CN113927596B - Width neural learning-based teleoperation limited time control method for time-varying output constraint robot - Google Patents
Width neural learning-based teleoperation limited time control method for time-varying output constraint robot Download PDFInfo
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Abstract
The invention discloses a time-varying output constraint robot finite time control method for width neural learning. Based on an integral obstacle Lyapunov function, a width neural learning algorithm and a finite time theory, a time-varying output constraint finite time controller based on width neural learning is innovatively provided. The direct integration barrier lyapunov function ensures that the output of the system is within the time-varying boundary; the width neural learning algorithm combines the advantages of the traditional neural network and the width learning, solves the problem of external force perception by utilizing the width learning algorithm based on the inverse dynamics observer, and simultaneously eliminates the control rate u xj Model uncertainty in the design process; the finite time theory ensures the high-precision and rapid tracking of the reference signal by the robot. In conclusion, the algorithm ensures stable, safe and efficient interaction between the robot and the environment, and improves the reliability and the efficiency of a teleoperation system.
Description
Technical Field
The invention belongs to the technical field of robot control, and particularly relates to a teleoperation limited time control method of a time-varying output constraint robot based on width neural learning.
Background
Teleoperation technology makes full use of human intelligence and the operational capabilities of robots. It extends the human perception and behavior ability in remote, unstructured and dangerous environments to a great extent, and is an indispensable key technology in deep space, deep sea and deep exploration. Compared with an intelligent robot, the teleoperation technology fully considers the defects of the current intelligent technology, such as decision-making problems related to emergency situations, and safety and constraint problems in operation. The technology combines human perception and decision making capability with the operation capability of the robot, integrally enhances the processing capability of a teleoperation system for emergency situations in an unstructured environment, and is the most realistic man-machine hybrid intelligent strategy at present.
The teleoperation system has complex composition, and an uncertain time delay is inevitably caused to the system by a generation mechanism of an operation instruction, remote transmission of the instruction and the like. The uncertain time delay seriously affects the stability of the system and degrades the performance of the system; model uncertainty also poses a threat to the stability of the system. At the same time, the robotic end effector needs to complete the operational tasks within the expected time while meeting the physical constraints, subject to constraints of the operational time and workspace. For example, in deep space exploration, when a robot performs an inspection task (such as through a narrow space), the robot needs to complete the on-orbit operation task in a limited time under the condition of ensuring safety.
Disclosure of Invention
The invention solves the technical problems that: based on the problems, the patent provides a robot teleoperation limited time control method with limited time-varying output, and provides a feasible scheme for a teleoperation system to develop actual work.
The technical scheme of the invention is as follows: a teleoperation limited time control method of a time-varying output constraint robot based on width neural learning is characterized by comprising the following steps:
step 1: carrying out dynamic modeling on the mechanical arm;
step 2: estimating the force of an operator and the environmental force through a width neural learning algorithm and an inverse dynamics observer;
step 3: and designing a time-varying output constraint finite time control law, resolving the uncertain influence of the model, and realizing high-precision tracking and rapid convergence of the teleoperation robot.
The invention further adopts the technical scheme that: the system is a pair of mechanical arms with n degrees of freedom, and the model expression is as follows:
wherein j is { m, s }, which are the master-slave robot identifications respectively.q j ∈R n×1 Acceleration, speed and position of joint space respectively; />x j ∈R n×1 Acceleration, speed and position of the operation space respectively; m is M xj As an inertial mass matrix, C xj For centrifugal and Golgi force matrices, g xj As a gravity term matrix, u xj Representing control inputs, F mν ＝F h Indicating the application of force by the operator, F sν ＝F e Representing the contact force between the slave robot and the environment.
The invention further adopts the technical scheme that: said step 2 comprises the sub-steps of:
step 2.1: linearizing the model to:
in the middle ofIs a linear regression matrix, eta is a parameter vector related to the mechanical arm, and theta epsilon R n×1 Is the product of the two;
step 2.2: external force assessment using a deep neural learning algorithm:
wherein q is j ,Is a neural networkIs input to the computer;
step 2.3: based on the inverse dynamics observer, the dynamics model (15) of the mechanical arm is further linearized into
In the middle ofIs a linear regression matrix, eta is a parameter vector related to the mechanical arm, and theta epsilon R n×1 Is the product of the two;
step 2.4: the deep neural learning algorithm is designed to realize the estimation of external force:
the input of the neural network is q j ,The interactive force can be estimated through a width neural learning algorithm.
