CN105108761B - Reduced-order adaptive robust cascading force control method for single-joint powered exoskeleton - Google Patents
Reduced-order adaptive robust cascading force control method for single-joint powered exoskeleton Download PDFInfo
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Abstract
The invention discloses a reduced-order adaptive robust cascading force control method for the single-joint powered exoskeleton. A reduced-order model is adopted by aiming at the power reinforcement and following problems of the single-joint powered exoskeleton driven by a hydraulic cylinder, errors caused by sensor precision problems are effectively eliminated, and controller design is simplified. The cascading force control method is adopted in the controller design, an upper-layer controller and a lower-layer controller are designed, a single-joint reference track is generated by the upper-layer controller, and the reference track is tracked through the lower-layer controller. When the powered exoskeleton bears weights, man-machine acting force is minimized to achieve power assistance and motion along with a person. The upper-layer controller and the lower-layer controller are designed through an adaptive robust control (ARC) algorithm, the influence of model nondeterminacy of the single-joint powered exoskeleton is effectively overcome, the good following and assisting effects of the powered exoskeleton to the motion of the person are achieved, and high application value is achieved.
Description
Technical Field
The invention relates to the field of robot control, in particular to a method for controlling adaptive robust cascade force of a single-joint power-assisted exoskeleton based on a reduced-order model.
Background
Army soldiers often need to carry heavy objects to walk or fight for a long distance, the heavy loads often cause certain damage to the bodies of the soldiers, and under the background, exoskeleton equipment capable of enhancing the speed, the strength and the endurance of the soldiers in a battlefield environment needs to be developed; in the fields of scientific investigation, fire rescue and the like, scientific investigation personnel and fire rescue personnel often need to walk for a long distance, bear heavy objects, transport wounded persons, fight in the field, climb mountain and explore and the like, and the traditional wheel type transportation tool is difficult to play a role in special occasions. In addition to this, the exoskeleton can also be used for goods handling in warehouses to reduce the labor intensity of the handlers. The combination of the exoskeleton and the human can adapt to unstructured environments, has excellent flexibility, and can finish some complex loading and unloading works, such as loading and unloading missiles for fighters, which is incomparable with other loading and unloading equipment. The application of exoskeletons in these areas will play a very positive role in these areas. In addition, the aging is spreading all over the world, and the appearance of the exoskeleton can help some old people to solve the problems of poor physical strength and unchanged walking and also help some people with mobility disabilities to recover partial mobility. The assistance exoskeleton is characterized in that cooperation with a wearer is required in an unstructured environment, researchers need to solve the problem of highly coordinated man-machine integration in the unstructured environment, including effective and reliable man-machine interaction, quick response to human movement intentions, light and flexible bionic structure design, safety problems of man-machine systems and the like, and the technical problems are still in a primary research stage, are not mature and need to be deeply researched.
Disclosure of Invention
The invention aims to provide a reduced-order single-joint power-assisted exoskeleton adaptive robust cascade force control method aiming at the defects of the prior art, which is effective and reliable in the problem of man-machine interaction and has the characteristic of quick response to human movement intention.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows: a method for controlling self-adaptive robust cascade force of a reduced-order single-joint power-assisted exoskeleton comprises a hydraulic cylinder, a joint rotary encoder, a force sensor, a first rod piece, a second rod piece, a bandage, an electro-hydraulic servo valve, a servo amplification plate, a real-time controller and the like; the first rod piece and the second rod piece are connected through a hinge, and a joint rotary encoder is arranged at the hinged position; one end of the hydraulic cylinder is hinged with the first rod piece, and the other end of the hydraulic cylinder is hinged with the second rod piece; the force sensor is arranged on the second rod piece, and the binding belt is connected with the force sensor; the hydraulic cylinder is connected with an electro-hydraulic servo valve, the electro-hydraulic servo valve is connected with a servo amplification plate, and the servo amplification plate, the joint rotary encoder and the force sensor are connected with real-time control; the method comprises the following steps:
