CN101816821B - Walking aid functional electrical stimulation precision control method based on ant colony fuzzy controller - Google Patents

Abstract

The invention relates to the field of rehabilitation devices and discloses a walking aid functional electrical stimulation precision control method based on an ant colony fuzzy controller, aiming at effectively improving the accuracy and the stability of an FES (Functional Electrical Stimulation) system. In the technical scheme of the invention, the walking aid FES precision control method based on the ant colony fuzzy controller comprises the steps of: firstly, converting a quantitative factor and a proportional factor of the fuzzy controller and the selection of 12 decision factors of a membership function parameter into a combination optimization problem applicable to an ant colony algorithm and carrying out encoding on the combination optimization problem and generating n initial urban agglomerations formed by individuals randomly; then, establishing a reasonable corresponding relationship target function of an actual joint angle and a muscle model output joint angle and determining the parameter configuration of the ant colony algorithm; entering an optimizing process; and regulating ant colony information quantity according to deviation, entering a next optimizing process, repeating the process, finally realizing the self-adaption on-line setting of the parameters of the fuzzy controller and applying to the FES system. The invention is mainly used for improving the accuracy and the stability of the FES system.

Description

Walk-aiding functional electric stimulation precision control method based on ant crowd fuzzy controller

Technical field

The present invention relates to the rehabilitation appliances field, especially based on the walk-aiding functional electric stimulation precision control method of ant crowd fuzzy controller.

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.At present, because the spinal cord regeneration ability is faint, to the spinal cord injury paralysed patient, Shang Weiyou can directly repair effective treatment method of damage, and implementing function rehabilitation training is effective measures.According to statistics, 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 real-time, accuracy and the stability of FES system greatly, but present effectively control method is still among exploring.

Fuzzy controller is a kind of method through fuzzy logic and approximate resoning; People's experience formalization, modelling; Become computing machine acceptable controlling models; Let computer generation replace the people to control the high-level policy and novel technical method of controlled device in real time; Can improve controllability, adaptability and the rationality of control algolithm effectively, especially be difficult to modeling and have the problem of enriching manual experience to have peculiar advantage with math equation, and human muscle's complicacy and time variation operating environment make it set up its mathematical model to complicacy; Cause traditional control method to be difficult to adapt to the strict demand in FES field, fuzzy controller is that the precision control of FES provides new departure.The fuzzy controller core technology is exactly to confirm the factors such as method of structure, the fuzzy rule that is adopted, compositional rule of inference algorithm and the fuzzy decision of fuzzy controller, and the key that fuzzy control will obtain optimum control effect is to adjust to fuzzy controller quantizing factor, scale factor and fuzzy control rule are isoparametric.In the FES field, system stability is required very strictness, so select also particularly important to Fuzzy Controller Parameters.

Ant in the ant group algorithm simulation biological world is having no prompting to seek down by the foraging behavior proposition of the shortest path of ant cave to the food source simulated evolutionary algorithm based on population; Has stronger adaptability; Distributed parallel calculates, and is easy to the integrated advantage of other algorithms.At present, also not on top of, experimental knowledge relatively is short of whole mechanism of muscle.Utilize ant group algorithm adjusting of Fuzzy Controller Parameters to be helped the control of the precision of functional electrostimulation.

Summary of the invention

For overcoming the deficiency of prior art, purport of the present invention is the precision control method that proposes a kind of new FES, the accurately stable current-mode of controlling the FES system in real time.The present invention can improve FES system accuracy and stability effectively, and obtains considerable social benefit and economic benefit.For achieving the above object, the technical scheme that the present invention adopts is: the walk-aiding functional electric stimulation precision control method based on ant crowd fuzzy controller comprises the following steps:

At first the selection with 12 decision variables of quantizing factor, scale factor and the subordinate function parameter of fuzzy controller is converted into the combinatorial optimization problem that ant group algorithm is suitable for, and it is encoded and produces the initial group of cities that the n individuals is formed at random;

Next sets up rational actual joint angles and the corresponding relation objective function of muscle model output joint angles and the parameter setting of definite ant group algorithm;

Searching process: utilize the ant random search; Optimize subordinate function and the quantizing factor and the scale factor of fuzzy controller; And call the fuzzy controller of having adjusted, whether checking reaches goal-selling, does not repeat above operation repeatedly if having; Parameter convergence up to ant group algorithm perhaps reaches predetermined index, and final output promptly gets the decision variable of fuzzy controller and the number of times of ant crowd operation;

The decision variable of the fuzzy controller that the aforementioned final output of foundation promptly gets; Calculate the deviation of output and this output and muscle model output by fuzzy controller; According to deviation ant crowd quantity of information is adjusted, and got into next searching process, repeatedly this process; The final self-adaptation on-line tuning of realizing Fuzzy Controller Parameters, and be used for the FES system.

