CN109048891A - Based on the neutral buoyancy robot pose and method for controlling trajectory from trigger model PREDICTIVE CONTROL - Google Patents
Based on the neutral buoyancy robot pose and method for controlling trajectory from trigger model PREDICTIVE CONTROL Download PDFInfo
- Publication number
- CN109048891A CN109048891A CN201810827124.5A CN201810827124A CN109048891A CN 109048891 A CN109048891 A CN 109048891A CN 201810827124 A CN201810827124 A CN 201810827124A CN 109048891 A CN109048891 A CN 109048891A
- Authority
- CN
- China
- Prior art keywords
- neutral buoyancy
- robot
- control
- trigger
- controller
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1602—Programme controls characterised by the control system, structure, architecture
- B25J9/1605—Simulation of manipulator lay-out, design, modelling of manipulator
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Programme-controlled manipulators
- B25J9/16—Programme controls
- B25J9/1656—Programme controls characterised by programming, planning systems for manipulators
- B25J9/1664—Programme controls characterised by programming, planning systems for manipulators characterised by motion, path, trajectory planning
Landscapes
- Engineering & Computer Science (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Automation & Control Theory (AREA)
- Feedback Control In General (AREA)
- Manipulator (AREA)
Abstract
The invention discloses a kind of neutral buoyancy robot poses and method for controlling trajectory based on from trigger model forecast Control Algorithm.The number of signal transmission is reduced by designing from triggering mode, to reduce signal transmission energy consumption.It is available a series of from moment when triggering by the design from trigger condition, so that the control track that controller generates sample and transmit these discrete control amounts to give neutral buoyancy robot.Furthermore, by considering the multiple and different performance indicator of neutral buoyancy robot, according to the switching law of setting, controller is directed to different performance indicators at different times and optimizes to obtain optimum control track, the comprehensive performance of neutral buoyancy robot is improved, to preferably complete the control to neutral buoyancy machine or engine device people posture and track.The characteristics of present invention combination computer controls is suitable for engineer application the characteristics of carrying out discretization according to from triggering moment by continuous optimum control track, meet computer discrete control.
Description
Technical field
The invention belongs to microgravity robot control field, it is related to a kind of neutral buoyancy robot pose and TRAJECTORY CONTROL
A kind of method, and in particular to neutral buoyancy robot pose and method for controlling trajectory based on from trigger model PREDICTIVE CONTROL.
Background technique
The experiment carried out under microgravity environment is to verify one of the basic step of ground space technology.Complex space behaviour
The test and demonstration of work require ground testing system provide for a long time, extensive, accurate, controllable and almost true microgravity
Environment is tested, to simulate the same degree of spatial movement and space.Therefore, microgravity ring is carried out using neutral buoyancy system
Experiment under border is widely studied.At the same time, wireless communication is increasingly being applied in the control of underwater robot, is made
With a major issue of wireless communication be communication energy be it is limited, i.e., the transmission power of signal with receive power be
It is restricted, while there is also data-bag losts and delay problem for wireless network.In addition, in neutral buoyancy system, robot
Underwater environment is worked in, is not only intercoupled between each control force, robot is seriously influenced by the viscous resistance of water, because
This, in neutral buoyancy robot pose and method for controlling trajectory design, seeking one kind can handle various constraint simultaneously
And the control method that can solve wireless network influence is particularly important.
Currently due to model predictive control method have to model accuracy it is of less demanding, system robustness is good, and stability is good
And the uncertainty due to caused by the factors such as model mismatch, distortion, interference can be made up in real time, dynamic property is good, can locate
The advantages that managing Multivariable Constrained, is widely used in electric system, big chemical process, aviation field etc..For wireless communication
Communication energy and number of communications can be effectively reduced from triggering mode in the case where energy constraint.It is a kind of spy from triggering mode
Different event triggered fashion generates a series of PREDICTIVE CONTROL amounts met from trigger condition by controller.Although using certainly
Triggering mode can reduce the burden of communication channel, but decrease the data that controller receives simultaneously, to influence to control
Performance processed.
