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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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 the technical field of robot control, and relates to a robot bilateral teleoperation symmetric prediction control method based on mode switching.
Background
The robot bilateral teleoperation system based on teleoperation technology and core can realize the operation of a remote target at a local end by a person, greatly extends and expands the operation capability of the human, and can perform a plurality of tasks which cannot or are inconvenient for human to participate in, such as part replacement and maintenance of a teleoperation control system, capture and grabbing of the remote target, replacement of nuclear raw materials, telemedicine and the like. The remote control and operation tasks are executed based on the robot teleoperation system, so that the robot teleoperation system can avoid directly executing dangerous operation tasks and improve the working efficiency and precision, and the characteristics also make the robot teleoperation system become a control system with very promising prospect in the field of robots and have great attention and development.
A typical robot bilateral teleoperation system is mainly formed by sequentially interconnecting an operator, a master-end robot, a master-slave communication link, a slave-end robot and a slave-end environment. The overall operation and working mechanism is as follows: in the first step, the operator at the master transmits control information to the master robot. In the second step, the master robot performs corresponding actions according to the control information received from the operator, and transmits the action information (mainly including position, angle, speed, etc.) to the slave robot via the uplink communication link. And thirdly, the slave-end robot repeats and reproduces the action of the master-end robot according to the received action information from the master-end robot, and acts on the operation target in the slave-end environment. And fourthly, measuring the motion information of the slave end robot by utilizing various sensors. And fed back to the master operator via the downlink communication link. And fifthly, comparing, analyzing and judging the received feedback action information and the action information of the original main-end robot by the operator so as to send out a control command of the next step. And (4) circularly executing the previous steps, and finally enabling the slave-end robot to follow the master-end robot to execute the same operation and complete the designated control and operation tasks.
Due to the existence of time delay (including transmission time delay, processing time delay and the like) in the robot bilateral teleoperation system, signals cannot be transmitted in real time, and further, the master end robot and the slave end robot are out of synchronization and deviate in action, so that the operation and control performance of the system is greatly reduced, and even the system finally tends to be unstable. Therefore, whether the influence of time delay on the bilateral teleoperation system of the robot can be eliminated or not can be eliminated, and the control performance of the system and the success or failure of the relation task are directly influenced. Therefore, designing an advanced control strategy to overcome the influence of a time delay system becomes a research focus of the teleoperation technology of the robot. However, the control methods such as passive control, variable structure control, fuzzy adaptive control, etc. can only reduce and cannot completely solve the influence of time delay, because the time delay always exists in the system control loop. However, the control method based on the state prediction can better avoid the influence of the time delay on the control loop, and convert the problem of overcoming the influence of the time delay on the system into the problem of overcoming the influence of the prediction error on the system. As long as the prediction accuracy is high enough, the effect of the prediction error can be completely overcome, which also means that the effect of the delay is completely eliminated. Therefore, predictive control will become an important development direction in the future teleoperation technology of robots. Furthermore, since the robotic system is a typical non-linear system, there are many uncertainties in modeling. Moreover, for the bilateral teleoperation system of the robot, because of the unknown of the remote environment, the bilateral teleoperation system also has the problems of uncertainty of a gravity term and the like.
Disclosure of Invention
Technical problem to be solved
In order to avoid the defects of the prior art, the invention provides a robot bilateral teleoperation symmetric prediction control method based on mode switching.
The invention discloses a bilateral teleoperation system of a robot, which aims at the problems of asymmetry and time-varying delay of a master-slave communication link, unknown and uncertain gravity items of dynamics modeling of a master-slave robot and the like based on a neural network technology, an adaptive theory, a homomorphic model prediction idea and a proportional differential control algorithm, and designs a mode-based (motion-waiting mode and predictor mode) switching symmetric prediction control method so as to thoroughly overcome the influences of asymmetry, time-varying delay and unknown and uncertain gravity items, realize stable, real-time, continuous and accurate master-slave synchronous control and complete a set teleoperation task.
