CN109143859A - A kind of adaptive consistency control method based on nonlinear object feedback system - Google Patents

A kind of adaptive consistency control method based on nonlinear object feedback system Download PDF

Info

Publication number
CN109143859A
CN109143859A CN201810995048.9A CN201810995048A CN109143859A CN 109143859 A CN109143859 A CN 109143859A CN 201810995048 A CN201810995048 A CN 201810995048A CN 109143859 A CN109143859 A CN 109143859A
Authority
CN
China
Prior art keywords
gain
unknown
design
adaptive
nussbaum
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
Application number
CN201810995048.9A
Other languages
Chinese (zh)
Other versions
CN109143859B (en
Inventor
任长娥
李桂露
施智平
关永
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Capital Normal University
Original Assignee
Capital Normal University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Capital Normal University filed Critical Capital Normal University
Priority to CN201810995048.9A priority Critical patent/CN109143859B/en
Publication of CN109143859A publication Critical patent/CN109143859A/en
Application granted granted Critical
Publication of CN109143859B publication Critical patent/CN109143859B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Feedback Control In General (AREA)

Abstract

The adaptive consistency control method based on nonlinear object feedback system that the present invention relates to a kind of, for the nonlinear system that unknown parameters and control gain are unknown, a kind of adaptive consistency adjusting controller based on novel Nussbaum gain and backstepping method is designed；Firstly, since system is any Relative order system, introduces filter and system is converted；Secondly, one Nussbaum gain of design, handles unknown control gain；Again, the property based on Nussbaum gain, adaptive consistency adjusting controller when design Relative order is 1；Finally, designing adaptive consistency adjusting controller when Relative order is greater than 1 in conjunction with backstepping method and filter；This method can be applied to the tracking of the Attitude Tracking and Aeronautics and Astronautics and underwater robot of aircraft for reference signal and the vehicle of the mass-spring-damper device system with nonlinear spring and the formation control of the robot with flexible joint.

