CN106997173A - The self-adaptation control method and system of a kind of pneumatic muscles - Google Patents
The self-adaptation control method and system of a kind of pneumatic muscles Download PDFInfo
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- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
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Abstract
The invention discloses a kind of self-adaptation control method of pneumatic muscles and system, the realization of wherein method includes：Real-time displacement during air-actuated muscle motion is gathered, the expectation displacement input adaptive controller of real-time displacement and desired trajectory is obtained into control instruction；Control instruction is converted into control voltage, controlling pneumatic muscles using control voltage, desirably track is run.The self-adaptation control method of the present invention, enables pneumatic muscles actively to adapt to time variation and uncertainty, and then improve control effect, lifting control performance, improve control accuracy.
Description
Technical field
The invention belongs to bio-robot technical field, more particularly, to a kind of Self Adaptive Control side of pneumatic muscles
Method and system.
Background technology
With the development of pneumatics, novel pneumatic element and application are continued to bring out, pneumatic muscles (Pneumatic
Muscle, PM) it is exactly Typical Representative therein.Pneumatic muscles have similar mechanical characteristic with biological muscles, and low with its
Advantage in terms of cost, high power/mass ratio, simple installation and in medical rehabilitation, remote control, intelligent robot, industry
The fields such as automation are able to extensive use.But pneumatic muscles have following technological difficulties in control aspect, it has strong
Nonlinear characteristic and time-varying characteristics, it is difficult to obtain accurate mathematical modeling.And following weak point is there is in the prior art：The
One, traditional control method depends on accurate mathematical modeling, when the load of pneumatic muscles and stroke change, it is impossible to obtain
Obtain preferable control effect.Second, prior art lacks strict stability analysis to pneumatic muscles system, therefore is difficult to from theory
The control performance of upper guarantee system.3rd, can although proposing the control method that such as synovial membrane controls a class in the prior art
The change of adaptive system characteristic, but be difficult to obtain higher control accuracy.Therefore a kind of suitable control strategy how is found
To handle the uncertainty of pneumatic muscles system, and it is effectively applied in real process, be the big skill of one urgently captured
Art difficulty.
As can be seen here, there is the technical problem that control effect is poor, control performance is poor, control accuracy is low in prior art.
The content of the invention
For the disadvantages described above or Improvement requirement of prior art, the invention provides a kind of Self Adaptive Control of pneumatic muscles
Method and system, its object is to the expectation displacement input adaptive controller of real-time displacement and desired trajectory is obtained into control to refer to
Order；Control instruction is converted into control voltage, controlling pneumatic muscles using control voltage, desirably track is run.Thus solve
There is the technical problem that control effect is poor, control performance is poor, control accuracy is low in prior art.
To achieve the above object, according to one aspect of the present invention, there is provided a kind of Self Adaptive Control side of pneumatic muscles
Method, including：
(1) real-time displacement during air-actuated muscle motion is gathered, by the expectation displacement input of real-time displacement and desired trajectory certainly
Adaptive controller obtains control instruction；
(2) control instruction is converted into control voltage, controlling pneumatic muscles using control voltage, desirably track is run；
Adaptive controller is：
Wherein, u is control instruction,It is adaptive dependent variable,It is the rate of change of adaptive dependent variable,It is stability controller,
K1It is the gain of Self Adaptive Control first, K2It is the gain of Self Adaptive Control second, λ is Self Adaptive Control weight, and ζ is desired trajectory
Kinematics parameters, It is according to the expectation acceleration for expecting displacement calculating, x1It is real-time displacement, x2It is according to reality
The real-time speed that Shi Weiyi is calculated, r is to expect displacement,It is according to the desired speed for expecting displacement calculating.
