CN-121989215-A - Sensorless force position control method for cable-driven robot

Abstract

The invention discloses a sensorless force position control method of a cable-driven robot, which comprises the steps of 1) establishing a dynamic model of the cable-driven robot containing unknown interference force, 2) establishing a control structure for seamlessly switching cable force control and position and posture control, 3) designing a real-time cable force distribution algorithm meeting given control constraint conditions and any rope number, obtaining position and posture estimation based on a positive kinematic model and a Newton iteration method, solving motor torque by utilizing the dynamic model and friction force compensation, 4) establishing a state observer for predicting the unknown interference force and a quick position and posture error tracking controller, and 5) designing a cable force compensation optimization algorithm to ensure that the output of the controller is always nonnegative. According to the invention, the manual control and the position control of the cable-driven robot and the seamless switching of the manual control and the position control of the cable-driven robot are realized under the condition of no sensor, and the dependence of the manual control and the position control of the cable-driven robot on a force or torque sensor is effectively solved.

Inventors

• SUN JUAN
• ZHENG WENJIE

Assignees

• 滨州市技师学院(博兴县职业中等专业学校)

Dates

Publication Date

20260508

Application Date

20240327

Claims (1)

1. 1. The sensorless force position control method of the cable-driven robot is characterized by comprising the following steps of: step 1, establishing a dynamic model of a cable-driven robot containing unknown interference force, wherein the model is as follows; Wherein, the X= [ x p ,y p ,z p ,α,β,γ] T ] is 6 degrees of freedom of the end platform of the cable-driven robot in the 3D space, T d is used for describing errors of predicted cable force and actual cable force, J is a jacobian matrix of the robot, u is a control input, m is the mass of the end platform, I p is the moment of inertia of the end platform about an origin of a relative coordinate system, And 2, constructing a state observer under unknown interference force. Firstly, establishing an error model: the observer for predicting the unknown disturbance force is established according to the error model as follows: Wherein x d ＝[x pd ,y pd ,z pd ,α d ,β d ,γ d ] T is the target track, the tracking error is e=x-x d , the diagonal matrix β 1 ＝diag([β 11 ,β 12 ,β 13 ,β 14 ,β 15 ,β 16 ) and β 2 ＝diag([β 21 ,β 22 ,β 23 ,β 24 ,β 25 ,β 26 ) take positive values. z 1 is the rate of change of tracking error in the error model Z 2 takes on the values indicated above, The approximate disturbance solved for the observer. Order the E z2 ＝z 2 -(-M -1 (x)T d ) to further obtain the observer error: Step 3, designing a quick pose error tracking controller, as follows: H=diag([D(1),D(2),D(3),D(4),D(5),D(6)]) Wherein, the Gamma 1 ,γ 2 and gamma 3 are positive values and 1< gamma 2 ＜2,γ 1 ＞γ 2 the diagonal elements of the diagonal matrices α＝diag([α 1 ,α 2 ,α 3 ,α 4 ,α 5 ,α 6 ]),β＝diag([β 1 ,β 2 ,β 3 ,β 4 ,β 5 ,β 6 ]),K 1 ＝diag([k 11 ,k 12 ,k 13 ,k 14 ,k 15 ,k 16 ]) and K 2 ＝diag([k 21 ,k 22 ,k 23 ,k 24 ,k 25 ,k 26 ) are positive values. Definition σ= [ σ 1 ,σ 2 ,σ 3 ,σ 4 ,σ 5 ,σ 6 ] T , And fsat(σ)＝[fsatσ 1 ,fsatσ 2 ,fsatσ 3 ,fsatσ 4 ,fsatσ 5 ,fsatσ 6 ] T ,fsat(x) is a saturation function, whose expression is as follows: ζ >0 and has a very small value, for example ζ=0.05. And 4, solving the pose of the end platform by utilizing a Newton iteration method based on the forward and reverse kinematic model. The inverse kinematics model is shown below: p=a i -l i - o R o' b i (i=1,2,...,m) Wherein p= [ x p ,y p ,z p ] T ] is the coordinate of the origin of the relative coordinate system fixed on the end platform under the global coordinate system, a i is the coordinate of the connecting anchor point of the rope and the frame under the global coordinate system, l i is the rope vector, l i is the length of the rope, b i is the coordinate of the connecting anchor point of the rope and the end platform under the relative coordinate system, o R o' is the rotation matrix of the relative coordinate system converted into the global coordinate system. Solving the positive kinematic model to obtain the position p of the terminal platform and the attitude [ alpha, beta, gamma ] T thereof under the condition of knowing the rope length l i , and accurately solving in real time based on The solution algorithm (matlab programming language representation) giving newton's iteration method is as follows: Wherein, the "Up_points" is b i , "down_points" is a i , and "start_global_position" is a given pose prediction initial value, for example, [0, 0] T can be given. "init_ legs _length" is the rope length l i , and "sol_tol" is the set resolution. And 5, establishing a real-time cable force distribution algorithm which meets the following control constraint and any number of ropes. min‖u|| u≥u min u≤u max Where u min ＝[u min ,...,u min ] T and u max ＝[u max ,...,u max ] T are m-dimensional vectors, m is the number of ropes, and u min and u max represent the minimum and maximum set rope forces, respectively. The inequality "u≥u max " and the inequality signs "gtoreq" and "≤u min " in the inequality "u≤u min " represent a one-to-one comparison between corresponding elements in two vectors, which applies to all vector inequalities in the following. And 6, designing a cable force compensation optimization algorithm. The cable force constraints given in step 5 are relaxed to ensure that there is a solution to the real-time cable force distribution algorithm. In this step, more strict constraint conditions are given to meet various constraint requirements of different applications on the cable force, two m-dimensional vectors u dmin ＝[u dmin ,...,u dmin ] T and u dmax ＝[u dmax ,...u dmax ] T are set, m is the number of ropes, and u dmin and u dmax respectively represent the more strict minimum and maximum constraint of the cable force. Solving jacobian matrix Is available: The rope force compensation optimization algorithm designed by Q＝αV 1 +βV 2 ,V 1 ＝[t 11 ,t 12 ,...t 1m ] T ,V 2 ＝[t 21 ,t 22 ,...t 2m ] T , is made as follows: (1) Solving to obtain a control input u according to the steps 3 and 5, and continuing to execute the step (2); (2) Judging whether the inequality condition u dmax ≥u≥u dmin is satisfied, if so, directly outputting the control input u, and if not, continuing to execute the step (3); (3) Solving a zero space Q of the jacobian matrix J, and continuing to execute (4); (4) Setting a value set { alpha 1 ,α 2 , } and { beta 1 ,β 2 , & gt, setting a maximum cycle number n max , assigning a count variable n=0, and assigning u d =u, and continuing execution (5); (5) Executing the operation n=n+1, and alpha=alpha n ,β＝β n , and continuing to execute (6); (6) Judging whether the inequality condition u dmin ≤u+αV 1 +βV 2 ≤u dmax is satisfied, if so, continuing to execute (7), and if not, returning to execute (5); (7) Executing operation u n ＝u+αV 1 +βV 2 , saving the calculated u n , and continuing to execute (8); (8) Judging whether the inequality condition n > n max is satisfied, if so, continuing to execute the step (9), and if not, returning to execute the step (5); (9) Through the above-described loop, a set of saved control inputs, { u 1 ,u 2 ,…,u i , }, From which the control input u i that minimizes u i -u d is selected as the final valued output, to which the algorithm ends.

