CN106406085A - Space manipulator trajectory tracking control method based on cross-scale model - Google Patents
Space manipulator trajectory tracking control method based on cross-scale model Download PDFInfo
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Abstract
The invention provides a space manipulator trajectory tracking control method based on a cross-scale model. In the case of analyzing the parameter and nonparameter cross-scale characteristics existed during the modeling of a free-floating space manipulator system, the manipulator joint space is subjected to real-time online tracking control. The control method introduces the radial basis neural network to approximate the variation item of the cross-scale characteristic in the dynamic model of the space manipulator and effectively inhibits the influence of the variation item on the system by means of the learning ability of the neural network, and designs the adaptive law to adjust the weight of the neural network in real time and performs the simulation verification by taking the plane two-connecting-rod space manipulator as an example, thereby realizing the fast and accurate tracking of the desired trajectory in the joint space of the space manipulator.
Description
Technical field
The invention belongs to Based Intelligent Control and technical field of system simulation, especially relate to a kind of based on the sky across Scale Model
Room machine arm Trajectory Tracking Control method.
Background technology
With the continuous development of space technology, space probation activity extends further.But space environment has microgravity, height
The features such as vacuum, intense radiation, big temperature difference, in so dangerous environment, assisted using space manipulator or replace spaceman
The arduous dangerous task in a large number that completes becomes the consistent target of each space-faring state in the world.
A significant difference with ground machine arm is the pedestal of space manipulator is motion, is a kind of sufficiently complex
The close coupling nonlinear and time-varying system of Multiple input-output is so that the control problem of space manipulator is compared with ground machine arm
Have the characteristics that many new.Being mainly manifested in parameter and nonparametric across scale feature and become in Space Manipulator System mathematical model
Change across yardstick.Parameter be mainly manifested in, across scale feature, kinetics, the kinematics parameters being difficult to be accurately obtained, such as each several part
Centroid position, rotary inertia, load quality etc., meanwhile, also a lot of low order time-varying parameters, such as with fuel consumption susceptor mass
Change etc..Non-parametric cannot be described with Stationary Parameter across scale feature, such as mechanical arm low-speed motion when non-linear friction
Power, mesomerism and some high-order unmodeled dynamiocs etc..Some can reach the controlling party of better effects for ground machine arm
Method, may not be applied to space manipulator.For Trajectory Tracking Control problem, the control method commonly used at present mainly have PID control,
Self Adaptive Control, robust control and Based Intelligent Control.
The control problem of space-based robot system is transformed into joint space by inertial space by Parlaktuna and Ozkan
Interior, after obtaining the linearizing kinetics equation of Space Manipulator System parameter, devise Free-floating in a kind of joint space empty
The PD control method of room machine arm track following[1].But PID control belongs to linear control method, have ignored space manipulator system
Non-linear factor in system and external interference, meanwhile, PID control generally requires larger control energy, is not suitable for following the tracks of essence
Degree requires higher situation.So when Space Manipulator System has parameter or nonparametric is across scale feature, control effect is simultaneously
Undesirable.
Wang H and Xie Y devises a kind of recursion self-adaptation control method for space-based robot system, using ginseng
Number adaptive law real-time estimation control parameter[2]；Liang Jie, Chen Li devise a kind of additional adaptive mode of nominal computed moment control
Paste compensates the composite control method controlling, can be efficiently against the impact of Space Manipulator System unknown parameter[3]；Zhang Fuhai
Etc. a kind of self adaptation Trajectory Tracking Control method devising in cartesian space, while ensureing that inertia matrix is reversible, and
Can be with real-time estimation control parameter[4].But above-mentioned self-adaptation control method only effectively overcomes Parameters variation to space mechanism
The impact of arm system, when free floating space manipulator systems exist external disturbance etc. non-parametric across scale feature when, merely
It is difficult to ensure that the stability of Space Manipulator System using self-adaptation control method, need and other advanced control strategy phase knots
Close and improve Space Manipulator System robustness.
