CN101816822A - Setting method of functional electrical stimulation PID (Proportion Integration Differentiation) parameter double source characteristic fusion particle swarm - Google Patents

Abstract

The invention relates to the field of instruments for extremity rehabilitation by utilizing electric pulse stimulation and provides a setting method of a functional electrical stimulation PID (Proportion Integration Differentiation) parameter double source characteristic fusion particle swarm. With the setting method, the current strength of an FES (Functional Electrical Stimulation) system can be accurately and stably controlled in real time and the accuracy and the stability of the FES system can be efficiently enhanced. The invention adopts the technical scheme that: firstly, a knee joint angle is predicted by utilizing a handle reaction vector (HRV) in the walk aid process; and secondly, a proportion calculus PID parameter is set by utilizing a chaos particle swarm algorithm, the FES current level strength is regulated in real time, and finally self-adaptive online setting of the proportion calculus PID parameter is realized, and the invention is also used for a functional electrical stimulation FES system. The invention is mainly applied to setting the PID parameter in functional electrical stimulation.

Description

Functional electric stimulation pid parameter double source characteristic fusion particle swarm setting method

Technical field

The present invention relates to carry out the instrument field of limb rehabilitating, especially the double source Feature Fusion chaos particle swarm setting method of pid parameter in the functional electric stimulation with electric pulse stimulation.

Background technology

(Functional Electrical Stimulation is to stimulate limb motion muscle group and peripheral nervous thereof by current pulse sequence FES) to functional electric stimulation, recovers or rebuild the technology of the componental movement function of paralytic patient effectively.According to statistics, because the spinal cord regeneration ability is faint, at the spinal cord injury paralysed patient, the effective treatment method that can directly repair damage is not arranged as yet at present, implementing function rehabilitation training is effective measures.Spinal cord injury paralysed patient number increases year by year, and function rehabilitation training is a technology of demanding demand urgently.The sixties in 20th century, Liberson successfully utilizes the electricity irritation peroneal nerve to correct the gait of hemiplegic patient's drop foot first, has started the new way that functional electric stimulation is used to move and Sensory rehabilitation is treated.At present, FES has become the componental movement function of recovering or rebuilding paralytic patient, is important rehabilitation means.Yet how accurate triggering sequential and the pulse current intensity of controlling FES can accurately be finished the key problem in technology that the intended function action is still FES with assurance electricity irritation action effect.According to statistics, the mode of the triggering of FES control is at present studied still few, and according to action effect and predetermined action deviation, automatically adjust FES stimulus intensity and time sequence parameter with closed loop control, thereby improved the accuracy and the stability of FES system greatly, but now effective control method is still among exploring.

Handle retroaction vector (handle reactions vector, HRV) be according in the process of standing and walking under the walker help, in fact the effectiveness that walker offers the patient can be divided into clear and definite independently 3 parts: sagittal trying hard to recommend into, about to dynamic balance and the power support of upward and downward, this also can be regarded as the patient in fact and keeps the new ideas that the required to external world additional mechanics demand of self normal stand walking proposes, promptly be that the patient is reduced to concentrfated load to the effect of walker is synthetic in the walking process of standing, represent with two mechanics vectors at handle mid point cross section centre of form place respectively, as shown in Figure 1, vector is at x, y, durection component on z axle size with joint efforts can characterize the patient respectively by trying hard to recommend into that walker obtained, dynamic balance and power support level.Wherein, the x axle forward that sets of definition coordinate system is patient's dextrad, and y axle forward be patient's a forward direction, z axle forward be the patient on to.Like this, the defined formula of HRV also can be written as:

[HRV]＝[HRV ₁，HRV _r] ^T＝[F _lx，F _ly，F _lz，F _rx，F _ry，F _rz] ^T (1)

At present, the situation when HRV is widely used in supervision patient walks in the electricity irritation process prevents that then patient from falling down, and causes the secondary injury.This patent proposes to utilize this parameter prediction knee joint angle, the accurate then levels of current intensity of controlling the FES system, and assurance electricity irritation action effect can accurately be finished the intended function action, and prevents muscle fatigue.

