CN101837164B - Double source feature fusion ant colony tuning method for PID (Proportion Integration Differention) parameter in functional electro-stimulation - Google Patents

Abstract

The invention relates to the instrument field for limbs rehabilitation by electronic impulse stimulation. The invention adopts the following technical scheme to control the current strength of an FES (Functional Electro-Simulation) system in real time accurately and stably so as to improve the accuracy and the stability of the FES system. A double source feature fusion ant colony tuning method for PID (Proportion Integration Differention) parameter in functional electro-stimulation comprises steps as follow: first, forecasting knee joint angle by a muscle model HRV in the processes of assisted walking; then, tuning the PID parameter through an ant colony optimization and adjusting current strength of the FES system in real time; controlling the PID parameter through ant colony optimization and keeping the optimization process till anticipatory goal is achieved; calculating system output and difference between the muscle model HRV with new PID parameter and then entering the step of self-learning of the ant colony optimization and self-regulating of weighting coefficient; repeating the former step for self-adapting on-line tuning of PID control parameter and using the tuned PID control parameter in the FES system. This invention is mainly applied for tuning PID parameter.

Description

The double source feature fusion ant colony setting method of pid parameter in the functional electrostimulation

Technical field

The present invention relates to carry out the instrument field of limb rehabilitating, especially the double source feature fusion ant colony setting method of pid parameter in the functional electrostimulation with electric pulse stimulation.

Background technology

(Functional Electrical Stimulation is to stimulate limb motion muscle group and peripheral nerve thereof through current pulse sequence FES) to functional electrostimulation, 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, to 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 electro photoluminescence nervus peronaeus to correct the gait of hemiplegic patient's drop foot first, has started the new way that functional electrostimulation 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 accomplished the key problem in technology that the intended function action is still FES with assurance electro photoluminescence 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; Come adjustment FES stimulus intensity and time sequence parameter automatically with closed-loop control, thereby improved the accuracy and the stability of FES system greatly, but present effectively control method is still among exploring.

Handle retroaction vector (handle reactions vector; HRV) be that in fact the effectiveness that walker offers the patient can be divided into clear and definite independently 3 parts in the process of standing and walking under helping according to walker: 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 centre-point load to the effect of walker is synthetic in the walking process of standing, represent with two mechanics vectors at handle mid point xsect centre of form place respectively, as shown in Figure 1; Vector is at x; Y, the size of making a concerted effort of the durection component on the z axle can characterize the patient respectively and tries hard to recommend into dynamic balance and power support level by what walker obtained.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 _l，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 electro photoluminescence 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 electro photoluminescence action effect can accurately be accomplished the intended function action, and prevents muscular fatigue.

Ratio infinitesimal analysis (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 complicacy and the time variation operating environment of muscle, because good stability, the reliable operation of PID have still obtained in the functional electrostimulation field using widely at present.The PID core technology is accurate confirm 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 deficiency of prior art; The double source feature fusion ant colony setting method of pid parameter in a kind of functional electrostimulation is provided; Can accurately stablize and control systematically strength of current 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 scheme that the present invention adopts is: the double source feature fusion ant colony setting method of pid parameter in the functional electrostimulation comprises the following steps:

At first, utilize the muscle model HRV forecasting knee joint angle of walk help process;

Secondly, utilize ant group algorithm tuning PID parameter, real-time monitoring FES levels of current intensity, its flow process of adjusting is: at first according to three decision variable K of PID _p, K _iAnd K _dThe bound of span; Confirm to comprise the parameter of ant crowd population size, search volume dimension; And it is encoded, utilize then through actual joint angles and export the fitness value of the corresponding relation of joint angles as appropriate evaluation function calculating with muscle model HRV; Adopt ant group algorithm that pid parameter is controlled, promptly confirm the parameter setting of ant group algorithm, utilize the ant random search to make its variable optimize the K of PID _p, K _iAnd K _dThree coefficients utilize fitness function to regulate at every turn explore path and judge whether to reach goal-selling of ant, as reach goal-selling, calculate the K that final best position promptly gets PID _p, K _iAnd K _dThree coefficients, as do not reach re-set target continuation optimizing, up to reaching goal-selling; Computing system output yout under the new PID coefficient and with the deviation of muscle model HRV after get into next step ant group algorithm again self study and weighting coefficient self-adjusting;

Last repeatedly step finally realizes the self-adaptation on-line tuning of pid control parameter, and is used for the FES system.

