CN112947066A - Manipulator improved finite time inversion control method - Google Patents
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Abstract
The invention discloses a manipulator improved finite time reversal control method, which comprises the following steps: s1, building a manipulator power model; s2, designing a manipulator finite time reversal controller; and S3, inverting the unknown state in the controller by using fuzzy neural network approximation. The control method of the invention utilizes the finite time inversion method to design the manipulator controller, so that the position of each joint of the manipulator reaches the expected target position, and adopts the structure and parameter full-regulation fuzzy neural network structure to design the self-adaptive fuzzy neural network control system, thereby overcoming the defect that the finite time inversion control strategy needs the accurate information of the system and further improving the robustness of the system.
Description
Technical Field
The invention relates to a manipulator control technology, in particular to an improved finite time reversal control method for a manipulator.
Background
The manipulator is a mechanical device which has the action function similar to that of a human arm, can grab and place objects in space or perform other operations, can replace heavy labor of people to realize mechanization and automation of production, can operate in a harmful environment to protect personal safety, and is widely applied to departments of mechanical manufacturing, metallurgy, electronics, light industry, atomic energy and the like.
Due to the fact that factors such as uncertainty of joint parameters of the manipulator exist, model uncertainty exists in a manipulator dynamic model, and the manipulator control accuracy based on model control is affected. At present, aiming at the problem that uncertainty factors such as model uncertainty and external interference influence the control precision of a manipulator joint, the uncertainty factors are mostly approximated or compensated through an uncertainty estimator, and the adaptive law of parameters in the estimator is determined through stability analysis. A neural network and a fuzzy system with universal approximation capability are widely applied to uncertainty approximation compensation, but the controller is used for manipulator control and needs to estimate system uncertainty upper bound information in advance, so that certain limitations exist.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a manipulator improved finite time reversal control method.
In order to achieve the purpose, the invention adopts the technical scheme that: a manipulator improved finite time reversal control method comprises the following steps:
step 1, building a mechanical arm power model
Consider a rigid manipulator with n joints, whose kinetic equation is:
wherein the ratio of q,the position, speed and acceleration vector of each joint of the manipulator, M (q) epsilon Rn×nIn order to include an inertial matrix of the additional mass,g (q) e R as a coupling matrix of centrifugal force and Coriolis forcen×1In the form of a gravity matrix vector,for friction torque, τd∈Rn×1For unknown applied interference, tau epsilon Rn×1For the control input of the movements of the individual joints,n is the joint degree of freedom;
step 2, designing a manipulator finite time reversal controller
2.1 define the tracking error as
e1=q-qd (2)
Wherein q isdIs the desired position of the joint;
derived from (2)
Selecting virtual control quantities
Wherein c is1Is a non-zero normal number;
taking the Lyapunov function as
If e2When the value is equal to 0, thenThe stability requirement is met, and the design is continued;
2.1 e2can be expressed as
Then designing a nonsingular terminal sliding mode surface as
Wherein λ is1>0 is a constant, p1,p2Is odd number, 1<p2/p1<2,
Further, we can get
The finite time inversion controller can be designed as
τBSC=u1+u2 (10)
Wherein, tauM≥||τd||;
S3, aiming at the formula (10), approximating the unknown state in the inversion controller by using the fuzzy neural network, and defining the input-output relationship of the fuzzy neural network as follows:
lk(k=1,...,Ny) The k-th output of the rule layer is represented,representing the connection weight matrix between the fuzzification layer and the regular layer, here taken as the unit vector, NyIs the total number of rule layers.
In a specific embodiment, the fuzzy neural network consists of an input layer, a fuzzy inference layer and an output layer, wherein the network input is the tracking deviation e1Output as control force tauFNNThe signal propagation and the function of each layer in the fuzzy neural network are represented as follows:
a first layer: input layer
Each node of the layer is directly connected with each component of the input quantity, and the input quantity is transmitted to the next layer;
a second layer: blurring layer
A gaussian-type function is used as the membership function,representing tracking offset vector e1The elements (A) and (B) in (B),andthe centre vector and the base width of the membership function of the ith input variable, the jth fuzzy set, respectively, i.e.