Ratio calculus (proportional-integral-differential, PID) be a kind of very practical feedback regulation algorithm, it detects according to system or the operation deviation, proportion of utilization, integration, the required regulated quantity of acquisition of differentiating are widely used in engineering practice so that system is carried out feedback control because of it is easy to operate.Especially indeterminate or when being difficult to timely on-line determination, safe closed loop control can be adopted the PID setting algorithm when the controlled system characterisitic parameter.In the face of the complexity and the time variation operating environment of muscle, because good stability, the reliable operation of PID have still obtained in the functional electric stimulation field using widely at present.The PID core technology is accurate determine wherein ratio, integration, differential coefficient, especially in the FES field, system stability is required very strictness, so select particularly important to pid parameter.PID control will obtain controls effect preferably, must adjust ratio, integration and three kinds of control actions of differential, forms in the controlled quentity controlled variable not only to cooperatively interact but also the relation of mutual restriction.

Summary of the invention

For overcoming the deficiencies in the prior art, the double source Feature Fusion chaos particle swarm setting method of pid parameter in a kind of functional electric stimulation is provided, can accurately stablize and control systematically current intensity of FES in real time, improve FES system accuracy and stability effectively, and obtain considerable social benefit and economic benefit.For achieving the above object, the technical solution used in the present invention is: the double source Feature Fusion chaos particle swarm setting method of pid parameter in the functional electric stimulation comprises:

At first, utilize the handle retroaction vector HRV forecasting knee joint angle of walk help process;

Secondly, utilize the chaos particle swarm optimization ratio calculus pid parameter of adjusting, real-time monitoring FES levels of current intensity, the flow process of adjusting is: at first according to three decision variable K of ratio calculus PID _p, K _iAnd K _dThe bound of span, determine the population population size, parameters such as search volume dimension, and the speed and the position of initialization particle colony, utilize the fitness value that calculates each particle in the population by the corresponding relation of actual joint angles and muscle model output joint angles as appropriate evaluation function then, and its fitness and optimum position fitness value itself made comparisons, and with it as the particle representative value, adjusting particle's velocity and other parameters then, change the optimum position of particle, till stable, calculate the K that final best position promptly gets ratio calculus PID _p, K _iAnd K _dThree coefficients, computing system output yout under the new ratio calculus PID coefficient and with the deviation of muscle model output joint angles after enter the self study and the weight coefficient self-adjusting of next step neutral net again, this process repeatedly, the self adaptation on-line tuning of final realization ratio calculus pid control parameter, and be used for functional electric stimulation FES system.

Described muscle model output joint angles is the method that adopts PLS, that is:

Be provided with m HRV variable HRV1 ..., HRVm, p M variable, M1 ..., Mp, common i (i=1 ..., the n) data set of individual observation, T, U are respectively the composition that extracts from HRV variable and M variable, be called the offset minimum binary factor,

Concentrate the linear combination of extracting first couple of composition T1, U1 to be from original variable:

T ₁＝ω ₁₁HRV ₁+…+ω _1mHRV _m＝ω ₁′HRV (4)

U ₁＝v ₁₁M ₁+…+v _1pM _p＝v ₁′M (5)

ω wherein ₁=(ω ₁₁..., ω _1m) ' be model effect weight, v ₁=(v ₁₁..., v _1p) ' be M variable weight is converted into the requirement of said extracted first composition and asks constrained extremal problem:

Wherein t1, u1 are the score vector of first pair of composition of being tried to achieve by sample, and HRV0, M0 are initializaing variable, utilize method of Lagrange multipliers, and the problems referred to above are converted into asks unit vector ω ₁And v ₁, make θ ₁=ω ₁' HRV ₀' M ₀V ₁Maximum is promptly asked matrix H RV ₀' M ₀M ₀' HRV ₀Eigenvalue and characteristic vector, its eigenvalue of maximum is θ ₁ ², corresponding unit character vector is exactly the ω that separates that is asked ₁, and v ₁By formula

Obtain;

Next sets up the equation of initializaing variable to T1

$\left\{\begin{array}{c}{\mathrm{HRV}}_0=t_1{\mathrm{\α}}_1^{\′}+E_1\\ M_0=t_1{\mathrm{\β}}_1^{\′}+F_1\end{array}\right.---\left(7\right)$

T wherein ₁Meaning is the same, α ₁'=(α ₁₁..., α _1m), β ₁'=(β ₁₁..., β _1p) be the parameter vector when only a M measures t1, E1, F1 are respectively n * m and n * p residual error battle array, can try to achieve coefficient vector α according to common method of least square ₁And β ₁, α wherein ₁Become model effect load capacity;

Can not reach the precision of regression model as first composition that extracts, utilization residual error battle array E1, F1 replace X0, Y0, repeat to extract composition, and the like, supposing finally to have extracted r composition, HRV0, M0 to the regression equation of r composition are:

$\left\{\begin{array}{c}{\mathrm{HRV}}_0=t_1{\mathrm{\α}}_1^{\′}+...+t_r{\mathrm{\α}}_r^{\′}+E_r\\ M_0=t_1{\mathrm{\β}}_1^{\′}+...+t_r{{\mathrm{\β}}_r}^{\′}+F_r\end{array}\right.---\left(8\right)$

The first step analyze extract in the gained HRV amount composition Tk (k=1 ..., r) regression equation that the M amount is set up r composition, i.e. t are brought in linear combination into _r=ω _K1HRV ₁+ ... + ω _KmHRV _mSubstitution M _j=t ₁β _1j+ ... + t _rβ _Rj(j=1 ..., p), promptly get the regression equation M of standardized variable _j=α _J1HRV ₁+ ... + α _JmHRV _m

At last according to formula L=M * HRV ^-1, can obtain L, M represents knee joint angle, and HRV represents that user is applied to the handle retroaction vector of power on the walker, and L represents the relation between HRV and the M.

The described chaos particle swarm optimization pid parameter of adjusting that utilizes further is refined as:

Ratio calculus PID adopts ratio unit P, integral unit I and differentiation element D three parts to form, according to the error of system, by the K that sets _p, K _iAnd K _dThree parameters are controlled system:

$\mathrm{yout}\left(t\right)=K_p\mathrm{error}\left(t\right)+K_i\underset{j=0}{\overset{t}{\mathrm{\Σ}}}\mathrm{error}\left(j\right)+K_d[\mathrm{error}\left(t\right)-\mathrm{error}(t-1)]---\left(9\right)$

K wherein _pBe proportionality coefficient, K _iBe integral coefficient, K _dBe differential coefficient, error is the deviation of default output with actual output, and u (t) is the output of PID, is again the input of controlled system simultaneously, by PID output formula $\mathrm{yout}\left(t\right)=K_p\mathrm{error}\left(t\right)+K_i\underset{j=0}{\overset{t}{\mathrm{\Σ}}}\mathrm{error}\left(j\right)+K_d[\mathrm{error}\left(t\right)-\mathrm{error}(t-1)]$ Can obtain

$u(t-1)=K_p\mathrm{error}(t-1)+K_i\underset{j=0}{\overset{t-1}{\mathrm{\Σ}}}\mathrm{error}\left(j\right)+K_d[\mathrm{error}(t-1)-\mathrm{error}(t-2)]---\left(10\right)$

According to:

Δu(t)＝u(t)-u(t-1)

＝K _p(error(t)-error(t-1))+K _ierror(t)+K _d(error(t)-2error(t-1)+error(t-2))

…………………………………………………………… (11)

Have:

u(t)＝Δu(t)+u(t-1)＝

u(t-1)+K _p(error(t)-error(t-1))+K _ierror(t)+K _d(error(t)-2error(t-1)+error(t-2))

………………(12)

Adopt the chaos particle swarm optimization to carry out the adaptive optimization of ratio calculus pid control parameter, selecting to receive the rope space is 3 dimensions, promptly is respectively three parameters of PID controller, chooses population size m=20, the initial velocity of colony and position produce in certain spatial dimension at random, are expressed as respectively: v _i=(v _I1, v _I2, v _I3), x _i=(x _I1, x _I2, x _I3), remember that it is p that i particle searches optimal location so far _i=(p _I1, p _I2, p _I3), whole population searches to such an extent that optimal location is p up to now _Gi=(p _Gi1, p _Gi2, p _Gi3), wherein, i=1,2 ..., 20, particle swarm optimization algorithm adopts following formula that population is operated,

v _id←v _id+c ₁r ₁(p _id-x _id)+c ₂r ₂(p _gid-x _id)+c ₃r ₃(q _id-x _id) (13)

x _id←x _id+v _id (14)

Wherein, i=1,2 ..., 20; Study factor c ₁, c ₂And c ₃Be nonnegative number, general value is 0.5; r ₁, r ₂And r ₃Be the random number between [0,1], q _IdIt is the picked at random particle position;

The concrete steps that realize are:

1, determines parameter: study factor c ₁, c ₂And c ₃And the scale N of colony, evolution number of times and chaos optimizing number of times;

2, producing N particle at random operates;

3, by formula operate particle (13) and (14);

4, to optimal location p _Gi=(p _Gi1, p _Gi2, p _Gi3) carry out chaos optimization, with p _Gid(i=1,2 ..., 20), be mapped to

Logistic equation z _I+1=μ z _i(1-z _i) i=0,1,2 ... the domain of definition [0,1];

I=1,2 ..., 20, then, carry out iteration with the Logistic equation and produce Chaos Variable

M=1,2 ... again the Chaos Variable sequence that produces (m=1,2 ...) by inverse mapping

(m=1,2 ...) turn back to former solution space, get

(m=1,2 ...)