Summary of the invention
The purpose of the present invention is to provide a kind of neutral buoyancy robot pose based on from trigger model PREDICTIVE CONTROL with
Method for controlling trajectory, this method can effectively solve the problem that neutral buoyancy robot using the communication energy that faces when wireless communication by
Limit problem, the various restricted problems that data-bag lost is subject in water with delay problem and neutral buoyancy robot.
The present invention is to be achieved through the following technical solutions:
A kind of neutral buoyancy robot pose and TRAJECTORY CONTROL based on from trigger model PREDICTIVE CONTROL disclosed by the invention
Method, comprising the following steps:
Step 1: neutral buoyancy system dynamics model is write as non-linear state space equation form；
Step 2: design a model predictive controller with from trigger condition, and according to trigger condition on optimum control track
Choose trigger point；
Step 3: cost function switching is carried out in condition triggering according to the switching condition of switching surfaces function；
Step 4: optimizing the cost function after switching, and screening obtains optimal control track.
Preferably, in step 1, neutral buoyancy system dynamics model is write as non-linear state space equation form
Concrete operations are as follows:
Consider kinetic model such as formula (1) of the neutral buoyancy robot under body coordinate system:
Wherein, M is inertia mass matrix, and C (v) is Coriolis force matrix, and D (v) is subject to glutinous in water for robot
Property resistance, g (η) is negative buoyancy coefficient, and τ is system input,For the acceleration of neutral buoyancy robot, v is neutral buoyancy machine
The speed of device people；
The neutral buoyancy robot considered is in geographic coordinate system OxnynznWith machine human body coordinate system OxbybzbRelationship
Such as following formula (2):
Wherein,It is the derivative of η,J(η)
For kinematic coefficient matrix；Robot is respectively referred in Oxn、OynAnd OyzThe position in direction； Respectively refer to machine
The roll angle of device people, pitch angle and yaw angle,For robot linear velocity vector,For machine
People's angular velocity vector；
Joint type (1) and formula (2), obtain the kinetic model under neutral buoyancy robot inertial coodinate system:
Wherein, Mη(η)=J-T(η)MJ-1(η)；
Dη(η, v)=J-T(η)D(v)J-1(η), gη(η)=J-T(η)g(η)；
Enable x1=η,With u=τ, then formula (3) indicates are as follows:
Wherein, It is the derivative of x (t)；
Formula (4) is expressed as following form (5):
And there is ‖ g (x (t)) ‖≤LG, LGIt is a normal number.
Consider to carry out the communication between neutral buoyancy robot and its controller, signal-to-noise ratio using wireless network are as follows:
Wherein, ∈2It is the variance of ambient noise, pTIt is the power that neutral buoyancy robot or controller send signal, pj
Represent other transmission powers for using wireless network user, and j ∈ S:=[1,2 ..., S], gR,TRepresent neutral buoyancy machine
Channel gain between people and controller, gR,jThe senders of other users is represented to the gain between recipient's channel；gR,TWith
gR,jIt is all the number between 0 to 1, the signal-to-noise ratio under unit speed can be written as γTb, and meet:
Wherein, fbFor the data rate of channel, BbFor channel width；The quantified tolerance of data turns to binary data,
Bit flows through transmission person and is sent after addition check code, under quadrature amplitude modulation, signal bit bit error rate pbDescription
Are as follows:
Wherein,
By p when signal-to-noise ratio is sufficiently largebIt is reduced to pb=exp (- γTb), then packet loss is written as α=1- (1-pb
)b+1, wherein a packet includes b data and 1 bit check code.