The invention further adopts the technical scheme that: the step 3 comprises the following substeps:
step 3.1: by designing the controller u xj Realize the reference trackLimited time tracking while guaranteeing output x η1 Within the restricted area, i.e.)>
The control law of the master-slave robot is that
The [ (x) ray ]22 In (c):is->Is generalized inverse of (1), satisfies the following conditions
The update law in equation (22) is selected as follows:
error variable e in formula (22) j1 ,e j2 J epsilon { m, s } is
Wherein:alpha is the reference track of the master and slave ends j1 Is the virtual control quantity to be designed.
Equation (10) may then generate a reference trajectory for the master based on operator behavior,
formula (27) whereinRepresenting an estimate of the operator force and the ambient force, respectively, wherein: />Acceleration, speed and position of the main end reference track respectively; />Scaling factors for the operator force estimate and the ambient force estimate, respectively; m is M r ,C r ,g r Target impedance parameters for the operator's behavior, respectively;
step 3.2: the reference track of the slave end is:T f (t) is the network transmission delay from the master end to the slave end; designing virtual control quantity alpha j1 ：
Wherein 0 < xi j ＜1，And gamma j1 The method comprises the following steps of:
in formula (22):the operator force and the contact force between the robot and the environment, k, respectively j1 ,k j2 ,χ j ,b j Is of normal number>Is a right-angle array. The weight vector of the neural network is W obtained by the step 2 wide neural learning algorithm j ，/>
Effects of the invention
The invention has the technical effects that: the invention aims to solve the problems of instability of a system, difficulty in external force perception of interaction between a robot and an unknown environment, limitation of operation space and operation time of the robot and the like caused by time delay and uncertainty in a teleoperation system, and provides a teleoperation limited time control method of a time-varying output constraint robot based on width neural learning. The width neural learning algorithm effectively combines incremental learning and RBF neural network, realizes estimation of operator force and environmental force, and simultaneously eliminates negative influence caused by uncertainty of a system model; the time-varying output constraint algorithm ensures that the end position of the robot does not exceed a time-varying boundary, and improves the safety of operation; the limited time control method ensures a fast tracking capability of the robot trajectory. The method does not need a force sensor, the model of the system does not need to be known accurately, and the high-precision tracking and the rapid control of the teleoperation robot are realized on the premise of ensuring the safe operation of the system.
Compared with the prior art, the invention has the following advantages:
(1) The invention designs a width neural learning algorithm to realize the estimation of the force of an operator and the environmental force; and meanwhile, the influence of model uncertainty in the design of the controller is eliminated. Compared with the traditional neural network, the network can ensure higher estimation accuracy, save a great amount of calculation pressure and does not need sufficient learning conditions;
(2) The finite time controller with strong robustness is designed, and finite time control of the robot under the condition of limited operation time is realized. The invention has faster convergence speed and achieves the aim of high-efficiency interaction between the robot and the environment.
(3) The invention considers the safety problem in the operation process, constrains the output of the robot, and the constraint boundary is a time-varying boundary, and the constant boundary is a special form of the time-varying boundary, so the method has stronger practicability and practical significance.
Drawings
FIG. 1 is a diagram of a teleoperation system control framework;
FIG. 2 is a broad neural learning algorithm framework;
FIG. 3 is a inverse dynamics based breadth-neural learning algorithm;
FIG. 4 is a graph of simulated effects (x, y, z axes as an example); (a) The graph shows the tracking effect on the x-axis and the y-axis
(b) The graph shows the tracking effect on the z-axis and the operation space
Detailed Description
Referring to fig. 1-4, the specific steps of the method are as follows:
step one: dynamic modeling of systems
Step two: a width neural learning algorithm is designed, and then estimation of operator force and environmental force is realized based on an inverse dynamics observer;
step three: the design of the time-varying output constraint finite time control law, the resolution of the uncertain influence of the model, the realization of the high-precision tracking and the rapid convergence of the teleoperation robot (the master end is similar to the slave end control law, so the variable j epsilon { m, s } is uniformly expressed)
By combining the steps, stable, safe and efficient interaction between the teleoperation system and the environment can be realized.
Teleoperation systems are complex and it is therefore necessary to outline the overall framework in order to develop the subsequent discussion more clearly.