In former solution space each feasible solution to the Chaos Variable experience

(m=1,2 ...) calculate its adaptive value, the feasible solution p that retention property is best ^*

5, a particle p who from current colony, selects at random ^*Replace;

6, if reach maximum algebraically or obtain satisfactory solution, then optimizing process finishes, otherwise returns step 3.

Characteristics of the present invention are: utilize the HRV variation prediction knee joint angle of walking aid to change, optimize proportionality coefficient, differential coefficient and the integral coefficient of PID then by the chaos particle cluster algorithm, then control the current impulse intensity of FES system, improved FES system accuracy and stability effectively.

Description of drawings

Fig. 1 handle retroaction vector (HRV) definition sketch map.

Fig. 2 is based on the FES system architecture diagram of HRV.

Fig. 3 chaos particle cluster algorithm structured flowchart of pid parameter control method of adjusting.

Anthropometric dummy in Fig. 4 walk-aiding functional electric stimulation.

Fig. 5 experiment scene.

The result is followed the trail of in the PID control that Fig. 6 chaos particle cluster algorithm is adjusted.

The relative error of angle and actual output is closed in the default down input of pid parameter control of adjusting of Fig. 7 chaos particle cluster algorithm.

The specific embodiment

Based on the structure of the precision in the functional electric stimulation walk help of HRV control The application of new technique as shown in Figure 2, its workflow is: at first, utilize the HRV forecasting knee joint angle of walk help process, secondly, utilize the chaos particle swarm optimization pid parameter of adjusting, real-time monitoring FES levels of current intensity.It adjusts structural representation as shown in Figure 3, for: at first according to three decision variable K of PID _p, K _iAnd K _dThe bound of span, determine the population population size, parameters such as search volume dimension, and the speed and the position of initialization particle colony, utilize the fitness value that calculates each particle in the population by the corresponding relation of actual joint angles and muscle model output joint angles as appropriate evaluation function then, and its fitness and optimum position fitness value itself made comparisons, and with it as the particle representative value, then in other parameters such as adjustment particle's velocity, change the optimum position of particle, till stable, calculate the K that final best position promptly gets PID _p, K _iAnd K _dThree coefficients.Computing system output yout under the new PID coefficient and with the deviation of muscle model after enter the self study and the weight coefficient self-adjusting of next step neutral net again.This process finally realizes the self adaptation on-line tuning of pid control parameter repeatedly, and is used for the FES system.

One, HRV forecasting knee joint angle model

In the walk help process, when user under the functional electric stimulation effect, when lifting lower limb and taking a step, in order to support body steadiness, user applied force on walker is then different, because varying in size of joint can make the gravity center of human body be in diverse location, it is also different then to overcome the gravity applied force, the residing plan-position of human body also changes to some extent simultaneously, applied force also changes to some extent for the position is tumbled then in the plane, therefore, joint angles and user have certain relation to the walker applied force, as shown in Figure 4.

M＝L·HRV+wPW (1)

Wherein, M represents knee joint angle, and HRV represents that user is applied to the handle retroaction vector of power on the walker, and L represents the relation between HRV and the M, and w represents coefficient, and W represents the center of gravity of upper arm, trunk and lower limb, and P represents the relation between three centers of gravity and the M.

In the reality, because the effect of walker, the gravity center of human body moves less, and knee joint angle then can be expressed as

M＝L·HRV (2)

Wherein, M represents knee joint angle, and HRV represents that user is applied to the handle retroaction vector of power on the walker, and L represents the relation between HRV and the M.Shown in formula 2, determine that L just can utilize HRV to take out the knee joint angle in the corresponding moment.

L＝M□HRV ^-1 (3)

When this patent is found the solution L, adopted the method for PLS.

Be provided with m HRV variable HRV1 ..., HRVm, p M variable, M1 ..., Mp, common i (i=1 ..., the n) data set of individual observation.T, U are respectively the composition that extracts from HRV variable and M variable, the composition of Ti Quing is commonly referred to the offset minimum binary factor here.