Easy to calculate, using NpiRepresenting individual numbers of membership functions, and we define the sets of adaptive parameter vectors b and c representing all the basis width and center vectors of Gaussian membership functions, respectively, i.e. WhereinRepresenting the total number of membership functions;
and a third layer: rule layer
The layer adopts a fuzzy inference mechanism, and the output of each node is the product of all input signals of the node, namely
In the formula Ik(k=1,...,Ny) The k-th output of the rule layer is represented,representing the connection weight matrix between the fuzzification layer and the regular layer, here taken as the unit vector, NyIs the total number of rules;
a fourth layer: output layer
The nodes of the layer represent output variables, each node yo (o 1.., N)o) The output of (2) being the sum of all input signals of the node, i.e.
Further, the input-output relationship of the fuzzy neural network is defined as follows:
as a specific embodiment, the fuzzy neural network includes two self-regulation strategies of data learning and data deletion, specifically:
data learning strategy
The fuzzy rule is determined step by step based on the condition thatWhere ψ is a spherical potential energy, representing the novelty of the input data, given by the following equation:
when a new fuzzy rule needs to be added, its parameters are initialized,
where k is the overlap parameter of a pre-given fuzzy rule,
data deletion policy
According to the universal approximation theory, there is an optimal control forceSatisfy the requirement of
Where ε is the minimum reconstruction error vector, W*,b*And c*Of W, b and c, respectivelyOptimizing parameters;
the output control force of the fuzzy neural network is assumed to be of the following form:
wherein the content of the first and second substances,andare respectively W*,b*And c*Is determined by the estimated value of (c),
defining an approximation error:
using Taylor series expansion, one can obtain
Wherein the content of the first and second substances,b*and c*Is the optimum value for b and c,andis b*And c*Estimated value of, OnvIs a high-order term of the magnetic field,
then (25) is substituted into (24) to obtain
further, the formula (9) can be rewritten as
the self-adaptive law of the weight, the base width and the central vector of the designed fuzzy neural network can be designed as follows:
wherein S isiIs an element of S, betaiIs an element in beta, sigmaω,σb,σcIs a normal number, and is,is thatIs determined by the estimated value of (c),is omegaiIs determined to be the optimum value of (c),
defining the lyapunov function as
Derivative and substitute (27) into
Bringing (28) - (30) into (32) to obtain
If τ is satisfiedMAnd the designed manipulator improved finite time inversion control method is stable.
Due to the application of the technical scheme, compared with the prior art, the invention has the following advantages: according to the manipulator-improved finite-time inversion control method, the adaptive fuzzy neural network control system is designed to approach the finite-time inversion controller by adopting the structure and parameter full-regulation fuzzy neural network structure, and the limit that uncertain functions and interference upper bound need to be predicted is relaxed, so that the defect that a finite-time inversion control strategy needs system accurate information is overcome, and the system robustness is further improved.
Drawings
FIG. 1 is a block diagram of a manipulator-improved finite time inversion control method according to the present invention.
Detailed Description
The technical solution of the present invention is further explained with reference to the drawings and the specific embodiments.
A manipulator improved finite time reversal control method comprises the following steps:
firstly, building a mechanical hand power model
Consider a rigid manipulator with n joints, whose kinetic equation is:
wherein the ratio of q,the position, speed and acceleration vector of each joint of the manipulator, M (q) epsilon Rn×nIn order to include an inertial matrix of the additional mass,g (q) e R as a coupling matrix of centrifugal force and Coriolis forcen×1In the form of a gravity matrix vector,for friction torque, τd∈Rn×1For unknown applied interference, tau epsilon Rn×1N is the degree of freedom of the joint;
(II) finite time reversal controller of design manipulator
The control object of the robot position tracking control system is to design an appropriate control input τ so that each joint position q of the robot tracks the desired position qdThe design steps are as follows:
2.1 define the tracking error as
e1=q-qd (2)
Derived from (2)
Selecting virtual control quantities
Wherein c is1Is a non-zero normal number;
taking the Lyapunov function as
If e2When the value is equal to 0, thenThe stability requirement is met, so the design is required to be continued;
2.1 e2can be expressed as
Then designing a nonsingular terminal sliding mode surface as
Wherein λ is1>0 is a constant, p1,p2Is odd number, 1<p2/p1<2,
Further, we can get
The finite time inversion controller can be designed as
τBSC=u1+u2 (10)
Wherein, tauM≥||τd||;
Constructing a Lyapunov function as
To V2Performing a time derivative may yield:
by substituting formula (9) for formula (14)
From the above formula, the designed manipulator finite time reversal controller is stable. However, the above-mentioned controllers require detailed system information and an upper uncertainty bound τ in practical systemsMIt is difficult to determine, and these problems indicate that the above controller is difficult to implement in practical applications. Therefore, to overcome these problems, an improved finite time inversion controller has been proposed.
