CN112327634B - Underwater robot attitude control method based on BP neural network S-surface control - Google Patents
Underwater robot attitude control method based on BP neural network S-surface control Download PDFInfo
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Abstract
The invention discloses an underwater robot attitude control method based on BP neural network S-surface control, which comprises the steps of establishing a dynamic model for an underwater robot, respectively carrying out modeling analysis on disturbance power of an umbilical cable and a manipulator, establishing an anti-disturbance control coordination controller mainly based on a global kinematics control loop and a disturbance power compensation item, and ensuring global convergence of the underwater robot coordination controller by adopting the S-surface control based on the BP neural network; in the design of the dynamics control law, the disturbance force of a manipulator and an umbilical cable on the underwater robot in the operation process is considered, the motion of a propeller is controlled to compensate, and the stable and accurate control of the posture of the underwater robot in the operation process is realized.
Description
Technical Field
The invention relates to underwater robot attitude control, in particular to an underwater robot attitude control method based on BP neural network S-surface control.
Background
The key point of underwater rescue operation is underwater search and underwater rescue operation, the search and rescue by manpower is limited, and the tasks can be completely finished by an underwater robot. The underwater robot has the greatest characteristics of strong deep water operation capability and simple and convenient operation, and an operator can remotely control the robot to perform high-difficulty operation underwater through a simple button of a control console in a ground control room. The underwater robot can complete high-strength and heavy-load underwater rescue operation in a depth and an unsafe water area which cannot be reached by divers. The control of the underwater robot posture is particularly important.
The attitude control algorithm of the current common underwater robot comprises the following steps: sliding mode control, PID algorithm control, fuzzy PID algorithm control, neural network algorithm control, S-surface control and the like. The chattering phenomenon is one of the biggest obstacles to the application of sliding mode control to practical control problems. The PID control is the control algorithm which is most widely applied, but in the area beyond the deviation working point, the PID controller is difficult to achieve satisfactory control performance, and the PID control does not have self-adaptive capacity. The adaptive control is based on an accurate mathematical model and is mainly established on the basis of a linear control theory, a closed-loop system is required to have strong inhibition capability on various interferences and low sensitivity on parameter change, the adaptive control can stably operate under various working conditions and environments, and the application of the adaptive control in the motion control of the underwater robot is influenced to a great extent by the limitation conditions.
The S-surface control algorithm has the characteristics of simple structure, small input quantity, suitability for the work of the underwater robot and the like. However, the control parameters depend on manual setting, which brings inconvenience to the actual field application. When the attitude of the underwater robot is controlled, the disturbance of a manipulator and an umbilical cable of the robot is also an important influence factor.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the defects, the invention provides the underwater robot attitude control method which adopts a BP neural network to autonomously complete the parameter initialization and online adjustment of an S-surface controller and considers the disturbance of a manipulator and an umbilical cable.
The technical scheme is as follows: in order to solve the problems, the invention adopts an underwater robot attitude control method based on BP neural network S-surface control, which comprises the following steps:
(1) constructing a body coordinate system and an inertia coordinate system of the underwater robot, establishing a coordinate system for an umbilical cable of the underwater robot and establishing a connecting rod coordinate system for a manipulator of the underwater robot;
(2) establishing an underwater robot dynamic model, and analyzing disturbance force of an umbilical cable and a manipulator to the underwater robot;
(3) ensuring the overall convergence of the underwater robot coordination controller by adopting S-surface control based on a BP neural network;
(4) and constructing the underwater robot coordination controller based on the dynamic model.
Further, in step 2, the underwater robot dynamics model is:
wherein M is an inertia matrix including additional mass, C (v) is a Coriolis and centripetal force matrix of the underwater robot, D (v) is a viscous water-like power matrix, G (eta) is a force or moment vector generated by gravity and buoyancy together, and QdForce or moment generated by interference of an umbilical cable and a manipulator on the underwater robot, wherein tau is driving force and moment generated by a propeller of the underwater robot; v is the velocity vector of the system and,
is the acceleration of the system.