Preferably, in step 2, design a model predictive controller with from trigger condition, and according to trigger condition optimal
Control the concrete operations that trigger point is chosen on track are as follows:
Consider following controlled optimization problem:
The constraint being subject to are as follows:
x(tk+T)∈Xf；
Wherein, { tkOptimization problem (7) are indicated at the time of be solved, T indicates prediction time domain, F () and Vf() difference
Indicate stepped cost and terminal cost, X is the constraint set of state, and U is that the constraint of input combines, XfBe the terminal of state about
Constriction closes；
Design the controller of following form；
Wherein, u*It (s) is the optimum control amount obtained by solving optimization problem (7), κloc(x (s)) is one offline
Assist local control；
It is as follows from the condition of trigger policy:
Wherein,
Wherein,It is next triggering moment, δ1,δ2,...,δNIt is that controller is calculated according to from trigger condition (8)
Series of discrete sampled point, Ex(δ1,δ2,...,δN) it is prediction error, LJIt is Li Puxici constant,It is the receipts of cost function
Hold back rate；
Obtain an optimum control track by solving optimization problem (7), then controller according to from trigger condition by this
Item controls track sampling, obtains discrete control amount, and these control amounts are transmitted and carry out appearance to neutral buoyancy robot
The control of state and track.
Preferably, consider the different system performance of neutral buoyancy robot, then need between different cost functions into
Row switching, so as to promote the overall performance of neutral buoyancy robot, multiple and different optimization problems is as follows:
Constraint are as follows:
u(s)∈U,s∈[tk,tk+T]
x(s)∈X,s∈[tk,tk+T]
x(tk+T)∈Xf
Wherein, ν indicates current cost label and ν ∈ θ,That is each renewable time of controller can be
It is selected and is optimized between h different optimization problems, obtain optimum control track u*(s), s ∈ [tk,tk+T]。
Compared with prior art, the invention has the following beneficial technical effects:
The present invention considers the shortcomings that wireless communication energy constraint, reduces signal transmission by designing from triggering mode
Number, to reduce signal transmission energy consumption.It is available a series of from moment when triggering by the design from trigger condition,
To which the control track for generating controller sample and transmit these discrete control amounts giving neutral buoyancy machine
People.Meanwhile by considering the multiple and different performance indicator of neutral buoyancy robot, according to the switching law of setting, controller exists
It optimizes to obtain optimum control track for different performance indicators at the time of different, improves neutral buoyancy machine
The comprehensive performance of people, to preferably complete the control to neutral buoyancy machine or engine device people posture and track.Present invention combination computer
Continuous optimum control track is carried out discretization according to from triggering moment, meets the discrete control of computer by the characteristics of control
Feature is suitable for engineer application.
Detailed description of the invention
Fig. 1 is the flow chart of the method for the present invention.
Specific embodiment
Below with reference to specific embodiment, the present invention is described in further detail, and described is explanation of the invention
Rather than it limits.
It is disclosed by the invention based on from the neutral buoyancy robot pose of trigger model PREDICTIVE CONTROL and track referring to Fig. 1
Control method is realized by following steps:
(a) consider kinetic model of the neutral buoyancy robot of formula (1) under body coordinate system
Wherein, M is inertia mass matrix, and C (v) is Coriolis force matrix, and D (v) is subject to glutinous in water for robot
Property resistance, g (η) is negative buoyancy coefficient, and τ is system input.The neutral buoyancy robot considered is in geographic coordinate system Oxnynzn
With machine human body coordinate system OxbybzbRelationship it is as follows
Wherein,Robot is respectively referred in Oxn、OynAnd OyzThe position in direction；It respectively refers to
The roll angle of robot, pitch angle and yaw angle,For robot linear velocity vector, For robot
Angular velocity vector,J (η) is kinematic coefficient matrix.
Joint type (1) and (2) obtain the kinetic model under neutral buoyancy robot inertial coodinate system
Wherein, J (η) is kinematic coefficient matrix, Mη(η)=J-T(η)MJ-1(η),
gη(η)=J-T(η)g(η).Enable x1=η,With u=τ, then formula (3) can be expressed as
Wherein
Formula (4) is written as following form
Wherein LG=1.
(b) consider to carry out the communication between neutral buoyancy robot and its controller using wireless network.