The said suitable combinatorial optimization problem of ant group algorithm that is converted into is to be converted into to find the solution shortest route problem, and concrete grammar is that the quantizing factor of ant crowd fuzzy controller and scale factor are that the basic quantization factor of fuzzy controller and basic scale factor multiply by factor kfuzzi (the i) (i=1 that is optimized by ant group algorithm respectively; 2,3), the adjustment of ant crowd fuzzy controller subordinate function promptly is adjusting to basic fuzzy controller subordinate function domain; On the basic domain basis of fuzzy controller, add or deduct factor kfuzzi (i) (i=4,5,6 that ant group algorithm is optimized; 7,8,9; 10,11,12); And to optimization factor kfuzzi (i) (i=1,2 of ant group algorithm ... The initial group of cities of 12) encoding and producing n individuals composition at random, the said binary coding that is encoded to.

Said binary coding, corresponding decoding formula does

$\mathrm{kfuzzi}\left(i\right)=x_{i\min }+(\underset{j=0}{\overset{l}{\mathrm{\Σ}}}{b}_j\×2^{j-1})\×\frac{x_{i\max }-x_{i\min }}{2^{l-1}}$

Wherein kfuzzi (i) is the variable quantity of the fuzzy controller adjusted, and l is the length of coding, b ∈ [0,1], x _ImaxAnd x _IminBe respectively the maximal value and the minimum value of decision content.

Adopt following method to confirm described fuzzy controller:

Input fuzzy controller initialization module variable is respectively the error e (k) and the error change rate ec (k) of actual output joint angles and expectation joint degree, and its domain is FE=[E, E]; FEC=[EC, EC], the stimulating current intensity u (k) of output; Its domain is FU=[U, U]

The quantification domain of error does

X＝{-n，-n+1，…0，…，n-1，n} (1)

The quantification domain of error rate does

X ₁＝{-m，-m+1，…0，…，m-1，m}； (2)

The quantification domain of controlled quentity controlled variable does

Y＝{-k，-k+1，…0，…，k-1，k} (3)

Quantizing factor is respectively

K _e＝n/X _e (4)

K _ec＝m/X _ec (5)

Scale factor does

K _u＝k/Y _u (6)

The opinion domain of error: { 3-2-1 012 3}; The domain of error rate be the domain of 3-2-1 012 3} output valves 3-2-1 012 3}, the control law of fuzzy control is: if E1 and EC1 then U1, if E2 and EC2 then U2 ... Ep and ECp be Up then;

Its total fuzzy control rule is:

R＝(E _i×CE _i) ^T1οC _i(8)

E wherein ₁=(a _1iA _Ni), EC ₁=(b _1iB _Mi), U ₁=(c _1iC _Ti) (i=1 ... P)

The reverse gelatinization method that adopts is a method of weighted mean:

$u_c=\left(\underset{i=-s}{\overset{s}{\mathrm{\Σ}}}{\mathrm{ik}}_i\right)/\left(\underset{i=-s}{\overset{s}{\mathrm{\Σ}}}{u}_i\right)---\left(9\right)$

For each concrete observed value deviation E ^*With its error rate EC ^*, use quantizing factor formula separately to become the element that quantizes in the domain more respectively, again its fuzzy E that turns to ^*And EC ^*,

E wherein ^*=(e ₁E _n), EC ^*=(f ₁F _m)

Can be by formula 8 in the hope of the accurate amount of output;

The method of said definite fuzzy controller adopts angular error and error rate thereof on [90 90], and domain is [3 3], then can use formula

$X=\left(\frac{2n}{b-a}(x^{\′}-\frac{a+b}{2})\right).$

Said subordinate function and quantizing factor and the scale factor that utilizes the ant random search to make its variable optimize fuzzy controller be,

$\mathrm{kfuzzi}\left(i\right)=x_{i\min }+(\underset{j=0}{\overset{l}{\mathrm{\Σ}}}{b}_j\×2^{j-1})\×\frac{x_{i\max }-x_{i\min }}{2^{l-1}}---\left(17\right)$

Wherein kfuzzi (i) is the variable quantity of the fuzzy controller adjusted, and l is the length of coding, b ∈ [0,1], x _ImaxAnd x _IminBe respectively the maximal value and the minimum value of decision content.