Concentrate the linear combination of extracting first couple of composition T1, U1 to be from original variable:

T ₁＝ω ₁₁HRV ₁+…+ω _1mHRV _m＝ω ₁′?HRV (4)

U ₁＝v ₁₁M ₁+…+v _1pM _p＝v ₁′M (5)

ω wherein ₁=(ω ₁₁..., ω _1m) ' be model effect weight, v ₁=(v ₁₁..., v _1p) be M variable weight.For guaranteeing that T1, U1 extract the variation information of place set of variables separately as much as possible, guarantee that simultaneously degree of correlation between the two reaches maximum, according to the character that the covariance of composition can be calculated by the inner product of the score vector of corresponding composition, the requirement of said extracted first composition is converted into asks conditional extremum to ask.

Wherein t1, u1 are the score vector of first pair of composition of being tried to achieve by sample, and HRV0, M0 are initializaing variable.Utilize method of Lagrange multipliers, the problems referred to above are converted into asks unit vector ω ₁And v ₁, make θ ₁=ω ₁HRV ₀' M ₀v ₁Maximum is promptly asked matrix H RV ₀' M ₀M ₀' HRV ₀Eigenvalue and characteristic vector, its eigenvalue of maximum is θ ₁ ², corresponding unit character vector is exactly the ω that separates that is asked ₁, and v ₁By formula

Obtain.

Next sets up the equation of initializaing variable to T1

$\left\{\begin{array}{c}{\mathrm{HRV}}_0=t_1{\mathrm{\α}}_1^{\′}+E_1\\ M_0=t_1{\mathrm{\β}}_1^{\′}+F_1\end{array}\right.---\left(7\right)$

Wherein the t1 meaning is the same, α ₁'=(α ₁₁..., α _1m), β ₁'=(β ₁₁..., β _1p) be the parameter vector when only a M measures t1, E1, F1 are respectively n * m and n * p residual error battle array.Can try to achieve coefficient vector α according to common method of least square ₁And β ₁, α wherein ₁Become model effect load capacity.

Can not reach the precision of regression model as first composition that extracts, utilization residual error battle array E1, F1 replace X0, Y0, repeat to extract composition, and the like.Suppose finally to have extracted r composition, HRV0, M0 to the regression equation of r composition are:

$\left\{\begin{array}{c}{\mathrm{HRV}}_0=t_1{\mathrm{\α}}_1^{\′}+...+t_r{\mathrm{\α}}_r^{\′}+E_r\\ M_0=t_1{\mathrm{\β}}_1^{\′}+...+t_r{{\mathrm{\β}}_r}^{\′}+F_r\end{array}\right.---\left(8\right)$

The first step analyze extract in the gained HRV amount composition Tk (k=1 ..., r) regression equation that the M amount is set up r composition, i.e. t are brought in linear combination into _r=ω _K1HRV ₁+ ... + ω _KmHRV _mSubstitution M _j=t ₁β _1j+ ... + t _rβ _Rj(j=1 ..., p), promptly get the regression equation M of standardized variable _j=α _J1HRV ₁+ ... + α _JmHRV _m

According to formula 3, can obtain L at last.

Two, the chaos particle cluster algorithm control of pid parameter of adjusting

PID is made up of ratio unit P, integral unit I and differentiation element D three parts, according to the error of system, by the K that sets _p, K _iAnd K _dThree parameters are controlled system.

$\mathrm{yout}\left(t\right)=K_p\mathrm{error}\left(t\right)+K_i\underset{j=0}{\overset{t}{\mathrm{\Σ}}}\mathrm{error}\left(j\right)+K_d[\mathrm{error}\left(t\right)-\mathrm{error}(t-1)]---\left(9\right)$

K wherein _pBe proportionality coefficient, K _iBe integral coefficient, K _dBe differential coefficient, error is the deviation of default output with actual output, and u (t) is the output of PID, is again the input of controlled system simultaneously.

Can obtain by PID output formula (1)

$u(t-1)=K_p\mathrm{error}(t-1)+K_i\underset{j=0}{\overset{t-1}{\mathrm{\Σ}}}\mathrm{error}\left(j\right)+K_d[\mathrm{error}(t-1)-\mathrm{error}(t-2)]---\left(10\right)$

According to:

Δu(t)＝u(t)-u(t-1)

＝K _p(error(t)-error(t-1))+K _ierror(t)+K _d(error(t)-2error(t-1)+error(t-2))

……………………………………………………………(11)

Have:

u(t)＝Δu(t)+u(t-1)＝

u(t-1)+K _p(error(t)-error(t-1))+K _ierror(t)+K _d(error(t)-2error(t-1)+error(t-2))

………………(12)