(1) initializing a sampling period T of the real-time controller, and taking the value of T between 10 and 20 milliseconds;
(2) rotating a first rod piece and a second rod piece of the single-joint power-assisted exoskeleton to parallel positions, initializing a joint rotary encoder on the single-joint power-assisted exoskeleton, and zeroing the numerical value of the joint rotary encoder;
(3) initializing a force sensor on the second rod piece, and zeroing the numerical value of the force sensor;
(4) establishing a physical model of the single-joint power-assisted exoskeleton and converting the physical model into a state equation; the physical model includes: the system comprises a man-machine interface model, a hydraulic cylinder load motion model, a hydraulic cylinder two-cavity pressure model and a flow model of a servo valve;
(5) carrying out order reduction processing on a state equation of a physical model of the single-joint power-assisted exoskeleton;
(6) the human body is connected with the force sensor on the exoskeleton single joint through the binding band, and the acting force T on the force sensor is measuredhmThe reference displacement α of the exoskeleton is obtained by the upper ARC controller1；
(7) Obtaining an actual angle value of the exoskeleton through the joint rotary encoder, and obtaining a reference displacement α of the exoskeleton according to step 61Comparing α the actual angular value of the exoskeleton with the reference displacement of the exoskeleton1Designing a lower-layer ARC position tracking controller as an input quantity of the lower-layer ARC position tracking controller, wherein the output of the lower-layer ARC position tracking controller is a control voltage u of the single-joint power-assisted exoskeleton;
(8) converting the control voltage u obtained in the step 7 into a control current of the servo valve through a servo valve amplification plate;
(9) the valve core displacement of the current control servo valve is controlled to control the pressure at two ends of the hydraulic cylinder, the hydraulic cylinder is pushed to move, and the following movement of the single-joint power-assisted exoskeleton is realized.
Further, the step 4 specifically comprises the following steps:
establishing a physical model of the single-joint assisted exoskeleton, wherein the physical model comprises:
a human-computer interface model:
the hydraulic cylinder load motion model is as follows:
two-cavity pressure model of the hydraulic cylinder:
flow model of the servo valve:
wherein, ThmIs the man-machine acting force, K is the stiffness of the man-machine interface, qhAnd q are the displacement of the person and the displacement of the exoskeleton respectively,is the first derivative of the displacement of the exoskeleton,is the second derivative of the displacement of the exoskeleton;is the centralized model uncertainty and interference on the human-computer interface, J is the rotational inertia of the single-joint power-assisted exoskeleton, h is the moment arm of the hydraulic cylinder output force, and P is1And P2Respectively, the pressure of two chambers of the hydraulic cylinder, A1And A2Is the area of the two chambers, m is the load mass, g is the gravitational acceleration, lcIs the distance from the joint to the force sensor, B is the damping viscous friction coefficient, a is the coulomb friction coefficient,is used to fit a symbolic functionIs a smooth function of (a) the average, is the central model uncertainty and interference, V, on the single-joint assisted exoskeleton1And V2Volume of two chambers of the hydraulic cylinder, β respectivelyeIs the bulk modulus of elasticity, Q, of the oil1And Q2Respectively the oil inlet flow and the oil outlet flow,the centralized model uncertainty and disturbance, x, on the inlet and outlet oil paths, respectivelyvIs the displacement of the valve core, kq1,kq2Respectively, the gain factor of the flow at the inlet and outlet, PsIs the supply pressure of the pump, PrIs the pressure at the oil outlet, u is the control voltage of the servo valve;
since the human-machine interface model is a static equation, Thm、qhAnd q is static in order to allow dynamic control of the man-machine force ThmIntegration of human action forceTo replace Thm；
The steps for converting the physical model into the equation of state are as follows:
order state variableWherein,let the centralized model uncertainty be:
the uncertainty of the centralized model is divided into two parts of constant and time-varying function, namelyWherein, DeltainIs a constant, ΔiIs a time-varying function; is provided withWherein,the state equation of the physical model of the single-joint assistance exoskeleton is as follows:
wherein:
further, the step of the order reduction processing in step 5 is as follows:
and (4) combining the formula (7) and the formula (8) obtained in the step (4) to obtain a second-order equation of the pressure model of the two cavities of the hydraulic cylinder:
wherein,will be provided withInto a low-frequency component thetaeAnd a high frequency time-varying component ΔeI.e. by
The natural frequencies of this second order system are:
the natural frequency w of the second-order system is determined according to the actual operation conditionnBetween 5 and 10 hz and the closed loop bandwidth of the single joint exoskeleton system is between 1 and 2 hz, the dynamics of the hydraulic cylinder two-chamber pressure model are negligible, so that the second order equation of the hydraulic cylinder two-chamber pressure model becomes the following form:
the state equation of the reduced-order model of the single-joint power-assisted exoskeleton obtained by the joint vertical type (5), the formula (6) and the formula (9) is as follows:
wherein,
further, the control method of the upper ARC controller in step 6 is as follows:
designing an upper ARC controller according to the formula (10) obtained in the step 5; let z1＝x1-x1dWherein x is1dThe integral of the expected man-machine acting force is taken as 0, and α is set1Reference displacement for the exoskeleton, α1Has the effect of making the human-machine force tracking error z1Fast trend towards zero, α1The determination method of (2) is as follows:
is provided withFor the tracking error z1Differentiation is carried out to obtain:
let α1＝α1a+α1s1+α1s2Wherein K1s1＝g1||1φ1||2+K1，K1,g1are all arbitrary non-negative numbers;is to the parameter theta1，θ2The two estimated values are obtained in the range of:whereinTo a parameter theta1Is estimated value ofThe minimum value of (a) is determined,to a parameter theta1Is estimated value ofThe maximum value of (a) is,to a parameter theta2Is estimated value ofThe minimum value of (a) is determined,to a parameter theta2Is estimated value ofMaximum value of (d); and the two estimated valuesIs determined by the adaptation rate in the upper layer ARC controllerTo obtain a mixture of, among others, τ1＝w1φ1z1，w1is a weight coefficient, the value of which is an arbitrary non-negative number; gamma ray1And gamma2Is any non-negative number; adaptive rateHas a mapping function of
Is provided withAccording to the ARC control algorithm, α1s2The following two conditions must be satisfied, namely:
wherein, 1is a threshold value, whose value is arbitrarily non-negative.
Further, the step of designing the lower ARC position tracking controller in step 7 is as follows:
setting the tracking error z according to (11) obtained in step 52＝x2-x2dWherein x is2d＝α1；
For the tracking error z2Differentiating to obtain:wherein,QLis a virtual control input;
let the virtual control input Q according to the ARC control algorithmL＝αL，αL＝αLa+αLs1+αLs2Wherein k2,g2are all arbitrary non-negative numbers;is to the parameter thetaeThe range of the estimated value obtained according to the actual physical model is:
whereinTo a parameter thetaeIs estimated value ofThe minimum value of (a) is determined,to a parameter thetaeIs estimated value ofMaximum value of (d); and this estimateIs determined by the adaptation rate in the lower layer ARC controllerTo obtain a mixture of, among others,w2is a weight coefficient, the value of which is an arbitrary non-negative number; gamma ray3Is an arbitrary non-negative number, the adaptation rateHas a mapping function of
Is provided withKnown from the ARC control algorithm, αLs2The following two conditions must be satisfied, namely:
wherein, 2is a threshold value, whose value is arbitrarily non-negative;
according to a virtual control input QLAnd obtaining the control voltage u of the servo valve as follows:
compared with the background technology, the invention has the following beneficial effects: the invention provides a method for controlling adaptive robust cascade force of a single-joint power-assisted exoskeleton based on a reduced-order model, and mainly aims at a control method of the single-joint power-assisted exoskeleton for assisting or enhancing the walking capability of people in a long-time load-bearing working environment. Aiming at the problems of force increasing and following of the hydraulic cylinder driving single-joint power-assisted exoskeleton, a cascade force control method is adopted, an upper layer controller and a lower layer controller are designed by utilizing an adaptive robust control Algorithm (ARC), the influence of model uncertainty of the single-joint power-assisted exoskeleton is effectively overcome, good following and force-assisting effects of the power-assisted exoskeleton on human motion are realized, meanwhile, a reduced-order model is adopted, the adverse influence of the sensor precision problem on the control of the single joint of the exoskeleton is effectively overcome, the controller is convenient to design, the cascade force control method has high application value, the whole control method is simple to realize, easy to realize in engineering and flexible to control.