Consider signal-to-noise ratio:
Wherein, ∈2=10-6, pT=-43.90dB, p1=-56.12dB, p2=-58.67dB, gR,T=0.1, gR,1=
0.05, gR,2Signal-to-noise ratio under=0.03 unit speed can be written as γTb, and meet
Wherein, fb=1.2, Bb=1.
(c) consider following controlled optimization problem
What is be subject to is constrained to
x(tk+T)∈Xf
Wherein, { tkOptimization problem (7) are indicated at the time of be solved, T indicates prediction time domain, F () and Vf() difference
Indicate stepped cost and terminal cost, X is the constraint set of state, and U is that the constraint of input combines, XfBe the terminal of state about
Constriction closes.
In addition, designing the controller of following form
Wherein, u*It (s) is the optimum control amount obtained by solving optimization problem (7), κloc(x (s)) is one offline
Assist local feedback control device, and κloc(x (t))=- 2.7138x1(t)-0.7163x2(t)。
(d) as follows from the condition of trigger policy
Wherein
Wherein, δ1,δ2,...,δNIt is controller according to the series of discrete sampled point calculated from trigger condition (8), Ex
(δ1,δ2,...,δN) it is prediction error, LJ=1.7808,
Obtain an optimum control track by solving optimization problem (7), then controller according to from trigger condition by this
Item controls track sampling, obtains discrete control amount.
Under normal conditions, a series of self-triggering discrete points are obtained come discretization control rail come approximate using following methods
Mark calculates the time to reduce, to be more in line with engineer application.
The method for seeking approximation sample point:
The first step, as t >=tkWhen, use control amount u*(tk) control neutral buoyancy robot until formula (9) be greater than 0, note
Recording this moment isAnd it obtainsAs N=1, setting
Second step divides as N >=2 in section [tk,tk+δ1] andUsing control amount u*(tk) and u*
(tk+δ1), pass through maximization
It obtainsIt usesUntil formula (9) be greater than 0, record fromTo formula (9) be greater than 0 this period beAs N=2, setting
Third step steps be repeated alternatively until to obtain N number of sampling interval.It can be obtained by following formula
As n=N, take
(e) consider 2 different system performances of neutral buoyancy robot, then need between 2 different cost functions
It switches over, so as to promote the overall performance of neutral buoyancy robot.
Multiple and different optimization problems is as follows
Constraint are as follows:
u(s)∈U,s∈[tk,tk+T]
x(s)∈X,s∈[tk,tk+T]
x(tk+T)∈Xf
Wherein.ν indicates current cost label and ν ∈ θ,I.e. each renewable time of controller can be at 2 not
It is selected, and is optimized between same optimization problem.Wherein
R1=1
R2=3
Wherein, once switched within two groups of different optimization problems every 30 seconds.
Model predictive controller can generate the control amount in a period of time, these control amounts be transmitted floating to neutrality
Power robot, the control amount that robot can select current time are controlled, to reduce wireless network packet loss and time delay
It influences.Meanwhile from trigger policy the transmission times of signal is greatly reduced, save transmission energy.The cost function of switching
The performance for promoting entire neutral buoyancy system can be integrated, the posture and TRAJECTORY CONTROL to neutral buoyancy system are completed with this.
In conclusion advantage of the invention is very significant:
(1) the shortcomings that present invention is by considering wireless communication energy constraint carries out model prediction control using from triggering mode
The design of device processed can greatly reduce data transmission times.
(2) consider neutral buoyancy robot multiple performance index, using switching surfaces function mode is in different control
Device renewable time optimizes different costs, improves the performance of neutral buoyancy robot on the whole.
Unspecified part of the present invention belongs to field technical staff's common knowledge.The above content is only to illustrate the invention
Technical idea, this does not limit the scope of protection of the present invention, it is all according to the technical idea provided by the invention, in technology
Any change done on the basis of scheme, each falls within the protection scope of claims of the present invention.