Description

Sensorless force position control method for cable-driven robot Technical Field The invention belongs to the technical field of machinery, relates to manual position control of a cable-driven robot, and particularly relates to a sensorless force position control method of the cable-driven robot. Background The cable-driven robot uses ropes instead of rigid transmission parts of the parallel robot and relies on the ropes to transfer motion and force to the end motion platform, enabling movement in up to 6 degrees of freedom in its space. Compared with a rigid parallel robot, the cable-driven robot has the unique advantages of small transmission part mass, large working space, high modularization degree, easy maintenance and the like. With the adoption of the dominant rope driving robots, the dominant rope driving robots have wide application in a plurality of fields, such as precise hoisting, microgravity motion simulation, wind tunnel experiments, radio telescope and the like, and enter a rapid development stage in recent years. However, the ropes can only exert pulling force and cannot bear compressive force, so the number of ropes of the rope-driven robot is always 1 more than the controllable degree of freedom, and the redundant constraint enables the rope force to have infinite groups of solutions. In addition, the rope force of each rope must be kept non-negative all the time in the motion process of the rope-driven robot, otherwise, the number of controllable degrees of freedom of the robot can be reduced by the rope which is drawn by the rope, and the motion precision of the tail end platform is reduced, so that the application requirements cannot be met. To monitor the cable force in real time, each cable of the cable driven robot needs to be provided with a force sensor or a torque sensor, and other accessories such as a signal amplifier and a collection card are also indispensable. This not only increases the complexity of the robot structure and electrical components, but also greatly increases the cost of the robot. And almost all researches on cable-driven robots in aspects of force control, position control, vibration control, dynamic control and the like depend on information feedback of force sensors. In addition, in order to solve the problem of infinite solution of rope force caused by redundancy constraint, many researches contribute to an effective solving algorithm, but two problems still exist, namely, firstly, real-time online solving cannot be realized, and secondly, part of the algorithms can realize real-time solving, but the requirement on the difference between the number of ropes and the controllable degree of freedom is 2. Disclosure of Invention In order to overcome the defects of the prior art, realize the force position control of the cable-driven robot under the condition of no sensing and the seamless switching of the force position control and the force position control, and complete the real-time distribution and compensation of the cable force under the constraint condition of the given cable force, the invention aims to provide a method for controlling the cable-driven robot under the condition of no sensing force position. Based on a dynamic model of the cable-driven robot containing unknown interference force, a state observer for predicting the unknown interference force, a quick pose error tracking controller and a cable force optimization and compensation algorithm are established, so that the dependence of the cable-driven robot on force or torque sensors under manual control and position control is reduced, and the structural complexity and the cost of the robot are reduced. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows: A sensorless force position control method of a cable-driven robot comprises the following steps: step 1, establishing a dynamic model of a cable-driven robot containing unknown interference force, wherein the model is as follows; Wherein, the X= [ x p,yp,zp,α,β,γ]T ] is 6 degrees of freedom of the end platform of the cable-driven robot in the 3D space, T d is used for describing errors of predicted cable force and actual cable force, J is a jacobian matrix of the robot, u is a control input, m is the mass of the end platform, I p is the moment of inertia of the end platform about an origin of a relative coordinate system, And 2, constructing a state observer under unknown interference force. Firstly, establishing an error model: The observer for predicting the unknown interference is established for the error model as follows: wherein x d＝[xpd,ypd,zpd,αd,βd,γd]T is a target track, tracking errors are e=x-x d, diagonal matrix beta 1＝diag([β11,β12,β13,β14,β15,β16) and beta 2＝diag([β21,β22,β23,β24,β25,β26) diagonal elements take positive values, and z 1 is a change rate of tracking errors in an error model Z 2 takes on the values indicated above,The approximate interference solved for the obser