Drawings
FIG. 1 is a schematic view of the overall shape structure of the present invention;
FIG. 2 is a control block diagram of the present invention;
FIG. 3 is a control flow diagram of the present invention;
in the figure, a hydraulic cylinder 1, a joint rotary encoder 2, a force sensor 3, a first rod 4, a second rod 5 and a bandage 6.
Detailed Description
The invention is further described below with reference to the figures and examples.
As shown in fig. 1, a single-joint assisted exoskeleton comprises: the device comprises a hydraulic cylinder 1, a joint rotary encoder 2, a force sensor 3, a first rod 4, a second rod 5, a bandage 6, an electro-hydraulic servo valve (not shown in the figure), a servo amplification board (not shown in the figure) and a real-time controller (not shown in the figure); the first rod piece 4 and the second rod piece 5 are connected through a hinge, and a joint rotary encoder 2 is arranged at the hinged position; one end of the hydraulic cylinder 1 is hinged with the first rod piece 4, and the other end is hinged with the second rod piece 5; the force sensor 3 is provided on the second rod member 5, and the binding band 6 is connected to the force sensor 3.
The hydraulic cylinder 1 is connected with an electro-hydraulic servo valve, the electro-hydraulic servo valve is connected with a servo amplification plate, and the servo amplification plate, the joint rotary encoder 2 and the force sensor 3 are all connected with a real-time controller. The real-time controller can adopt a product with the model of NI cRIO-9031, but is not limited to the product; the servo valve amplification plate can be made of a product of type Star WO36829/1, but is not limited thereto.
In order to overcome the influence of model uncertainty in the modeling process of the single-joint power-assisted exoskeleton and realize good follow-up and power-assisted effects of the power-assisted exoskeleton on human motion, the single-joint power-assisted exoskeleton control method adopts Adaptive Robust Control (ARC) which can well overcome the influence of the model uncertainty. The principle of Adaptive Robust Control (ARC) is to design adaptive rate to continuously adjust model parameters, perform feedforward compensation on a control model to ensure zero tracking error under a static state, and ensure the dynamic characteristic and stability of a single-joint power-assisted exoskeleton system through designed robust feedback. Meanwhile, an upper layer controller and a lower layer controller are designed by utilizing a cascade control method, so that the track planning and the track tracking of the single-joint power-assisted exoskeleton are realized, meanwhile, a reduced-order model is adopted, the problem of sensor precision is effectively overcome, the adverse effect on the control of the single joint of the exoskeleton is effectively avoided, the design of the controllers is convenient, the application value is high, the whole control algorithm is simple to realize, the engineering is easy to realize, and the control is flexible.
As shown in fig. 2, a method for adaptive robust cascade force control of a reduced-order single-joint power-assisted exoskeleton specifically includes the following steps:
(1) the single-joint power-assisted exoskeleton is fixed on the crus of a person through a binding band 6; initializing a sampling period T of the real-time controller, and taking the value of T between 10 and 20 milliseconds;
(2) rotating a first rod 4 and a second rod 5 of the single-joint power-assisted exoskeleton to parallel positions, initializing a joint rotary encoder 2 on the single-joint power-assisted exoskeleton, and zeroing the numerical value of the joint rotary encoder 2;
(3) initializing the force sensor 3 on the second rod 5, and zeroing the value of the force sensor 3;
(4) establishing a physical model of the single-joint assistance exoskeleton and converting the physical model into a state equation, wherein the physical model comprises:
a human-computer interface model:
the hydraulic cylinder load motion model is as follows:
two-cavity pressure model of the hydraulic cylinder:
flow model of the servo valve:
wherein, ThmIs the man-machine acting force, K is the stiffness of the man-machine interface, qhAnd q are the displacement of the person and the displacement of the exoskeleton respectively,is the first derivative of the displacement of the exoskeleton,is the second derivative of the displacement of the exoskeleton;is the centralized model uncertainty and interference on the human-computer interface, J is the rotational inertia of the single-joint power-assisted exoskeleton, h is the moment arm of the hydraulic cylinder output force, and P is1And P2Respectively, the pressure of two chambers of the hydraulic cylinder, A1And A2Is the area of the two chambers, m is the load mass, g is the gravitational acceleration, lcIs the joint-to-force sensing distance, B is the damping and viscous friction coefficient, a is the coulomb friction coefficient,is used to fit a symbolic functionIs a smooth function of (a) the average,，is the central model uncertainty and interference, V, on the single-joint assisted exoskeleton1And V2Volume of two chambers of the hydraulic cylinder, β respectivelyeIs the bulk modulus of elasticity, Q, of the oil1,Q2Respectively the oil inlet flow and the oil outlet flow,the centralized model uncertainty and disturbance, x, on the inlet and outlet oil paths, respectivelyvIs the displacement of the valve core, kq1,kq2Respectively, the gain factor of the flow at the inlet and outlet, PsIs the supply pressure of the pump, PrIs the pressure at the oil outlet, u is the control voltage of the servo valve;
since the human-machine interface model is a static equation, Thm、qhAnd q is static in order to allow dynamic control of the man-machine force ThmIntegration of human action forceTo replace Thm；
The steps for converting the physical model into the equation of state are as follows:
order state variableWherein,let the centralized model uncertainty be:
the uncertainty of the centralized model is divided into two parts of constant and time-varying function, namelyWherein, DeltainIs a constant, ΔiIs a time-varying function; is provided withWherein,
the state equation of the physical model of the single-joint assistance exoskeleton is as follows:
wherein:
(5) reducing the order of a state equation of a physical model of the single-joint power-assisted exoskeleton to enable the single-joint power-assisted exoskeleton control system to be suitable for a low-precision sensor; the steps of the order reduction treatment are as follows:
and (4) combining the formula (7) and the formula (8) obtained in the step (4) to obtain a second-order equation of the pressure model of the two cavities of the hydraulic cylinder:
wherein,will be provided withInto a low-frequency component thetaeAnd a high frequency time-varying component ΔeI.e. by
The natural frequencies of this second order system are:
the natural frequency w of the second-order system is determined according to the actual operation conditionnBetween 5 and 10 hz and the closed loop bandwidth of the single joint exoskeleton system is between 1 and 2 hz, the dynamics of the hydraulic cylinder two-chamber pressure model are negligible, so that the second order equation of the hydraulic cylinder two-chamber pressure model becomes the following form:
the state equation of the reduced-order model of the single-joint power-assisted exoskeleton obtained by the joint vertical type (5), the formula (6) and the formula (9) is as follows:
wherein,
(6) the human body is connected with the force sensor 3 on the exoskeleton single joint through the binding belt 6, and the acting force T on the force sensor is measuredhmThe reference displacement α of the exoskeleton is obtained by the upper ARC controller1The control method of the upper ARC controller is as follows:
designing an upper ARC controller according to the formula (10) obtained in the step 5; let z1＝x1-x1dWherein x is1dThe integral of the expected man-machine acting force is taken as 0, and α is set1Is the reference displacement of the exoskeleton, the reference displacement α1Has the effect of making the human-machine force tracking error z1Fast trend towards zero, α1The determination method of (2) is as follows:
is provided withFor the tracking error z1Differentiation is carried out to obtain:
let α1＝α1a+α1s1+α1s2Wherein K1s1＝g1||1φ1||2+K1，K1,g1are all arbitrary non-negative numbers; in this example, K is selected1s1＝g1||1φ1||2+K1＝20，Is to the parameter theta1，θ2The two estimated values are obtained in the range of:whereinTo a parameter theta1Is estimated value ofThe minimum value of (a) is determined,to a parameter theta1Is estimated value ofThe maximum value of (a) is,to a parameter theta2Is estimated value ofThe minimum value of (a) is determined,to a parameter theta2Is estimated value ofMaximum value of (d); and the two estimated valuesIs determined by the adaptation rate in the upper layer ARC controllerTo obtain a mixture of, among others, τ1＝w1φ1z1，w1is a weight coefficient, whose value is an arbitrary non-negative number, set to 1 in this embodiment; gamma ray1And gamma2Is an arbitrary non-negative number, and is set to γ in this embodiment1＝0，γ260; adaptive rateHas a mapping function of