Further, the scope of the gain of Self Adaptive Control first and the gain of Self Adaptive Control second：
Wherein, π1It is the border coefficient of Self Adaptive Control first, bmaxIt is the maximum friction force coefficient of unit mass, kmaxIt is single
The maximum spring ratio of position quality, π2Self Adaptive Control the second boundary coefficient, d beIn the range of appoint
Meaning constant, γminIt is the minimum of contraction member force coefficient of unit mass, αmaxIt is the maximum damping factor coefficient of unit mass,It is
The maximal rate of desired trajectory, βmaxIt is the maximum spring factor coefficient of unit mass, rmaxIt is the maximum displacement of desired trajectory,
bminIt is the minimized friction force coefficient of unit mass.
Further, the maximum friction force coefficient of unit mass, the maximum spring ratio of unit mass, unit mass be most
It is small to shrink first force coefficient, the maximum damping factor coefficient of unit mass, the maximum spring factor coefficient of unit mass and unit matter
The minimized friction force coefficient of amount is entered by setting up three element models of pneumatic muscles, the parameter to three element models of pneumatic muscles
Row identification is obtained.
It is another aspect of this invention to provide that there is provided a kind of adaptive control system of pneumatic muscles, including：
First module, for gathering real-time displacement during air-actuated muscle motion, by the expectation of real-time displacement and desired trajectory
Displacement input adaptive controller obtains control instruction；
Second module, for control instruction to be converted into control voltage, pneumatic muscles are controlled according to the phase using control voltage
Hope track operation；
Adaptive controller is：
Wherein, u is control instruction,It is adaptive dependent variable,It is the rate of change of adaptive dependent variable,It is stability controller,
K1It is the gain of Self Adaptive Control first, K2It is the gain of Self Adaptive Control second, λ is Self Adaptive Control weight, and ζ is desired trajectory
Kinematics parameters, It is according to the expectation acceleration for expecting displacement calculating, x1It is real-time displacement, x2It is according to reality
The real-time speed that Shi Weiyi is calculated, r is to expect displacement,It is according to the desired speed for expecting displacement calculating.
Further, the scope of the gain of Self Adaptive Control first and the gain of Self Adaptive Control second：
Wherein, π1It is the border coefficient of Self Adaptive Control first, bmaxIt is the maximum friction force coefficient of unit mass, kmaxIt is single
The maximum spring ratio of position quality, π2Self Adaptive Control the second boundary coefficient, d beIn the range of appoint
Meaning constant, γminIt is the minimum of contraction member force coefficient of unit mass, αmaxIt is the maximum damping factor coefficient of unit mass,It is
The maximal rate of desired trajectory, βmaxIt is the maximum spring factor coefficient of unit mass, rmaxIt is the maximum displacement of desired trajectory,
bminIt is the minimized friction force coefficient of unit mass.
Further, the maximum friction force coefficient of unit mass, the maximum spring ratio of unit mass, unit mass be most
It is small to shrink first force coefficient, the maximum damping factor coefficient of unit mass, the maximum spring factor coefficient of unit mass and unit matter
The minimized friction force coefficient of amount is entered by setting up three element models of pneumatic muscles, the parameter to three element models of pneumatic muscles
Row identification is obtained.
In general, by the contemplated above technical scheme of the present invention compared with prior art, it can obtain down and show
Beneficial effect：
(1) the expectation displacement input adaptive controller of real-time displacement and desired trajectory is obtained control instruction by the present invention；
Control instruction is converted into control voltage, controlling pneumatic muscles using control voltage, desirably track is run.Present invention design
Adaptive controller, can in the case where there is strong time variation and be uncertain in pneumatic muscles systematic parameter, it is online from
Adaptively estimate parameter, pneumatic muscles system is actively adapted to time variation and uncertainty, and then improve control effect, carry
Rise control performance, improve control accuracy.
(2) preferred, limiting adaptive of the present invention controls the scope of the first gain and the gain of Self Adaptive Control second, is changing
The stability of pneumatic muscles is ensure that while kind control effect, lifting control performance, raising control accuracy.
(3) preferred, the present invention is by setting up three element models of pneumatic muscles, to three element models of pneumatic muscles
Parameter is recognized, and then optimizes the estimation to adaptive dependent variable.
Brief description of the drawings
Fig. 1 is a kind of experiment porch structure chart of pneumatic muscles control system provided in an embodiment of the present invention；
Fig. 2 is pneumatic muscles control principle block diagram provided in an embodiment of the present invention；
Fig. 3 is a kind of flow chart of the self-adaptation control method of pneumatic muscles provided in an embodiment of the present invention；
Fig. 4 is the identification result figure of the spring ratio in the embodiment of the present invention；
Fig. 5 is the identification result figure for shrinking first force coefficient in the embodiment of the present invention；
Fig. 6 is the identification result figure of the friction coefficient under pneumatic muscles inflated condition of the embodiment of the present invention；
Fig. 7 is the identification result figure of the friction coefficient under pneumatic muscles deflation status of the embodiment of the present invention；
Fig. 8 is adaptive controller, non-self-adapting controller, the tracing property of PID controller in emulation of the embodiment of the present invention
Can 0-12s comparison schematic diagram；
Fig. 9 is adaptive controller, non-self-adapting controller, the tracing property of PID controller in emulation of the embodiment of the present invention
Can 52-68s comparison schematic diagram；
Figure 10 be the embodiment of the present invention experiment in adaptive controller, non-self-adapting controller, PID controller low frequency with
Comparison schematic diagram of the track performance in 0-12s；
Figure 11 be the embodiment of the present invention experiment in adaptive controller, non-self-adapting controller, PID controller low frequency with
Comparison schematic diagram of the track performance in 52-68s；
Figure 12 be the embodiment of the present invention experiment in adaptive controller, non-self-adapting controller, PID controller high frequency with
Track performance comparision schematic diagram.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.As long as in addition, technical characteristic involved in each embodiment of invention described below
Not constituting conflict each other can just be mutually combined.
Present invention firstly provides a kind of experiment porch of pneumatic muscles control system, as shown in figure 1, its part
Have：It is stay wire displacement sensor 1, load 2, pneumatic muscles 3, air compressor 4, proportioning valve 5, NI controllers 6, dc source 7, aobvious
Show device 8.The platform uses the pneumatic muscles FESTO MAXM-20-AA that FESTO companies produce, and its one end is fixed on the branch of platform
On frame, and the other end is movable end, is connected with load 2.Diameter increases when pneumatic muscles are inflated, and length reduces；Otherwise when deflating
Diameter reduces, length increase.Air compressor 4 provides the air pressure input of whole system, and the regulation of passing ratio valve 5 can be controlled
System actually enters the amount of gas pressure in pneumatic muscles 3.Stay wire displacement sensor 1 is used for the displacement for measuring the movable end of pneumatic muscles 3,
NI controllers 6 gather displacement signal by the analog-to-digital conversion module of data collecting card, and controller utilizes obtained feedback signal, root
Calculate controlled quentity controlled variable according to adaptive servo control algolithm, then by the D/A converter module of capture card export actual control voltage to
Proportioning valve 5, the action of pneumatic muscles is controlled by the output pressure of proportioning valve 5.
Control principle block diagram for above-mentioned pneumatic muscles 3 is as shown in Fig. 2 controller sends voltage signal is simulated by NI
Output slab is exported to proportioning valve, so as to adjust the air pressure for entering pneumatic muscles and then change the amount of contraction of pneumatic muscles, gas
The amount of contraction of dynamic muscle is that the stay wire displacement sensor that is displaced through of movable end feeds back to NI data acquisition boards, by feedback quantity with giving
Determine input quantity to be compared, constantly correct deviation and realize the tracking to given input.
The generation of the signal acquisition and pumping signal of above-mentioned NI controllers 6 is the development environment based on Labview,
Labview is the abbreviation of laboratory virtual instrument the integration environment, is also current most widely used, with fastest developing speed, function most strong figure
Shape software development the integration environment.It is equally simple just as heap building blocks using Labview programmings, both made the people without programming experience
Beautiful interface can also be developed in a short period of time, powerful automated procedures.Programmer need not know each figure
Target internal structure, it is only necessary to know that its concrete function, and connect them with line according to certain logical order, just can be complete
Into the task of analog data collection all the way.
NI simulation output panels have used the simulation output function of NI controllers 6, and NI data acquisition boards have used NI controls
The data acquisition function of device 6.
Set up three element models：
Wherein, ys,The displacement of pneumatic muscles is represented respectively, and speed, acceleration, P represents the inside of pneumatic muscles
Air pressure, m is the quality of pneumatic muscles, and g is the nonlinear terms in acceleration of gravity, formulak(P)ysAnd f (P) is respectively
Frictional force, elastic force and contraction elementary force, make ys=0 corresponds to the state that pneumatic muscles are deflated and stretched completely, then works as pneumatic muscles
During contraction, ysIncrease.Friction coefficient b (P), spring ratio k (P), shrinking member force coefficient f (P) has following expression-form：
B (P)=α0-α1P
K (P)=β0-β1P
F (P)=γ0+γ1P
In formula, α0For the coefficient of damping factor first of pneumatic muscles, α1For the coefficient of damping factor second of pneumatic muscles, β0
For the coefficient of spring factor first of pneumatic muscles, β1For the coefficient of spring factor second of pneumatic muscles, γ0For the receipts of pneumatic muscles
Contracting power the first coefficient of the factor, γ1For the coefficient of the convergent force factor second of pneumatic muscles.
Preset an atmospheric pressure value P0, pneumatic muscles equalization point y is sets=y0, according to equilibrium condition, there is f (P0)-k(P0)ys
=mg；
Equalization point using setting makes control instruction u=Δs P=P-P as benchmark0, real-time displacement y=ys-y0,To be real-time
Acceleration,For real-time speed, α is the damping factor coefficient of unit quality, and β is the spring factor coefficient of unit quality, and γ is single
The contraction member force coefficient of position quality, b is unit matter
The friction coefficient of amount, k is the spring ratio of unit quality, reconstructs three element models：
B=(α0-α1P0)/m, k=(β0-β1P0)/m, α=α1/ m, β=β1/ m, γ=(γ1+β1y0)/m；
For three element models of reconstruct, first state variable x is made1=y,X=[x1, x2]T,For first state
The rate of change of variable,For the rate of change of the second state variable, the state-space expression for setting up three element models is as follows：
Coordinate transform is carried out to step state-space expressionThe then kinetic simulation of error
Type is：R is expectation displacement,For desired speed,To expect acceleration,
It is desirable that pneumatic muscles can track given desired trajectory, this condition can be described as：Y (t) represents the real-time displacement of t, and r (t) represents the expectation position of t
Move.
Required control input can be solved from state-space expression, i.e.,
U is linearized：
U=μTζ+O(ζ[2])
Wherein, μ is linearisation coefficient, μ=[μ1, μ2, μ3]T,ζ is the kinematics parameters of desired trajectory, O
(ζ[2]) be ζ second order it is infinitely small, take
Structure after being linearized according to u, design adaptive controller is as follows：
Wherein,It is adaptive dependent variable, represents the estimation to μ,It is the rate of change of adaptive dependent variable,It is point stabilization
Device, K1It is the gain of Self Adaptive Control first, K2It is the gain of Self Adaptive Control second, λ is Self Adaptive Control weight.
As shown in figure 3, a kind of self-adaptation control method of pneumatic muscles, including：
(1) real-time displacement during air-actuated muscle motion is gathered, by the expectation displacement input of real-time displacement and desired trajectory certainly
Adaptive controller obtains control instruction；
(2) control instruction is converted into control voltage, controlling pneumatic muscles using control voltage, desirably track is run；
Adaptive controller is：
Wherein, u is control instruction,It is adaptive dependent variable,It is the rate of change of adaptive dependent variable,It is stability controller,
K1It is the gain of Self Adaptive Control first, K2It is the gain of Self Adaptive Control second, λ is Self Adaptive Control weight, and ζ is desired trajectory
Kinematics parameters, It is according to the expectation acceleration for expecting displacement calculating, x1It is real-time displacement, x2It is according to reality
The real-time speed that Shi Weiyi is calculated, r is to expect displacement,It is according to the desired speed for expecting displacement calculating.
Further, the scope of the gain of Self Adaptive Control first and the gain of Self Adaptive Control second：
Wherein, π1It is the border coefficient of Self Adaptive Control first, bmaxIt is the maximum friction force coefficient of unit mass, kmaxIt is single
The maximum spring ratio of position quality, π2Self Adaptive Control the second boundary coefficient, d beIn the range of appoint
Meaning constant, γminIt is the minimum of contraction member force coefficient of unit mass, αmaxIt is the maximum damping factor coefficient of unit mass,It is
The maximal rate of desired trajectory, βmaxIt is the maximum spring factor coefficient of unit mass, rmaxIt is the maximum displacement of desired trajectory,
bminIt is the minimized friction force coefficient of unit mass.
Further, the maximum friction force coefficient of unit mass, the maximum spring ratio of unit mass, unit mass be most
It is small to shrink first force coefficient, the maximum damping factor coefficient of unit mass, the maximum spring factor coefficient of unit mass and unit matter
The minimized friction force coefficient of amount is entered by setting up three element models of pneumatic muscles, the parameter to three element models of pneumatic muscles
Row identification is obtained.
Further, in order to compare the performance of adaptive servo control algolithm and other control algolithms, the present invention is also to upper
The systematic parameter stated in three element models is recognized.As shown in figure 4, k (P) is in approximate piecewise linear relationship between P, such as
Shown in Fig. 5, f (P) is in linear approximate relationship between P.And the identification to friction coefficient b (P) needs to enter in dynamic process
OK, it is notable that b (P) is not simple linear relationship between P, and for identical air pressure P, different loads
It correspond to different friction coefficient b (P), it means that friction coefficient b (P) is not simply related to air pressure P, Ke Nengcun
In increasingly complex functional relation.But in view of on the whole, the value of friction coefficient is smaller, finally still employs one
The straight line of fitting and its issuable error boundary describe relations of the b (P) between P.Relations of the b (P) between P is as schemed
Shown in 6 and Fig. 7, as seen from the figure, b (P) is in inflated condition with pneumatic muscles with P relation or deflation status is relevant.From debating
Knowledge process understands the uncertainty and strong nonlinearity of pneumatic muscles systematic parameter.
Then, the range of indeterminacy of pneumatic muscles systematic parameter is calculated as follows：
713≤b≤1024
5.2×103≤k≤6.1×103
6.44×10-5≤|α|≤9.1×10-4
0≤|β|≤0.061
5.38×10-4≤γ≤0.0024
B unit isK unit isα unit isβ unit isγ's
Unit isAccording to above-mentioned indeterminacy of calculation result, any selection numerical value comes as systematic parameter in its span
The traditional controller of design, for comparing.
We are respectively by emulating with the adaptive controller in the experimental verification present invention to giving sinusoidal input signal
Ability of tracking, and by adaptive controller Adaptive and conventional PID controllers, other non-self-adapting controllers Non-
Adaptive is compared, and Fig. 8 is adaptive controller, non-self-adapting controller, PID control in emulation of the embodiment of the present invention
Comparison schematic diagram of the tracking performance of device in 0-12s；Fig. 9 is adaptive controller, non-self-adapting in emulation of the embodiment of the present invention
Controller, PID controller tracking performance 52-68s comparison schematic diagram；In simulations, the tracking of adaptive controller is missed
Difference is 0.03mm, and PID controller and the tracking error of non-self-adapting controller are respectively 0.75mm, 0.013mm.Figure 10 is this
Adaptive controller, non-self-adapting controller, the low frequency tracking performance of PID controller are 0-12s's in inventive embodiments experiment
Comparison schematic diagram；Figure 11 be the embodiment of the present invention experiment in adaptive controller, non-self-adapting controller, PID controller it is low
Comparison schematic diagram of the frequency tracking performance in 52-68s；Figure 12 is adaptive controller, non-self-adapting in experiment of the embodiment of the present invention
The high frequency tracking performance comparison schematic diagram of controller, PID controller.More careful comparison has been done in an experiment, has been respectively compared
Ability of tracking of three kinds of controllers to low frequency signal and high-frequency signal.Emulate and experimental result shows, adaptive controller
Ability of tracking be better than other two kinds of controllers.
As it will be easily appreciated by one skilled in the art that the foregoing is merely illustrative of the preferred embodiments of the present invention, it is not used to
The limitation present invention, any modifications, equivalent substitutions and improvements made within the spirit and principles of the invention etc., it all should include
Within protection scope of the present invention.
Claims (6)
1. a kind of self-adaptation control method of pneumatic muscles, it is characterised in that including：
(1) real-time displacement during air-actuated muscle motion is gathered, by the expectation displacement input adaptive of real-time displacement and desired trajectory
Controller obtains control instruction；
(2) control instruction is converted into control voltage, controlling pneumatic muscles using control voltage, desirably track is run；
The adaptive controller is：
Wherein, u is control instruction,It is adaptive dependent variable,It is the rate of change of adaptive dependent variable,It is stability controller, K1It is
The gain of Self Adaptive Control first, K2It is the gain of Self Adaptive Control second, λ is Self Adaptive Control weight, and ζ is the motion of desired trajectory
Learn parameter, It is according to the expectation acceleration for expecting displacement calculating, x1It is real-time displacement, x2It is according to real-time position
The real-time speed calculated is moved, r is to expect displacement,It is according to the desired speed for expecting displacement calculating.
2. a kind of self-adaptation control method of pneumatic muscles as claimed in claim 1, it is characterised in that the Self Adaptive Control
First gain and the scope of the gain of Self Adaptive Control second：
Wherein, π1It is the border coefficient of Self Adaptive Control first, bmaxIt is the maximum friction force coefficient of unit mass, kmaxIt is unit matter
The maximum spring ratio of amount, π2Self Adaptive Control the second boundary coefficient, d beIn the range of it is any often
Number, γminIt is the minimum of contraction member force coefficient of unit mass, αmaxIt is the maximum damping factor coefficient of unit mass,It is to expect
The maximal rate of track, βmaxIt is the maximum spring factor coefficient of unit mass, rmaxIt is the maximum displacement of desired trajectory, bminIt is
The minimized friction force coefficient of unit mass.
3. a kind of self-adaptation control method of pneumatic muscles as claimed in claim 2, it is characterised in that the unit mass
Maximum friction force coefficient, the maximum spring ratio of unit mass, the minimum of contraction member force coefficient of unit mass, unit mass are most
The minimized friction force coefficient of big damping factor coefficient, the maximum spring factor coefficient of unit mass and unit mass is by setting up gas
Three element models of dynamic muscle, the parameter to three element models of pneumatic muscles is recognized.
4. a kind of adaptive control system of pneumatic muscles, it is characterised in that including：
First module, for gathering real-time displacement during air-actuated muscle motion, by the expectation displacement of real-time displacement and desired trajectory
Input adaptive controller obtains control instruction；
Second module, for control instruction to be converted into control voltage, pneumatic muscles desirably rail is controlled using control voltage
Mark is run；
The adaptive controller is：
Wherein, u is control instruction,It is adaptive dependent variable,It is the rate of change of adaptive dependent variable,It is stability controller, K1It is
The gain of Self Adaptive Control first, K2It is the gain of Self Adaptive Control second, λ is Self Adaptive Control weight, and ζ is the motion of desired trajectory
Learn parameter, It is according to the expectation acceleration for expecting displacement calculating, x1It is real-time displacement, x2It is according to real-time position
The real-time speed calculated is moved, r is to expect displacement,It is according to the desired speed for expecting displacement calculating.
5. a kind of adaptive control system of pneumatic muscles as claimed in claim 4, it is characterised in that the Self Adaptive Control
First gain and the scope of the gain of Self Adaptive Control second：
Wherein, π1It is the border coefficient of Self Adaptive Control first, bmaxIt is the maximum friction force coefficient of unit mass, kmaxIt is unit matter
The maximum spring ratio of amount, π2Self Adaptive Control the second boundary coefficient, d beIn the range of it is any often
Number, γminIt is the minimum of contraction member force coefficient of unit mass, αmaxIt is the maximum damping factor coefficient of unit mass,It is to expect
The maximal rate of track, βmaxIt is the maximum spring factor coefficient of unit mass, rmaxIt is the maximum displacement of desired trajectory, bminIt is
The minimized friction force coefficient of unit mass.
6. a kind of adaptive control system of pneumatic muscles as claimed in claim 5, it is characterised in that the unit mass
Maximum friction force coefficient, the maximum spring ratio of unit mass, the minimum of contraction member force coefficient of unit mass, unit mass are most
The minimized friction force coefficient of big damping factor coefficient, the maximum spring factor coefficient of unit mass and unit mass is by setting up gas
Three element models of dynamic muscle, the parameter to three element models of pneumatic muscles is recognized.
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| CN109407513A (en) * | 2018-12-14 | 2019-03-01 | 江南大学 | The tracking and controlling method based on Iterative feedback tuning of joint actuated by pneumatic muscles |
| CN109613831A (en) * | 2019-02-01 | 2019-04-12 | 河海大学常州校区 | Pneumatic artificial muscle control system and method based on dynamic mathematical models feedforward PID |
| CN109814376A (en) * | 2019-02-01 | 2019-05-28 | 河海大学常州校区 | Pneumatic artificial muscle displacement control system and method based on nonlinear fitting network |
| CN110015609A (en) * | 2019-03-12 | 2019-07-16 | 华中科技大学 | A kind of petrochemical industry Lift-on/Lift-off System and hanging method based on Pneumatic artificial muscle |
| CN111360852A (en) * | 2020-04-27 | 2020-07-03 | 徐航 | Control method of follow-up mechanical arm |
| CN111596610A (en) * | 2020-05-19 | 2020-08-28 | 苏州诺达佳自动化技术有限公司 | Industrial control machine control system with operation track measurement and control function |
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| CN109407513A (en) * | 2018-12-14 | 2019-03-01 | 江南大学 | The tracking and controlling method based on Iterative feedback tuning of joint actuated by pneumatic muscles |
| CN109613831A (en) * | 2019-02-01 | 2019-04-12 | 河海大学常州校区 | Pneumatic artificial muscle control system and method based on dynamic mathematical models feedforward PID |
| CN109814376A (en) * | 2019-02-01 | 2019-05-28 | 河海大学常州校区 | Pneumatic artificial muscle displacement control system and method based on nonlinear fitting network |
| CN109613831B (en) * | 2019-02-01 | 2021-09-28 | 河海大学常州校区 | Pneumatic artificial muscle control system and method based on dynamic mathematical model feedforward PID |
| CN110015609A (en) * | 2019-03-12 | 2019-07-16 | 华中科技大学 | A kind of petrochemical industry Lift-on/Lift-off System and hanging method based on Pneumatic artificial muscle |
| CN110015609B (en) * | 2019-03-12 | 2023-09-29 | 华中科技大学 | Petrochemical hoisting system and hoisting method based on pneumatic artificial muscles |
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