Claims (4)
1. a kind of neutral buoyancy robot pose and method for controlling trajectory based on from trigger model PREDICTIVE CONTROL, feature exist
In, comprising the following steps:
Step 1: neutral buoyancy system dynamics model is write as non-linear state space equation form；
Step 2: it designs a model and predictive controller and is chosen on optimum control track from trigger condition, and according to trigger condition
Trigger point；
Step 3: cost function switching is carried out in condition triggering according to the switching condition of switching surfaces function；
Step 4: optimizing the cost function after switching, and screening obtains optimal control track.
2. the neutral buoyancy robot pose and TRAJECTORY CONTROL according to claim 1 based on from trigger model PREDICTIVE CONTROL
Method, which is characterized in that in step 1, write neutral buoyancy system dynamics model as non-linear state space equation form
Concrete operations are as follows:
Consider kinetic model such as formula (1) of the neutral buoyancy robot under body coordinate system:
Wherein, M is inertia mass matrix, and C (v) is Coriolis force matrix, and D (v) is the stickiness resistance that robot is subject in water
Power, g (η) are negative buoyancy coefficient, and τ is system input,For the acceleration of neutral buoyancy robot, v is neutral buoyancy robot
Speed；
The neutral buoyancy robot considered is in geographic coordinate system OxnynznWith machine human body coordinate system OxbybzbRelationship it is as follows
Formula (2):
Wherein,It is the derivative of η,J (η) is movement
Coefficient matrix；Robot is respectively referred in Oxn、OynAnd OyzThe position in direction； Respectively refer to robot
Roll angle, pitch angle and yaw angle,For robot linear velocity vector,For Schemes of Angular Velocity Estimation for Robots
Vector；
Joint type (1) and formula (2), obtain the kinetic model under neutral buoyancy robot inertial coodinate system:
Wherein, Mη(η)=J-T(η)MJ-1(η)；
Dη(η, v)=J-T(η)D(v)J-1(η), gη(η)=J-T(η)g(η)；
Enable x1=η,With u=τ, then formula (3) indicates are as follows:
Wherein, It is the derivative of x (t)；
Formula (4) is expressed as following form (5):
And there is ‖ g (x (t)) ‖≤LG, LGIt is a normal number；
Consider to carry out the communication between neutral buoyancy robot and its controller, signal-to-noise ratio using wireless network are as follows:
Wherein, ∈2It is the variance of ambient noise, pTIt is the power that neutral buoyancy robot or controller send signal, pjRepresent it
He uses the transmission power of wireless network user, and j ∈ S:=[1,2 ..., S], gR,TRepresent neutral buoyancy robot and control
Channel gain between device, gR,jThe senders of other users is represented to the gain between recipient's channel；gR,TAnd gR,jIt is all 0
Number between to 1, the signal-to-noise ratio under unit speed are written as γTb, and meet:
Wherein, fbFor the data rate of channel, BbFor channel width；The quantified tolerance of data turns to binary data, and school is being added
It tests bit after code and flows through transmission person and sent, under quadrature amplitude modulation, signal bit bit error rate pbDescription are as follows:
Wherein,
By p when signal-to-noise ratio is sufficiently largebIt is reduced to pb=exp (- γTb), then packet loss is written as α=1- (1-pb)b+1,
In, a packet includes b data and 1 bit check code.
3. the neutral buoyancy robot pose and TRAJECTORY CONTROL according to claim 2 based on from trigger model PREDICTIVE CONTROL
Method, which is characterized in that in step 2, design a model predictive controller with from trigger condition, and according to trigger condition optimal
Control the concrete operations that trigger point is chosen on track are as follows:
Consider following controlled optimization problem:
The constraint being subject to are as follows:
Wherein, { tkOptimization problem (7) are indicated at the time of be solved, T indicates prediction time domain, F () and Vf() respectively indicates rank
Duan Chengben and terminal cost,For the constraint set of state,It is combined for the constraint of input,It is the end conswtraint collection of state
It closes；
Design the controller of following form；
Wherein, u*It (s) is the optimum control amount obtained by solving optimization problem (7), κloc(x (s)) is an offline auxiliary office
Portion's controller；
It is as follows from the condition of trigger policy:
Wherein,
Wherein,It is next triggering moment, δ1,δ2,...,δNIt is that controller is a series of according to calculating from trigger condition (8)
Discrete sampling point, Ex(δ1,δ2,...,δN) it is prediction error, LJIt is Li Puxici constant,It is the convergency factor of cost function；
An optimum control track is obtained by solving optimization problem (7), then controller controls this according to from trigger condition
Track processed sampling, obtains discrete control amount, and by these control amounts transmit to neutral buoyancy robot carry out posture with
The control of track.