The present invention adopts chaos particle swarm algorithm to carry out the adaptive optimization of pid control parameter, selecting to receive the rope space is 3 dimensions, promptly is respectively three parameters of PID controller, chooses population size m=20, the initial velocity of colony and position produce in certain spatial dimension at random, are expressed as respectively: v _i=(v _I1, v _I2, v _I3), x _i=(x _I1, x _I2, x _I3), remember that it is p that i particle searches optimal location so far _i=(p _I1, p _I2, p _I3), whole population searches to such an extent that optimal location is p up to now _Gi=(p _Gi1, p _Gi2, p _Gi3).Wherein, i=1,2 ..., 20, particle swarm optimization algorithm adopts following formula that population is operated.

v _id←v _id+c ₁r ₁(p _id-x _id)+c ₂r ₂(p _gid-x _id)+c ₃r ₃(q _id-x _id) (13)

x _id←x _id+v _id (14)

Wherein, i=1,2 ..., 20; Study factor c ₁, c ₂And c ₃Be nonnegative number, general value is 0.5; r ₁, r ₂And r ₃Be the random number between [0,1], q _IdIt is the picked at random particle position.

The concrete steps of its realization are:

1, determines parameter: study factor c ₁, c ₂And c ₃And the scale N of colony, evolution number of times and chaos optimizing number of times;

2, producing N particle at random operates;

3, by formula operate particle (13) and (14);

4, to optimal location p _Gi=(p _Gi1, p _Gi2, p _Gi3) carry out chaos optimization, with p _Gid(i=1,2 ..., 20), be mapped to Logistic equation z _I+1=μ z _i(1-z _i) i=0,1,2 ... the domain of definition [0,1];

I=1,2 ..., 20, then, carry out iteration with the Logistic equation and produce Chaos Variable

M=1,2 ..., again the Chaos Variable sequence that produces

(m=1,2 ...) by inverse mapping

(m=1,2 ...) turn back to former solution space, get

(m=1,2 ...)

In former solution space each feasible solution to the Chaos Variable experience

(m=1,2 ...) calculate its adaptive value, the feasible solution p that retention property is best ^*

5, a particle p who from current colony, selects at random ^*Replace.

6, if reach maximum algebraically or obtain satisfactory solution, then optimizing process finishes, otherwise returns step 3.

Three, experimental program

Experimental provision adopts the walker system of wireless transmission and the Parastep functional electric stimulation system that U.S. SIGMEDICS company produces, and this system comprises microprocessor and boost pulse generation circuit, contains six stimulation channels, battery powered.Experiment content is: utilize the FES system that the relevant muscle group of lower limb is stimulated, make the experimenter according to predetermined actions, record is applied to HRV on the walker at first by being installed in voltage signal and the knee joint angle movement locus that foil gauge (BX3506AA) network of electrical bridge changes into that lead of 12 on the walker simultaneously.Require the experimenter healthy, no lower limb muscles, skeleton illness, impassivity illness and severe cardiac pulmonary disease.Before the experimenter sits on walker during experiment, stimulating electrode is fixed in corresponding position, when not applying electricity irritation, it is light that the experimenter keeps.The FES experiment scene as shown in Figure 5.The electric stimulation pulse sequence adopts classical Lilly waveform, and pulse frequency is 25Hz, pulsewidth 150 μ s, and pulse current is adjustable in 0～120m scope.In the experiment, write down HRV in real time and can adjust stimulus intensity to change the knee joint angle that produces by stimulating by changing the pulse current size.Before the experiment, set the knee joint angle movement locus of expectation, utilize the angular surveying meter to detect the knee joint subtended angle in real time in the experiment and change.The experimental data sample rate is 128Hz, and the data record duration is 60s.

Beneficial effect

The adjust new algorithm of pid parameter of chaos particle swarm algorithm is calculated the FES pulse current amplitude and is adjusted, the knee joint angle that the FES effect is produced move the movement locus of expection.The result is followed the trail of in the PID control that Fig. 6 adjusts for chaos particle swarm algorithm.Red line represents that desired movement track, blue line are actual output joint angles among the figure.X-axis is the time, and Y-axis is the motion of knee joint angle.For more clearly observing the departure that the chaos particle cluster algorithm is adjusted PID, shown in the relative error of default input knee joint angle and actual knee joint angle under Fig. 7 chaos particle swarm Tuning PID Controller, then error can reach accurate control all within 5% as can be seen.