Description

A kind of adaptive consistency control method based on nonlinear object feedback system
Technical field
The adaptive consistency control method based on nonlinear object feedback system that the present invention relates to a kind of, can be applied to fly The Attitude Tracking and Aeronautics and Astronautics and underwater robot of row device are for the tracking of reference signal and with nonlinear spring Mass-spring-damper device system vehicle and the robot with flexible joint formation control.
Background technique
For spring mass-damping system as the most common mechanical vibrating system, the application in real production and living is ten Divide widely, the buffer of autonomous vehicle is exactly very typical spring mass-damping system application.Spring mass-damping In system, the force analysis of previous mass block first has to pass through stress since adjacent latter mass block force analysis It analyzes and according to differential equation of motion listed by Newton's second law, and establishes the mathematical model come out and studied with this method System be characterized in it is almost the same.
The formation control problem of autonomous vehicle causes the concern of researcher as hot issue.But due to severe Climatic factor, the problems such as unknown environmental factor, can the formation problem to autonomous vehicle cause the challenge of very severe, if The influence for not considering these factors when designing controller frequently can lead to the performance decline of system, or even unstable.However These uncertain factors are difficult to carry out Analysis on Mechanism and Accurate Model again.
In order to which to environmental factor, the uncertain factors such as climatic factor and unknown control gain are studied, both domestic and external Many scholars have carried out a large amount of research.First against uncertain factor, domestic and foreign scholars propose a series of solution party Method, for example, uncertain factor keeps bounded rather than converges to zero in distributing self-adaptation control method；Application parameter estimation Method estimates uncertain factor, then carries out corresponding control design case；Some scholars devise based on disturbance compensation Control strategy.It can be used under the premise of guaranteeing the estimation of control gain far from zero for unknown control gain problemIn the case where there is unknown gain in systems, controller design is carried out；Known to the symbol of unknown control gain Under the premise of, the design of controller can be carried out using the method for the sign function of unknown control gain when designing controller； Nussbaum gain can be used to solve some System Stabilizations with uncertain control coefrficient, and select suitably Nussbaum gain.
It is found by retrieval, follows the research of formation control many about Autonomous Vehicle navigator in the prior art, there has also been Certain research achievement, but do not account under the influence of the uncertain factors such as weather, environment mostly, the sensor of vehicle Information transmission will receive certain restrictions.
Summary of the invention
Technology of the invention solves the problems, such as: overcome the deficiencies in the prior art, unknown for unknown parameters and control gain Nonlinear object feedback system, devise a kind of adaptive consistent with backstepping method based on Nussbaum gain Property control method, solve unknown parameters and control the adaptive consistency control of the unknown non-linear multi-agent system of gain Problem improves the control precision and adaptivity of system.
A kind of technical solution of the invention are as follows: nonlinear object feedback unknown based on unknown parameters and control gain The adaptive consistency control method of system, it is characterised in that the following steps are included: firstly, since the Relative order of system is unknown, Design filter；Secondly, one Nussbaum gain of design carries out self adaptive control to unknown gain；Again, Relative order is designed Adaptive consistency adjusting controller when being 1；Finally, being greater than in conjunction with backstepping method and filter design Relative order Adaptive consistency adjusting controller when 1,
Specific step is as follows:
The first step establishes the state-space model of system
The state space side of N+1 subsystem of the nonlinear object feedback system with unknown parameter and unknown gain Journey is as follows:
Wherein,For the state vector of system, niIt indicates subsystem order, is known Normal integer, yi,ui∈ R is the output variable and control input of system respectively,It is unknown non-thread Property function,It is unknown parameter vector, if bi,ρThe Relative order of ≠ 0 system is ρ.
It is as follows to define output tracking error:
ei=yi-y0, i=1, N
Wherein, y0For root node state and as pilotage people, the information exchange relationship between subsystem is by one by side ε The digraph G constituted with node ν is indicated, and digraph G has directed spanning tree of the cluster with subsystem 0 for root node, section Point ν indicates that subsystem, each edge ε indicate an information connection.It will be by the element a of expression connection relationshipijThe adjacency matrix A of composition It is associated with figure, if there is from subsystem i to the path of subsystem j, then aij=1, otherwise aij=0.Define Laplce's square Battle array L, whereinAnd as i ≠ j, lij=-aij。
Nonlinear function φ (yi) meeting condition, following inequality is set up:
Wherein, γφFor unknown positive real number, p is known positive integer.
The Laplacian Matrix of grid connection can be blocked into:
Convenient for being designed later, definition consistency tracking error is as follows:
And the form that can be expressed as vector is ζ=Qe.
