CN108227506A - A kind of robot admittance control system based on adaptive optimization method - Google Patents
A kind of robot admittance control system based on adaptive optimization method Download PDFInfo
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- CN108227506A CN108227506A CN201810127238.9A CN201810127238A CN108227506A CN 108227506 A CN108227506 A CN 108227506A CN 201810127238 A CN201810127238 A CN 201810127238A CN 108227506 A CN108227506 A CN 108227506A
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- robot
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- mechanical arm
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- 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
Abstract
The invention discloses a kind of robot admittance control systems based on adaptive optimization method, include the following steps：Method based on generalized momentum obtains the external torque that mechanical arm is subject to by force observer；Optimal admittance model is obtained using adaptive Optimal Control；Observation torque is input to, the mechanical arm for adapting to external torque amendment reference locus is available in optimal admittance model；Adaptive controller of the design based on neural network makes tracking error reach satisfactory value；Finally so that cost function value is minimum.The present invention is compared to traditional robot and environmental interaction control system, it can efficiently reduce the inconvenience brought due to the addition of force sensor apparatus to system, while robot generation can preferably be allowed to be adapted to the interbehavior of external environment using the method that adaptive optimal admittance controls.
Description
Technical field
The present invention relates to robot and circumstances not known interactive controlling fields more particularly to one kind to be based on adaptive optimization side
The robot admittance control system of method.
Background technology
Under the overall situation of artificial intelligence, the development of robot technology has important influence to the progress of science and technology.Artificial
The related industry of intelligence, such as industrial production, military affairs, medical treatment, amusement industry, to robot in yard more extensively, complicated
Application in scape proposes higher requirement.Robot needs to interact with external environment in task process is completed, with
The development of robot technology and the continuous improvement to independence requirement, interactive controlling of the robot under circumstances not known have obtained more
More concerns.
At present, the research for robot and external environment interactive controlling mainly has following methods：(1) based on impedance control
The method of system using robot impedance model, establishes external force and the relationship of robot location, and design controller adjusts machinery resistance
It is anti-.（2）Based on the method for admittance control, using the admittance model of robot, external force and the relationship of robot location, root are established
The external force being subject to according to robot, the reference locus of constantly regulate robot make robot although can also reach under the effect of external force
To good interaction effect.
Invention content
In order to overcome shortcoming and deficiency of the existing technology, the present invention provides a kind of based on adaptive optimization method
Robot admittance control system.The external estimated value for being applied to mechanical arm tail end torque, profit are obtained using state space observer
Obtain optimal admittance model with adaptive Optimal Control method, the estimated value of external force is input in admittance model obtain one it is suitable
The amendment reference locus of torque, reaches satisfied control effect finally by neural network control device outside Ying Yu.
In order to solve the above technical problems, the present invention provides following technical solution：Machine based on adaptive optimization method
People's admittance control system, includes the following steps：
S1, according to generalized momentum method and machine arm dynamical equation, build state space force observer；
S2, using adaptive Optimal Control method, obtain being adapted to the optimal admittance model of external torque and will be observed that torque
Numerical value be input in admittance model, obtain being adapted to reference locus after the amendment of moment of face；
S3, design neural network control device allow reference of the mechanical arm actual motion track well after tracking correction
Track；
Further, the kinetics equation of n connecting rod mechanical arms is in the step S1
Wherein,,Inertial matrix, coriolis force matrix and the gravity item of system.、Acceleration, speed and joint angle vector are represented respectively.,。
Further, the generalized momentum of mechanical arm is expressed as
Further, the differential equation of first order of momentum is
Further, the model of external torque is defined
Wherein,
Further, state space observer is built
=
y=
Wherein.
Further, the step S2 is specially：
External environment, can be regarded as a single order or second order spring-damper system by S21, under normal circumstances；
S22, in joint space, the admittance model of mechanical arm is
。
S23, consider that a continuous linear system is expressed as
Definition：
Wherein, A and B are unknown.