Step one: the system consists of a pair of mechanical arms with n degrees of freedom, the dynamics form in an operation space is as follows, and for simplicity, the dynamics of a master end and a slave end are uniformly written as follows:
wherein: j is { m, s }, is the master-slave robot identity, respectively.q j ∈R n×1 Acceleration, velocity and position of the joint space, respectively. />x j ∈R n×1 Acceleration, speed and position of the operating space, respectively. M is M xj As an inertial mass matrix, C xj For centrifugal and Golgi force matrices, g xj As a gravity term matrix, u xj Representing control inputs, F mν ＝F h Indicating the application of force by the operator, F sν ＝F e Representing the contact force between the slave robot and the environment.
Step two: based on the inverse dynamics observer, a width neural learning algorithm is designed to estimate the interaction force (the contact force between the master end operator and the master end robot, the contact force between the slave end robot and the environment). The kinetic model (15) of the mechanical arm can be linearized into
In the middle ofIs a linear regression matrix, eta is a parameter vector related to the mechanical arm, and theta epsilon R n×1 Is the product of the two. Because the mechanical arm cannot be accurately modeled and the model itself has certain deviation, the traditional inverse dynamics observer cannot accurately estimate the external force. Therefore, the deep neural learning algorithm is designed to realize the estimation of the external force:
the input of the neural network is q j ,The interactive force can be estimated through a width neural learning algorithm. Because of the change of external environment and uncertainty of the system, the traditional algorithm needs to adjust the parameters of the neural network and retrain the parameters, while the wide nerve designed by the inventionThe learning algorithm combines RBF and incremental learning, and adaptively increases nodes by setting threshold bias to achieve a better training network, see FIG. 2. Based on the inverse dynamics observer, the interactive force can be estimated by using the wide neural network algorithm; meanwhile, the width neural learning algorithm can compensate uncertainty of a system model. Pseudo codes based on the breadth-neural learning algorithm are shown in Table one.
Table one learning algorithm based on width nerve
In the table: x is heel q j ,The related input vectors, W and beta are respectively the weight vector and radial basis vector of the RBF neural network, A m And A m+1 Are all defined pattern matrices.
Wherein: d= (a m ) + H m+1 ，
Wherein c=h m+1 -A m And D, the weight is thus available:
W ei ＝(λI+(A m ) T A m ) -1 (A m ) T Y (20)
step three: in order to secure the safety of the operation, it is necessary to limit the position output of the robot, that is, the end position of the robot is within the time-varying boundary. The patent adopts a direct obstacle Lyapunov function (IBLF), a width neural learning algorithm and a finite time theory, and firstly proposes a time-varying output constraint finite time control method based on the width neural learning.
Control target: by designing the controller u xj Realize the reference trackLimited time tracking while guaranteeing output x η1 Within the restricted area, i.e.)>
The control law of the master-slave robot is designed as
In formula (22):is->Is generalized inverse of (1), satisfies the following conditions
The update law in equation (22) is selected as follows:
error variable e in formula (22) j1 ,e j2 J epsilon { m, s } is
Wherein:alpha is the reference track of the master and slave ends j1 Is the virtual control quantity to be designed.
Equation (26) may then generate a reference trajectory for the master based on operator behavior,
formula (27) whereinRepresenting the estimated values of the operator force and the environmental force, respectively (without the force measuring device, estimated in step one using the width neural learning algorithm, see step two), wherein: />Acceleration, speed and position of the main end reference track respectively; />Scaling factors for the operator force estimate and the ambient force estimate, respectively; m is M r ,C r ,g r Respectively, target impedance parameters of the operator's behavior.
The reference track of the slave end is:T f and (t) is the network transmission delay from the master end to the slave end. Equation (22), designed virtual control amount α j1
Wherein 0 < xi j ＜1，And gamma j1 The method comprises the following steps of:
in formula (22):the operator force and the contact force between the robot and the environment, k, respectively j1 ,k j2 ,χ j ,b j Is of normal number>Is a right-angle array. The weight vector of the neural network is W obtained by the step 2 wide neural learning algorithm j ，/>
For a teleoperation system (15), virtual control quantity (28), control quantity (22) and update law (24) are selected, so that the closed-loop stability of the teleoperation system is ensured, and meanwhile, the safe and efficient interaction between the robot and the environment is realized.