Is provided withAccording to the ARC control algorithm, α1s2The following two conditions must be satisfied, namely:
wherein,is an estimated valueSubtracting the actual valueNamely, it is 1Is a threshold value, which is arbitrarily non-negative, and is set to be in this embodiment1Choose α for 11s2＝0；
(7) Obtaining the actual angle value of the exoskeleton through the joint rotary encoder 2, and obtaining the reference displacement α of the exoskeleton according to step 61Comparing α the actual angular value of the exoskeleton with the reference displacement of the exoskeleton1The output of the lower-layer ARC position tracking controller is the control voltage of the single-joint power-assisted exoskeleton;
setting the tracking error z according to (11) obtained in step 52＝x2-x2dWherein x is2d＝α1；
For the tracking error z2Differentiating to obtain:wherein,QLis a virtual control input;
let the virtual control input Q according to the ARC control algorithmL＝αL，αL＝αLa+αLs1+αLs2Wherein k2,g2are all arbitrary non-negative numbers; in this embodiment, k is selected2s1＝g2||2φ2||2+k2＝30，Is to the parameter thetaeThe range of the estimated value obtained according to the actual physical model is:whereinTo a parameter thetaeIs estimated value ofThe minimum value of (a) is determined,to a parameter thetaeIs estimated value ofMaximum value of (d); and this estimateIs determined by the adaptation rate in the lower layer ARC controllerTo obtain a mixture of, among others,2＝γ3，τ2＝w2φ2z2，w2is a weight coefficient, whose value is an arbitrary non-negative number, set to 1 in this embodiment; gamma ray3The value of (A) is arbitrary non-negative number, which is set to 2000 in the present embodiment, the adaptation rateHas a mapping function of
Is provided withKnown from the ARC control algorithm, αLs2The following two conditions must be satisfied, namely:
wherein,is an estimated valueMinus the actual value theta I.e. by 2Is a threshold value, which is arbitrarily non-negative, and is set to be in this embodiment2Choose α for 1Ls2＝0；
According to a virtual control input QLAnd obtaining the control voltage u of the servo valve as follows:
(8) converting the control voltage u obtained in the step 7 into a control current of the servo valve through a servo valve amplification plate;
(9) the valve core displacement of the current control servo valve is controlled to control the pressure at two ends of the hydraulic cylinder, the hydraulic cylinder is pushed to move, and the following movement of the single-joint power-assisted exoskeleton is realized.
The basic principle of the present invention is described above, the main features of the present invention are not limited to the technical solutions described in the present invention, and all the technical solutions and modifications thereof without departing from the spirit and scope of the present invention should be covered by the claims of the present invention.
Claims (5)
1. A method for controlling adaptive robust cascade force of a reduced-order single-joint power-assisted exoskeleton comprises a hydraulic cylinder (1), a joint rotary encoder (2), a force sensor (3), a first rod piece (4), a second rod piece (5), a bandage (6), an electro-hydraulic servo valve, a servo amplification plate and a real-time controller; the first rod piece (4) is connected with the second rod piece (5) through a hinge, and a joint rotary encoder (2) is arranged at the hinged position; one end of the hydraulic cylinder (1) is hinged with the first rod piece (4), and the other end of the hydraulic cylinder is hinged with the second rod piece (5); the force sensor (3) is arranged on the second rod piece (5), and the binding band (6) is connected with the force sensor (3); the hydraulic cylinder (1) is connected with an electro-hydraulic servo valve, the electro-hydraulic servo valve is connected with a servo amplification plate, and the servo amplification plate, the joint rotary encoder (2) and the force sensor (3) are all connected with a real-time controller; the method is characterized by comprising the following steps:
(1) initializing a sampling period T of the real-time controller, and taking the value of T between 10 and 20 milliseconds;
(2) rotating a first rod (4) and a second rod (5) of the single-joint power-assisted exoskeleton to parallel positions, initializing a joint rotary encoder (2) on the single-joint power-assisted exoskeleton, and zeroing the numerical value of the joint rotary encoder (2);