Wherein, qij=lij, Q=[qij]∈RN×N,L0c=[l0j]∈R1×N, i, j=1 ..., N, e ∈ RNIt is by element ei The vector of composition, ζ ∈ RNIt is by element ζiThe vector of composition.
Since ζ=Qe meets, then following inequality sets up any positive integer p
Wherein, σ (Q) indicates the singular value of Q.
Second step designs filter
Due to the Relative order ρ of system be it is unknown, design following filter as Relative order ρ > 1:
Wherein, λj> 0, j=1, ρ -1 is the parameter of design, and introduces auxiliary variable
WhereinAndFurther system equation is converted to
Wherein,And di,1=bi,ρ.Introduce auxiliary variable Wherein,It indicates by the 2nd row to n-thiRow constituted vector or matrix
System model can be re-expressed as:
Wherein, coefficient matrix
Auxiliary variable
Auxiliary variable
Enable auxiliary variable errorAuxiliary variable error can be obtainedTracking error ei Dynamic are as follows:
Wherein, auxiliary variableAuxiliary variable
Third step designs novel Nussbaum gain
Since the control gain of system is unknown, the boundedness of function when designed for proving that gain is unknown Nussbaum gain is as follows:
Wherein,For designed Nussbaum gain,Meet Property.
If meetingWherein, r (t) is one A bounded function, V (t) are a positive definite integral form, kiIt is a continuous function and ki(0)=0.Utilize above-mentioned Nussbaum gain The available function V (t) of property and kiBoundedness.
4th step designs adaptive consistency adjusting controller when Relative order ρ=1
Adaptive consistency adjusting controller when Relative order ρ=1 is as follows:
Wherein,Nussbaum gainFor what is designed in step (3), γ is The normal number of one design.For the ease of the simplicity expression of controller
Design the adaptive law of unknown parameter are as follows:
Wherein, ζiConsistency tracking error between intelligent body, c1>=2 be the normal number of design.
5th step designs adaptive consistency adjusting controller when Relative order ρ > 1
Adaptive consistency adjusting controller when Relative order ρ > 1 is as follows:
Wherein, the adaptive rate of unknown parameter is as follows:
Wherein, ξi,ρ-1For based on filter and backstepping method design ρ -1 step in controller,It is Unknown control gain bi,ρEstimated value, evaluated errorAnd filtering errorFor the normal number of design.
The advantages of the present invention over the prior art are that:
(1) the nonlinear object feedback system unknown with control gain based on unknown parameters of the invention is adaptive consistent Property control method is the controller obtained based on Nussbaum gain and backstepping method, its main feature is that it is non-linear not Know that function design adaptive parameter control is restrained and design continuous item is offset in the controller；For controlling, gain is unknown to be asked Topic calms to system using Nussbaum gain；Since the Relative order of system is unknown, in conjunction with backstepping method and Controller of the filter design when the Relative order of system is greater than 1, solves the adaptive consistency control problem in system, again Improve the control precision and adaptivity of system.
(2) present invention has comprehensively considered the influence of uncertain factor and unknown control gain, by Nussbaum gain method Backstepping method is merged, and good by the system tracking effect that this method controls.
Detailed description of the invention
Fig. 1 is adaptive the one of the nonlinear object feedback system unknown based on unknown parameters and control gain of the invention The design flow diagram of cause property control method；
Fig. 2 is the topological network of the present invention in specific implementation.
Specific embodiment
The following describes the present invention in detail with reference to the accompanying drawings and embodiments.
As shown in Figure 1, the present invention relates to a kind of adaptive consistency control method based on nonlinear object feedback system, For unknown parameters and the unknown nonlinear system of control gain, design it is a kind of based on novel Nussbaum gain and The adaptive consistency adjusting controller of backstepping method；Firstly, since system is any Relative order system, filter is introduced Wave device converts system；Secondly, one Nussbaum gain of design, handles unknown control gain；Again, it is based on The property of Nussbaum gain, adaptive consistency adjusting controller when design Relative order is 1；Finally, in conjunction with Backstepping method and filter design Relative order are greater than adaptive consistency adjusting controller when 1；This method can answer Attitude Tracking and Aeronautics and Astronautics and underwater robot for aircraft is for the tracking of reference signal and with non-thread Property spring mass-spring-damper device system vehicle and the robot with flexible joint formation control, this method solve The adaptive consistency control problem of unknown parameters and the unknown nonlinear systems of control gain, can handle the rank of subsystem The identical multi-agent system of the not identical but Relative order of number, has a wide range of application, improves the control precision and adaptivity of system.
As shown in Figure 1, carry out the specific implementation of illustration method by taking the control of autonomous driving automobile as an example, specific reality of the invention It is existing that steps are as follows:
1, the state-space model of system is established
The buffer system model of autonomous driving automobile describes are as follows:
Wherein,
xi=(xi,1,xi,2), i=0,1,2,3,4 is the state vector of system, and the autonomous driving automobile of i=0 is as navigator Person, yi,ui∈ R is the output variable and control input of system, y respectivelyiIt indicates the displacement of Autonomous Vehicle, and enables u0=0.μ,bi,2 It is unknown positive real number, because of bi,2≠ 0, so the Relative order of system is 2.miFor the quality of vehicle, κiFor the coefficient of elasticity of spring, ai> 0 is the viscous friction coefficient of buffer.