Overall flow framework of the system: a teleoperational system control framework based on time-varying output constraints is shown in fig. 1. Position signal x of main terminal m (t) transmitting the reference signal to the slave terminal through the communication link to obtain the reference signal of the slave terminalTime-varying output constraint finite time controller u based on width neural learning is then designed xs (see step 3) the slave robot can be enabled to do +.>Is provided. Meanwhile, the environment force of the slave end is estimated by using a width neural learning algorithm (see step 2), and virtual environment parameters are transmitted to the master end. And at the master end, reconstructing the environment force of the slave end at the master end by utilizing the motion information of the master end and the virtual environment parameters of the slave end. In order to enable an operator to have better force perception, a reference track of the main end is generated based on the behavior of the operator. Then, a time-varying output constraint finite time controller u based on width neural learning is designed at the main end xm (see step 3) to realize the main end robot to the main end reference signal +.>Is provided. (due to master control law u) xm And slave control law u xs The form is the same, so the expression is unified
In summary, the invention discloses a time-varying output constraint robot finite time control method for wide neural learning. Based on an integral obstacle Lyapunov function, a width neural learning algorithm and a finite time theory, a time-varying output constraint finite time controller based on width neural learning is innovatively provided. The direct integration barrier lyapunov function ensures that the output of the system is within the time-varying boundary; the width neural learning algorithm combines the advantages of the traditional neural network and the width learning, solves the problem of external force perception by utilizing the width learning algorithm based on the inverse dynamics observer, and simultaneously eliminates the control rate u xj Model uncertainty in the design process; the finite time theory ensures the high-precision and rapid tracking of the reference signal by the robot. In conclusion, the algorithm ensures stable, safe and efficient interaction between the robot and the environment, and improves the reliability and the efficiency of a teleoperation system.
Claims (1)
1. A teleoperation limited time control method of a time-varying output constraint robot based on width neural learning is characterized by comprising the following steps:
step 1: carrying out dynamic modeling on the mechanical arm; the mechanical arm is a pair of mechanical arms with n degrees of freedom, and the model expression is as follows:
wherein j is { m, s, is the master-slave robot identity respectively;acceleration, speed and position of joint space respectively; />Acceleration, speed and position of the operation space respectively; m is M xj As an inertial mass matrix, C xj For centrifugal and Golgi force matrices, g xj As a gravity term matrix, u xj Representing a control input;
step 2: estimating the force of an operator and the environmental force through a width neural learning algorithm and an inverse dynamics observer;
step 2.1: linearizing the model to:
in the method, in the process of the invention,is a linear regression matrix, eta is a parameter vector related to the mechanical arm, and theta epsilon R n×1 Is the product of the two;
step 2.2: external force assessment using a deep neural learning algorithm:
wherein the method comprises the steps ofIs an input to the neural network;
step 2.3: based on the inverse dynamics observer, the dynamics model (1) of the mechanical arm is further linearized into:
in the middle ofIs a linear regression matrix, eta is a parameter vector related to the mechanical arm, and theta epsilon R n×1 Is the product of the two;
step 2.4: the deep neural learning algorithm is designed to realize the estimation of external force:
the input of the neural network isThe interactive force can be estimated through a width neural learning algorithm;
step 3: and designing a time-varying output constraint finite time control law, resolving the uncertain influence of the model, and realizing high-precision tracking and rapid convergence of the teleoperation robot.
Step 3.1: by designing the controller u xj Realize the reference trackLimited time tracking while guaranteeing output x η1 Within the restricted area, i.e.)>
The control law of the master-slave robot is as follows:
in formula (6):the operator force and the contact force between the robot and the environment, respectively +.>Is->Is generalized inverse of (1), satisfies the following conditions
The update law in equation (6) is selected as follows:
error variable e in formula (6) j1 ,e j2 J epsilon { m, s } is
Wherein:alpha is the reference track of the master and slave ends j1 The virtual control quantity to be designed;
a reference trajectory of the master may be generated based on operator behavior,
in (10) whereinRepresenting an estimate of the operator force and the ambient force, respectively, wherein: />Acceleration, speed and position of the main end reference track respectively; />Scaling factors for the operator force estimate and the ambient force estimate, respectively; mr, cr and gr are respectively target impedance parameters of the operator behavior;
step 3.2: the reference track of the slave end is:tf (t) is the network transmission delay from the master end to the slave end; designing virtual control quantity alpha j1 ：
Wherein, the xi j is more than 0 and less than 1,and γj1 are respectively:
wherein: k (k) j1 ,k j2 ,χ j ,b j Is a normal number of times, and the number of times is equal to the normal number,is a right-angle array; the weight vector of the neural network is W obtained by the step 2 wide neural learning algorithm j ，/>
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