Purport of the present invention is the precision control method that proposes a kind of new FES, utilize the error of knee joint angle and the joint angles of actual knee joint angle prediction of the HRV parameter prediction of walker, by proportionality coefficient, integral coefficient and the differential coefficient of chaos swarm optimization algorithm PID, the accurately stable then systematically current intensity of FES of controlling in real time.This invention can improve FES system accuracy and stability effectively, and obtains considerable social benefit and economic benefit.Optimum implementation intends adopting patent transfer, technological cooperation or product development.

Claims (3)

1. a functional electric stimulation pid parameter double source characteristic fusion particle swarm setting method is characterized in that, comprises the following steps:

At first, utilize the handle retroaction vector HRV forecasting knee joint angle of walk help process;

Secondly, utilize the chaos particle swarm optimization ratio calculus pid parameter of adjusting, real-time monitoring FES levels of current intensity, the flow process of adjusting is: at first according to three decision variable K of ratio calculus PID _p, K _iAnd K _dThe bound of span, determine the population population size, parameters such as search volume dimension, and the speed and the position of initialization particle colony, utilize the fitness value that calculates each particle in the population by the corresponding relation of actual joint angles and muscle model output joint angles as appropriate evaluation function then, and its fitness and optimum position fitness value itself made comparisons, and with it as the particle representative value, adjusting particle's velocity and other parameters then, change the optimum position of particle, till stable, calculate the K that final best position promptly gets ratio calculus PID _p, K _iAnd K _dThree coefficients, computing system output yout under the new ratio calculus PID coefficient and with the deviation of muscle model output joint angles after enter the self study and the weight coefficient self-adjusting of next step neutral net again, this process repeatedly, the self adaptation on-line tuning of final realization ratio calculus pid control parameter, and be used for functional electric stimulation FES system.

2. a kind of functional electric stimulation pid parameter double source characteristic fusion particle swarm setting method according to claim 1 is characterized in that, muscle model output joint angles is the method that adopts PLS, that is:

Be provided with m HRV variable HRV1 ..., HRVm, p M variable, M1 ..., Mp, common i (i=1 ..., the n) data set of individual observation, T, U are respectively the composition that extracts from HRV variable and M variable, be called the offset minimum binary factor,

Concentrate the linear combination of extracting first couple of composition T1, U1 to be from original variable:

T ₁＝ω ₁₁HRV ₁+…+ω _1mHRV _m＝ω′ ₁HRV (4)

U ₁＝v ₁₁M ₁+…+v _1pM _p＝v′ ₁M (5)

ω wherein ₁=(ω ₁₁..., ω _1m) ' be model effect weight, v ₁=(v ₁₁..., v _1p) ' be M variable weight is converted into the requirement of said extracted first composition and asks constrained extremal problem:

Wherein t1, u1 are the score vector of first pair of composition of being tried to achieve by sample, and HRVO, MO are initializaing variable, utilize method of Lagrange multipliers, and the problems referred to above are converted into asks unit vector ω ₁And v ₁, make θ ₁=ω ' ₁HRV ' ₀M ₀v ₁Maximum is promptly asked matrix H RV ' ₀M ₀M ' ₀HRV ₀Eigenvalue and characteristic vector, its eigenvalue of maximum is θ ₁ ², corresponding unit character vector is exactly the ω that separates that is asked ₁, and v ₁By formula

Obtain;

Next sets up the equation of initializaing variable to T1

$\left\{\begin{array}{c}{\mathrm{HRV}}_0=t_1{\mathrm{\α}}_1^{\′}+E_1\\ M_0=t_1{\mathrm{\β}}_1^{\′}+F_1\end{array}\right.---\left(7\right)$

Wherein the t1 meaning is the same, α ' ₁=(α ₁₁..., α _1m), β ' ₁=(β ₁₁..., β _1p) be the parameter vector when only a M measures t1, E1, F1 are respectively n * m and n * p residual error battle array, can try to achieve coefficient vector α according to common method of least square ₁And β ₁, α wherein ₁Become model effect load capacity;

Can not reach the precision of regression model as first composition that extracts, utilization residual error battle array E1, F1 replace XO, YO, repeat to extract composition, and the like, supposing finally to have extracted r composition, HRVO, M0 to the regression equation of r composition are:

$\left\{\begin{array}{c}{\mathrm{HRV}}_0=t_1{\mathrm{\α}}_1^{\′}+...+t_r{\mathrm{\α}}_r^{\′}+E_r\\ M_0=t_1{\mathrm{\β}}_1^{\′}+...+t_r{{\mathrm{\β}}_r}^{\′}+F_r\end{array}\right.---\left(8\right)$

The first step analyze extract in the gained HRV amount composition Tk (k=1 ..., r) regression equation that the M amount is set up r composition, i.e. t are brought in linear combination into _r=ω _K1HRV ₁+ ... + ω _KmHRV _mSubstitution M _j=t ₁β _1j+ ... + t _rβ _Rj(j=1 ..., p), promptly get the regression equation M of standardized variable _j=α _J1HRV ₁+ ... + α _JmHRV _m

At last according to formula L=M * HRV ^-1, can obtain L, M represents knee joint angle, and HRV represents that user is applied to the handle retroaction vector of power on the walker, and L represents the relation between HRV and the M.