(III) design mechanical arm improved finite time inversion controller
The designed manipulator-improved finite-time inversion controller adopts a fuzzy neural network to approximate the inversion controller (10), thereby overcoming the defect that the inversion controller needs detailed system information. Fuzzy neural network adoptedThe network tracking error detection method is composed of an input layer, a fuzzy inference layer and an output layer, wherein the network input is tracking error e1Output as control force tauFNN. The signal propagation and the function of the layers in the fuzzy neural network are represented as follows:
a first layer: input layer
Each node of the layer is directly connected with each component of the input quantity, and the input quantity is transmitted to the next layer;
a second layer: blurring layer
A gaussian-type function is used as the membership function,representing tracking offset vector e1The elements (A) and (B) in (B),andthe centre vector and the base width of the membership function of the ith input variable, the jth fuzzy set, respectively, i.e.
Easy to calculate, using NpiRepresenting individual numbers of membership functions, and we define the sets of adaptive parameter vectors b and c representing all the basis width and center vectors of Gaussian membership functions, respectively, i.e. WhereinRepresenting the total number of membership functions;
and a third layer: rule layer
The layer adopts a fuzzy inference mechanism, and the output of each node is the product of all input signals of the node, namely
In the formula Ik(k=1,...,Ny) The k-th output of the rule layer is represented,representing the connection weight matrix between the fuzzification layer and the regular layer, here taken as the unit vector, NyIs the total number of rules;
a fourth layer: output layer
The nodes of the layer represent output variables, each node yo(o=1,...,No) The output of (2) being the sum of all input signals of the node, i.e.
Further, the input-output relationship of the fuzzy neural network is defined as follows:
y=[y1 y2…yNo]=τFNN=Wl
particularly, the meta-cognition fuzzy neural model provided by the invention considers two self-regulation strategies of data learning and data deletion, and is beneficial to efficiently executing a real-time control task.
First is a data learning strategy. The data learning process of the meta-cognitive fuzzy neural network involves online evolution and parameter updating of rules closest to current input data.
The fuzzy rule is based on the following conditionsDetermined stepwise, i.e.And psi<EsWhere ψ is a spherical potential energy, representing the novelty of the input data, given by the following equation:
when a new fuzzy rule needs to be added, its parameters are initialized,
where κ is an overlap parameter of a predefined fuzzy ruleWhen the time is needed, the rule parameters are adjusted,
in the learning process, the contribution degree of a certain rule to the output may be reduced. In this case, insignificant rules should be removed from the rule base to avoid overcomputing. The degree of contribution of the qth rule is given by the following equation:
If the contribution degree of the q rule to the input is lower than the threshold value EpThen the rule is deleted.
Data deletion policy
Specifically, when the current tracking error is close to the error in the last iterative calculation process of the neural network, network parameters do not need to be updated, excessive learning is avoided, and the calculation burden is reduced.
According to the universal approximation theory, there is an optimal control forceSatisfy the requirement of
Where ε is the minimum reconstruction error vector, W*,b*And c*Optimal parameters of W, b and c respectively;
the output control force of the fuzzy neural network is assumed to be of the following form:
wherein the content of the first and second substances,andare respectively W*,b*And c*Is determined by the estimated value of (c),
defining an approximation error:
using Taylor series expansion, one can obtain
Wherein the content of the first and second substances,b*and c*Is the optimum value for b and c,andis b*And c*Estimated value of, OnvIs a high-order term of the magnetic field,
then (25) is substituted into (24) to obtain
further, the formula (9) can be rewritten as
the self-adaptive law of the weight, the base width and the central vector of the designed fuzzy neural network can be designed as follows:
wherein S isiIs an element of S, betaiIs an element in beta, sigmaω,σb,σcIs a normal number, and is,is thatIs determined by the estimated value of (c),is omegaiIs determined to be the optimum value of (c),
defining the lyapunov function as
Derivative and substitute (27) into
Bringing (28) - (30) into (32) to obtain
If τ is satisfiedMAnd the designed manipulator improved finite time inversion control method is stable.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and the purpose thereof is to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the protection scope of the present invention. All equivalent changes and modifications made according to the spirit of the present invention should be covered within the protection scope of the present invention.
Claims (3)
1. A manipulator improved finite time reversal control method is characterized by comprising the following steps:
step 1, building a mechanical arm power model
Consider a rigid manipulator with n joints, whose kinetic equation is:
wherein the ratio of q,the position, speed and acceleration vector of each joint of the manipulator, M (q) epsilon Rn×nIn order to include an inertial matrix of the additional mass,g (q) e R as a coupling matrix of centrifugal force and Coriolis forcen×1In the form of a gravity matrix vector,for friction torque, τd∈Rn×1For unknown applied interference, tau epsilon Rn×1N is the degree of freedom of the joint;
step 2, designing a manipulator finite time reversal controller
2.1 define the tracking error as
e1=q-qd (2)
Wherein q isdIs the desired position of the joint;
derived from (2)
Selecting virtual control quantities
Wherein c is1Is a non-zero normal number;
taking the Lyapunov function as
If e2When the value is equal to 0, thenThe stability requirement is met, and the design is continued;
2.1 e2can be expressed as
Then designing a nonsingular terminal sliding mode surface as
Wherein λ is1>0 is a constant, p1,p2Is odd number, 1<p2/p1<2,
Further, we can get
The finite time inversion controller can be designed as
τBSC=u1+u2 (10)
Wherein, tauM≥||τd||;
S3, aiming at the formula (10), approximating the unknown state in the inversion controller by using the fuzzy neural network, and defining the input-output relationship of the fuzzy neural network as follows:
2. The manipulator-improved finite time reversal control method according to claim 1, wherein the fuzzy neural network is composed of an input layer, a fuzzy inference layer and an output layer, wherein the network input is a tracking deviation e1Output as control force tauFNNThe signal propagation and the function of each layer in the fuzzy neural network are represented as follows:
a first layer: input layer
Each node of the layer is directly connected with each component of the input quantity, and the input quantity is transmitted to the next layer;
a second layer: blurring layer
A gaussian-type function is used as the membership function,representing tracking offset vector e1The elements (A) and (B) in (B),andthe centre vector and the base width of the membership function of the ith input variable, the jth fuzzy set, respectively, i.e.
Easy to calculate, using NpiRepresenting individual numbers of membership functions, and we define the sets of adaptive parameter vectors b and c representing all the basis width and center vectors of Gaussian membership functions, respectively, i.e. WhereinRepresenting the total number of membership functions;
and a third layer: rule layer
The layer adopts a fuzzy inference mechanism, and the output of each node is the product of all input signals of the node, namely
In the formula Ik(k=1,...,Ny) The k-th output of the rule layer is represented,representing the connection weight matrix between the fuzzification layer and the regular layer, here taken as the unit vector, NyIs the total number of rules;
a fourth layer: output layer
The nodes of the layer represent output variables, each node yo(o=1,...,No) The output of (2) being the sum of all input signals of the node, i.e.
Further, the input-output relationship of the fuzzy neural network is defined as follows:
3. the manipulator-improved finite time reversal control method according to claim 2, wherein the fuzzy neural network comprises two self-regulation strategies of data learning and data deletion, specifically:
data learning strategy
The fuzzy rule is determined step by step based on the condition thatAnd psi<EsWhere ψ is a spherical potential energy, representing the novelty of the input data, given by the following equation:
when a new fuzzy rule needs to be added, its parameters are initialized,
where k is the overlap parameter of a pre-given fuzzy rule,
data deletion policy
According to the universal approximation theory, there is an optimal control forceSatisfy the requirement of
Where ε is the minimum reconstruction error vector, W*,b*And c*Optimal parameters of W, b and c respectively;
the output control force of the fuzzy neural network is assumed to be of the following form:
wherein the content of the first and second substances,andare respectively W*,b*And c*Is determined by the estimated value of (c),
defining an approximation error:
using Taylor series expansion, one can obtain
Wherein the content of the first and second substances,b*and c*Is the optimum value for b and c,andis b*And c*Estimated value of, OnvIs a high-order term of the magnetic field,
then (25) is substituted into (24) to obtain
further, the formula (9) can be rewritten as
the self-adaptive law of the weight, the base width and the central vector of the designed fuzzy neural network can be designed as follows:
wherein S isiIs an element of S, betaiIs an element in beta, sigmaω,σb,σcIs a normal number, and is,is thatIs determined by the estimated value of (c),is omegaiIs determined to be the optimum value of (c),
defining the lyapunov function as
Derivative and substitute (27) into
Bringing (28) - (30) into (32) to obtain
If τ is satisfiedMAnd the designed manipulator improved finite time inversion control method is stable.
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Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114844430A (en) * | 2022-06-06 | 2022-08-02 | 苏州泰科贝尔直驱电机有限公司 | Fuzzy neural network control method for magnetic suspension switched reluctance motor |
Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108227504A (en) * | 2018-01-25 | 2018-06-29 | 河海大学常州校区 | Microthrust test fractional order adaptive fuzzy nerve inverting TSM control method |
CN108284442A (en) * | 2017-01-24 | 2018-07-17 | 中国北方车辆研究所 | A kind of mechanical arm flexible joint control method based on fuzzy neural network |
CN108828961A (en) * | 2018-09-18 | 2018-11-16 | 河海大学常州校区 | Active Power Filter-APF sliding-mode control based on metacognition fuzzy neural network |
CN109103884A (en) * | 2018-09-18 | 2018-12-28 | 河海大学常州校区 | Active Power Filter-APF back stepping control method based on metacognition fuzzy neural network |
CN109103885A (en) * | 2018-09-18 | 2018-12-28 | 河海大学常州校区 | Active Power Filter-APF metacognition fuzzy Neural Network Control Method |
CN110877333A (en) * | 2019-04-12 | 2020-03-13 | 国网宁夏电力有限公司电力科学研究院 | Flexible joint mechanical arm control method |
-
2021
- 2021-01-26 CN CN202110105530.2A patent/CN112947066B/en active Active
Patent Citations (6)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN108284442A (en) * | 2017-01-24 | 2018-07-17 | 中国北方车辆研究所 | A kind of mechanical arm flexible joint control method based on fuzzy neural network |
CN108227504A (en) * | 2018-01-25 | 2018-06-29 | 河海大学常州校区 | Microthrust test fractional order adaptive fuzzy nerve inverting TSM control method |
CN108828961A (en) * | 2018-09-18 | 2018-11-16 | 河海大学常州校区 | Active Power Filter-APF sliding-mode control based on metacognition fuzzy neural network |
CN109103884A (en) * | 2018-09-18 | 2018-12-28 | 河海大学常州校区 | Active Power Filter-APF back stepping control method based on metacognition fuzzy neural network |
CN109103885A (en) * | 2018-09-18 | 2018-12-28 | 河海大学常州校区 | Active Power Filter-APF metacognition fuzzy Neural Network Control Method |
CN110877333A (en) * | 2019-04-12 | 2020-03-13 | 国网宁夏电力有限公司电力科学研究院 | Flexible joint mechanical arm control method |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN114844430A (en) * | 2022-06-06 | 2022-08-02 | 苏州泰科贝尔直驱电机有限公司 | Fuzzy neural network control method for magnetic suspension switched reluctance motor |
CN114844430B (en) * | 2022-06-06 | 2024-03-01 | 苏州泰科贝尔直驱电机有限公司 | Fuzzy neural network control method for magnetic suspension switch reluctance motor |
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