The corresponding relation of said actual joint angles and muscle model HRV output joint angles is:

L＝M×HRV ^-1 (3)

M representes knee joint angle, and HRV representes that the user is applied to the handle retroaction vector of power on the walker, and L representes the relation between HRV and the M, adopts the method for PLS to find the solution L:

Be provided with m HRV variable HRV1 ..., HRVm, p M variable; M1 ..., Mp, i (i=1 altogether;, the n) data set of individual observed reading, T, U are respectively the composition that from HRV variable and M variable, extracts, and 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 HRV0, M0 are initializaing variable, utilize method of Lagrange multipliers, and the problems referred to above are converted into asks vector of unit length ω ₁And v ₁, make θ ₁=ω ' ₁HRV ' ₀M ₀v ₁Maximum is promptly asked matrix H RV ' ₀M ₀M ' ₀HRV ₀Eigenwert and proper 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 least square method ₁And β ₁, α wherein ₁Become model effect load capacity;

Can not reach the precision of regression model like 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

According to formula (3), can obtain L at last.

The said coding is that according to knee joint angle and current-mode, and three parameters of actual error situation setting PID are 5 position effective digitals, wherein K _pPreceding 2 of radix point, behind the radix point 3; K _iAnd K _dPreceding 1 of radix point, behind the radix point 4, specific coding such as following formula:

K _p＝y _1，j×10 ¹+y _2，j×10 ⁰+y _3，j×10 ^-1+y _4，j×10 ^-2+y _5，j×10 ^-3

K _i＝y _6，j×10 ⁰+y _7，j×10 ^-1+y _8，j×10 ^-2+y _9，j×10 ^-3+y _10，j×10 ^-4

K _d＝y _6，j×10 ⁰+y _7，j×10 ^-1+y _8，j×10 ^-2+y _9，j×10 ^-3+y _10，j×10 ^-4

And the concrete function of overshoot equal error design adaptation function based on output is:

fit＝α1×σ+β1×t+c×error (9)

Wherein, σ is an overshoot, and t is the rise time, and error is the relative error of output joint angles and preset joint angles, α 1=0.1, β 1=0.8, c=2;

The concrete workflow of ant group algorithm Tuning PID Controller device parameter is:

Step1: parameter initialization makes time t=0 and cycle index N _Max=0, maximum cycle N is set _Cmax, m ant placed starting point;

Step2: ant number and cycle index are set;

Step3: ant random search; After the end of once creeping, determine the actual input variable of the selected conduct of which characteristic variable, revise the taboo list index; After promptly choosing ant is moved to new element, and move this element in the individual taboo table of this ant;

Step4: calculate the probability that the ant individuality calculates according to the state transition probability formula, select element according to this probability;

Step5:, change Step3, otherwise be Step6 if the ant element has not traveled through;

Step6: the pheromone concentration that the plain concentration of lastest imformation is divided the high characteristic variable of accuracy is enhanced, and next time can be selected with bigger probability when searching for;

Step7: satisfy and finish to regulate the end of adjusting.

Characteristics of the present invention are: utilize the HRV variation prediction knee joint angle of walk helper to change; Pass through scale-up factor, differential coefficient and the integral coefficient of ant crowd crowd algorithm optimization PID then; 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 synoptic diagram.

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

The structured flowchart of Fig. 3 ant crowd crowd algorithm tuning PID parameter control method.

Manikin in Fig. 4 walk-aiding functional electric stimulation.

Fig. 5 ant group algorithm tuning PID parameter coding synoptic diagram.