4. the neutral buoyancy robot pose and TRAJECTORY CONTROL according to claim 3 based on from trigger model PREDICTIVE CONTROL
Method, which is characterized in that consider the different system performance of neutral buoyancy robot, cut between different cost functions
It changes, multiple and different optimization problems is as follows:
Constraint are as follows:
Wherein, ν indicates current cost label and ν ∈ θ,I.e. each renewable time of controller can be at h not
It is selected and is optimized between same optimization problem, obtain optimum control track u*(s), s ∈ [tk,tk+T]。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810827124.5A CN109048891B (en) | 2018-07-25 | 2018-07-25 | Neutral buoyancy robot posture and track control method based on self-triggering model predictive control |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810827124.5A CN109048891B (en) | 2018-07-25 | 2018-07-25 | Neutral buoyancy robot posture and track control method based on self-triggering model predictive control |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109048891A true CN109048891A (en) | 2018-12-21 |
| CN109048891B CN109048891B (en) | 2021-12-07 |
Family
ID=64835517
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810827124.5A Active CN109048891B (en) | 2018-07-25 | 2018-07-25 | Neutral buoyancy robot posture and track control method based on self-triggering model predictive control |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109048891B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110554701A (en) * | 2019-09-17 | 2019-12-10 | 哈尔滨工业大学 | Motion control system of curling throwing robot and rolling time domain optimization method thereof |
| CN112327622A (en) * | 2020-11-02 | 2021-02-05 | 西北工业大学 | Consistency fault-tolerant control method for neutral buoyancy robot |
| CN112428278A (en) * | 2020-10-26 | 2021-03-02 | 北京理工大学 | Control method and device of mechanical arm and training method of man-machine cooperation model |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002020310A2 (en) * | 2000-09-04 | 2002-03-14 | Merhav - A.A.P. Ltd. | Self triggering impact protection system |
| CN106078781A (en) * | 2016-08-05 | 2016-11-09 | 广州市轻工职业学校 | Connecting rod belt wheel straight line flat folder perception self-adaption robot finger apparatus |
| CN107065564A (en) * | 2017-05-18 | 2017-08-18 | 西北工业大学 | A kind of neutral buoyancy robot pose and method for controlling trajectory based on active disturbance rejection |
| CN107656530A (en) * | 2016-07-26 | 2018-02-02 | 深圳华清精密科技有限公司 | Variable-parameter open-frame type ocean underwater robot trajectory tracking control method, device and system |
| CN107942671A (en) * | 2017-12-04 | 2018-04-20 | 国网山东省电力公司电力科学研究院 | A kind of improved underwater robot Work machine arm generalized forecast control method |
| CN108181914A (en) * | 2017-12-11 | 2018-06-19 | 西北工业大学 | A kind of neutral buoyancy robot pose and track Auto-disturbance-rejection Control |
-
2018
- 2018-07-25 CN CN201810827124.5A patent/CN109048891B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2002020310A2 (en) * | 2000-09-04 | 2002-03-14 | Merhav - A.A.P. Ltd. | Self triggering impact protection system |
| CN107656530A (en) * | 2016-07-26 | 2018-02-02 | 深圳华清精密科技有限公司 | Variable-parameter open-frame type ocean underwater robot trajectory tracking control method, device and system |
| CN106078781A (en) * | 2016-08-05 | 2016-11-09 | 广州市轻工职业学校 | Connecting rod belt wheel straight line flat folder perception self-adaption robot finger apparatus |
| CN107065564A (en) * | 2017-05-18 | 2017-08-18 | 西北工业大学 | A kind of neutral buoyancy robot pose and method for controlling trajectory based on active disturbance rejection |
| CN107942671A (en) * | 2017-12-04 | 2018-04-20 | 国网山东省电力公司电力科学研究院 | A kind of improved underwater robot Work machine arm generalized forecast control method |