Further, in step 3, a Sigmoid curve is adopted to perform nonlinear fitting on the fuzzy control rule table, and the obtained S-plane controller is:
wherein k is1，k2Is a control parameter of the S-plane controller, k1The proportional parameter influences the rise time and overshoot amplitude of control; k is a radical of2The derivative coefficient influences the overshoot amplitude and the stability of the control. e, the number of the first and second groups,
deviation and deviation change rate; u is the control output, and the range of the reverse maximum output to the forward maximum output is [ -1, 1 [ ]]。
Further, a BP neural network is adopted to input control parameters for the S-plane controller, and the specific steps are as follows:
(3.1) selecting the number i of nodes of an input layer, the number j of nodes of a hidden layer and the number k of nodes of an output layer, initializing a network weight, and selecting a learning rate, wherein t is equal to 1;
(3.2) the deviation at time t is compared with the derivative e of the deviation(t)、
As BP neural network input, calculating each layer output in forward propagation to obtain S-plane controller parameter k1And k is2；
(3.3) calculating S-plane controller output, sampling control object response, and updating e (t) and
(3.4) online learning of the BP neural network, and adjusting the weights of the output layer and the hidden layer through back propagation;
(3.5) let t be t +1, return to step 2.
Further, in step 4, the underwater robot coordination controller based on the dynamic model is:
wherein the content of the first and second substances,
for the observation matrix representing mass, inertia and additional mass,
is an S-surface kinematic control loop; k is a radical of1，k2By being a control parameter, the control unit is,
to expect acceleration, I ═ 1,1, 1,1, 1,1]T，
And
input deviation and deviation change rate respectively;
an observation matrix of coriolis forces induced by the mass and the additional mass;
is a coriolis force (including that caused by the additional mass); q is the position of the underwater robot in the body coordinate system,
is the speed of the underwater robot and,
force τ is controlled for observed disturbance forces caused by umbilical and robot motionctrlAnd finally, the underwater robot thrust distribution module is cooperatively realized by all the propellers.
Has the advantages that: compared with the prior art, the method has the obvious advantages that the global convergence of the underwater robot coordination controller is ensured by adopting a BP neural network S-surface control method; in the design of the dynamics control law, the disturbance force of a manipulator and an umbilical cable on the underwater robot in the operation process is considered, the motion of a propeller is controlled to compensate, and the stable and accurate control of the posture of the underwater robot in the operation process is realized.
Drawings
FIG. 1 is a flow chart of a coordination controller of an underwater robot based on a dynamic model;
fig. 2 is a coordinate system constructed for the underwater robot according to the present invention.
Detailed Description
As shown in fig. 1, an underwater robot attitude control method based on the S-plane control of the BP neural network in this embodiment includes the following steps:
(1) constructing a body coordinate system and an inertia coordinate system of the underwater robot, establishing a coordinate system for an umbilical cable of the underwater robot, and establishing a connecting rod coordinate system for joints of a manipulator of the underwater robot by adopting a D-H method;
(2) establishing a dynamic model of the underwater robot, and analyzing and modeling disturbance force of the umbilical cable and the manipulator to the underwater robot;
as shown in figure 2 of the drawings, in which,
η＝[η1 η2]Tposition and attitude angles of the underwater robot under a motion coordinate system, wherein eta1＝[x y z]T，
v＝[v1 v2]TLinear and angular velocities, v, of underwater robots in a fixed coordinate system1＝[u v w]T，v2＝[p q r]T；
τ＝[τ1 τ2]TAll acting forces and moments, tau, received by the underwater robot in a body coordinate system1＝[X Y Z]T，τ2＝[K M N]T；
The underwater robot dynamic model is as follows:
wherein M is an inertia matrix including additional mass, C (v) is a Coriolis and centripetal force matrix of the underwater robot, D (v) is a viscous water-like power matrix, G (eta) is a force or moment vector generated by gravity and buoyancy together, and QdForce or moment generated by external interference (an umbilical cable and a manipulator) on the underwater robot, wherein tau is driving force and moment generated by the underwater robot propeller; v is the velocity vector of the system and,
is the acceleration of the system.