(3) initializing a force sensor (3) on the second rod (5) and zeroing the value of the force sensor (3);
(4) establishing a physical model of the single-joint power-assisted exoskeleton and converting the physical model into a state equation; the physical model includes: the system comprises a man-machine interface model, a hydraulic cylinder load motion model, a hydraulic cylinder two-cavity pressure model and a flow model of an electro-hydraulic servo valve;
(5) carrying out order reduction processing on a state equation of a physical model of the single-joint power-assisted exoskeleton;
(6) the human body is connected with the force sensor (3) on the exoskeleton single joint through the binding band (6) to measure the acting force T on the force sensorhmThe reference displacement α of the exoskeleton is obtained by the upper ARC controller1；
(7) Obtaining an actual angle value of the exoskeleton through the joint rotary encoder (2), and obtaining a reference displacement α of the exoskeleton according to the step 61Comparing α the actual angular value of the exoskeleton with the reference displacement of the exoskeleton1Designing a lower-layer ARC position tracking controller as an input quantity of the lower-layer ARC position tracking controller, wherein the output of the lower-layer ARC position tracking controller is a control voltage u of the single-joint power-assisted exoskeleton;
(8) converting the control voltage u obtained in the step 7 into control current of the electro-hydraulic servo valve through an electro-hydraulic servo valve amplification plate;
(9) the valve core displacement of the electro-hydraulic servo valve is controlled by controlling the current to control the pressure at two ends of the hydraulic cylinder, so that the hydraulic cylinder is pushed to move, and the following movement of the single-joint power-assisted exoskeleton is realized.
2. The method for adaptive robust cascade force control of a reduced order single joint assist exoskeleton of claim 1, wherein step 4 comprises the specific steps of:
establishing a physical model of the single-joint assisted exoskeleton, wherein the physical model comprises:
a human-computer interface model:
the hydraulic cylinder load motion model is as follows:
two-cavity pressure model of the hydraulic cylinder:
flow model of electrohydraulic servo valve:
wherein, ThmIs the man-machine acting force, K is the stiffness of the man-machine interface, qhAnd q are the displacement of the person and the displacement of the exoskeleton respectively,is the first derivative of the displacement of the exoskeleton,is the second derivative of the displacement of the exoskeleton;is the centralized model uncertainty and interference on the human-computer interface, J is the rotational inertia of the single-joint power-assisted exoskeleton, h is the moment arm of the hydraulic cylinder output force, and P is1And P2Are respectively provided withIs the pressure of two chambers of the hydraulic cylinder, A1And A2Is the area of the two chambers, m is the load mass, g is the gravitational acceleration, lcIs the distance from the joint to the force sensor, B is the damping viscous friction coefficient, a is the coulomb friction coefficient,is used to fit a symbolic functionIs a smooth function of (a) the average, is the central model uncertainty and interference, V, on the single-joint assisted exoskeleton1And V2Volume of two chambers of the hydraulic cylinder, β respectivelyeIs the bulk modulus of elasticity, Q, of the oil1And Q2Respectively the oil inlet flow and the oil outlet flow,the centralized model uncertainty and disturbance, x, on the inlet and outlet oil paths, respectivelyvIs the displacement of the valve core, kq1,kq2Respectively, the gain factor of the flow at the inlet and outlet, PsIs the supply pressure of the pump, PrIs the pressure on the oil outlet, u is the control voltage of the electro-hydraulic servo valve;
since the human-machine interface model is a static equation, Thm、qhAnd q is static in order to allow dynamic control of the man-machine force ThmIntegration of human action forceTo replace Thm；
The steps for converting the physical model into the equation of state are as follows:
order state variableWherein,x2＝q,x4＝P1,x5＝P2let the centralized model uncertainty be:
the uncertainty of the centralized model is divided into two parts of constant and time-varying function, namelyi is 1,3,4, wherein ΔinIs a constant, ΔiIs a time-varying function; is provided withWherein, theta1＝K,θ2＝Δ1n,θ7＝Δ3n,θ8＝βe,θ9＝Δ4nThen, the state equation of the physical model of the single-joint assistance exoskeleton is as follows:
wherein:
3. the method for adaptive robust cascade force control of a reduced order single joint assist exoskeleton of claim 1 wherein the step of the step 5 reduction process is as follows:
and (4) combining the formula (7) and the formula (8) obtained in the step (4) to obtain a second-order equation of the pressure model of the two cavities of the hydraulic cylinder:
wherein,will be provided withInto a low-frequency component thetaeAnd a high frequency time-varying component ΔeI.e. by
The second order system is fixedThe frequency is:
the natural frequency w of the second-order system is determined according to the actual operation conditionnBetween 5 and 10 hz and the closed loop bandwidth of the single joint exoskeleton system is between 1 and 2 hz, the dynamics of the hydraulic cylinder two-chamber pressure model are negligible, so that the second order equation of the hydraulic cylinder two-chamber pressure model becomes the following form:
the state equation of the reduced-order model of the single-joint power-assisted exoskeleton obtained by the joint vertical type (5), the formula (6) and the formula (9) is as follows:
wherein,
4. the method for adaptive robust cascade force control of a reduced order single joint assistance exoskeleton of claim 1, wherein the control method of the upper ARC controller in step 6 is as follows:
designing an upper ARC controller according to the formula (10) obtained in the step 5; let z1＝x1-x1dWherein x is1dThe integral of the expected man-machine acting force is taken as 0, and α is set1For reference displacement of the exoskeleton, the exoskeletonReference displacement of skeleton α1Has the effect of making the human-machine force tracking error z1Fast trend towards zero, α1The determination method of (2) is as follows:
let β be ═ θ1θ2]TFor the tracking error z1Differentiation is carried out to obtain:
let α1＝α1a+α1s1+α1s2WhereinK1s1＝g1||1φ1||2+K1，K1,g1are all arbitrary non-negative numbers;is to the parameter theta1，θ2The two estimated values are obtained in the range of:whereinTo a parameter theta1Is estimated value ofThe minimum value of (a) is determined,to a parameter theta1Is estimated value ofThe maximum value of (a) is,to a parameter theta2Is estimated value ofThe minimum value of (a) is determined,to a parameter theta2Is estimated value ofMaximum value of (d); and the two estimated valuesIs determined by the adaptation rate in the upper layer ARC controllerTo obtain a mixture of, among others,τ1＝w1φ1z1，φ1＝[-α1a1]T，w1is a weight coefficient, the value of which is an arbitrary non-negative number; gamma ray1And gamma2Is any non-negative number; adaptive rateHas a mapping function of
WhereiniIs an independent variable;
is provided withAccording to the ARC control algorithm, α1s2The following two conditions must be satisfied, namely:
wherein, 1is a threshold value, whose value is arbitrarily non-negative.
5. The method for adaptive robust cascade force control of a reduced order single joint assist exoskeleton of claim 1 wherein the step of designing the lower ARC position tracking controller in step 7 is as follows:
setting the tracking error z according to (11) obtained in step 52＝x2-x2dWherein x is2d＝α1；
For the tracking error z2Differentiating to obtain:wherein,QLis a virtual control input;
let the virtual control input Q according to the ARC control algorithmL＝αL，αL＝αLa+αLs1+αLs2Whereink2,g2are all arbitrary non-negative numbers;is to the parameter thetaeThe range of the estimated value obtained according to the actual physical model is:whereinTo a parameter thetaeIs estimated value ofThe minimum value of (a) is determined,to a parameter thetaeIs estimated value ofMaximum value of (d); and this estimateIs determined by the adaptation rate in the lower layer ARC controllerTo obtain a mixture of, among others,2＝γ3，τ2＝w2φ2z2，w2is a weight coefficient, the value of which is an arbitrary non-negative number; gamma ray3Is an arbitrary non-negative number, the adaptation rateHas a mapping function of
WhereiniIs an independent variable;
is provided withKnown from the ARC control algorithm, αLs2The following two conditions must be satisfied, namely:
wherein, 2is a threshold value, whose value is arbitrarily non-negative;
according to a virtual control input QLAnd obtaining the control voltage u of the electro-hydraulic servo valve as follows:
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