Adjacency matrix are as follows:
Then, it is as follows to define output tracking error:
ei=yi-y0, i=1,4
Wherein, y0For root node 0 state and as pilotage people, the information exchange relationship between subsystem is by one by side The digraph G that ε and node ν are constituted is indicated, and digraph G has directed spanning tree of the cluster with subsystem 0 for root node, such as Shown in Fig. 2, pilotage people 0 is by status information y0It is transferred to follower 1, follower 1 will need the status information followed to pass through communication Topology connection is transferred to other follower.
Nonlinear function φ (yi) meeting condition, following inequality is set up:
Wherein, γφFor unknown positive real number, p is known positive integer.
The Laplacian Matrix of grid connection can be blocked into:
Convenient for being designed later, definition consistency tracking error is as follows:
And the form that can be expressed as vector is ζ=Qe.
Wherein, qij=lij, Q=[qij]∈RN×N,L0c=[l0j]∈R1×N, i, j=1 ..., N, e ∈ RNIt is by element ei The vector of composition, ζ ∈ RNIt is by element ζiThe vector of composition.
Since ζ=Qe meets, then following inequality sets up any positive integer p:
Wherein, σ (Q) indicates the singular value of Q.
2, the filter of design controller is convenient in design
Since the Relative order of the buffer system of autonomous driving automobile is 2, for the ease of the controller of designing system, design Following filter:
Wherein, λ1> 0 is the parameter of design, and introduces auxiliary variable:
WhereinAnd
Further system equation is converted to:
Wherein,And di,1=bi,2.Introduce auxiliary variable Wherein,It indicates by the 2nd row to n-thiRow constituted vector or matrix.
System model can be re-expressed as:
Wherein,
The auxiliary variable error is enabled to beAuxiliary variable error can be obtainedTracking error eiDynamic are as follows:
Wherein, auxiliary variableAuxiliary variable
3, novel Nussbaum gain is designed
Due to the control gain of the buffer system of autonomous driving automobile be it is unknown, design handle unknown gain as follows Nussbaum gain:
Wherein,For designed Nussbaum gain,Meet Property.
If the liapunov function of design meets Utilize the available function V (t) of property of above-mentioned Nussbaum gain and kiBoundedness, and from which further follow that tracking error exists In 0 contiguous range.
4, adaptive consistency adjusting controller when design Relative order is 1
Since the Relative order of the buffer system of the autonomous driving automobile considered here is 2, the controller of system cannot be straight It connects design to obtain, the controller for needing first to design when system Relative order is 1 is as follows:
Wherein, the variable of Nussbaum gainNussbaum gainFor in step (3) Middle design, γ are the normal numbers of a design, for the ease of the simplicity expression of controller
Design the adaptive law of unknown parameter are as follows:
Wherein, ζiConsistency tracking error between intelligent body, c1>=2 be the normal number of design.
Consider the liapunov function about tracking error e:
Wherein, the error of unknown parameterQ is the matrix of known intersubsystem communication connection.
Consider the liapunov function about auxiliary variable z:
Wherein, Pi,zFor the positive definite matrix of design, meet condition
Design total liapunov function:
V=Ve+βVz
Wherein, the parameter beta of design meets
The adaptive law of the controller of design and parameter, which is substituted into total liapunov function, to be obtained:
It can be obtained according to the property of Nussbaum gain function when Relative order is 1, the controller of design can make system Tracking error is in 0 contiguous range.
5, adaptive consistency adjusting controller when Relative order ρ > 1 is designed
For the buffer system of autonomous driving automobile, controller when according to Relative order being 1, when carrying out Relative order ρ=2 The first step for designing controller is as follows:
Wherein,Variable for the Virtual Controller designed in backstepping method, in Nussbaum gainEasy expression for the ease of the Virtual Controller designed in the first step
According to Nussbaum gain and above-mentioned formula, the dynamic of available tracking error:
Wherein,For the error of auxiliary variable,It is unknown control gain bi,2Estimated value, adaptive law is below It indicates, the error of unknown control gainAnd filtering error For the normal number of design.
Then it in second step, is obtained according to filter
Wherein,For aboutFunction, thenTo seek partial differential to each variable.
It can obtain,
It is as follows that design obtains final controller:
Wherein, uiIt is inputted for the control of i-th of subsystem, in the Virtual Controller of second step designSince Relative order is 2 at this time,It is obtained according to the filter of designDynamic be The adaptive law of unknown parameter is θ i=ζ i2+ ζ i2p in system, and the adaptive law of unknown control gain is
Consider the liapunov function about tracking error e:
Wherein,It is the error of unknown parameter in system,For filtering error.
Consider the liapunov function about auxiliary variable z:
Wherein, Pi,zFor the positive definite matrix of design, meet condition
Design total liapunov function:
V=Ve+βVz
Wherein, factor beta meets
The adaptive law of the controller of design and parameter, which is substituted into total liapunov function, to be obtained:
It can be obtained in more autonomous driving automotive systems according to the property of Nussbaum gain function, the controller of design can Make the tracking error for pilotage people of system in 0 contiguous range, can realize Autonomous Vehicle to navigator's automobile 0 with Track.
The content that description in the present invention is not described in detail belongs to the prior art well known to professional and technical personnel in the field.

