
where x represents the position of COM in the sagittal plane,is the natural frequency of pendulum, p x Is the location of the zero momentum point, which is the point on the ground plane at which the ground reaction forces act to compensate for gravity and inertia.
Specifically, after the zero momentum point trajectory is generated, a trajectory of the centroid should be calculated, which can be obtained by solving the above-described kinetic equation as a side value problem. The boundary condition considered to solve the differential equation is the location of the centroid at the beginning and end of the walking step. Thus, the trajectory of the centroid can be obtained using the following function:
wherein t is 0 、t f 、x 0 、x f The time and the corresponding position of the centroid at the beginning and end of the step, respectively.
Specifically, the divergent motion component planner is based on divergent motion component theory. The divergent motion component is an unstable part of the centroid dynamics, which is the point at which the robot should rest straddling the support foot. The divergent motion component is dynamically defined as follows:
in the formula, zeta,representing the speed of the divergent motion component and the centroid, respectively. Taking the derivatives from the two sides of the equation (4), and substituting the equation (4) into the equation, a state space expression of a dynamics equation of the linear inverted pendulum model can be obtained, as follows:
according to the state space matrix, the centroid always converges to the divergent motion component under the uncontrolled condition, so that stable walking can be realized only by controlling the divergent motion component.
The divergent motion component reference trajectory may be generated by substituting the generated centroid trajectory and its derivative into equation (4).
The linear quadratic gaussian controller is used to achieve a robust tracking of the required reference trajectory in the presence of uncertainty and to achieve a controller that is also robust in the presence of process disturbances and measurement noise. In particular, the linear quadratic gaussian controller uses the concept of divergent motion components, simplifying the complexity of the step planning by decoupling the motion of the sagittal and coronal planes; an optimal closed-loop controller based on linear quadratic Gaussian distribution is provided, and a Kalman filter and integrator are combined to reduce measurement errors and steady-state errors so as to generate robust and stable walking of the lower limb exoskeleton robot.
Specifically, the linear quadratic gaussian controller comprises a kalman filter for estimating the state of the system in the presence of measurement and process noise, and based on the observability of the state error in each control period, an integrator is used to eliminate steady state error, and based on the estimated state and output of the integrator, the tracking optimal state feedback control law is designed as follows:
wherein x, x des Representing the estimated state and the expected state, respectively. X is x i Is the integrator output, K represents the optimal gain matrix designed to minimize the following cost function, as follows:
wherein the method comprises the steps ofz T For the transposed matrix of z, Q is the performance index function of the linear quadratic function, R is the weight of the control quantity in the linear quadratic function, which are trade-offs between tracking performance and control cost, and can be adjusted by testing as needed.
Those skilled in the art will appreciate that the invention provides a system and its individual devices, modules, units, etc. that can be implemented entirely by logic programming of method steps, in addition to being implemented as pure computer readable program code, in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, the system and various devices, modules and units thereof provided by the invention can be regarded as a hardware component, and the devices, modules and units for realizing various functions included in the system can also be regarded as structures in the hardware component; means, modules, and units for implementing the various functions may also be considered as either software modules for implementing the methods or structures within hardware components.
The foregoing describes specific embodiments of the present invention. It is to be understood that the invention is not limited to the particular embodiments described above, and that various changes or modifications may be made by those skilled in the art within the scope of the appended claims without affecting the spirit of the invention. The embodiments of the present application and features in the embodiments may be combined with each other arbitrarily without conflict.

Claims (6)

1. The lower limb exoskeleton robot control system based on the divergent motion component is characterized by comprising a reference track planner and a linear quadratic Gaussian controller;
the reference track planner comprises a footstep planner, a zero momentum point planner, a centroid planner and a divergent motion component planner;
the reference trajectory planner obtains actual parameters of the lower limb exoskeleton robot through the step planner, then a zero momentum point planner is utilized to generate a zero momentum point trajectory according to steps, a centroid trajectory is calculated based on a linear inverted pendulum model, and finally divergent motion components in centroid motion are controlled based on divergent motion component theory, so that a reference trajectory of robot feedforward walking is obtained;
the linear quadratic gaussian controller enables robust tracking of the reference trajectory in the presence of uncertainty and robustness in the presence of process disturbances and measurement noise based on the divergent motion component;
the linear quadratic gaussian controller uses the concept of divergent motion components, simplifying the complexity of step planning by decoupling the motion of the sagittal and coronal planes;
the linear quadratic gaussian controller comprises a kalman filter for estimating the state of the system in the presence of measurement and process noise and from the observability of the state error in each control period, an integrator; the integrator is used for eliminating steady-state errors and is used for estimating states and outputs according to the integrator.
2. The divergent motion component based lower extremity exoskeleton robot control system of claim 1 wherein said step planner generates a set of footprint locations from input step size information including step Size (SL), step Width (SW), step Size Duration (SD), single Support Duration (SSD), and Double Support Duration (DSD).
3. The divergent motion component based lower extremity exoskeleton robot control system of claim 2 wherein said zero momentum point planner generates a zero momentum point trajectory based on a specified footprint and using the formula:
where T represents time, reset at the end of each step, (t.gtoreq.T) ss +T ds )，T ss 、T ds The duration of the single support phase and the double support phase, respectively, fi= [ f i,x f i,y ]Is a set of planned foot positions (i e N) on the 2D plane.
4. A lower extremity exoskeleton robot control system based on divergent motion components as claimed in claim 3, wherein the dynamics model of said centroid planner is based on a linear inverted pendulum model, according to which the centroid is limited to move only along a predefined horizontal plane, whereby the motion of the sagittal and coronal planes is decoupled, independent; the dynamics model represents the overall dynamics of the humanoid robot through a first-order stable dynamics equation, and is as follows:
where x represents the position of COM in the sagittal plane,is the natural frequency of pendulum, p x Is the location of a zero momentum point, which is a point on the ground plane where ground reaction forces act to compensate for gravity and inertia;
calculating a centroid trace, wherein the centroid trace can be obtained by solving the dynamic equation as a boundary value problem, and the boundary condition considered by solving the differential equation is the position of the centroid at the beginning and the end of the walking step; thus, the centroid trace is obtained using the following function:
wherein t is 0 、t f 、x 0 、x f The time and the corresponding position of the centroid at the beginning and end of the step, respectively.
5. The divergent motion component based lower extremity exoskeleton robot control system of claim 4 wherein the dynamic definition of said divergent motion component is as follows:
in the formula, zeta,respectively representing the speed of the divergent motion component and the centroid, obtaining the corresponding derivative equation by taking the derivatives on both sides of the equation, and adding the equation +.>Substituting the derivative equation can obtain a state space expression of a dynamics equation of the linear inverted pendulum model, as follows:
from this state space matrix, the centroid always converges, without control, to the divergent motion component, the divergent motion component reference trajectory can be obtained by substituting the generated centroid trajectory and its derivative intoFang ChengTo be generated.
6. The divergent motion component based lower extremity exoskeleton robot control system of claim 1 wherein said linear quadratic gaussian controller tracked optimal state feedback control law is designed as follows:
wherein x, x des Respectively represents an estimated state and an expected state, x i Is the integrator output, K represents the optimal gain matrix designed to minimize the following cost function, as follows:
wherein the method comprises the steps ofz T For the transposed matrix of z, Q is the performance index function of the linear quadratic function, R is the weight of the control quantity in the linear quadratic function, which are trade-offs between tracking performance and control cost, and can be adjusted by testing as needed.
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