CN108803344A - A kind of symmetrical forecast Control Algorithm of robot bilateral teleoperation based on Mode-switch - Google Patents
A kind of symmetrical forecast Control Algorithm of robot bilateral teleoperation based on Mode-switch Download PDFInfo
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- G—PHYSICS
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- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
Abstract
The symmetrical forecast Control Algorithm of robot bilateral teleoperation based on Mode-switch that the present invention relates to a kind of, based on nerual network technique, (wherein the estimated capacity of neural network is used for estimating uncertain gravity item, and predictive ability is used for building fallout predictor kernel), adaptation theory (for eliminating every estimation and prediction error), homomorphic model prediction thought (is and surveys the additional conditions for exporting and needing to meet, for improving precision of prediction) and proportion-plus-derivative control algorithm (for designing master, from side controller), it is proposed a kind of symmetrical predictive control strategy based on mode (" movement-waiting mode " and " fallout predictor mode ") switching, realize the stabilization of principal and subordinate end robot, in real time, continuously, precise synchronization controls, complete expected remote operating task.
Description
Technical field
The invention belongs to technical field of robot control, are related to a kind of robot bilateral teleoperation pair based on Mode-switch
Claim forecast Control Algorithm.
Background technology
With the robot bilateral teleoperation system of core, it can be achieved that people is in local side and to remote based on teleoperation
Hold target operation, extend and extend the operational capacity of the mankind significantly, can carry out include distant end control system parts more
It changes and safeguards, distant end target acquistion can not or be not easy to join in person with numerous mankind such as crawl, the replacement of nuclear fuel material, tele-medicines
With task.Remote control and operation task are executed based on tele-robotic system, it is can avoid and directly executes dangerous behaviour
Make task and raising working efficiency and precision, these features have also made it a kind of very promising in robot field
Control system, and obtained greatly paying close attention to and developing.
Typical robot bilateral teleoperation system is mainly by operator, main side robot, master-slave communication link, from terminal
Device people and composition is interconnected successively from five part of end ring border.Its overall operation and working mechanism be：The first step, the operator of main side
Control information is transmitted to main side robot.Second step, main side robot foundation receive the control information from operator and make
Corresponding action, and via uplink communication links by these action messages (including mainly position, angle and speed etc.) be transmitted to from
Hold robot.Third walks, and from end robot according to the action message from main side robot received, repeats, reproduces main side
The action of robot, and act on from the operation target in end ring border.4th step is measured using various kinds of sensors from end robot
Action message.And main side operator is fed back to by downstream communications link.5th step, operator is to receiving feedback action letter
The action message of breath and original main side robot is compared, analyzes and judges, to send out the control command of next step.Cycle is held
The step of row front, finally enables and follows robot execution in main side similarly to operate from end robot and complete specified control
System and operation task.
The presence of time delay (including propagation delay time and processing delay etc.), leads to signal not in robot bilateral teleoperation system
Energy real-time Transmission, so that the asynchronous and deviation of action occurs in main and slave terminal robot, this, which is greatly reduced, is
The operation of system and control performance, the system that even results in finally tend to be unstable.Therefore it is bilateral to robot distant time delay can be eliminated
The influence of operating system directly affects the success or failure of the control performance and relational task of system.So the control strategy of advanced design
The influence of time-delay system is overcome to become the research emphasis of teleoperation of robot technology.However either Passive Shape Control, structure changes
The control methods such as control or fuzzy adaptivecontroller can only all reduce and can not thoroughly solve the influence of time delay, because time delay begins
It is present in system control loop eventually.But the controlling party rule based on state forecast can preferably avoid time delay to control loop
Influence, will overcome the problems, such as time delay systematic influence is converted to overcome the problems, such as predict error to systematic influence, because in order to control
Used in circuit it is all there are the information of time lag to be substituted by corresponding predictive information, so thoroughly avoiding the shadow of time delay
It rings.As long as precision of prediction is sufficiently high, predict that the influence of error can thoroughly overcome, also implies that the influence of time delay is thoroughly disappeared
It removes.So PREDICTIVE CONTROL will be at an important development direction in future robot teleoperation.Further, since robot
System is a kind of typical nonlinear system, so modeling has many uncertainties.And it is directed to the bilateral distant behaviour of robot
Make system, due to the non-intellectual of distal environment, so its problems such as there is also the uncertainties of gravity item.
Invention content
Technical problems to be solved
In order to avoid the shortcomings of the prior art, the present invention proposes a kind of bilateral distant behaviour of the robot based on Mode-switch
Make symmetrical forecast Control Algorithm, since refreshing network technology and fuzzy mathematics theory are used equally for the uncertainty of processing system,
Here the estimation of uncertain gravity item is realized using nerual network technique, it is final to compensate and eliminate shadow of the gravity item to system
It rings.
The present invention is based on nerual network technique, adaptation theory, homomorphic model prediction thought and proportion-plus-derivative controls to calculate
Method, for " there are asymmetric, time-vary delay systems for master-slave communication link ", " there are unknown, not true for principal and subordinate end Dynamic Modeling in Robotics
Determine gravity item " the problems such as robot bilateral teleoperation system, devise one based on mode (" movement-waiting mode " and " in advance
Survey device mode ") the symmetrical forecast Control Algorithm of switching, to thoroughly overcome asymmetric, time-vary delay system and unknown, uncertain gravity item
Influence, realize that stables, real-time, continuous, accurate master-slave synchronisation controls, complete set remote operating task.
Technical solution
A kind of symmetrical forecast Control Algorithm of robot bilateral teleoperation based on Mode-switch, it is characterised in that step is such as
Under：
Step 1：
1, the kinetic model of master and slave end robot is established
Wherein, subscript m and s indicate the main and slave terminal of robot bilateral teleoperation system, q respectivelymAnd qsMaster is indicated respectively
End and from end joint of robot angular displacement,WithMain and slave terminal joint of robot angular speed is indicated respectively,WithRespectively
Indicate main and slave terminal joint of robot angular acceleration, Mm(qm) and Ms(qs) indicate main and slave terminal robot symmetrically just respectively
Determine inertial matrix,WithThe centrifugal force and coriolis force item of main and slave terminal robot, G are indicated respectivelym
(qm) and Gs(qs) the gravity item of main and slave terminal robot, τ are indicated respectivelymAnd τsThe control of main and slave terminal robot is indicated respectively
Torque processed, FhAnd FeThe active force of operator and environment are indicated respectively；
The active force of the operator and environment is expressed as follows：
Wherein km0、km1、km2、ks0、ks1And ks2It is any given positive definite constant；
2, unknown, uncertain gravity item estimation：
It is assumed that the gravity item G of main and slave terminal robotm(qm) and Gs(qs) it is unknown, uncertain, using BRF god
They are estimated through network, corresponding expression formula is as follows：
Wherein,WithG is indicated respectivelym(qm) and Gs(qs) estimated value,WithIt is master and slave end god respectively
Through network parameter θmAnd θsEstimated value, Lm(qm) and Ls(qs) it is the corresponding radial basis function of master and slave end RBF neural；
Due to the gravity item G of main and slave terminal robotm(qm) and Gs(qs) size, probably due to structure or environment
Change and change.So in order to improve the estimated accuracy to them, the RBF neural at master and slave end is separately designed
Corresponding adaptive law is as follows：
Wherein,Withθ is indicated respectivelymAnd θsEvaluated error, ΓmAnd ΓsRespectively arbitrarily give
Fixed symmetric positive definite matrix；
Step 2, the master and slave end fallout predictor of structure：
(a) master and slave end fallout predictor prediction output：
Wherein,WithThe prediction output of main and slave terminal fallout predictor is indicated respectively,WithIt is main and slave terminal respectively
Predictor parameter wsAnd wmPredicted value,WithIt is the corresponding prediction of main and slave terminal fallout predictor
Function,WithThe prediction of main and slave terminal fallout predictor is indicated respectively
Output error.Master and slave end fallout predictor prediction outputWithThe condition that need to meet：
The corresponding homomorphic model of main side robot：
From the corresponding homomorphic model of end robot：
Wherein,WithThe symmetric positive definite of the corresponding homomorphic model of main and slave terminal robot is indicated respectively
Inertial matrix,WithIndicate respectively the corresponding homomorphic model of main and slave terminal robot centrifugal force and
Coriolis force item,WithIndicate that main and slave terminal robot corresponds to respectively
Homomorphic model gravity itemWithEstimated value,WithThe control moment of the corresponding homomorphic model of main and slave terminal robot is indicated respectively,WithOperator and environment in corresponding homomorphic model are indicated respectively
Active force.
(b) it is that the corresponding adaptive law of master and slave fallout predictor design is：
Wherein,WithW is indicated respectivelymAnd wsEvaluated error, and be respectively
WithRespectively any given symmetric positive definite matrix；
Step 3：
1, master and slave controller design
" movement-waiting mode " controller design
Wherein, αm, βm, αsAnd βsFor controller parameter to be solved；
" fallout predictor mode " controller design
Wherein, αm, βm, αsAnd βsFor controller parameter to be solved；
2, Mode-switch mechanism
When robot bilateral teleoperation system brings into operation, all switchings switch is connected to contact 1 in system,
I.e. system works in " movement-waiting mode " at this time, which is continued until that the prediction error of the fallout predictor at master and slave end is small
In the desired value of setting；
When the prediction error at master and slave end is respectively less than the desired value set, then all switching switches are all connected again
To contact 2, namely system works in " fallout predictor mode " and is always maintained at system stalls at this time.
Advantageous effect
A kind of symmetrical forecast Control Algorithm of robot bilateral teleoperation based on Mode-switch proposed by the present invention, for
" there are asymmetric, time-vary delay systems for master-slave communication link ", " there are unknown, uncertain gravity for principal and subordinate end Dynamic Modeling in Robotics
The principal and subordinate end synchronisation control means of the robot bilateral teleoperation system of the problems such as item ".It is (wherein neural based on nerual network technique
The estimated capacity of network is used for estimating uncertain gravity item, and predictive ability is used for building fallout predictor kernel), adaptation theory
(for eliminating every estimation and prediction error), homomorphic model prediction thought (it is to survey the additional conditions that output needs to meet with survey,
For improving precision of prediction) and proportion-plus-derivative control algorithm (for designing master and slave side controller), it proposes a kind of based on mode
The symmetrical predictive control strategy of (" movement-waiting mode " and " fallout predictor mode ") switching, the stabilization of realization principal and subordinate end robot,
In real time, continuously, precise synchronization control, complete expected remote operating task.
Description of the drawings
Fig. 1：Symmetrical forecast Control Algorithm based on Mode-switch realizes the robot stablize, is real-time, is continuous, accurately controlling
Bilateral teleoperation system framework figure
Specific implementation mode
In conjunction with embodiment, attached drawing, the invention will be further described：
Fig. 1 gives what the realization of the symmetrical forecast Control Algorithm based on Mode-switch stablized, is real-time, is continuous, accurately controlling
Robot bilateral teleoperation system framework figure, it is corresponding to realize that steps are as follows：
Step 1：The kinetic model of master and slave end robot is provided, and uncertainty is estimated；
Step 2：Master and slave end fallout predictor is built, realizes symmetrical PREDICTIVE CONTROL；
Step 3：Design corresponds respectively to the master and slave side controller of " movement-waiting mode " and " fallout predictor mode ", and makes
The fixed mechanism based on Mode-switch.Finally, comprehensive work above forms the symmetrical forecast Control Algorithm based on Mode-switch.
Step 1：
The step groundwork is the master and slave end kinetic model provided in robot bilateral teleoperation system, and to not
Know, uncertain gravity item is estimated.
(1) master and slave end Dynamic Models of Robot Manipulators：
The master and slave end Dynamic Models of Robot Manipulators in robot bilateral teleoperation system is provided in conjunction with Fig. 1：
Wherein, subscript m and s indicate the main and slave terminal of robot bilateral teleoperation system, corresponding symbolic indication respectively
It has been presented in Fig. 1 with meaning.
The active force of operator and environment are indicated using second order mass-spring-damper model, and are expressed as follows：
Wherein km0、km1、km2、ks0、ks1And ks2It is positive definite constant.
(2) unknown, uncertain gravity item estimation：
Because when there is enough neurons, RBF neural has approaches arbitrary continuation function with arbitrary accuracy
Ability.So being estimated herein unknown, uncertain gravity item to realize using BRF neural networks, corresponding expression
Formula is as follows：
Wherein,WithIt is neural network parameter θmAnd θsEstimated value, Lm(qm) and Ls(qs) it is radial basis function.
Due to the presence of evaluated error, so the adaptive adjustment of parameter is realized using adaptive approach, to realize
Eliminate evaluated error.Adaptive design is as follows：
Wherein
Step 2：
The step mainly builds master and slave end fallout predictor, wherein the framework of master and slave end fallout predictor is consistent, so being referred to as
For " symmetrical PREDICTIVE CONTROL ".
Symmetrical fallout predictor design：
(a) fallout predictor kernel (predictive ability for utilizing RBF neural)
Wherein,Corresponding adaptive law is：
Wherein,
(b) homomorphic model (in conjunction with homomorphic model thought)
Wherein
(c)WithThe estimation item estimated capacity of RBF neural (utilize)
Wherein, the adaptive law of the corresponding RBF neural estimation parameter of the estimation function is：
Wherein
Above-mentioned (a), (b) and (c) three synthesis give the design method of master and slave fallout predictor, wherein (a) gives prediction
Expression formula is exported, (b) and (c) then indicates that prediction output equation needs the condition met, such a building mode that can greatly carry
High precision of prediction.
Step 3：
The step groundwork is the switchover policy of the master and slave controller of design and formulation based on Mode-switch.Finally, comprehensive
The work for closing face forms the symmetrical forecast Control Algorithm based on Mode-switch.
(1) master and slave controller design
Due to robot bilateral teleoperation system shown in FIG. 1, two different mode are worked in respectively：" movement-waiting
Mode " and " fallout predictor mode ".So design in master and slave control design case also respectively with correspond to " movement-waiting mode " and
" fallout predictor mode " divides.
" movement-waiting mode " controller design
" fallout predictor mode " controller design
(2) handover mechanism based on Mode-switch
Mode-switch mechanism：
The step mainly provides the handover mechanism based on Mode-switch, realizes and utilizes the bilateral distant behaviour of robot to the greatest extent
Limited window time in work simultaneously saves the energy, resource.Specific Mode-switch mechanism is as follows：
When robot bilateral teleoperation system shown in Fig. 1 brings into operation, all switchings switch in Fig. 1
(Switch) it is connected to contact 1, system works in " movement-waiting mode " at this time, which is continued until the pre- of master and slave end
The prediction error for surveying device is respectively less than the desired value set；
When the prediction error at master and slave end is respectively less than the desired value set, then all switchings are switched again
(Switch) it is connected to contact 2, system works in " fallout predictor mode " and is always maintained at system stalls at this time.
Claims (1)
1. a kind of symmetrical forecast Control Algorithm of robot bilateral teleoperation based on Mode-switch, it is characterised in that steps are as follows：
Step 1：
1) kinetic model of master and slave end robot, is established
Wherein, subscript m and s indicate the main and slave terminal of robot bilateral teleoperation system, q respectivelymAnd qsRespectively indicate main side and
From end joint of robot angular displacement,WithMain and slave terminal joint of robot angular speed is indicated respectively,WithIt indicates respectively
Main and slave terminal joint of robot angular acceleration, Mm(qm) and Ms(qs) indicate that main and slave terminal robot symmetric positive definite is used respectively
Property matrix,WithThe centrifugal force and coriolis force item of main and slave terminal robot, G are indicated respectivelym(qm) and
Gs(qs) the gravity item of main and slave terminal robot, τ are indicated respectivelymAnd τsThe control force of main and slave terminal robot is indicated respectively
Square, FhAnd FeThe active force of operator and environment are indicated respectively；
The active force of the operator and environment is expressed as follows：
Wherein km0、km1、km2、ks0、ks1And ks2It is any given positive definite constant；
2), unknown, uncertain gravity item estimation：
It is assumed that the gravity item G of main and slave terminal robotm(qm) and Gs(qs) it is unknown, uncertain, using BRF neural networks
They are estimated, corresponding expression formula is as follows：
Wherein,WithG is indicated respectivelym(qm) and Gs(qs) estimated value,WithIt is master and slave terminal nerve net respectively
Network parameter θmAnd θsEstimated value, Lm(qm) and Ls(qs) it is the corresponding radial basis function of master and slave end RBF neural；
Due to the gravity item G of main and slave terminal robotm(qm) and Gs(qs) size, probably due to the change of structure or environment
And it changes.So in order to improve the estimated accuracy to them, the RBF neural at master and slave end is separately designed accordingly
Adaptive law is as follows：
Wherein,Withθ is indicated respectivelymAnd θsEvaluated error, ΓmAnd ΓsIt is respectively any given
Symmetric positive definite matrix；
Step 2, the master and slave end fallout predictor of structure：
(a) master and slave end fallout predictor prediction output：
Wherein,WithThe prediction output of main and slave terminal fallout predictor is indicated respectively,WithIt is main and slave terminal prediction respectively
Device parameter wsAnd wmPredicted value,WithIt is the corresponding prediction letter of main and slave terminal fallout predictor
Number,WithIndicate that the prediction of main and slave terminal fallout predictor is defeated respectively
Go out error.Master and slave end fallout predictor prediction outputWithThe condition that need to meet：
The corresponding homomorphic model of main side robot：
From the corresponding homomorphic model of end robot：
Wherein,WithThe symmetric positive definite the moment of inertia of the corresponding homomorphic model of main and slave terminal robot is indicated respectively
Battle array,WithThe centrifugal force and coriolis force of the corresponding homomorphic model of main and slave terminal robot are indicated respectively
,WithThe corresponding homomorphism of main and slave terminal robot is indicated respectively
The gravity item of modelWithEstimated value,WithThe control moment of the corresponding homomorphic model of main and slave terminal robot is indicated respectively,WithOperator and environment in corresponding homomorphic model are indicated respectively
Active force.
(b) it is that the corresponding adaptive law of master and slave fallout predictor design is：
Wherein,WithW is indicated respectivelymAnd wsEvaluated error, and be respectively With
Respectively any given symmetric positive definite matrix；
Step 3：
1), master and slave controller design
" movement-waiting mode " controller design
Wherein, αm, βm, αsAnd βsFor controller parameter to be solved；
" fallout predictor mode " controller design
Wherein, αm, βm, αsAnd βsFor controller parameter to be solved；
2), Mode-switch mechanism
When robot bilateral teleoperation system brings into operation, in system all switching switches be connected to contact 1 namely this
When system work in " movement-waiting mode ", the mode be continued until the fallout predictor at master and slave end prediction error be respectively less than set
Fixed desired value；
When the prediction error at master and slave end is respectively less than the desired value set, then all switching switches are connected to again tactile
Point 2, namely system works in " fallout predictor mode " and is always maintained at system stalls at this time.
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| CN110253584A (en) * | 2019-07-03 | 2019-09-20 | 西北工业大学 | A kind of discrete time variable boundary bilateral control method towards remote control |
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| CN110253584B (en) * | 2019-07-03 | 2022-04-22 | 西北工业大学 | Remote control-oriented discrete time-varying boundary bilateral control method |
| CN110253584A (en) * | 2019-07-03 | 2019-09-20 | 西北工业大学 | A kind of discrete time variable boundary bilateral control method towards remote control |
| CN110340894A (en) * | 2019-07-18 | 2019-10-18 | 浙江大学 | A kind of adaptive polygon control method of the remote control system based on fuzzy logic |
| CN111571594A (en) * | 2020-05-26 | 2020-08-25 | 南通大学 | Method for improving transparency and stability of teleoperation robot |
| CN111679644A (en) * | 2020-07-07 | 2020-09-18 | 南京航空航天大学 | Uncertain industrial robot motion control method considering system delay |
| CN112025708A (en) * | 2020-08-31 | 2020-12-04 | 北京理工大学 | Control system and method for completing knocking task by using field tool |
| CN112025708B (en) * | 2020-08-31 | 2021-09-21 | 北京理工大学 | Control system and method for completing knocking task by using field tool |
| CN112363389A (en) * | 2020-11-11 | 2021-02-12 | 西北工业大学 | Shared autonomous formation planning control method for single-master multi-slave teleoperation mode |
| CN112363389B (en) * | 2020-11-11 | 2022-07-05 | 西北工业大学 | Shared autonomous formation planning control method for single-master multi-slave teleoperation mode |
| US11497001B2 (en) | 2020-11-19 | 2022-11-08 | Kabushiki Kaisha Toshiba | Edge-intelligence for stability guaranteed real-time control systems |
| CN112783046A (en) * | 2020-12-31 | 2021-05-11 | 西北工业大学 | Bilateral teleoperation terminal smooth behavior planning control method based on fuzzy strategy |
| CN112783046B (en) * | 2020-12-31 | 2022-03-15 | 西北工业大学 | Bilateral teleoperation terminal smooth behavior planning control method based on fuzzy strategy |
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