Wherein C (v) ═ CRB+CA，vξ＝v-vd，CRBIs a matrix of Coriolis centripetal forces, CAIs an additional quality matrix.
The center of gravity of the underwater robot is xG＝yG＝zGThe inertia matrix M is 0:
c (v) from a Coriolis centripetal force matrix CRBAnd an additional quality matrix CAComposition, expressed as:
the G (η) restoring force matrix is the gravity and buoyancy vectors, expressed as:
wherein W and B are gravity and buoyancy, respectively, and xB、yBAnd zBAnd the floating center coordinates of the underwater robot.
Analyzing the disturbance force of the umbilical and the manipulator, the dynamic model of the umbilical in the fluid can be described as:
wherein: s is an unstretched lagrange coordinate system; t is the tension applied to s, m1Mass per unit length of umbilical; a is inertia acceleration; w is the force of gravity in the water for an umbilical of unit length and F is the fluid force per unit length.
The underwater robot is subjected to the interference force of the manipulator as follows:
Fdis＝g(η)+τmom
wherein, taumomCoupling torque brought to the robot by the movement of the mechanical arm; g (eta) is the recovery moment of the underwater robot system; eta is the position and the posture of each part of the underwater robot and the manipulator. Using Newton-Euler method to reversely recur and perform dynamic analysis on the system, and using interference torque Q of mechanical armmCan be expressed as:
wherein the content of the first and second substances,
and
the gravity and the buoyancy of the ith generalized connecting rod of the manipulator under a carrier coordinate system;
and
respectively the gravity and the buoyancy force borne by the carrier under the boat body coordinate system;
and
the gravity center and the floating center of the ith generalized connecting rod of the manipulator are respectively relative to the position of the unmanned underwater robot.
(3) Ensuring the overall convergence of the underwater robot coordination controller by adopting S-surface control based on a BP neural network; firstly, nonlinear fitting is carried out on a fuzzy control rule table by adopting a Sigmoid curve to obtain an S-surface controller:
wherein k is1，k2Is a control parameter of the S-plane controller, k1The proportional parameter influences the rise time and overshoot amplitude of control; k is a radical of2The derivative coefficient influences the overshoot amplitude and the stability of the control. e, the number of the first and second groups,
deviation and deviation change rate; u is the control output, and the range of the reverse maximum output to the forward maximum output is [ -1, 1 [ ]]。
Then, a BP neural network is adopted to input control parameters for an S-plane controller, adjustment is carried out through online learning, and the setting process of the control parameters from initial value selection to online setting is automatically completed, and the method specifically comprises the following steps:
(3.1) selecting the number i of nodes of an input layer, the number j of nodes of a hidden layer and the number k of nodes of an output layer, initializing a network weight, and selecting a learning rate, wherein t is equal to 1;
(3.2) comparing the deviation at time t with the derivative of the deviation e (t),
As BP neural network input, calculating each layer output in forward propagation to obtain S-plane controller parameter k1And k is2；
(3.3) calculating S-plane controller output, sampling control object response youtAnd update e (t) and
and selecting a performance index function as follows:
wherein r isinIs a control input.
(3.4) online learning of the BP neural network, and adjusting the weights of the output layer and the hidden layer through back propagation;
(3.5) let t be t +1, return to step (3.2).
(4) The method for constructing the underwater robot coordination controller based on the dynamic model comprises the following steps:
wherein the content of the first and second substances,
an observation matrix representing mass, inertia, and additional mass;
is an S-surface kinematic control loop; k is a radical of1，k2Adopting BP neural network control as control parameter;
is a desired acceleration; i ═ 1,1, 1,1, 1]T；
And
input deviation and deviation change rate respectively;
an observation matrix of coriolis forces induced by the mass and the additional mass;
disturbance forces caused by observed umbilical and robot motion;
is a coriolis force (including that caused by the additional mass); q is the position of the underwater robot in the body coordinate system,
for the velocity of the underwater robot, controlling the force tauctrlAnd finally, the underwater robot thrust distribution module is cooperatively realized by all the propellers.
The first item in the underwater robot coordination controller forms a kinematics control law of the controller, the last three items form a dynamics control law based on an observer, in the coordination control process, the dynamics control law observes and compensates interference force, and the kinematics control law compensates position deviation of the interference force on the underwater robot.
Claims (5)
1. The underwater robot attitude control method based on BP neural network S-surface control is characterized by comprising the following steps:
(1) constructing a body coordinate system and an inertia coordinate system of the underwater robot, establishing a coordinate system for an umbilical cable of the underwater robot and establishing a connecting rod coordinate system for a manipulator of the underwater robot;
(2) establishing an underwater robot dynamic model, and analyzing disturbance force of an umbilical cable and a manipulator to the underwater robot;
(3) ensuring the overall convergence of the underwater robot coordination controller by adopting S-surface control based on a BP neural network;
(4) constructing an underwater robot coordination controller based on a dynamic model; the underwater robot coordination controller based on the dynamic model comprises:
wherein the content of the first and second substances,
for the observation matrix representing mass, inertia and additional mass,
is an S-surface kinematic control loop; k is a radical of1，k2In order to control the parameters of the device,
to expect acceleration, I ═ 1,1, 1,1, 1,1]T，
And
are respectively the input deviationAnd a rate of change of deviation;
an observation matrix of coriolis forces induced by the mass and the additional mass;
is a Coriolis force, q is the position of the underwater robot in a body coordinate system,
is the speed of the underwater robot and,
force τ is controlled for observed disturbance forces caused by umbilical and robot motionctrlAnd finally, the underwater robot thrust distribution module is cooperatively realized by all the propellers.
2. The attitude control method of an underwater robot according to claim 1, wherein a link coordinate system is established for a manipulator of the underwater robot by a D-H method.
3. The attitude control method of an underwater robot as claimed in claim 1, wherein in the step (2), the dynamics model of the underwater robot is:
wherein M is an inertia matrix including additional mass, C (v) is a Coriolis and centripetal force matrix of the underwater robot, D (v) is a viscous water-like power matrix, G (eta) is a force or moment vector generated by gravity and buoyancy together, and QdForce or moment generated by interference of an umbilical cable and a manipulator on the underwater robot, wherein tau is driving force and moment generated by a propeller of the underwater robot; v is the velocity vector of the system and,
is the acceleration of the system.
4. The underwater robot attitude control method according to claim 1, wherein in the step (3), a Sigmoid curve is adopted to perform nonlinear fitting on a fuzzy control rule table, and an S-plane controller is obtained as follows:
wherein k is1，k2Is a control parameter of the S-plane controller, k1The proportional parameter influences the rise time and overshoot amplitude of control; k is a radical of2The derivative coefficient influences the overshoot amplitude and the stability of control; e, the number of the first and second groups,
deviation and deviation change rate; u is the control output, and the range of the reverse maximum output to the forward maximum output is [ -1, 1 [ ]]。
5. The attitude control method of the underwater robot as claimed in claim 4, wherein a BP neural network is adopted to input control parameters for an S-plane controller, and the method comprises the following specific steps:
(3.1) selecting the number i of nodes of an input layer, the number i of nodes of a hidden layer and the number k of nodes of an output layer, initializing a network weight, and selecting a learning rate, wherein t is equal to 1;
(3.2) comparing the deviation at time t with the derivative of the deviation e (t),
As BP neural network input, calculating each layer output in forward propagation to obtain S-plane controller parameter k1And k is2；
(3.3) calculating S-plane controller output, sampling control object response, and updating e (t) and
(3.4) online learning of the BP neural network, and adjusting the weights of the output layer and the hidden layer through back propagation;
(3.5) let t be t +1, return to step (3.2).
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| CN113093771B (en) * | 2021-04-07 | 2022-07-26 | 安徽大学 | Neural network-based modeling method and system for underwater robot-manipulator system |
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| CN114035591B (en) * | 2021-11-15 | 2023-05-02 | 哈尔滨工程大学 | Motion switching control method of underwater variable-curvature wall surface motion robot |
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