Claims (6)

1. a kind of adaptive consistency control method based on nonlinear object feedback system, it is characterised in that including following step It is rapid:
It (1) is non-thread with unknown parameter and unknown gain according to spring mass-damping system of more autonomous driving automobiles Property output feedback system, the state-space model of foundation；
(2) be based on the state-space model, due to more autonomous driving automobiles spring mass-damping system Relative order not Know, designs the filter of i-th of subsystem；
(3) it is based on the state-space model, since control gain is unknown, the Nussbaum that design tracking error goes to zero increases Benefit；
(4) it is based on above-mentioned Nussbaum gain, the spring mass-damping system Relative order for designing more autonomous driving automobiles is 1 When adaptive consistency adjusting controller；
(5) according to Relative order be 1 when adaptive consistency adjusting controller, in conjunction with backstepping method and the step (2) filter and Nussbaum gain, design Relative order are greater than adaptive consistency adjusting controller when 1, realize more Tracking of the Autonomous Vehicle to navigator's automobile.
2. the adaptive consistency control method according to claim 1 based on nonlinear object feedback system, feature Be: in the step (1), the state-space model of foundation is as follows:
Wherein,For the state vector of system, niIt indicates subsystem order, is known just whole Number, yi,ui∈ R is that the output variable of system and control input respectively, φ:It is unknown non-linear letter Number,It is unknown parameter vector, if bi,ρSpring mass-damping system of autonomous driving automobile more than ≠ 0 is opposite Rank is ρ,Indicate the n of real number fieldiDimensional vector, RmIndicate the m dimensional vector in real number field.
3. the adaptive consistency control method according to claim 1 based on nonlinear object feedback system, feature It is: in the step (2), designs the filter of i-th of subsystem are as follows:
Spring mass-damping system Relative order ρ of more autonomous driving automobiles is unknown, wherein λj> 0, j=1 ..., ρ -1 It is the parameter of design, then ξi=[ξi,1…ξi,ρ-1]TIt is the filter of i-th of subsystem, uiFor i-th of the subsystem designed hereinafter Controller input.
4. the adaptive consistency control method according to claim 1 based on nonlinear object feedback system, feature It is: in the step (3), Nussbaum gain that the tracking error of design goes to zero are as follows:
Wherein,For designed Nussbaum gain,Meet Property, the variable of k representative function, for function related with time t；
If meetingWherein r (t), which is one, has Bound function, V (t) is a positive definite integral form, for the liapunov function of design, siWhen to be integrated to function the right and left Auxiliary variable, the variable k of Nussbaum gainiIt (t) is a continuous function and ki(0)=0, increased using above-mentioned Nussbaum The property of benefit obtains function V (t) and variable ki(t) boundedness.
5. the adaptive consistency control method according to claim 1 based on nonlinear object feedback system, feature It is: the adaptive consistency adjusting controller in the step (4), when Relative order is 1 are as follows:
Wherein, uiIt is inputted for the control of i-th of subsystem,ki(0)=0, kiFor the variable of Nussbaum gain, For the Nussbaum gain designed in step (3), γ is the normal number of a design, and Wherein, c1>=2 be the normal number of design, unknown parameter θiAdaptive law are as follows:θiIt is by φ (yi) in Unknown parameter and unknown control gain biThe unknown parameter vector of composition, ζiConsistency tracking error between the intelligent body of definition, The Laplacian Matrix piecemeal of grid connection are as follows:Wherein, qij=lij, Q=[qij]∈ RN×N,L0c=[l0j]∈R1×N, i, j=1 ..., N, ejFor the tracking error of definition, ei=yi-y0, i=1 ..., N, y0For root section The state put and the status information as pilotage people.
6. the adaptive consistency control method according to claim 1 based on nonlinear object feedback system, feature Be: in the step (5), Relative order is greater than adaptive consistency adjusting controller when 1 are as follows:
Wherein, the adaptive rate of unknown parameter is as follows:
Wherein, ξi,ρ-1For based on filter and backstepping method design ρ -1 step in controller,It is unknown Control gain bi,ρEstimated value, evaluated errorAnd filtering error For the normal number of design, according to design Filter obtainsDynamic be ζiFor consistency tracking error defined in step (5).
CN201810995048.9A 2018-08-29 2018-08-29 Self-adaptive consistency control method based on nonlinear output feedback system Active CN109143859B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810995048.9A CN109143859B (en) 2018-08-29 2018-08-29 Self-adaptive consistency control method based on nonlinear output feedback system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810995048.9A CN109143859B (en) 2018-08-29 2018-08-29 Self-adaptive consistency control method based on nonlinear output feedback system