Technical scheme
A robot bilateral teleoperation symmetric prediction control method based on modality switching is characterized by comprising the following steps:
step 1:
1. establishing a dynamic model of a master-slave end robot
Wherein, subscripts m and s respectively represent a master end and a slave end of the robot bilateral teleoperation system, and qmAnd q issRespectively representing the angular displacement of the joints of the robot at the master end and the slave end,andrespectively representing the angular velocity of the joints of the robot at the master end and the slave end,andrespectively representing angular accelerations, M, of joints of the robot at the master end and at the slave endm(qm) And Ms(qs) Respectively representing the symmetric positive definite inertia matrixes of the robot at the master end and the robot at the slave end,andrepresenting centrifugal and Copenforces terms, G, of the master and slave robots, respectivelym(qm) And Gs(qs) Representing the gravity terms, τ, of the master and slave robots, respectivelymAnd τsIndicating the control moments of the master and slave robots, respectively, FhAnd FeRepresenting the effort of the operator and the environment, respectively;
the forces of the operator and the environment are respectively expressed as follows:
wherein k ism0、km1、km2、ks0、ks1And ks2Are all any given positive definite constant;
2. estimation of unknown and uncertain gravity terms:
assuming the gravity term G of the master and slave robotsm(qm) And Gs(qs) The method is unknown and uncertain, and a BRF neural network is adopted to estimate the unknown and uncertain, and the corresponding expressions are as follows:
wherein,andeach represents Gm(qm) And Gs(qs) Is determined by the estimated value of (c),andthe parameters theta of the main end neural network and the slave end neural network are respectivelymAnd thetasEstimated value of, Lm(qm) And Ls(qs) The radial basis functions correspond to the RBF neural networks at the master end and the slave end;
gravity item G due to master and slave robotsm(qm) And Gs(qs) May vary due to structural or environmental changes. Therefore, in order to improve the estimation accuracy of the RBF neural networks, corresponding adaptive laws are respectively designed for the RBF neural networks of the master end and the slave end as follows:
wherein,andrespectively represent thetamAnd thetasOf the estimated error of ΓmAnd ΓsRespectively any given symmetric positive definite matrix;
step 2, constructing a master-slave end predictor:
(a) and (3) prediction output of the master end predictor and the slave end predictor:
wherein,andrepresenting the prediction outputs of the master and slave predictors respectively,andprimary and secondary predictor parameters w, respectivelysAnd wmThe predicted value of (a) is determined,andis the prediction function corresponding to the predictor of the master end and the slave end,andthe prediction output error of the predictor of the master end and the slave end is respectively expressed. Prediction output of main and slave end predictorAndthe conditions to be satisfied are as follows:
the homomorphic model corresponding to the master robot is as follows:
the homomorphism model corresponding to the slave robot is as follows:
wherein,andrespectively representing symmetrical positive definite inertia matrixes of homomorphic models corresponding to the master-end robot and the slave-end robot,andrespectively representing the centrifugal force and the Copenforces of the homomorphic models corresponding to the master end robot and the slave end robot,andgravity terms representing homomorphic models corresponding to master-end and slave-end robots respectivelyAndis determined by the estimated value of (c),andrespectively representing the control moments of homomorphic models corresponding to the master-end robot and the slave-end robot,andrepresenting the forces of the operator and the environment in the corresponding homomorphic model, respectively.
(b) Designing corresponding adaptive laws for the master predictor and the slave predictor as follows:
wherein,andrespectively represents wmAnd wsAnd are respectivelyAndrespectively any given symmetric positive definite matrix;
and step 3:
1. master and slave controller design
"motion-waiting mode" controller design
wherein alpha ism，βm，αsand betasThe controller parameters to be solved;
'predictor modality' controller design
wherein alpha ism，βm，αsand betasThe controller parameters to be solved;
2. modal switching mechanism
On the robot bilateral teleoperation systemWhen the system starts to operate, all the change-over switches in the system are connected to the contact 1, namely the system works in a motion-waiting mode at the moment, and the mode continues until the prediction errors of the predictors of the master end and the slave end are smaller than the set expected value;
when the prediction errors of the master end and the slave end are smaller than the set expected value, all the change-over switches are connected to the contact 2, namely, the system works in a predictor mode at the moment and is kept until the system stops working.
Advantageous effects
The invention provides a symmetric prediction control method for robot bilateral teleoperation based on modality switching, which is a master-slave end synchronous control method for a robot bilateral teleoperation system aiming at the problems of asymmetric and time-varying time delay of master-slave communication links, unknown and uncertain gravity items of master-slave end robot dynamics modeling and the like. Based on a neural network technology (wherein the estimation capability of the neural network is used for estimating uncertain gravity terms, and the prediction capability is used for constructing a predictor kernel), an adaptive theory (used for eliminating various estimation and prediction errors), a homomorphic model prediction idea (which is an additional condition required to be met by measurement and measurement output and used for improving prediction precision) and a proportional-derivative control algorithm (used for designing a master controller and a slave controller), a symmetric prediction control strategy based on mode switching (a motion-waiting mode and a predictor mode) is provided, stable, real-time, continuous and accurate synchronous control of a master-slave robot is realized, and an expected teleoperation task is completed.
Drawings
FIG. 1: robot bilateral teleoperation system framework diagram realizing stable, real-time, continuous and accurate control based on mode switching symmetric prediction control method
Detailed Description
The invention will now be further described with reference to the following examples and drawings:
fig. 1 shows a framework diagram of a robot bilateral teleoperation system for realizing stable, real-time, continuous and accurate control by a symmetric predictive control method based on modality switching, and the corresponding implementation steps are as follows:
step 1: giving out dynamic models of the master and slave end robots, and estimating uncertainty;
step 2: constructing a main end predictor and a slave end predictor to realize symmetric prediction control;
and step 3: master and slave controllers corresponding to a "motion-wait modality" and a "predictor modality", respectively, are designed, and a modality switching-based mechanism is formulated. Finally, the above work is combined to form a symmetrical predictive control method based on mode switching.
Step 1:
the main work of the step is to provide a master-slave end dynamic model in a robot bilateral teleoperation system and estimate unknown and uncertain gravity items.
(1) Master and slave end robot dynamics models:
the dynamic models of the master and slave robots in the robot bilateral teleoperation system are given in conjunction with fig. 1:
where the subscripts m and s denote the master and slave, respectively, of the robot bilateral teleoperation system, and the corresponding notation and meaning have been given in fig. 1.
The forces of the operator and the environment are represented using a second order mass-spring-damping model and are respectively expressed as follows:
wherein k ism0、km1、km2、ks0、ks1And ks2Are all positive constants.
(2) Estimation of unknown and uncertain gravity terms:
because when there are enough neurons, the RBF neural network has the ability to approximate any continuous function with any precision. Therefore, the BRF neural network is used to estimate the unknown and uncertain gravity term, and the corresponding expression is as follows:
wherein,andis the neural network parameter θmAnd thetasEstimated value of, Lm(qm) And Ls(qs) Is the radial basis function.
Due to the existence of the estimation error, the adaptive adjustment of the parameters is realized by using an adaptive method so as to eliminate the estimation error. The adaptive design is as follows:
wherein
Step 2:
the method mainly constructs a master-slave end predictor, wherein the architectures of the master-slave end predictor and the slave-slave end predictor are consistent, so the method is called as symmetric prediction control.
Designing a symmetric predictor:
(a) predictor core (prediction ability using RBF neural network)
Wherein,the corresponding adaptation law is:
wherein,
(b) homomorphic model (idea of homomorphic model combination)
Wherein
(c)Andestimate term (using the estimating ability of RBF neural network)
The self-adaptive law of the RBF neural network estimation parameters corresponding to the estimation function is as follows:
wherein
The items (a), (b) and (c) are integrated to provide a design method of the master predictor and the slave predictor, wherein (a) provides a prediction output expression, and (b) and (c) represent conditions required to be met by a prediction output equation, so that the construction mode can greatly improve the prediction accuracy.
And step 3:
the main work of the step is to design a master controller and a slave controller and to make a switching strategy based on mode switching. Finally, the above work is combined to form a symmetrical predictive control method based on mode switching.
(1) Master and slave controller design
Due to the bilateral teleoperation system of the robot shown in fig. 1, the robot works in two different modes: the "motion-waiting modality" and the "predictor modality". Therefore, the design is also divided in the master and slave control designs corresponding to the "motion-wait modality" and the "predictor modality", respectively.
"motion-waiting mode" controller design
'predictor modality' controller design
(2) Switching mechanism based on modality switching
A mode switching mechanism:
the step mainly provides a switching mechanism based on mode switching, so that the limited window time in the bilateral teleoperation of the robot is utilized to the maximum degree, and energy and resources are saved. The specific modality switching mechanism is as follows:
when the robot bilateral teleoperation system shown in fig. 1 starts to run, all the change-over switches (Switch) in fig. 1 are connected to the contact 1, and the system works in a motion-waiting mode which continues until the prediction errors of the predictors at the master end and the slave end are smaller than the set expected value;
when the prediction error of the master end and the slave end is smaller than the set expected value, all the change-over switches (Switch) are connected to the contact 2, and the system works in a predictor mode until the system stops working.
Claims (1)
1. A robot bilateral teleoperation symmetric prediction control method based on modality switching is characterized by comprising the following steps:
step 1:
1) establishing a dynamic model of the master-slave end robot
Wherein, subscripts m and s respectively represent a master end and a slave end of the robot bilateral teleoperation system, and qmAnd q issRespectively representing the angular displacement of the joints of the robot at the master end and the slave end,andrespectively representing the angular velocity of the joints of the robot at the master end and the slave end,andrespectively representing angular accelerations, M, of joints of the robot at the master end and at the slave endm(qm) And Ms(qs) Respectively representing the symmetric positive definite inertia matrixes of the robot at the master end and the robot at the slave end,andrepresenting centrifugal and Copenforces terms, G, of the master and slave robots, respectivelym(qm) And Gs(qs) Representing the gravity terms, τ, of the master and slave robots, respectivelymAnd τsIndicating the control moments of the master and slave robots, respectively, FhAnd FeRepresenting the effort of the operator and the environment, respectively;
the forces of the operator and the environment are respectively expressed as follows:
wherein k ism0、km1、km2、ks0、ks1And ks2Are all any given positive definite constant;
2) unknown, uncertain gravity term estimation:
assuming the gravity term G of the master and slave robotsm(qm) And Gs(qs) The method is unknown and uncertain, and a BRF neural network is adopted to estimate the unknown and uncertain, and the corresponding expressions are as follows:
wherein,andeach represents Gm(qm) And Gs(qs) Is determined by the estimated value of (c),andthe parameters theta of the main end neural network and the slave end neural network are respectivelymAnd thetasEstimated value of, Lm(qm) And Ls(qs) The radial basis functions correspond to the RBF neural networks at the master end and the slave end;
gravity item G due to master and slave robotsm(qm) And Gs(qs) May vary due to structural or environmental changes. Therefore, toThe estimation precision of the RBF neural networks is improved, and corresponding adaptive laws are respectively designed for the RBF neural networks of the master end and the slave end as follows:
wherein,andrespectively represent thetamAnd thetasOf the estimated error of ΓmAnd ΓsRespectively any given symmetric positive definite matrix;
step 2, constructing a master-slave end predictor:
(a) and (3) prediction output of the master end predictor and the slave end predictor:
wherein,andrepresenting the prediction outputs of the master and slave predictors respectively,andprimary and secondary predictor parameters w, respectivelysAnd wmThe predicted value of (a) is determined,andis the prediction function corresponding to the predictor of the master end and the slave end,andthe prediction output error of the predictor of the master end and the slave end is respectively expressed. Prediction output of main and slave end predictorAndthe conditions to be satisfied are as follows:
the homomorphic model corresponding to the master robot is as follows:
the homomorphism model corresponding to the slave robot is as follows:
wherein,andrespectively representing symmetrical positive definite inertia matrixes of homomorphic models corresponding to the master-end robot and the slave-end robot,andrespectively representing the centrifugal force and the Copenforces of the homomorphic models corresponding to the master end robot and the slave end robot,andgravity terms representing homomorphic models corresponding to master-end and slave-end robots respectivelyAndis determined by the estimated value of (c),andrespectively representing the control moments of homomorphic models corresponding to the master-end robot and the slave-end robot,andrepresenting the forces of the operator and the environment in the corresponding homomorphic model, respectively.
(b) Designing corresponding adaptive laws for the master predictor and the slave predictor as follows:
wherein,andrespectively represents wmAnd wsAnd are respectively Andrespectively any given symmetric positive definite matrix;
and step 3:
1) master and slave controller design
"motion-waiting mode" controller design
wherein alpha ism，βm，αsand betasThe controller parameters to be solved;
'predictor mode' controllerDesign of
wherein alpha ism，βm，αsand betasThe controller parameters to be solved;
2) mode switching mechanism
When the robot bilateral teleoperation system starts to run, all the change-over switches in the system are connected to the contact 1, namely the system works in a motion-waiting mode at the moment, and the mode continues until the prediction errors of the predictors of the master end and the slave end are smaller than the set expected value;
when the prediction errors of the master end and the slave end are smaller than the set expected value, all the change-over switches are connected to the contact 2, namely, the system works in a predictor mode at the moment and is kept until the system stops working.
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