3. a kind of functional electric stimulation pid parameter double source characteristic fusion particle swarm setting method according to claim 1 is characterized in that, utilizes the chaos particle swarm optimization pid parameter of adjusting, and further is refined as:

Ratio calculus PID adopts ratio unit P, integral unit I and differentiation element D three parts to form, according to the error of system, by the K that sets _p, K _iAnd K _dThree parameters are controlled system:

$\mathrm{yout}\left(t\right)=K_p\mathrm{error}\left(t\right)+K_i\underset{j=0}{\overset{t}{\mathrm{\Σ}}}\mathrm{error}\left(j\right)+K_d[\mathrm{error}\left(t\right)-\mathrm{error}(t-1)]---\left(9\right)$

K wherein _pBe proportionality coefficient, K _iBe integral coefficient, K _dBe differential coefficient, error is the deviation of default output with actual output, and u (t) is the output of PID, is again the input of controlled system simultaneously, by PID output formula

Can obtain

$u(t-1)=K_p\mathrm{error}(t-1)+K_i\underset{j=0}{\overset{t-1}{\mathrm{\Σ}}}\mathrm{error}\left(j\right)+K_d[\mathrm{error}(t-1)-\mathrm{error}(t-2)]---\left(10\right)$

According to:

Δu(t)＝u(t)-u(t-1)

＝K _p(error(t)-error(t-1))+K _ierror(t)+K _d(error(t)-2error(t-1)+error(t-2))

……………………………………………………………(11)

Have:

u(t)＝Δu(t)+u(t-1)＝

u(t-1)+K _p(error(t)-error(t-1))+K _ierror(t)+K _d(error(t)-2error(t-1)+error(t-2))

………………(12)

Adopt the chaos particle swarm optimization to carry out the adaptive optimization of ratio calculus pid control parameter, selecting to receive the rope space is 3 dimensions, promptly is respectively three parameters of PID controller, chooses population size m=20, the initial velocity of colony and position produce in certain spatial dimension at random, are expressed as respectively: v _i=(v _I1, v _I2, v _I3), x _i=(x _I1, x _I2, x _I3), remember that it is p that i particle searches optimal location so far _i=(p _I1, p _I2, p _I3), whole population searches to such an extent that optimal location is p up to now _Gi=(p _Gi1, p _Gi2, p _Gi3), wherein, i=1,2 ..., 20, particle swarm optimization algorithm adopts following formula that population is operated,

v _id←v _id+c ₁r ₁(p _id-x _id)+c ₂r ₂(p _gid-x _id)+c ₃r ₃(q _id-x _id) (13)

x _id←x _id+v _id (14)

Wherein, i=1,2 ..., 20; Study factor c ₁, c ₂And c ₃Be nonnegative number, general value is 0.5; r ₁, r ₂And r ₃Be the random number between [0,1], q _IdIt is the picked at random particle position;

The concrete steps that realize are:

1, determines parameter: study factor c ₁, c ₂And c ₃And the scale N of colony, evolution number of times and chaos optimizing number of times;

2, producing N particle at random operates;

3, by formula operate particle (13) and (14);

4, to optimal location p _Gi=(p _Gi1, p _Gi2, p _Gi3) carry out chaos optimization, with p _Gid(i=1,2 ..., 20), be mapped to Logistic equation z _I+1=μ z _i(1-z _i) i=0,1,2 ... the domain of definition [0,1]; I=1,2 ..., 20, then, carry out iteration with the Logistic equation and produce Chaos Variable

M=1.2. ..., again the Chaos Variable sequence that produces (m=1,2 ...) by inverse mapping (m=1,2 ...) turn back to former solution space, get

(m=1,2 ...)

In former solution space each feasible solution to the Chaos Variable experience (m=1,2 ...) calculate its adaptive value, the feasible solution p that retention property is best ^*

5, a particle p who from current colony, selects at random ^*Replace;

6, if reach maximum algebraically or obtain satisfactory solution, then optimizing process finishes, otherwise returns step 3.

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