Fig. 6 experiment scene.

The result is followed the trail of in the PID control that Fig. 7 ant crowd crowd algorithm is adjusted.

Embodiment

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; Through scale-up factor, integral coefficient and the differential coefficient of ant group algorithm optimization PID, the accurately stable then systematically strength of current 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.

Structure based on the control of the precision in the functional electrostimulation walk help of HRV The application of new technique is as shown in Figure 2; Its workflow is: at first, utilize the HRV forecasting knee joint angle of walk help process, secondly; Utilize ant group algorithm tuning PID parameter, real-time monitoring FES levels of current intensity.Its structural representation of adjusting is as shown in Figure 3, for: at first according to three decision variable K of PID _p, K _iAnd K _dThe bound of span; Confirm parameters such as ant crowd population size, search volume dimension; And it is encoded; Utilize the fitness value that calculates as appropriate evaluation function through the corresponding relation of actual joint angles and muscle model output joint angles then, and the parameter setting of definite ant group algorithm, utilize ant to receive rope at random and make its variable optimize the K of PID _p, K _iAnd K _dThree coefficients utilize fitness function to regulate at every turn explore path and judge whether to reach goal-selling of ant.As reach goal-selling, calculate the K that final best position promptly gets PID _p, K _iAnd K _dThree coefficients, as do not reach re-set target continuation optimizing, up to reaching goal-selling.Computing system output yout under the new PID coefficient and with the deviation of muscle model after get into next step ant group algorithm again self study and weighting coefficient self-adjusting.This process finally realizes the self-adaptation on-line tuning of pid control parameter repeatedly, and is used for the FES system.

1 HRV forecasting knee joint angle model

In the walk help process, when the user under the functional electrostimulation effect, when lifting leg and taking a step; In order to support body steadiness, user's 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 planimetric position of human body also changes to some extent simultaneously, and 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, and be as shown in Figure 4.

M＝L·HRV+wPW (1)

Wherein, M representes knee joint angle, and HRV representes that the user is applied to the handle retroaction vector of power on the walker, and L representes the relation between HRV and the M, and w representes coefficient, and W representes the center of gravity of upper arm, trunk and lower limb, and P representes 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 representes knee joint angle, and HRV representes that the user is applied to the handle retroaction vector of power on the walker, and L representes the relation between HRV and the M.Shown in formula 2, confirm that L just can utilize HRV to take out the knee joint angle in the corresponding moment.

L＝M×HRV ^-1 (3)

When the present invention finds 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 observed reading.T, U are respectively the composition that from HRV variable and M variable, extracts, and the composition that extracts here is commonly referred to 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.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 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, the problems referred to above are converted into asks vector of unit length ω ₁And v ₁, make θ ₁=ω ' ₁HRV ' ₀M ₀v ₁Maximum is promptly asked matrix H RV ' ₀M ₀M ' ₀HRV ₀Eigenwert and proper 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 least square method ₁And β ₁, α wherein ₁Become model effect load capacity.

Can not reach the precision of regression model like 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

Based on formula 3, can obtain L at last.

Ant group algorithm is at first encoded to three parameters of PID to the control of pid parameter, and according to knee joint angle and current-mode, and three parameters that situation such as actual error is set PID are 5 position effective digitals, wherein K _pPreceding 2 of radix point, behind the radix point 3; K _iAnd K _dPreceding 1 of radix point, behind the radix point 4.Its specific coding synoptic diagram is as shown in Figure 5.

Like following formula

K _p＝y _1，j×10 ¹+y _2，j×10 ⁰+y _3，j×10 ^-1+y _4，j×10 ^-2+y _5，j×10 ^-3

K _i＝y _6，j×10 ⁰+y _7，j×10 ^-1+y _8，j×10 ^-2+y _9，j×10 ^-3+y _10，j×10 ^-4

K _d＝y _6，j×10 ⁰+y _7，j×10 ^-1+y _8，j×10 ^-2+y _9，j×10 ^-3+y _10，j×10 ^-4

And the concrete function of overshoot equal error design adaptation function based on output is:

fit＝α1×σ+β1×t+c×error (9)

Wherein, σ is an overshoot, and t is the rise time, and error is the relative error of output joint angles and preset joint angles, α 1=0.1, β 1=0.8, c=2.