Xie Limin etc. is directed to limited by space manipulator joint control input torque amplitude and Space Manipulator System exists not
Determine the complex situations of parameter, devise a kind of robust and adaptive composite control method, robust adaptive tune is carried out to parameter
Section[5]；Pazelli etc. is directed to the free floating space manipulator systems that there is parametric variations and external disturbance, to all kinds of non-thread
Property H∞Control method is researched and analysed[6].But above-mentioned robust control method is design based on the priori upper bound,
It is a kind of more conservative control strategy of ratio, be not therefore Optimal Control.
Guo Yishen and Chen Li utilizes radial base neural net it is proposed that a kind of self adaptation without Manipulator Dynamic
Neural network control method[7], but be not discussed model and there is solution when scale feature；Xie Jian etc. proposes one
Plant the Neural Network Adaptive Control method for space-based robot system, non-by radial base neural net approximate model
Linear function and upper bound, the adaptive control laws of proposition ensure that the boundedness of weights[8]But, designed from
Adapt to rule complex, affect calculating speed；Zhang Wenhui etc. devises a kind of radial base neural net Robust Adaptive Control side
Method, is applied to free floating space manipulator systems[9], free floating space manipulator systems limited by control moment for the thunderclap,
Devise a kind of Neural Network Adaptive Control method[10], but both approaches are directed to the compensation rule designed by Parameters variation
Contain the full detail of kinetic model, the wherein nominal section of model is Given information, belongs to redundancy portion in compensating rule
Point.
Content of the invention
It is an object of the invention to provide a kind of Neural Network Adaptive Control method, in kinetic model, there is parameter
And nonparametric is across the Space Manipulator System of yardstick, realize the quick accurate tracking to desired trajectory for the joint space.
For achieving the above object, the present invention provide a kind of based on the space manipulator Trajectory Tracking Control side across Scale Model
Method it is characterised in that：Design Neural Network Adaptive Control rule, will exist across yardstick in Space Manipulator System kinetic model
The change item of feature is expressed asUsing neutral net, change item f is approached, thus realizing to change
Change the compensation of item f, ANN Control is restrained and isV is for overcoming nerve net
The robust item of network approximate error, its value is v=Kvsgn(r)；Error function isNeutral net
Form is radial base neural net, and the input of radial base neural net takesPreferably approximate algorithm isThen network is output asRadial base neural net weights are adjusted
Whole adaptive law isD0、C0For the nominal plant model of object, D0=D- Δ D, C0=C- Δ C, Δ D, Δ C are to build
Mould error matrix, d is summation disturbance, e=qd- q andIt is respectively joint angle tracking error and angular velocity tracking error,
qdIt is respectively expectation and actual joint vector, K with qP、KIIt is positive certainty ratio and storage gain matrix respectively, KvFor robust term system
Number,For the weight vector of neutral net,For the output vector of Gaussian bases, ciFor i-th of network
The center vector of node, biBase width parameter for node i.
Compared with prior art the invention has the beneficial effects as follows：
In view of the modeling error in Space Manipulator System and external interference, with radial base neural net to space mechanism
Exist in arm system kinetic model and carry out online approximating across the parameter of scale feature and nonparametric item f, f only includes modeling error
ΔD(q)、And unknown disturbancesKnown nominal model need not be considered.Study energy using neutral net
Power, effectively inhibits parameter and nonparametric impact to Space Manipulator System across dimensional variation, adaptive law can on-line tuning
Neural network weight, it is ensured that the boundedness of weights, solves the problems, such as unknown upper bound bounded.
Simulation result of the present invention draws, in 2s, rail is expected in the angular displacement in joint 1 and joint 2, the rapid tracking of angular velocity
Mark.Joint angle tracking error is stablized ± 5 × 10-3Within rad, joint angle velocity error is stablized ± 5 × 10-3Rad/s with
Interior, realize the quick accurate tracking to desired trajectory in space manipulator joint space.
Brief description
Fig. 1 is plane 2 connecting rod space manipulator illustraton of model；
Fig. 2 is Neural Network Adaptive Control method structured flowchart；
Fig. 3 is the time dependent curve of joint 1 angleonly tracking；
Fig. 4 is the time dependent curve of joint 2 angleonly tracking；
Fig. 5 is that joint 1 angular velocity follows the tracks of time dependent curve；
Fig. 6 is that joint 2 angular velocity follows the tracks of time dependent curve；
Fig. 7 is the time dependent curve of joint 1 joint angle tracking error；
Fig. 8 is the time dependent curve of joint 2 joint angle tracking error；
Fig. 9 is the time dependent curve of joint 1 joint angle speed Tracking error；
Figure 10 is the time dependent curve of joint 2 joint angle speed Tracking error；
Figure 11 is the time dependent curve of control moment in joint 1；
Figure 12 is the time dependent curve of control moment in joint 2；
Wherein：∑ I, inertial coodinate system；∑ 0, moving base coordinate system；O, inertial coodinate system initial point；CM, space manipulator
The total barycenter of system；Bi, rigid body i, i-th connecting rod of mechanical arm；B0, moving base, connecting rod 0；Ci, the barycenter of connecting rod i；C0, pedestal
Barycenter；ri∈R2, the position vector of connecting rod i barycenter；r0, the position vector of pedestal barycenter；rc∈R2, Space Manipulator System matter
The position vector of heart CM；pi∈R2, the position vector of connecting rod i；ai, from joint JiVector to connecting rod i barycenter；bi, from connecting rod i matter
The heart is to joint Ji+1Vector；b0, from pedestal barycenter to joint J1Vector.
Specific embodiment
Plane 2 connecting rod space manipulator model such as Fig. 1, by the moving base B that can freely float0With two armed lever B1、B2Group
Become.
Space Manipulator System items kinetic parameter as described by table 1, by initial position and attitude angle and the company of pedestal
Bar 1, the vector of the initial attitude angle composition of connecting rod 2 are [qb,qs]T=[x, y, q0,q1,q2]T, pedestal and connecting rod 1, connecting rod 2
Initial velocity vector isThe initial value of parameters and desired trajectory is as shown in table 2.
Table 1 plane 2 connecting rod Space Manipulator System parameter list
Table 2 space manipulator Neural Network Adaptive Control emulates initial value
Setting control parameter is KP=diag { 100,100,100,100,100 }, KI=diag 250,250,250,250,
250 }, Kv=0.2, FW=diag { 0.0005,0.0005,0.0005,0.0005,0.0005 }, according to the control method knot of Fig. 2
Structure block diagram carries out simulating, verifying, the present invention with plane 2 connecting rod free floating space manipulator systems as object of study, kinetics equation
For
Wherein, q=[q1q2]TFor joint angular displacement, D (q)5×5For the inertial matrix of space manipulator,Table
Show the matrix including non-linear centrifugal force and coriolis force, τ is control moment.
The kinetics equation of space manipulator meets following property：
Property 1 inertial matrix D (q) is symmetrical, positive definite, bounded matrix.
It is suitable that property 2 selectsCan make D (q) andMeet
There is k in property 3c＞ 0 and positive definite integral formSo that
Property 4 assigned error matrix meets Δ Dl≤||ΔD||≤ΔDh, Δ Cl≤||ΔC||≤ΔCh, wherein h and l
It is respectively upper and lower dividing value.
In order to realize the quick accurate tracking to desired trajectory in joint space, also need during modeling to consider space manipulator system
There is parameter with nonparametric across scale feature in system.Introduce external disturbance, can be so that kinetics equation to be rewritten as following form
Wherein,It is summation disturbance, including moment of friction disturbance and other external disturbances.
In Practical Project, the realistic model of object hardly results in, cannot obtain accurate D (q),Can only
Set up preferable nominal plant model.The shape that kinetics equation is write as ideal model and be there is the change item sum across scale feature
Formula, then can be expressed as
Wherein, D0、C0For the nominal plant model of object, D0(q)=D (q)-Δ D (q),
ΔD(q)、For error matrix,It is to exist across scale feature including modeling error and external disturbance moment etc.
Parameter and nonparametric item, be a unknown nonlinear time-varying function, concrete form is
Because D (q) is reversible, can obtain
Design error function is
Wherein, e=qd- q andIt is respectively joint angle tracking error and acceleration tracking error, qdWith q respectively
For expectation and actual joint vector, KP、KIIt is positive certainty ratio and storage gain matrix respectively.
Derivation can obtain
OrderDerive Equivalent control law
Therefore obtaining stable closed loop system is
For nominal plant model, design of control law is
In view of unknown disturbances it can be deduced that
?
As can be seen here, there is the change item across scale feature in model is
F is approached using radial base neural net, network inputs takeThen network is output as
Then
Design control law is
Wherein, v=KvSgn (r) is robust item, for the impact overcoming neutral net approximate error to cause.
Defining Lyapunov function is
Wherein, D and FWFor positively definite matrix, that is,Then V is positive definite.
Derivation, and binding property 2 can obtain
Arrangement can obtain
According to conditionIn view of v=KvSgn (r), can obtain
TakeFollowing adaptive law then can be designed to adjust the weights of radial base neural net
Then can obtain
Describe for convenience, definition
Then
K be can use by property 3 and property 4vIn＞ | Q |, wherein In=[1,1 ..., 1]T∈Rn, then
The input of control structure is the desired trajectory of joint angle, and the actual value of output joint angle is as negative feedback and joint angle
Actual value make comparisons, according to the tracking error of joint angle, error function and robust item design Neural Network Adaptive Control system
System, simulation result is as shown in Fig. 3-Figure 12.
The angle in joint 1 and joint 2 changes over curve such as Fig. 3, Fig. 4, angular velocity change over curve such as Fig. 5,
Fig. 6.Redness dotted line therein represents the expected value of track following, and blue solid lines represent actual joint vector.As can be seen that closing
The angular displacement in section 1 and joint 2, angular velocity are rapid in 2s to follow the tracks of desired trajectory.
The angle error in tracking in joint 1 and joint 2 changes over curve such as Fig. 7, Fig. 8 it can be seen that joint angle tracking
Error is maintained at ± 5 × 10-3In the range of rad.
The angular velocity tracking error in joint 1 and joint 2 changes over curve such as Fig. 9, Figure 10 it can be seen that joint angle
Speed Tracking error is maintained at ± 5 × 10-3In the range of rad/s.
The control moment in joint 1 and joint 2 changes over curve such as Figure 11, Figure 12 it can be seen that each joint control power
Square is maintained in attainable scope.
The position of moving base and the time dependent curve of attitude angle are it can be seen that the motion of connecting rod 1 and connecting rod 2 is made
The change becoming base position and attitude more gentle it is adaptable to the Trajectory Tracking Control of space-based robot system.
, there is parameter in kinetic model in the experiment show effectiveness of Neural Network Adaptive Control algorithm
With nonparametric across the situation of scale feature, rapidly can follow the tracks of desired trajectory online, there is certain robustness and anti-interference
Property.
Claims (1)
1. a kind of based on across Scale Model space manipulator Trajectory Tracking Control method it is characterised in that：Design neutral net
Adaptive control laws, the change item existing in Space Manipulator System kinetic model across scale feature is expressed asUsing neutral net, change item f is approached, thus realizing the compensation to change item f, nerve net
Network control law isV is the robust item for overcoming neutral net approximate error,
Its value is v=Kvsgn(r)；Error function isThe form of neutral net is radial base neural net,
The input of radial base neural net takesPreferably approximate algorithm is
Then network is output asRadial base neural net weighed value adjusting adaptive law isD0、
C0For the nominal plant model of object, D0=D- Δ D, C0=C- Δ C, Δ D, Δ C are modeling error matrix, and d is summation disturbance, e=
qd- q andIt is respectively joint angle tracking error and angular velocity tracking error, qdIt is respectively expectation and actual joint with q
Vector, KP、KIIt is positive certainty ratio and storage gain matrix respectively, KvFor robust term coefficient,For the weight vector of neutral net,For the output vector of Gaussian bases, ciFor the center vector of i-th node of network, biFor node i
Base width parameter.
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| CN107203141A (en) * | 2017-08-02 | 2017-09-26 | 合肥工业大学 | A kind of track following algorithm of the decentralized neural robust control of mechanical arm |
| CN107505835A (en) * | 2017-08-11 | 2017-12-22 | 广东工业大学 | A kind of method, apparatus and system of control machinery hands movement |
| CN108445768A (en) * | 2018-05-29 | 2018-08-24 | 福州大学 | The augmentation adaptive fuzzy control method of robot for space operating space track following |
| CN109176529A (en) * | 2018-10-19 | 2019-01-11 | 福州大学 | A kind of NEW ADAPTIVE fuzzy control method of the robot for space coordinated movement of various economic factors |
| CN109227550A (en) * | 2018-11-12 | 2019-01-18 | 吉林大学 | A kind of Mechanical arm control method based on RBF neural |
| CN111061216A (en) * | 2019-12-28 | 2020-04-24 | 哈尔滨工业大学 | Intelligent chip mounter motion system control method based on binary spline scale function |
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| CN112152539A (en) * | 2020-09-29 | 2020-12-29 | 中国船舶重工集团公司第七二四研究所 | Neural network compensation motor load moment observer implementation method |
| CN113219825A (en) * | 2021-03-26 | 2021-08-06 | 齐鲁工业大学 | Single-leg track tracking control method and system for quadruped robot |
| CN113253610A (en) * | 2021-04-20 | 2021-08-13 | 中国科学院自动化研究所 | Aircraft control method and device |
| CN113296393A (en) * | 2021-05-27 | 2021-08-24 | 安徽工业大学 | Two-link mechanical arm control method, device and medium based on self-adjusting fuzzy iterative learning |
| CN113370205A (en) * | 2021-05-08 | 2021-09-10 | 浙江工业大学 | Baxter mechanical arm track tracking control method based on machine learning |
| CN114516047A (en) * | 2022-02-14 | 2022-05-20 | 安徽大学 | Method and system for controlling track of mechanical arm based on radial basis function neural network terminal sliding mode |
| CN114700938A (en) * | 2022-03-04 | 2022-07-05 | 华南理工大学 | Redundant mechanical arm motion planning method based on jump gain integral neural network |
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| CN107203141A (en) * | 2017-08-02 | 2017-09-26 | 合肥工业大学 | A kind of track following algorithm of the decentralized neural robust control of mechanical arm |
| CN107505835A (en) * | 2017-08-11 | 2017-12-22 | 广东工业大学 | A kind of method, apparatus and system of control machinery hands movement |
| CN108445768A (en) * | 2018-05-29 | 2018-08-24 | 福州大学 | The augmentation adaptive fuzzy control method of robot for space operating space track following |
| CN108445768B (en) * | 2018-05-29 | 2020-12-25 | 福州大学 | Augmented self-adaptive fuzzy control method for operation space trajectory tracking of space robot |
| CN109176529A (en) * | 2018-10-19 | 2019-01-11 | 福州大学 | A kind of NEW ADAPTIVE fuzzy control method of the robot for space coordinated movement of various economic factors |
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| CN111399397B (en) * | 2020-04-01 | 2022-03-04 | 合肥工业大学 | Robot control method, controller and control system |
| CN111399397A (en) * | 2020-04-01 | 2020-07-10 | 合肥工业大学 | Robot control method, controller and control system |
| CN112152539A (en) * | 2020-09-29 | 2020-12-29 | 中国船舶重工集团公司第七二四研究所 | Neural network compensation motor load moment observer implementation method |
| CN113219825A (en) * | 2021-03-26 | 2021-08-06 | 齐鲁工业大学 | Single-leg track tracking control method and system for quadruped robot |
| CN113253610A (en) * | 2021-04-20 | 2021-08-13 | 中国科学院自动化研究所 | Aircraft control method and device |
| CN113370205A (en) * | 2021-05-08 | 2021-09-10 | 浙江工业大学 | Baxter mechanical arm track tracking control method based on machine learning |
| CN113370205B (en) * | 2021-05-08 | 2022-06-17 | 浙江工业大学 | Baxter mechanical arm track tracking control method based on machine learning |
| CN113296393A (en) * | 2021-05-27 | 2021-08-24 | 安徽工业大学 | Two-link mechanical arm control method, device and medium based on self-adjusting fuzzy iterative learning |
| CN114516047A (en) * | 2022-02-14 | 2022-05-20 | 安徽大学 | Method and system for controlling track of mechanical arm based on radial basis function neural network terminal sliding mode |
| CN114700938A (en) * | 2022-03-04 | 2022-07-05 | 华南理工大学 | Redundant mechanical arm motion planning method based on jump gain integral neural network |
| CN114700938B (en) * | 2022-03-04 | 2023-06-16 | 华南理工大学 | Redundant mechanical arm motion planning method based on jump gain integral neural network |
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