S24, using adaptive Optimal Control algorithm, obtain optimal input in the case where A B are unknown；
Assume initially that a gain that can stablize systemSide
Journey obtains a positive definite matrix, cycle calculations K+1 → K, whenIt can obtain feedback gain matrix。
Further, the step S3 is specially：
S31, neural network function model expression formula it is as follows：
+b
Wherein,The feature vector of input data,It is weight column vector,It is bias vector,Base is function；
S32, tracking error is defined：
Wherein,q-,
S33, the adaptive controller based on neural network are
After adopting the above technical scheme, the present invention at least has the advantages that：
（1）, method of the method for the present invention based on state space observer and adaptive Optimal Control blend, can overcome in reality
Due to inconvenience that added force sensor is brought in the control system of border；Mechanical arm can be obtained by the method for adaptive Optimal Control
Optimal admittance model, so meet mechanical arm and circumstances not known interactive controlling by correcting mechanical arm movement locus will
It asks.
（2）, the present invention is based on neural network controls, approach energy using radial basis function (RBF) neural network
Power estimates the unknown in control system, improve due in control system due to control accuracy that model uncertainty brings
The problem of loss；Simultaneously so that controller meets the requirement of real-time.
Description of the drawings
Fig. 1 is that the present invention is based on the schematic diagrames of state space force observer；
Fig. 2 is the algorithm steps flow chart the present invention is based on adaptive Optimal Control；
Fig. 3 is the system construction drawing of the robot admittance control system the present invention is based on adaptive optimization method；
Specific embodiment
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the application can phase
It mutually combines, the application is described in further detail below in conjunction with the accompanying drawings.
Step S1 is as shown in Figure 1, for schematic diagram of this example based on state space observer, the side based on generalized momentum
Method, using the Theory Construction state space equation of linear system, detailed process is as follows：
S11, in joint space, the dynamical equation of n connecting rod mechanical arms is：
Wherein,,Inertial matrix, coriolis force matrix and the gravity item of system.
、Acceleration, speed and joint angle vector are represented respectively.,。
S12, under joint space, the momentum of mechanical arm can be defined as
S13, it is applied to moment model outside mechanical arm tail end and is
=+
S14, above formula is write to state equation form as
S15, due toIt is an observable vector, a system with observer, definition status can be designed
Variable, then the state space equation of system be
=
y=
Wherein.
Step S2 adaptive Optimal Control algorithms are inputted according to system groupThe reciprocal force of mechanical arm and external environment
With the optimal admittance model obtained by the method for on-line study, it can be deduced that revised reference locus is defeated as the inner ring of system
Enter, be as follows
S21, mechanical arm reference locus can need to set according to specific tasks by operator, in situation without loss of generality
Under, the model of external environment can be defined as；
Wherein,With。
S22, secondly, on joint space, the admittance model of mechanical arm is
=-
When observing mechanical arm and extraneous reciprocal forceWhen, the inner ring input of system
WhenWhen for non-vanishing vector, inner ring inputIt will be revised track.
S23, the form that environmental model is write as to state space；
And it defines
∈
Wherein
,B=.A and B is unknown in the algorithm.
S25, generalAs the input of system, using the method for on-line study, the optimal input of state space is obtained；
WhereinIt is the system optimal gain obtained using the method for on-line study, this input can reduce cost function
V=
Wherein cost function is the target of entire interactive controlling, its definition is to weigh the interactive controlling of control system effect
Fruit, cost function is smaller, shows that the effect of interactive controlling is best；Q and R is weight matrix.
S26, define system initial input be
Wherein,For initial gain,It is noise.Definition
WhereinIt is fraction,, pass through calculating
=
It is obtained by solving homogeneous equation,
Definition
Wherein。
Finally, K+1 → K repeats the above steps, until, obtain
Step S3, the adaptive controller based on neural network is designed, primarily to ensuring a good track following effect
Fruit；
S31, first of all for reducing since model does not know the negative effect brought to controller, introduce neural network to estimate not
Perception model, expression formula are as follows：
+b
Wherein,The feature vector of input data,It is weight column vector,It is bias vector,It is basic function.
We introduce RBF (radial basis function) neural network in this example, the reason is that the structure of RBFNN is simple, training speed is fast, can be with
Meet the needs controlled in real time well.
S32, tracking error function variable is defined：
Wherein,q-,
The ANN Control input of S33, design with compensation term
Wherein,
Estimate item
+
+
+
As shown in figure 3, the signal for a kind of optimal interaction control method total system of robot without force snesor of this example
Figure.The present invention is based on force observer, the admittance model obtained by adaptive Optimal Control.Force observer estimates machinery first
Arm is obtained an optimal admittance model, then thus admittance model by the external force from environment by adaptive Optimal Control
Produce one can be adapted to external force very well accomplish reference locus, cause finally by neural network control device
Mechanical arm can be very good the input of tracking system inner ring, cost function be made to reach a minimum value by this control method, most
Reach good interaction effect eventually.
It although an embodiment of the present invention has been shown and described, for the ordinary skill in the art, can be with
Understand, can carry out a variety of equivalent changes to these embodiments without departing from the principles and spirit of the present invention
Change, change, replacing and modification, the scope of the present invention are limited by appended claims and its equivalency range.
Claims (6)
1. a kind of robot admittance control system based on adaptive optimization method, which is characterized in that include the following steps：
S1, according to generalized momentum method and machine arm dynamical equation, build state space force observer；
S2, using adaptive Optimal Control method, obtain being adapted to the optimal admittance model of external torque and will observe what is obtained
Torque is input in admittance model, obtains being adapted to reference locus after the amendment of moment of face；
S3, design neural network control device allow reference of the mechanical arm actual motion track well after tracking correction
Track.
2. robot admittance control system according to claim 1, which is characterized in that state space is seen in the step S1
It surveys device and includes the generalized momentum model of mechanical arm, the kinetics equation of mechanical arm and external moment model.
3. robot admittance control system according to claim 2, which is characterized in that the power of the connecting rod mechanical arm
Learning equation is
Wherein,,Inertial matrix, coriolis force matrix and the gravity item of system；
、Acceleration, speed and joint angle vector are represented respectively；,；
The generalized momentum of mechanical arm is expressed as
The model of the external torque of definition
。
4. robot admittance control system according to claim 3, which is characterized in that the first differential side of the momentum
Cheng Wei
State space observer is expressed as
=
y=
Wherein。
5. robot admittance control system according to claim 1, which is characterized in that the step S2 is specially：
S21, under normal circumstances, regards external environment as a single order or second order spring-damper system；
S22, in joint space, the admittance model of mechanical arm is
；
S23, a continuous linear system are expressed as
Definition：
Wherein, A and B are unknown；
S24, using adaptive Optimal Control algorithm, obtain optimal input in the case where A B are unknown；It is false first
If one can make the gain that system is stablizedEquation obtains
One positive definite matrix, cycle calculations K+1 → K, whenIt can obtain feedback gain matrix。
6. robot admittance control system according to claim 1, which is characterized in that the step S3 is specially：
S31, neural network function model expression formula it is as follows：
+b
Wherein,The feature vector of input data,It is weight column vector,It is bias vector,It is basic function；
S32, tracking error are defined as follows：
Wherein,q-,
S33, adaptive controller of the design based on neural network are
。
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109062032A (en) * | 2018-10-19 | 2018-12-21 | 江苏省（扬州）数控机床研究院 | A kind of robot PID impedance control method based on Approximate dynamic inversion |
| CN110597072A (en) * | 2019-10-22 | 2019-12-20 | 上海电气集团股份有限公司 | Robot admittance compliance control method and system |
| WO2020214787A1 (en) * | 2019-04-16 | 2020-10-22 | University Of Louisville Research Foundation, Inc. | Adaptive robotic nursing assistant |
| CN112689552A (en) * | 2018-07-16 | 2021-04-20 | 快砖知识产权私人有限公司 | Active damping system |
| CN112733423A (en) * | 2020-12-03 | 2021-04-30 | 重庆邮智机器人研究院有限公司 | Industrial robot inverse kinematics solving method based on PSO-RBFNN |
| CN112809666A (en) * | 2020-12-17 | 2021-05-18 | 安徽工业大学 | 5-DOF mechanical arm force and position tracking algorithm based on neural network |
| CN113352322A (en) * | 2021-05-19 | 2021-09-07 | 浙江工业大学 | Adaptive man-machine cooperation control method based on optimal admittance parameters |
| CN113568313A (en) * | 2021-09-24 | 2021-10-29 | 南京航空航天大学 | Variable admittance auxiliary large component assembly method and system based on operation intention identification |
| CN114800490A (en) * | 2022-03-22 | 2022-07-29 | 浙江工业大学 | Smart hand self-adaptive admittance control system and method for fine grabbing |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105159084A (en) * | 2015-09-06 | 2015-12-16 | 台州学院 | Manipulator nerve network control system with interference observer and control method |
| CN105242533A (en) * | 2015-09-01 | 2016-01-13 | 西北工业大学 | Variable-admittance teleoperation control method with fusion of multi-information |
| CN106483964A (en) * | 2015-08-31 | 2017-03-08 | 中南大学 | A kind of robot Shared control method based on contact force observer |
-
2018
- 2018-02-08 CN CN201810127238.9A patent/CN108227506A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106483964A (en) * | 2015-08-31 | 2017-03-08 | 中南大学 | A kind of robot Shared control method based on contact force observer |
| CN105242533A (en) * | 2015-09-01 | 2016-01-13 | 西北工业大学 | Variable-admittance teleoperation control method with fusion of multi-information |
| CN105159084A (en) * | 2015-09-06 | 2015-12-16 | 台州学院 | Manipulator nerve network control system with interference observer and control method |
Non-Patent Citations (4)
| Title |
|---|
| GUANGZHU PENG 等: ""Neural-learning Enhanced Admittance Control of a Robot Manipulator With Input Saturation"", 《2017 CHINESE AUTOMATION CONGRESS》 * |
| ISURA RANATUNGA 等: ""Adaptive Admittance Control for Human–Robot Interaction Using Model Reference Design and Adaptive Inverse Filtering"", 《IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY》 * |
| MARTINO CAPURSO 等: ""Sensorless Kinesthetic Teaching of Robotic Manipulators Assisted by Observer-based Force Control"", 《2017 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION》 * |
| YU JIANG 等: ""Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics"", 《AUTOMATICA》 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112689552A (en) * | 2018-07-16 | 2021-04-20 | 快砖知识产权私人有限公司 | Active damping system |
| CN109062032A (en) * | 2018-10-19 | 2018-12-21 | 江苏省（扬州）数控机床研究院 | A kind of robot PID impedance control method based on Approximate dynamic inversion |
| WO2020214787A1 (en) * | 2019-04-16 | 2020-10-22 | University Of Louisville Research Foundation, Inc. | Adaptive robotic nursing assistant |
| CN110597072A (en) * | 2019-10-22 | 2019-12-20 | 上海电气集团股份有限公司 | Robot admittance compliance control method and system |
| CN110597072B (en) * | 2019-10-22 | 2022-06-10 | 上海电气集团股份有限公司 | Robot admittance compliance control method and system |
| CN112733423A (en) * | 2020-12-03 | 2021-04-30 | 重庆邮智机器人研究院有限公司 | Industrial robot inverse kinematics solving method based on PSO-RBFNN |
| CN112809666A (en) * | 2020-12-17 | 2021-05-18 | 安徽工业大学 | 5-DOF mechanical arm force and position tracking algorithm based on neural network |
| CN113352322A (en) * | 2021-05-19 | 2021-09-07 | 浙江工业大学 | Adaptive man-machine cooperation control method based on optimal admittance parameters |
| CN113568313A (en) * | 2021-09-24 | 2021-10-29 | 南京航空航天大学 | Variable admittance auxiliary large component assembly method and system based on operation intention identification |
| CN114800490A (en) * | 2022-03-22 | 2022-07-29 | 浙江工业大学 | Smart hand self-adaptive admittance control system and method for fine grabbing |
| CN114800490B (en) * | 2022-03-22 | 2023-09-08 | 浙江工业大学 | Fine grabbing-oriented smart hand self-adaptive admittance control system and method |
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Application publication date: 20180629 |