Ant crowd fuzzy controller is to utilize ant group algorithm to the optimization of adjusting of the relevant parameter of fuzzy controller, and concrete steps are following:

Ant group algorithm is to the control of Fuzzy Controller Parameters, like formula (18), (19), shown in (20):

K _e1＝kfuzzi(1)*K _e (18)

K _c1＝kfuzzi(2)*K _c (19)

K _u1＝kfuzzi(3)*K _u (20)

The error domain of fuzzy controller does

{-3-kfuzzi(4)，-2-kfuzzi(5)，-1-kfuzzi(6)，0，1+kfuzzi(6)，2+kfuzzi(5)，3+kfuzzi(4)}

The error rate domain of fuzzy controller does

{-3-kfuzzi(7)，-2-kfuzzi(8)，-1-kfuzzi(9)，0，1+kfuzzi(9)，2+kfuzzi(8)，3+kfuzzi(7)}

The domain of the output valve of fuzzy controller

{-3-kfuzzi(10)，-2-kfuzzi(11)，-1-kfuzzi(12)，0，1+kfuzzi(12)，2+kfuzzi(11)，3+kfuzzi(10)}。

Said subordinate function and quantizing factor and the scale factor course of work of utilizing the ant random search to make its variable optimize fuzzy controller 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 subordinate function, the probability that ant individual state transition probability formula calculates is selected element

Step5: utilize the condition of the information generating fuzzy rule that training sample provides, the accuracy of check fuzzy model

Step6:, change Step3, otherwise be Step7 if the ant element has not traveled through

Step7: 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

Step8: satisfy and finish to regulate the end of adjusting.

Characteristics of the present invention are: utilize the ant group algorithm Fuzzy Controller Parameters of adjusting, and the accurately stable then strength of current of controlling the FES system in real time effectively, and can improve FES system real time, accuracy and stability effectively.

Description of drawings

Fig. 1 ant group algorithm structured flowchart of Fuzzy Controller Parameters of adjusting.

Fig. 2 ant group algorithm structural map.

Fig. 3 experiment scene figure.

The fuzzy controller that Fig. 4 ant group algorithm is adjusted is followed the trail of the result.

The adjust relative error of the preset down input joint angles of Fuzzy Controller Parameters control and actual output of Fig. 5 ant group algorithm.

Embodiment

Proposed to adjust fuzzy controller (Fuzzy Controller) parameter with accurate control function property electro photoluminescence (Functional Electrical Stimulation, FES) new method of current-mode by ant group algorithm (Ant Algorithm) self-adaptation.Its techniqueflow is: optimize subordinate function and the quantizing factor and the scale factor of fuzzy controller through the ant group algorithm self-adjusting, control the current-mode of FES system then.This method is a kind of brand-new functional electrostimulation accurate control technique.

Structure based on the application of the walk-aiding functional electric stimulation precision control method of ant crowd fuzzy controller is as shown in Figure 1.Its workflow is: at first the selection with 12 decision variables of quantizing factor, scale factor and the subordinate function parameter of fuzzy controller is converted into the combinatorial optimization problem that ant group algorithm is suitable for; And to its initial group of cities of encoding and producing n individuals composition at random; Next sets up rational actual joint angles and the corresponding relation objective function of muscle model output joint angles and the parameter setting of definite ant group algorithm; Utilize the ant random search to make its variable optimize subordinate function and the quantizing factor and the scale factor of fuzzy controller; And call the fuzzy controller of having adjusted; Whether checking reaches goal-selling, does not repeat above operation repeatedly if having, and perhaps reaches predetermined index up to parameter convergence; Final output promptly gets the decision variable of fuzzy controller and the number of times of ant crowd operation.Computing system output under the new fuzzy controller and with the deviation of muscle model after to the adjustment of ant crowd quantity of information, make it get into next searching process.This process finally realizes the self-adaptation on-line tuning of Fuzzy Controller Parameters repeatedly, and is used for the FES system.

1 design of fuzzy control

Because people's singularity, the FES field is strict to controller stability, robustness, real-time, designs fuzzy controller equalization stable property and real-time and has selected two-dimensional fuzzy controller; Promptly two input variable difference reality are exported joint angles and the error e (k) and the error change rate ec (k) that expect the joint degree, and its domain is FE=[E, E]; FEC=[EC, EC], the stimulating current intensity u (k) of output; Its domain is FU=[U, U].

The quantification domain of error does

X＝{-n，-n+1，…0，…，n-1，n} (1)

The quantification domain of error rate does

X ₁＝{-m，-m+1，…0，…，m-1，m}； (2)

The quantification domain of controlled quentity controlled variable does

Y＝{-k，-k+1，…0，…，k-1，k} (3)

Quantizing factor is respectively

K _e＝n/X _e (4)

K _ec＝m/X _ec (5)

Scale factor does

K _u＝k/Y _u (6)

The present invention adopts the opinion domain of error: { 3-2-1 012 3}; The domain of error rate is the { domain of 3-2-1 01 23} output valves { 3-2-1 012 3}.Control rule tables is: if E1 and EC1 then U1, if E2 and EC2 then U2 ... Ep and ECp be Up then;

Its total fuzzy control rule does

R＝(E _i×CE _i) ^T1οC _i(8)

E wherein ₁=(a _1iA _Ni), EC ₁=(b _1iB _Mi), U ₁=(c _1iC _Ti) (i=1 ... P)

The reverse gelatinization method that adopts is a method of weighted mean:

$u_c=\left(\underset{i=-s}{\overset{s}{\mathrm{\Σ}}}{\mathrm{ik}}_i\right)/\left(\underset{i=-s}{\overset{s}{\mathrm{\Σ}}}{u}_i\right)---\left(9\right)$

For each concrete observed value deviation E ^*With its error rate EC ^*, use quantizing factor formula separately to become the element that quantizes in the domain more respectively, again its fuzzy E that turns to ^*And EC ^*,

E wherein ^*=(e ₁E _n), EC ^*=(f ₁F _m)

Have formula 8 can in the hope of output accurate amount.

Angular error of the present invention and error rate thereof are on [90 90], and domain is [3 3], then can use formula

$X=\left(\frac{2n}{b-a}(x^{\′}-\frac{a+b}{2})\right)---\left(11\right)$

The 2 ant group algorithms Fuzzy Controller Parameters of adjusting

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 ₀₀Or N ₀₁Set out, N in proper order passes by ₁, N ₂, a wherein child node, up to destination node N _K0, N _K1Form path (N _0tN _1tN _Kt), t ∈ [0,1].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) is the inverse of distance.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's path local updating information that finds so far: 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.

Basic fuzzy controller steady-state behaviour can not reach the requirement in FES field; It promptly is that correlation parameter with fuzzy controller utilizes binary coding to be converted into to find the solution shortest route problem that the present invention will adjust to quantizing factor and scale factor and fuzzy control rule, and the structure of ant group algorithm is as shown in Figure 2.When basic domain and word set were constant, the quantizing factor variation can cause deviation and the pairing language value of rate of change thereof to change, and the controlled quentity controlled variable that the variation of scale factor can directly cause acting on controlled device changes.Be specially: K _eBig more, system's rise time is short more, otherwise long more; K _EcBig more, the reaction of system is sensitiveer, otherwise blunt more, K _uBig more, system's rise time is short more, but causes vibration easily, and K _uToo smallly be prone to make the dynamic process of system elongated.

What the present invention adopted is binary coding, and then corresponding decoding formula does

$\mathrm{kfuzzi}\left(i\right)=x_{i\min }+(\underset{j=0}{\overset{l}{\mathrm{\Σ}}}{b}_j\×2^{j-1})\×\frac{x_{i\max }-x_{i\min }}{2^{l-1}}---\left(17\right)$

Wherein kfuzzi (i) is the variable quantity of the fuzzy controller adjusted, and l is the length of coding, b ∈ [0,1], x _ImaxAnd x _IminBe respectively the maximal value and the minimum value of decision content.

Ant group algorithm is to the control such as the following formula of Fuzzy Controller Parameters:

K _e1＝kfuzzi(1)*K _e (18)

K _c1＝kfuzzi(2)*K _c (19)

K _u1＝kfuzzi(3)*K _u (20)

The error domain does

{-3-kfuzzi(4)，-2-kfuzzi(5)，-1-kfuzzi(6)，0，1+kfuzzi(6)，2+kfuzzi(5)，3+kfuzzi(4)}

The error rate domain does

{-3-kfuzzi(7)，-2-kfuzzi(8)，-1-kfuzzi(9)，0，1+kfuzzi(9)，2+kfuzzi(8)，3+kfuzzi(7)}

The domain of output valve

{-3-kfuzzi(10)，-2-kfuzzi(11)，-1-kfuzzi(12)，0，1+kfuzzi(12)，2+kfuzzi(11)，3+kfuzzi(10)}

The adjust concrete workflow of Fuzzy Controller Parameters of ant group algorithm 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 subordinate function, the probability that ant individual state transition probability formula calculates is selected element

Step5: utilize the condition of the information generating fuzzy rule that training sample provides, the accuracy of check fuzzy model

Step6:, change Step3, otherwise be Step7 if the ant element has not traveled through

Step7: 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

Step8: satisfy and finish to regulate the end of adjusting.

3 experimental programs

Parastep functional electrostimulation walk help system and the PS-2137 of PASCO company protractor and Data Studio software that experimental provision adopts U.S. SIGMEDICS company to produce.The Parastep 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, utilize the PS-2137 of PASCO company protractor to gather knee joint angle and the measured knee joint angle of Data Studio software records.Require the experimenter healthy, no lower limb muscles, bone illness, impassivity illness and severe cardiac pulmonary disease.The experimenter sits idly on test board during experiment, and stimulating electrode is fixed in the end positions of quadriceps muscle of thigh, and protractor is fixed on thigh and the shank, makes the joint motion point press close to knee joint moving point position.Shank does not loosen, keeps vertical vacant state when applying electro photoluminescence, and the FES experiment scene is as shown in Figure 3.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.Can adjust stimulus intensity to change the knee joint angle that produces by stimulating through changing the pulse current size in the experiment.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.

The Fuzzy Controller Parameters new algorithm that the ant crowd adjusts 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. 4 follows the trail of the result for the fuzzy controller that the ant group algorithm adaptive optimization 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 that ant group algorithm is adjusted fuzzy controller; Shown in the relative error of preset input knee joint angle and actual knee joint angle under Fig. 5 ant group algorithm Tuning PID Controller; Can find out that then error all within 3%, can reach accurate control.

Purport of the present invention is the precision control method that proposes a kind of new FES, through the ant group algorithm self-adaptation Fuzzy Controller Parameters of adjusting, the accurately stable then strength of current of controlling the FES system in real time effectively.This invention can improve FES system real time, 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 (4)

1. the walk-aiding functional electric stimulation precision control method based on ant crowd fuzzy controller is characterized in that, comprises the following steps:

At first the selection with 12 decision variables of quantizing factor, scale factor and the subordinate function parameter of fuzzy controller is converted into the combinatorial optimization problem that ant group algorithm is suitable for; And to its initial group of cities of encoding and producing n individuals composition at random; What said coding adopted is binary coding, and corresponding decoding formula is:

Wherein kfuzzi (i) is the variable quantity of the fuzzy controller adjusted, and l is the length of coding, b ∈ [0,1], x _ImaxAnd x _IminBe respectively the maximal value and the minimum value of decision content,

Ant crowd fuzzy controller promptly is to utilize ant group algorithm to the optimization of adjusting of the relevant parameter of fuzzy controller, and concrete steps are following:

Ant group algorithm is to the control of Fuzzy Controller Parameters, like formula (18), (19), shown in (20):

K _e1＝kfuzzi(1)*K _e (18)

K _c1＝kfuzzi(2)*K _c (19)

K _u1＝kfuzzi(3)*K _u (20)

The error domain of fuzzy controller is:

{-3-kfuzzi(4)，-2-kfuzzi(5)，-1-kfuzzi(6)，0，1+kfuzzi(6)，2+kfuzzi(5)，3+kfuzzi(4)}

The error rate domain of fuzzy controller is:

{-3-kfuzzi(7)，-2-kfuzzi(8)，-1-kfuzzi(9)，0，1+kfuzzi(9)，2+kfuzzi(8)，3+kfuzzi(7)}

The domain of the output valve of fuzzy controller:

{-3-kfuzzi(10)，-2-kfuzzi(11)，-1-kfuzzi(12)，0，1+kfuzzi(12)，2+kfuzzi(11)，3+kfuzzi(10)}；

Next sets up rational actual joint angles and the corresponding relation objective function of muscle model output joint angles and the parameter setting of definite ant group algorithm;

Searching process: utilize the ant random search; Optimize subordinate function and the quantizing factor and the scale factor of fuzzy controller; And call the fuzzy controller of having adjusted, whether checking reaches goal-selling, does not repeat above operation repeatedly if having; Parameter convergence up to ant group algorithm perhaps reaches predetermined index, and final output promptly gets the decision variable of fuzzy controller and the number of times of ant crowd operation;

The decision variable of the fuzzy controller that the aforementioned final output of foundation promptly gets; Calculate the deviation of output and this output and muscle model output by fuzzy controller; According to deviation ant crowd quantity of information is adjusted, and got into next searching process, repeatedly this process; The final self-adaptation on-line tuning of realizing Fuzzy Controller Parameters, and be used for the FES system.

2. a kind of walk-aiding functional electric stimulation precision control method based on ant crowd fuzzy controller according to claim 1 is characterized in that, the said suitable combinatorial optimization problem of ant group algorithm that is converted into is to be converted into to find the solution shortest route problem; Concrete grammar is that the quantizing factor of ant crowd fuzzy controller and scale factor are that the basic quantization factor of fuzzy controller and basic scale factor multiply by the factor kfuzzi (i) that is optimized by ant group algorithm, i=1,2 respectively; 3, the adjustment of ant crowd fuzzy controller subordinate function promptly is the adjustment to the subordinate function domain of basic fuzzy controller, on the basic domain basis of fuzzy controller, adds or deduct the factor kfuzzi (i) that ant group algorithm is optimized, i=4; 5,6,7,8; 9,10,11; 12, and to the optimization factor kfuzzi (i) of ant group algorithm, i=1; 2 ... 12, the initial group of cities of encoding and producing n individuals composition at random, the said binary coding that is encoded to.

3. a kind of walk-aiding functional electric stimulation precision control method based on ant crowd fuzzy controller according to claim 1 is characterized in that, adopts following method to confirm described fuzzy controller:

Input fuzzy controller initialization module variable is respectively the error e (k) and the error change rate ec (k) of actual output joint angles and expectation joint degree, and its domain is FE=[E, E]; FEC=[EC, EC], the stimulating current intensity u (k) of output; Its domain is FU=[U, U]

The quantification domain of error does

X＝{-n，-n+1，…0，…，n-1，n} (1)

The quantification domain of error rate does

X ₁＝{-m，-m+1，…0，…，m-1，m}； (2)

The quantification domain of controlled quentity controlled variable does

Y＝{-k，-k+1，…0，…，k-1，k} (3)

Quantizing factor is respectively

K _e＝n/X _e (4)

K _ec＝m/X _ec (5)

Scale factor does

K _u＝k/Y _u (6)

Adopt the domain of error: { 3-2-1 012 3}; The domain of error rate be the domain of 3-2-1 012 3} output valves 3-2-1 012 3}, control rule tables is: if E1 and EC1 then U1, if E2 and EC2 then U2 ... Ep and ECp be Up then;

Its total fuzzy control rule is:

R＝(E _i×EC _i) ^T1οC _i?(8)

E wherein ₁=(a _1iA _Ni), EC ₁=(b _1iB _Mi), U ₁=(c _1iC _Ti) (i=1 ... P)

The reverse gelatinization method that adopts is a method of weighted mean:

For each concrete observed value deviation E ^*With its error rate EC ^*, use quantizing factor formula separately to become the element that quantizes in the domain more respectively, again its fuzzy E that turns to ^*And EC ^*,

E wherein ^*=(e ₁E _n), EC ^*=(f ₁F _m)

Ask the accurate amount of output by formula (9);

The method of said definite fuzzy controller adopts angular error and error rate thereof on [90 90], and domain is [3 3], then uses formula

transforms, and angular error and error rate commentaries on classics thereof are calculated its domain space.

4. a kind of walk-aiding functional electric stimulation precision control method according to claim 1 based on ant crowd fuzzy controller; It is characterized in that said subordinate function and quantizing factor and the scale factor course of work of utilizing the ant random search to make its variable optimize fuzzy controller 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 subordinate function, the probability that ant individual state transition probability formula calculates is selected element;

Step5: utilize the condition of the information generating fuzzy rule that training sample provides, the accuracy of check fuzzy model;

Step6:, change Step3, otherwise be Step7 if the ant element has not traveled through;

Step7: 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;

Step8: satisfy termination condition, the end of adjusting.

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