Publications (2)

Publication Number Publication Date
CN109143859A true CN109143859A (en) 2019-01-04
CN109143859B CN109143859B (en) 2021-05-28

Family

ID=64828975

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810995048.9A Active CN109143859B (en) 2018-08-29 2018-08-29 Self-adaptive consistency control method based on nonlinear output feedback system

Country Status (1)

Country Link
CN (1) CN109143859B (en)

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110187637A (en) * 2019-06-03 2019-08-30 重庆大学 Robot system control method in the case where control direction and desired trajectory are uncertain
CN113126496A (en) * 2021-04-14 2021-07-16 西北工业大学 Aircraft self-adaptive tracking control method based on improved EKF state observer
CN113703451A (en) * 2021-08-24 2021-11-26 黄山学院 Self-adaptive fault-tolerant control method for formation of multiple mobile robots with preset performance
CN114609915A (en) * 2022-04-02 2022-06-10 东北电力大学 Time-varying multi-agent cooperative control method with unknown control direction

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6611823B1 (en) * 2000-04-20 2003-08-26 Board Of Regents, The University Of Texas System Backlash compensation using neural network
US8411872B2 (en) * 2003-05-14 2013-04-02 Ultra Electronics Limited Adaptive control unit with feedback compensation
CN103365296A (en) * 2013-06-29 2013-10-23 天津大学 Nonlinear output feedback flight control method for quad-rotor unmanned aerial vehicle
CN103645736A (en) * 2013-12-05 2014-03-19 哈尔滨工程大学 Non-linear H infinite inverse optimization output feedback controller-based ship course motion control method
CN106338919A (en) * 2016-11-02 2017-01-18 哈尔滨工程大学 USV (Unmanned Surface Vehicle) track tracking control method based on enhanced learning type intelligent algorithm
CN106647277A (en) * 2017-01-06 2017-05-10 淮阴工学院 Self-adaptive dynamic surface control method of arc-shaped mini-sized electromechanical chaotic system

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6611823B1 (en) * 2000-04-20 2003-08-26 Board Of Regents, The University Of Texas System Backlash compensation using neural network
US8411872B2 (en) * 2003-05-14 2013-04-02 Ultra Electronics Limited Adaptive control unit with feedback compensation
CN103365296A (en) * 2013-06-29 2013-10-23 天津大学 Nonlinear output feedback flight control method for quad-rotor unmanned aerial vehicle
CN103645736A (en) * 2013-12-05 2014-03-19 哈尔滨工程大学 Non-linear H infinite inverse optimization output feedback controller-based ship course motion control method
CN106338919A (en) * 2016-11-02 2017-01-18 哈尔滨工程大学 USV (Unmanned Surface Vehicle) track tracking control method based on enhanced learning type intelligent algorithm
CN106647277A (en) * 2017-01-06 2017-05-10 淮阴工学院 Self-adaptive dynamic surface control method of arc-shaped mini-sized electromechanical chaotic system

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
LI YONGMING等: "Observer-Based Adaptive Fuzzy Tracking Control of MIMO Stochastic Nonlinear Systems With Unknown Control Directions and Unknown Dead Zones", 《IEEE TRANSACTIONS ON FUZZY SYSTEMS》 *
ZHOU YING等: "Output feedback adaptive control of multivariable nonlinear systems using Nussbaum gain method", 《JOURNAL OF SYSTEMS ENGINEERING AND ELECTRONIC》 *
任长娥等: "基于高增益观测器的SISO非线性系统模糊自适应输出反馈控制", 《基于高增益观测器的SISO非线性系统模糊自适应输出反馈控制》 *

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110187637A (en) * 2019-06-03 2019-08-30 重庆大学 Robot system control method in the case where control direction and desired trajectory are uncertain
CN110187637B (en) * 2019-06-03 2021-12-10 重庆大学 Robot system control method under uncertain control direction and expected track
CN113126496A (en) * 2021-04-14 2021-07-16 西北工业大学 Aircraft self-adaptive tracking control method based on improved EKF state observer
CN113126496B (en) * 2021-04-14 2022-10-28 西北工业大学 Aircraft self-adaptive tracking control method based on improved EKF state observer
CN113703451A (en) * 2021-08-24 2021-11-26 黄山学院 Self-adaptive fault-tolerant control method for formation of multiple mobile robots with preset performance
CN113703451B (en) * 2021-08-24 2023-03-07 黄山学院 Self-adaptive fault-tolerant control method for formation of multiple mobile robots with preset performance
CN114609915A (en) * 2022-04-02 2022-06-10 东北电力大学 Time-varying multi-agent cooperative control method with unknown control direction
CN114609915B (en) * 2022-04-02 2023-01-31 东北电力大学 Time-varying multi-agent cooperative control method with unknown control direction

Also Published As

Publication number Publication date
CN109143859B (en) 2021-05-28

Similar Documents

Publication Publication Date Title
CN109143859A (en) A kind of adaptive consistency control method based on nonlinear object feedback system
CN108803316A (en) For the Active Fault-tolerant Control Method of multi-agent system actuator failures
CN110119087B (en) Fixed-time consistency tracking method for second-order multi-agent system under directed communication
CN112987567B (en) Fixed time self-adaptive neural network sliding mode control method of nonlinear system
CN110879535B (en) Sliding mode fault-tolerant control method of T-S fuzzy UMV
CN104950677A (en) Mechanical arm system saturation compensation control method based on back-stepping sliding mode control
CN108333949A (en) For the sliding formwork fault tolerant control method of multi-agent system actuator partial failure failure
CN105182743A (en) Robust H-infinity-based variable-gain decoupling control method
CN108388114A (en) A kind of flexible mechanical arm composite control method based on Output Redefinition
CN112305918A (en) Multi-agent system sliding mode fault-tolerant consistency control algorithm under supercoiled observer
CN106681345A (en) Crowd-searching-algorithm-based active-disturbance-rejection control method for unmanned plane
Liu et al. Distributed adaptive fuzzy control approach for prescribed-time containment of uncertain nonlinear multi-agent systems with unknown hysteresis
Gao et al. Adaptive integral backstepping control for a 3-DOF helicopter
CN110362103B (en) Distributed autonomous underwater vehicle attitude collaborative optimization control method
CN106647241B (en) Novel nonlinear PID controller
CN111439392B (en) Spacecraft formation position cooperative control method
Ye et al. Adaptive dynamic surface control of switched MIMO nonlinear systems with input saturation and its application to NSVs
CN109709807B (en) Self-adaptive neural network control method and device based on friction compensation
CN109108964B (en) Spatial mechanical arm coordination control method based on adaptive dynamic programming Nash game
CN113220007B (en) Flexible spacecraft finite time attitude cooperative control method for executing mechanism faults
CN111694272B (en) Self-adaptive control method and device of nonlinear multi-agent based on fuzzy logic system
CN110162084B (en) Formation control method of flying missile cluster system based on consistency theory
CN116466588A (en) Finite time-varying formation tracking control method and system for multi-agent system
Yi et al. Fixed-time connectivity-preserving consensus of periodically disturbed nonlinear multi-agent systems with limited communication ranges
Chen et al. Research on Adaptive Trajectory Tracking Algorithm for a Quadrotor Based on Backstepping and the Sigma‐Pi Neural Network

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