The concrete workflow of ant group algorithm Tuning PID Controller device parameter is:

Step1: parameter initialization.Make time t=0 and cycle index N _Max=0, maximum cycle N is set _Cmax, m ant placed starting point.

Step2: ant number and cycle index are set

Step3: the ant random search, after the end of once creeping, determine the actual input variable of the selected conduct of which characteristic variable, revise the taboo list index, after promptly choosing ant is moved to new element, and move this element in the individual taboo table of this ant

Step4: calculate the probability that the ant individuality calculates according to the state transition probability formula, select element according to this probability

Step5:, change Step3, otherwise be Step6 if the ant element has not traveled through

Step6: the pheromone concentration that the plain concentration of lastest imformation is divided the high characteristic variable of accuracy is enhanced, and next time can be selected with bigger probability when searching for

Step7: satisfy and finish to regulate the end of adjusting.

2 ant crowd crowd algorithm tuning PID parameter control

PID is made up of ratio unit P, integral unit I and differentiation element D three parts, according to the error of system, through 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 scale-up factor, K _iBe integral coefficient, K _dBe differential coefficient, error is the deviation of preset 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 ant group algorithm to carry out the adaptive optimization of pid control parameter, as a combination, utilizes three parameters of PID the ant group algorithm optimizing to solve this combinatorial problem.Ant group algorithm is a kind of novel bionic Algorithm that comes from the Nature biological world, when finding the solution optimization problem with ant group algorithm, at first optimization problem is transformed in order to find the solution shortest route problem.Every ant is from initial contact N ₀₀, N ₀₁... N _0nSet out, N in proper order passes by ₁, N ₂..., a wherein child node, up to destination node N _K0, N _K1... N _KnForm path (N _0tN _1t... N _Kt), t ∈ [0,1,2 ... 9].A binary feasible solution can be represented in its path.Following characteristic is arranged during each ant visit city:

The state transformation rule: the state transformation rule that ant group algorithm uses is the rule of ratio at random that proposes based on the TSP problem, and it provides the probability that the ant k that is positioned at city i selects to move to city j,

τ wherein _Ij(i j) is (i, fitness j), η _Ij(i, j)=(10-|y (i)-y (i) ^*|)/10, during y (i) ant group hunting in the value at i place, y (i) ^*When searching for for last time in the value at i place.α is the relative significance level of residual risk, the relative significance level that β is expectation value.

In ant group algorithm, selection mode does

Wherein, q is for being evenly distributed on a random number on [0,1], q ₀Be the parameter on [0,1].

Overall situation update rule: ant algorithm has different update algorithm; The overall situation that ant crowd system adopts is upgraded principle; Only allowing the ant release pheromone of globally optimal solution, is the neighborhood that mainly concentrates on the best path of being found out till the current circulation for the search that makes ant like this.

τ _ij(i，j)←(1-ρ)□τ _ij(i，j)+ρ·Δτ _ij(i，j) (14)

Wherein ρ is that information is counted volatility coefficient, L _GbBe the global optimum path of finding so far

Local updating information: every ant is set up the renewal that the plain mark of the information of carrying out number is also arranged in the process of separating

τ _ij(i，j)←(1-γ)□τ _ij(i，j)+γ·Δτ _ij(i，j) (16)

γ ∈ [0,1] wherein.

Ant group algorithm is at first encoded to three parameters of PID to the control of pid parameter, and according to knee joint angle and current-mode, and three parameters that situation such as actual error is set PID are 5 position effective digitals, wherein K _pPreceding 2 of radix point, behind the radix point 3; K _iAnd K _dPreceding 1 of radix point, behind the radix point 4.Its specific coding synoptic diagram is as shown in Figure 5.

Like following formula

K _p＝y _1，j×10 ¹+y _2，j×10 ⁰+y _3，j×10 ^-1+y _4，j×10 ^-2+y _5，j×10 ^-3

K _i＝y _6，j×10 ⁰+y _7，j×10 ^-1+y _8，j×10 ^-2+y _9，j×10 ^-3+y _10，j×10 ^-4

K _d＝y _6，j×10 ⁰+y _7，j×10 ^-1+y _8，j×10 ^-2+y _9，j?×10 ^-3+y _10，j?×10 ^-4

The concrete workflow of ant group algorithm Tuning PID Controller device parameter is:

Step1: parameter initialization.Make time t=0 and cycle index N _Max=0, maximum cycle N is set _Cmax, m ant placed starting point.

Step2: ant number and cycle index are set

Step3: the ant random search, after the end of once creeping, determine the actual input variable of the selected conduct of which characteristic variable, revise the taboo list index, after promptly choosing ant is moved to new element, and move this element in the individual taboo table of this ant

Step4: calculate the probability that the ant individuality calculates according to the state transition probability formula, select element according to this probability

Step5:, change Step3, otherwise be Step6 if the ant element has not traveled through

Step6: the pheromone concentration that the plain concentration of lastest imformation is divided the high characteristic variable of accuracy is enhanced, and next time can be selected with bigger probability when searching for

Step7: satisfy and finish to regulate the end of adjusting.

3 experimental programs

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, powered battery.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 through 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, bone illness, impassivity illness and severe cardiac pulmonary disease.The experimenter sits idly before walker during experiment,

Stimulating electrode is fixed in corresponding position, and when not applying electro photoluminescence, it is light that the experimenter keeps.The FES experiment scene is as shown in Figure 5.The electric stimulation pulse sequence adopts classical Lilly waveform, and pulsed frequency is 25Hz, pulsewidth 150 μ s, and pulse current is adjustable in 0～120m scope.In the experiment, write down in real time HRV with

And can adjust stimulus intensity to change the knee joint angle that produces by stimulating through changing the pulse current size.Before the experiment, set the knee joint angle movement locus of expectation, utilize the measurement of angle meter to detect the knee joint subtended angle in real time in the experiment and change.The experimental data sampling rate is 128Hz, and the data recording duration is 60s.

Beneficial effect

The new algorithm of ant group algorithm tuning PID parameter 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.Fig. 7 follows the trail of the result for the PID control that ant group algorithm is adjusted.Red line representes that desired movement track, blue line are actual output joint angles among the figure.The X axle is the time, and the Y axle is the motion of knee joint angle.For more clearly observing the departure of ant group algorithm tuning PID; Shown in the relative error of preset input knee joint angle and actual knee joint angle under Fig. 8 ant group algorithm Tuning PID Controller; Can find out that then error all within 5%, can reach accurate control.

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; Through scale-up factor, integral coefficient and the differential coefficient of ant group algorithm optimization PID, the accurately stable then systematically strength of current 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 is intended and is adopted patent transfer, technological cooperation or product development.

Claims (3)

1. the double source feature fusion ant colony setting method of pid parameter in the functional electrostimulation is characterized in that, comprises the following steps:

At first, utilize the muscle model HRV forecasting knee joint angle of walk help process;

Secondly, utilize ant group algorithm tuning PID parameter, real-time monitoring FES levels of current intensity, its flow process of adjusting is: at first according to three decision variable K of PID controller _p, K _iAnd K _dThe bound of span; Confirm to comprise the parameter of ant crowd population size, search volume dimension; And it is encoded; Utilize then through actual joint angles and muscle model HRV and export the fitness value of the corresponding relation of joint angles as the fitness evaluation function calculation, corresponding relation is L=M * HRV ^-1, wherein, M representes knee joint angle, and HRV representes that the user is applied to the handle retroaction vector of power on the walker, and L representes the relation between HRV and the M, adopts the method for PLS to confirm muscle model HRV output joint angles; Adopt ant group algorithm that the PID controller parameter is controlled, promptly confirm the parameter setting of ant group algorithm, utilize the ant random search to make its variable optimize three decision variable K of PID controller _p, K _iAnd K _d, utilize the fitness evaluation function adjusting each searching route of ant and judge whether to reach goal-selling, as reach goal-selling, calculate three decision variable K that final best position promptly gets the PID controller _p, K _iAnd K _d, as do not reach re-set target continuation optimizing, up to reaching goal-selling; Computing function property electro photoluminescence FES system output yout under the new PID controller parameter and with the deviation of muscle model HRV after get into the self study of next step ant group algorithm and three decision variable K of PID controller again _p, K _iAnd K _dThe weighting coefficient self-adjusting;

Last repeatedly step finally realizes the self-adaptation on-line tuning of PID controller parameter, and is used for functional electrostimulation FES system.

2. the double source feature fusion ant colony setting method of pid parameter is characterized in that in a kind of functional electrostimulation according to claim 1, adopts the method for PLS to find the solution L:

Be provided with m HRV variable HRV ₁..., HRV _m, p M variable, M ₁..., M _p, the data set of common i observed reading, i=1 ..., n, T, U are respectively the composition that from HRV variable and M variable, extracts, and concentrate from original variable and extract first couple of composition T ₁, U ₁Linear combination be:

T ₁＝ω ₁₁HRV ₁+…+ω _1mHRV _m＝ω′ ₁?HRV (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 first pair of composition of said extracted and asks constrained extremal problem:

T wherein ₁, u ₁Be the score vector of first pair of composition of trying to achieve, HRV by sample ₀, M ₀Be initializaing variable, utilize method of Lagrange multipliers, the problems referred to above are converted into asks vector of unit length ω ₁And v ₁, make θ ₁=ω ' ₁HRV ' ₀M ' ₀v ₁' maximum is promptly asked matrix H RV ' ₀M ₀M ' ₀HRV ₀Eigenwert and proper vector, its eigenvalue of maximum is θ ₁ ², corresponding unit character vector is exactly the ω that separates that is asked ₁, and v1 is by 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 M variable t only ₁The time parameter vector, E ₁, F ₁Be respectively n * m and n * p residual error battle array, try to achieve coefficient vector α according to common least square method ₁And β ₁, α wherein ₁Be called model effect load capacity;

Wherein, E ₁=HRV ₀- _t1 α ' ₁,

F ₁＝HRV ₀-t ₁β′ ₁

Can not reach the precision of regression model, utilization residual error battle array E like the first pair of composition that extracts ₁, F ₁Replace HRV ₀, M ₀, repeat to extract composition, and the like, suppose finally to have extracted r composition, HRV ₀, M ₀Regression equation to r composition is:

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

The first step analyze extract in the gained HRV amount composition Tk (k=1 ..., r) regression equation that the M variable 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.

3. the double source feature fusion ant colony setting method of pid parameter is characterized in that in a kind of functional electrostimulation according to claim 1, and the concrete workflow of ant group algorithm Tuning PID Controller device parameter is:

Step1: parameter initialization makes time t=0 and cycle index N _Max=0, maximum cycle N is set _Cmax, m ant placed starting point;

Step2: ant number and cycle index are set;

Step3: ant random search; After the end of once creeping, determine the actual input variable of the selected conduct of which characteristic variable, revise the taboo list index; After promptly choosing ant is moved to new element, and move to this element in the individual taboo table of ant;

Step4: calculate the probability that the ant individuality calculates according to the state transition probability formula, select element according to this probability;

Step5:, change Step3, otherwise be Step6 if the ant element has not traveled through;

Step6: the plain concentration of lastest imformation, the pheromone concentration of dividing the high characteristic variable of accuracy is enhanced, and next time can be selected with bigger probability when searching for;

Step7: satisfy termination condition, the end of adjusting.

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