| CN108181914A (en) * | 2017-12-11 | 2018-06-19 | 西北工业大学 | A kind of neutral buoyancy robot pose and track Auto-disturbance-rejection Control |
Non-Patent Citations (1)
| Title |
|---|
| 姜毅龙: "基于自适应自触发远程控制的机器人路径跟踪研究", 《机械研究与应用》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110554701A (en) * | 2019-09-17 | 2019-12-10 | 哈尔滨工业大学 | Motion control system of curling throwing robot and rolling time domain optimization method thereof |
| CN110554701B (en) * | 2019-09-17 | 2022-09-02 | 哈尔滨工业大学 | Motion control system of curling throwing robot and rolling time domain optimization method thereof |
| CN112428278A (en) * | 2020-10-26 | 2021-03-02 | 北京理工大学 | Control method and device of mechanical arm and training method of man-machine cooperation model |
| CN112327622A (en) * | 2020-11-02 | 2021-02-05 | 西北工业大学 | Consistency fault-tolerant control method for neutral buoyancy robot |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109048891B (en) | 2021-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102298326B (en) | Underactuated autonomous underwater vehicle (AUV) adaptive trajectory tracking control device and control method | |
| CN109240086B (en) | Self-adaptive robust control method of nonlinear bilateral teleoperation system | |
| Polushin et al. | On the model-based approach to nonlinear networked control systems | |
| CN109048891A (en) | Based on the neutral buoyancy robot pose and method for controlling trajectory from trigger model PREDICTIVE CONTROL | |
| CN103838145B (en) | VTOL aircraft Robust Fault-Tolerant Control Systems based on cascade observer and method | |
| CN110262256B (en) | Multilateral self-adaptive sliding mode control method of nonlinear teleoperation system | |
| CN108241292B (en) | Underwater robot sliding mode control method based on extended state observer | |
| CN107024863A (en) | A kind of UUV Trajectory Tracking Control methods for avoiding differential from exploding | |
| CN106227223A (en) | A kind of UUV trace tracking method based on dynamic sliding mode control | |
| CN106970594B (en) | A kind of method for planning track of flexible mechanical arm | |
| CN110116409B (en) | Four-channel teleoperation bilateral control method based on disturbance observer | |
| CN105807789B (en) | UUV control methods based on the compensation of T-S Fuzzy Observers | |
| CN109241552A (en) | A kind of underwater robot motion planning method based on multiple constraint target | |
| CN102880052A (en) | Time scale function decomposition based hypersonic aircraft actuator saturation control method | |
| CN102853833B (en) | Rapid damping method of strap-down inertial navigation system | |
| Zhang et al. | Intelligent event-based output feedback control with Q-learning for unmanned marine vehicle systems | |
| CN108267952B (en) | Self-adaptive finite time control method for underwater robot | |
| CN110340898A (en) | A kind of Free-floating space manipulator adaptive fusion method with specified tracking performance | |
| Xiang et al. | A new hybrid network traffic prediction method | |
| CN103831831A (en) | Non-linear teleoperation system position and force tracking and controlling system with time-varying delay | |
| CN107065564A (en) | A kind of neutral buoyancy robot pose and method for controlling trajectory based on active disturbance rejection | |
| CN105182990B (en) | Robust control method with the limited Three Degree Of Freedom model copter of output | |
| CN109828468A (en) | A kind of control method for the non-linear robot system of magnetic hysteresis | |
| CN109933074A (en) | A kind of more unmanned boat flocking control device structures and design method having leader | |
| CN102880056A (en) | Equivalence model based discrete sliding mode control method for hypersonic velocity aircraft |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |