2. Manipulator kinematics and dynamics

GR630ST is a typical 6-DOF serial spray-painting industrial robot. Each joint is driven by an independent motor. Figure 2 shows CAD model of robot and kinematics coordinate system. The calibrated kinematics parameters are shown in Table I.

Table I.

The calibrated kinematics parameters.

Figure 2.

CAD model of spray-painting robot and the coordinate system.

The dynamic model of the robot can be written as:

(1) \begin{equation} M\!\left(q\right)\!\ddot{q}+C\!\left(q,\dot{q}\right)\!\dot{q}+g\!\left(q\right)+\tau _{f}=Ki_{c} \end{equation}

where $M(q)\in R^{6\times 6}$ is the inertia matrix, $C(q,\dot{q})\in R^{6\times 6}$ is the Coriolis and centrifugal matrix, $g(q)\in R^{6}$ is the gravitational torque, $\tau _{f}$ is friction torque. $i_{c}\in R^{6}$ is the motor current, $K\in R^{6\times 6}$ is constant diagonal positive definite matrix of drive gains.

Equation (1) has the following properties [Reference Spong, Hutchinson and Vidyasagar35]:

Propertie 1: The inertia matrix $M(q)$ is symmetric and uniformly positive definite and $\dot{M}(q)-2C(q,\dot{q})$ is skew-symmetric.

Propertie 2: The dynamics (1) depend linearly on a constant dynamic parameter vector $a_{d}$ ,

(2) \begin{equation} M\!\left(q\right)\!\dot{\varsigma }+C\!\left(q,\dot{q}\right)\!\varsigma +B\varsigma +g\!\left(q\right)=Y_{d}\!\left(q,\dot{q},\varsigma,\dot{\varsigma }\right)\!a_{d} \end{equation}

where $Y_{d}(q,\dot{q},\varsigma,\dot{\varsigma })\in \mathrm{\mathbb{R}}^{n\times {n_{a}}}$ is the dynamic regressor matrix. $\varsigma$ is a differentiable vector, $a_{d}$ is dynamic parameter vector.

Figure 3.

Compensation torque analysis of joint 2.

Figure 4.

Compensation torque analysis of joint 3.

In order to meet the requirements of explosion-proof performance of the spraying robot from the perspective of structural design, the size of the driving motor of the joint 2 and 3 should not be too large, so auxiliary cylinders are installed. The installation position and structure of the cylinders are shown in the Figs. 3 and 4. In order to facilitate the follow-up work, the relationship between the cylinder driving torque $T_{2}, T_{3}$ and the cylinder pressure $P_{2}, P_{3}$ is given here.

As shown in Fig. 3, when the rotation angle of joint 2 is $\theta _{2}$ , the force provided by cylinder 2 is decomposed along the direction and vertical direction, and the following can be obtained

(3) \begin{equation} T_{2}=F'_{\!\!2}\left| A_{2}B_{2}\right| \end{equation}

Follows from the law of cosines

(4) \begin{equation} cos\!\left(\angle B_{2}A_{2}C_{2}\right)=\frac{\left| A_{2}B_{2}\right| ^{2}+\left| A_{2}C_{2}\right| ^{2}-\left| B_{2}C_{2}\right| ^{2}}{2\!\left| A_{2}B_{2}\right|\! \left| A_{2}C_{2}\right| } \end{equation}

We can get

(5) \begin{equation} \left| B_{2}C_{2}\right| =\sqrt{\left| A_{2}B_{2}\right| ^{2}+\left| A_{2}C_{2}\right| ^{2}-2\left| A_{2}B_{2}\right| \!\left| A_{2}C_{2}\right| cos\!\left(\angle B_{2}A_{2}C_{2}\right)} \end{equation}

And,

(6) \begin{equation} \frac{\left| B_{2}C_{2}\right| }{sin\!\left(\angle B_{2}A_{2}C_{2}\right)}=\frac{\left| A_{2}C_{2}\right| }{sin\!\left(\angle A_{2}B_{2}C_{2}\right)} \end{equation}

(7) \begin{equation} F'_{\!\!2}=F_{2}\!\left| A_{2}C_{2}\right| \frac{sin\!\left(\angle B_{2}A_{2}C_{2}\right)}{\left| B_{2}C_{2}\right| } \end{equation}

Namely,

(8) \begin{align} F_{2} &= \frac{T_{2}}{\left| A_{2}B_{2}\right| }\frac{1}{\left| A_{2}C_{2}\right| }\frac{\left| B_{2}C_{2}\right| }{sin\!\left(\angle B_{2}A_{2}C_{2}\right)} \nonumber \\[5pt] & =\frac{T_{2}\sqrt{\left| A_{2}B_{2}\right| ^{2}+\left| A_{2}C_{2}\right| ^{2}-2\left| A_{2}B_{2}\right| \! \left| A_{2}C_{2}\right| cos\!\left(\pi -\theta _{2}\right)}}{\left| A_{2}B_{2}\right| \!\left| A_{2}C_{2}\right| sin\!\left(\pi -\theta _{2}\right)} \end{align}

The piston diameter of cylinder 2 is 0.1 m, then the piston area $S_{2}=7.854\times 10^{-3}\,{\rm m}^{2}$ , and we can have

(9) \begin{equation} P_{2}=\frac{F_{2}}{S_{2}} \end{equation}

Namely,

(10) \begin{equation} T_{2}=\frac{P_{2}S_{2}\left| A_{2}B_{2}\right| \! \left| A_{2}C_{2}\right| sin\!\left(\pi -\theta _{2}\right)}{\sqrt{\left| A_{2}B_{2}\right| ^{2}+\left| A_{2}C_{2}\right| ^{2}-2\left| A_{2}B_{2}\right| \! \left| A_{2}C_{2}\right| cos\!\left(\pi -\theta _{2}\right)}} \end{equation}

where $| A_{2}B_{2}|$ = 0.125 m, $| A_{2}C_{2}|$ = 0.335 m.

Similarly, the driving force $F_{3}$ of cylinder 3 is solved. when the rotation angle of joint 2 is $\theta _{3}$ , we have

(11) \begin{equation} F_{3}=\enspace \frac{T_{3}}{\left| A_{3}B_{3}\right| cos\!\left(\theta _{3}\right)} \end{equation}

where $| A_{3}B_{3}|$ = 0.1 m.

Similar to $T_{2}$ , we have

(12) \begin{equation} \mathrm{T}_{3}=P_{3}S_{3}\left| A_{3}B_{3}\right| cos\!\left(\theta _{3}\right) \end{equation}

3. Controller design

3.1. Model identification

3.1.1. Friction model

Traditional friction model includes Coulomb friction, viscous friction and static friction. According to the joint velocity of the robot, the state of the robot is divided into three kinds [Reference Lee, Kim and Song36]:

(13) \begin{equation} \tau _{f}=\left\{\begin{array}{l} \tau _{c}\;\mathrm{sgn}\!\left(i_{c}\right)\!,\left| \dot{q}\right| \lt\! \varepsilon,\dot{q}_{d}=0\\[5pt] \tau _{s}\;\mathrm{sgn}\!\left(i_{c}\right)\!,\left| \dot{q}\right| \lt \! \varepsilon,\dot{q}_{d}\neq 0\\[5pt] \tau _{c}\;\mathrm{sgn}\!\left(i_{c}\right)+\tau _{v}\!\left(\dot{q}\right)\!,\left| \dot{q}\right| \geq \varepsilon \end{array}\right. \end{equation}

which $\tau _{c}$ , $\tau _{s}$ , $\tau _{v}$ is coulomb, static friction and viscous friction components, $i_{c}$ is the motor current, $\dot{q}_{d}$ is the motor speed required for the corresponding joint. $\varepsilon$ is the critical speed value for transition to the operating state. However, this model cannot well represent the nonlinear characteristics of friction. Therefore, another method is proposed to simulate the rotational angle-dependent characteristics of friction in harmonic drive by describing the low-speed fluctuation and steady-state Coulomb friction and viscous friction through the second-order Fourier series [Reference Xiao, Zhang, Hong, Wang and Zeng37]. The friction force of joint $i$ is expressed as

(14) \begin{equation} \begin{array}{l} \tau _{fi}=f_{i1}\sin \!\left(q_{i}\right)+f_{i2}\cos \!\left(q_{i}\right)+f_{i3}\sin \!\left(2q_{i}\right)\\[5pt] +\;f_{i4}\cos \!\left(2q_{i}\right)+f_{ic}\sin \!\left(q_{i}\right)+f_{is}e^{-{f_{ti}}{\dot{q}_{i}}}\;\mathrm{sgn}\!\left(q_{i}\right)+f_{iv}\dot{q}_{i} \end{array} \end{equation}

which $q_{i}, \dot{q}_{i}$ is the position and velocity of joint $i,\, f_{i1}\sim f_{i4}$ is the Fourier friction coefficient, $\;f_{ic}\;\; f_{is}\;\; f_{iv}$ are coulomb, static friction and viscous friction coefficients, respectively, $f_{ti}$ is the transition index from rest to motion.

The friction model used in this paper combines Eqs. (13) and (14) with static friction estimation. The model uses Fourier series to represent the low-speed fluctuations of joint rotation, the static friction component that depends on the direction of the motor current and the friction during the transition period (the static torque between joint motions) by using the Sigmoid function. Then, the friction model of joint $i$ is:

(15) \begin{equation} \begin{array}{l} \tau _{fi}=\tau _{si}\;\mathrm{sgn}\!\left(i_{c}\right)S^{-1}\!\left(\dot{q}_{i}\right)+\\[7pt] \!\left(f_{i1}\sin \!\left(\dfrac{q_{i}}{R_{i}}\right)+f_{i2}\cos \!\left(\dfrac{q_{i}}{R_{i}}\right)+f_{i3}\sin \!\left(\dfrac{2q_{i}}{R_{i}}\right)+f_{i4}\cos \!\left(\dfrac{2q_{i}}{R_{i}}\right)+\tau _{ci}\right)\\[11pt] \times\; \mathrm{sgn}\!\left(\dot{q}\right)+\left(\,f_{5}\sin \!\left(q_{i}\right)+f_{6}\cos \!\left(q_{i}\right)+\tau _{v1i}\dot{q}+\tau _{v2i}\dot{q}^{2}\right)S\!\left(\dot{q}_{i}\right) \end{array} \end{equation}

where $\tau _{si} \tau _{ci}$ is static and Coulomb friction coefficient, $R_{i}$ is the ratio of each joint, $\tau _{v1i}\; \tau _{v2i}$ is the coefficient of viscous friction, $f_{i1}\sim f_{i6}$ is the Fourier friction coefficient, and

(16) \begin{equation} S\!\left(\dot{q}_{i}\right)=\frac{1}{1+e^{-\frac{\dot{q}_{i}}{\dot{q}_{i\varepsilon }}}} \end{equation}

(17) \begin{equation} S^{-1}\!\left(\dot{q}_{i}\right)=e^{-\frac{\dot{q}_{i}}{\dot{q}_{i\varepsilon }}} \end{equation}

where $\dot{q}_{i\varepsilon }$ is the transition velocity.

3.1.2. Gravity and inertia identification

In order to achieve zero-moment lead-through teaching, robot dynamics parameters are needed. The traditional dynamic parameter identification method calculates the dynamic parameters of the robot through the data collection in the process of the robot moving under the optimized identification excitation trajectory combined with the optimized identification algorithm. In order to identify all the dynamic parameters, the most important part of the whole process is to design a reasonable identification trajectory. However, standard industrial inner controllers can only generate very simple trajectories (like line, circles, Point to Point, etc.), and this becomes an obstacle in the practical application of traditional parameter identification methods which optimized trajectories usually require to generate arbitrary joint motions.

The inertia matrix, gravity matrix and Coriolis matrix are decoupled by linearization of dynamic model (1), and the models related to inertia parameters are obtained, respectively. While the influence of Coriolis force is ignored, because the inertial matrix and gravity matrix are only related to the pose of the robot, and the robot runs slowly in the lead-through teaching process. So, the related terms of the inertia matrix and gravity matrix can be solved separately. Based on the two-step identification method proposed in ref. [Reference Grotjahn, Daemi and Heimann38], the dynamics parameters of the robot in (5) are divided into three parts:

(a) The gravitational parameters $a_{g}$ which only associated with gravity of robot $g(q)$ .

(b) The diagonal parameters $a_{Md}$ which only associated with the diagonal elements of inertia matrix $M(q)$ .

(c) The off-diagonal parameters $a_{Mod}$ which only associated with the off-diagonal elements of inertia matrix $M(q)$ .

Furthermore, typically the number of elements of $a_{Mod}$ is small due to the symmetric structure of the links. The grouping leads to a separation of regressor matrix $Y_{d}(q,\dot{q},\ddot{q})$ as

(18) \begin{equation} \tau =\underset{M\left(q\right)\ddot{q}}{\underbrace{Y_{Md}\!\left(q,\ddot{q}\right)\!a_{Md}+Y_{Mod}\!\left(q,\ddot{q}\right)\!a_{Mod}+Y_{Mg}\!\left(q,\ddot{q}\right)\!a_{g}}}+\underset{C\left(q,\dot{q}\right)\dot{q}}{\underbrace{Y_{c}\!\left(q,\dot{q}\right)\!a_{d}}}+\underset{g\left(q\right)}{\underbrace{Y_{g}\!\left(q\right)\!a_{g}}} \end{equation}

where $Y_{Md}$ and $Y_{Mod}$ are the regressor which only associated with the diagonal and off-diagonal elements of inertia matrix, $Y_{Mg}$ is the regressor which both associated with elements of inertia matrix and gravity matrix, $Y_{c}$ is the regressor which only associated with the elements of $C(q,\dot{q}), Y_{g}$ is the regressor which only associated with the elements of gravity matrix.

According to property 2, the inertia parameters of each link in the dynamic equation can be separated to realize the linearization of the dynamic model. For each link $i$ , the dynamic parameters include the mass of the link $m_{i}$ , the position of the center of mass $r_{i}=[x_{i},y_{i},z_{i}]^{T}$ and the inertia tensor $I_{i}$ which respect to the origin of the link coordinate system. Thus, we can regroup the dynamic parameters of each link as

\begin{equation*} a_{i}=[m_{i},\,m_{i}x_{i},\,m_{i}y_{i},\,m_{i}z_{i},\,I_{xx}^{i},\,\,I_{xy}^{i},\,\,I_{xz}^{i},\,\,I_{yy}^{i},\,\,I_{yz}^{i},\,\,I_{zz}^{i}]^{\mathrm{T}} \end{equation*}

In general, not all link parameters affect the dynamics equation of the robot. For example, for link 1, only the inertia $I_{zz}$ is involved in the calculation of the dynamic model. In addition, due to the coupling effect between links, the dynamic parameters are not independent of each other, but linearly combined together, which leads to the existence of zero column elements and linearly correlated columns in the regression matrix $Y(\!\boldsymbol\cdot\!)$ . Recursive solution and numerical methods (such as QR [Reference Mamedov and Mikhel39] and SVD [Reference Gautier40] decomposition method) are commonly used to solve the singularity by reorganizing the linearly related columns in $Y(\!\boldsymbol\cdot\!)$ and removing the zero element columns to obtain a new regression matrix. Accordingly, the original dynamic parameters of the connecting rod are reassembled and removing the quantity that does not affect the dynamic output to obtain a new base parameter set $a_{b}$ . To be exact, the element in $a_{b}$ is the dynamic coefficient after linear combination, rather than the standard dynamic parameter [Reference Gaz, Flacco and De Luca41].

The measurement of gravitational torque must include long periods with constant velocity in order to excite gravity and eliminate inertial effects. The average torques of joint torque in forward and backward motion give the required gravitational torque. Acceleration accounts for a greater proportion of the motion used to identify the diagonal elements of inertia matrix.

When we get the gravity torque, we can use the least square method to get the corresponding gravity parameter $a_{g}$ [Reference Mamedov and Mikhel39]. We can use the same way as gravitational measurements to measure the off-diagonal elements of the inertia matrix. Joint $i$ is moved at a very low constant velocity, and joint $j$ is accelerated at the same time. Because the influence of Coriolis term is ignored in the experiment, there is an error between the final model and the actual one. Considering the uncertainty [Reference Zhou, Zi and Qian42] of the model, ${\Delta} (q,\dot{q},\ddot{q})$ is introduced and is required to satisfy $\| \!{\Delta} (q,\dot{q},\ddot{q})\| \leq \nu$ . In the experiment, we chose ${\Delta} (q,\dot{q},\ddot{q})=10\cdot \sin\!(t)$ , and $\nu =12$ .

3.2. Control strategy design

Ideally, our goal is to calculate the gravity, friction and inertia forces at each axis position and speed in real time during the robot operation, and then realize the robot balance by compensating each torque, so that the user only needs to overcome the Coriolis force to achieve the robot lead-through teaching. Since the inner torque control loop of the robot cannot be directly accessed by users, the joint velocity/position commands are designed by an adaptive method to achieve the output of target control torque. The control block diagram is shown in the Fig. 5.

Figure 5.

Adaptive lead-through teaching control block diagram for closed control system of industrial robot.

For the spray-painting robot in this paper, an adaptive controller is designed considering the PI velocity controller used in the inner control loop:

(19) \begin{equation} i_{c}=-K_{P}\!\left(\dot{q}-\dot{q}_{c}\right)-K_{I}\!\left(q-q_{c}\right) \end{equation}

where $K_{P}, K_{I}$ are proportional and integral gains (positive definite diagonal matrices) respectively, $q_{c}$ and $\dot{q}_{c}$ are joint position and speed commands.

To solve the problem of measurement without joint torque sensor, we can take $Ki_{c}$ in (1) a s the “torque” input and our goal becomes

(20) \begin{equation} \tau =Ki_{c}=-KK_{P}\!\left(\dot{q}-\dot{q}_{c}\right)-KK_{I}\!\left(q-q_{c}\right)\rightarrow \tau _{d} \end{equation}

Define $K^{*}=KK_{P}=diag[k_{ii}^{*},i=1,\ldots,6]$ being positive constants. We defined the joint velocity command as

(21) \begin{equation} \dot{q}_{c}+\hat{\Upsilon}_{I}q_{c}=\dot{q}+\hat{\Upsilon}_{I}q+diag\!\left[\hat{\omega }\right]\!\tau _{d} \end{equation}

where $\hat{\Upsilon}_{I}$ is the estimate of $\Upsilon_{I}=K_{P}^{-1}K_{I}$ , which can be expressed as $\hat{\Upsilon}_{I}=diag[\hat{\omega }_{I}]$ . And $\hat{\omega }=[\hat{\omega }_{i},i=1,\ldots,6]^{T}$ denotes the scale weight is the estimate of $\omega =[k_{ii}^{\ast-1},i=1,\ldots,6]^{T}$ .

We then have that

(22) \begin{equation} \!\left(\dot{q}_{c}-\dot{q}\right)+\hat{\Upsilon}_{I}\!\left(q_{c}-q\right)=diag\!\left[\omega \right]\tau _{d}+diag\!\left[{\Delta} \omega \right]\tau _{d}-{\Delta} \hat{\Upsilon}_{I}\!\left(q_{c}-q\right) \end{equation}

where ${\Delta} \omega =\hat{\omega }-\omega =[\hat{\omega }_{i}-k_{ii}^{\ast-1},i=1,\ldots,6]^{T}$ , which leads us to obtain that

(23) \begin{equation} \tau =\tau _{d}+K^{*}diag\!\left[{\Delta} \omega \right]\tau _{d}-K^{*}\mathrm{{\Delta} \Upsilon}_{I}\!\left(q_{c}-q\right) \end{equation}

where $\mathrm{{\Delta} \Upsilon}_{I}=\hat{\Upsilon}_{I}-\Upsilon_{I}$ . By the joint velocity command proposed before, the closed-loop of robot dynamics become

(24) \begin{equation} M\!\left(q\right)\!\ddot{q}+C\!\left(q,\dot{q}\right)\!\dot{q}+g\!\left(q\right)+\tau _{f}=\tau _{d}+{\Delta} \tau _{d} \end{equation}

where

\begin{equation*} {\Delta} \tau _{d}=\tau -\tau _{d}=K^{*}diag[{\Delta} \omega ]\tau _{d}-K^{*}\mathrm{{\Delta} \Upsilon}_{I}(q_{c}-q) \end{equation*}

To meet the requirements of the control model (24), the updating laws for $\hat{\omega }$ and $\hat{\omega }_{I}$ should be properly developed so that ${\Delta} \tau _{d}$ decays to zero. So, when we come to the adaptive method, we have

(25) \begin{equation} \hat{\tau }_{d}=\hat{M}\!\left(q\right)\!\ddot{q}+\hat{C}\!\left(q,\dot{q}\right)\dot{q}+\hat{g}\!\left(q\right)+\hat{\tau }_{f}=Y_{d}\!\left(q,\dot{q},\dot{q},\ddot{q}\right)\!\hat{a}_{d} \end{equation}

Then, we have

(26) \begin{equation} \hat{\tau }_{d}-\tau _{d}=Y_{d}\!\left(q,\dot{q},\dot{q},\ddot{q}\right){\Delta} a_{d}+K^{*}diag\!\left[\tau _{d}\right]{\Delta} \omega -K^{*}diag\!\left[q_{c}-q\right]{\Delta} \omega _{I} \end{equation}

Define

(27) \begin{equation} e=\hat{\tau }_{d}-\tau _{d} \end{equation}

Then, we have the adaptation law for $\hat{a}_{d}, \hat{\omega }, \hat{\omega }_{I}$ defined as

(28) \begin{equation} \dot{\hat{\omega }}=-{\Lambda} diag\!\left[\tau _{d}\right]\!e \end{equation}

(29) \begin{equation} \dot{\hat{a}}_{d}=-{\Gamma} _{d}Y_{d}^{T}\!\left(q,\dot{q},\dot{q},\ddot{q}\right)\!e \end{equation}

(30) \begin{equation} \dot{\hat{\omega }}_{I}=-{\Lambda} _{I}diag\!\left[q_{c}-q\right]\!e \end{equation}

where ${\Lambda}$ and ${\Lambda} _{I}$ are diagonal positive definite matrices, and ${\Gamma} _{d}$ is symmetric positive definite matrices. The advantage of joint velocity command given by (21) is included the dynamic influence which can be considered as scaled compensation in the model.

Suppose that $\hat{\Upsilon}_{I}$ is uniformly definite, then the adaptive outer loop controller given by (21) and (28)–(30) for the robot system (1) under the inner PI controller (19) ensures the stability of the system and convergence to the desired torque.

Proof :

Consider the Lyapunov function:

(31) \begin{equation} V=\frac{1}{2}\left[{\Delta} \omega ^{T}{\Lambda} ^{-1}K^{*}{\Delta} \omega +{\Delta} {a_{d}}^{T}{{\Gamma} _{d}}^{-1}{\Delta} a_{d}+{\Delta} {\omega _{I}}^{T}{{\Lambda} _{I}}^{-1}K^{*}{\Delta} \omega _{I}\right] \end{equation}

whose derivative $\dot{V}=-e^{T}e\leq 0$ . Thus, it can be shown that $\hat{a}_{d}\in L_{\infty }, \hat{\omega }\in L_{\infty }, \hat{\omega }_{I}\in L_{\infty }$ , and $e\in L_{2}$ . If $e$ is further uniformly continuous, we obtain that $e\rightarrow 0$ , that is, $\tau _{d}\rightarrow \hat{M}(q)\ddot{q}+\hat{C}(q,\dot{q})\dot{q}+\hat{g}(q)+\hat{\tau }_{f}$ as $t\rightarrow \infty$ . This is based on the properties of square-integrable and uniformly continuous functions.

It can be obtained from the above results that $\tau _{d}$ converges to $\tau =M(q)\ddot{q}+C(q,\dot{q})\dot{q}+g(q)+\tau _{f}$ in the sense of certainty equivalence [Reference Ciliz43]. In this way, we can use the standard deterministic equivalence principle to design the adaptive controller and can realize the application of torque-based control scheme when the internal control system of the robot is closed.

4. Experiments design and implementation

The robot can drag and teach the spraying track and monitor the tracking error. After the coordinate system is established, the calibrated kinematics parameters are shown in Table I. A teaching handle is installed at the end of the robot, and a six-dimensional force sensor is installed at the end to measure the force and torque applied by the operator in the teaching process. There are no external forces in the process of friction model identification, gravity and inertia measuring, and no relevant components are installed at the end of the robot. The experiment setup and data acquisition are shown in Fig. 6.

Figure 6.

Joint friction, gravity and inertia identification experiment and data acquisition.

The parameter identification of friction, gravity and inertia provides the basis for the lead-through teaching experiment. According to the robot dynamics model (1), the output torque of the motor provides inertia force, Coriolis force and gravity after overcoming friction. When a single joint uniformly rotates at low speed, the influence of inertia force and Coriolis force can be ignored. Since at any position with the same joint angle, the gravity torque is equal while the friction force is equal and opposite, the two times friction force values at the same joint angle can be obtained by approximating the difference of the output torque of the two motors. Similarly, the sum of the output torques of two motors at the same joint angle is twice the magnitude of the gravity torques. After the joint friction model and gravity parameters are obtained, the inertial parameters can be obtained according to the identification method in Section 3.1.2.

Experiments are carried out according to the friction model established in Section 3.1.1. Because each joint friction model is independent, the parameters of each joint friction model can be identified separately. The drive gain $K=[46.2166.183911.1511.155.58]^{T}$ .

In the friction identification experiment, the angular velocity increment range of each joint is defined as 0.5-4 degrees/s for low-speed operation. Each joint reciprocates forward and backward over the range of joint motion, defining the joint trajectory in the initial and final 5% of the path following a constant velocity profile with parabolic mixing (constant acceleration). Define residence time for each end of the path which equal to the time of one-way joint movement. The joint trajectory is shown as blue dash line in Fig. 7 for the data fitting results of the joint friction model of joint 1 to joint 3, and Fig. 8 respectively shows the comparison results of the actual measured friction of robot joint with the model calculation. The measured friction torques were compared with those of the model, and the mean square error of the calculated joint friction model was in the range of 5-15 Nm. The large error of joint 1 to joint 3 is largely due to the large joint size and transmission, especially the spray-painting robot used in the experiment used a cylinder auxiliary device at joint 2 and joint 3 as an extra power input, which further increased the measurement error. Due to the smaller joint size and transmission ratio, joints 4-6 exhibit smaller torques and torque prediction errors. After the friction force, gravity and inertia force experiment, the robot dynamics model by ignoring Coriolis force is obtained. Meanwhile, the model results of Eq. (14) are compared with the model results of Eq. (15) in this paper. It can be seen from Fig. 7 that the trends of the two models are roughly the same, but the model in this paper can better simulate the friction force when the joint motion state changes, and the simulation effect is better than that of Eq. (14). Figures 9 and 10 show the actual measuring each joint torque and the torque-based on the model. It can be seen from the figure that there is a certain error between the estimated torque and the actual torque of each joint, which is caused by the error of friction model and ignoring the Coriolis force. But from the point of view of lead-through teaching, the model works.

Figure 7.

Parameter fitting of robot joint friction model and joint motion trajectory of robot joint 1-3.

Figure 8.

Compare between friction model and measurement results of robot joint 4-6.

Figure 9.

Measured and estimated torques for joint 1-3.

Figure 10.

Measured and estimated torques for joint 4-6.

After the characterization, zero-moment control is adopted. When the robot switches to the lead-through teaching mode, the spraying operator can flexibly move in the robot workspace. The goal is to make the motion feel as free as possible so that the operator can easily guide the spray gun to follow the desired trajectory. Lead-through teaching experiment hardware and the operating force of end-effector collected during the experiment are shown in Fig. 11(a). According to the collaborative robot design standard ISO 15066, desired experimental trajectory is shown in Fig. 11(b). During the experiment, drag the end of the robot to move from point P10 to point P35 in turn. The designed adaptive controller parameters are chosen as ${\Gamma} _{d}=I_{36}, {\Lambda} =0.01I_{6}, {\Lambda} _{I}=0.02I_{6}$ , the initial value of $\hat{a}_{d}$ is set as $\hat{a}_{d}(0)=0_{36}$ , here we do not include the parameters associated with the friction and the base dynamic parameters of robot are obtained by QR decomposition method in ref. [Reference Leite32]. The initial value of $\hat{\omega }$ and $\hat{\omega }_{I}$ are chosen as $\hat{\omega }(0)=0_{6}$ , and $\hat{\omega }_{I}(0)=[1.2\;1.2\;1.2\;1.2\;1.2\;1.2]^{T}$ .

Figure 11.

Lead-through teaching experiment setup and desired trajectory. (a) Experiment setup of lead-through teaching. (b) Desired trajectory of end-effector.

Figure 12.

The force and torque of lead-through teaching based on power-off gravity compensation method.

Figure 13.

The force and torque of lead-through teaching based on adaptive method.

First of all, a lead-through teaching experiment based on gravity compensation method was carried out in the off-servo state. This scheme is a robot’s own scheme, which can realize partial gravity compensation by only two cylinders of the joint 2 and joint 3 when the servo power of each joint is cut off. As can be seen from Fig. 12, when the robot moves along the target trajectory, each movement inflection point corresponds to a larger resistance. On average, dragging the end of the robot along the target trajectory requires about 25 N of force to move the robot and much less force to move along a straight line. It is worth noting that the Z-force suddenly increases during the operation of the robot, and a higher force of 130 N is observed in the Z-direction, which is caused by the unstable cylinder pressure and the large error of the cylinder compensation model due to the gravity changes with the position during the operation.

Figure 13 shows the operator force and torque in the lead-through teaching with low speed based on adaptive outer loop control in this paper. Ideally, when the robot is under model-based zero-moment control, the external force exerted by the operator during the lead-through teaching process should be close to zero. However, due to the friction model, gravity, inertia force error and the neglect of Coriolis force, the operator still needs to apply a certain amount of force during the dragging instruction process. The pressure change of the auxiliary cylinder in the corresponding lead-through teaching process is shown in Fig. 14. The actual cylinder pressure can follow the set pressure value well. As can be seen from Fig. 15, when the robot moves along the target trajectory, each movement inflection point still corresponds to a large force. On average, when the end of the robot moves along the target trajectory, the force required is about 15 N, which is significantly reduced compared with the original control scheme. The comparison between actual trajectory and desired trajectory in the teaching process based on the adaptive method is shown in Fig. 15. It can be seen that due to the lack of contact between the end-effector and the plane in the teaching process and the natural shaking of operator hands, the actual trajectory of the end-effector is not completely coincident with the desired trajectory, and the error is large at the inflection points of each trajectory. In general, the desired trajectory can be tracked well at low speed. Compared with the off-servo control, there is no sudden change in the operator force, and the operation process is more stable.

Figure 14.

The cylinder pressure during the experiment of lead-through teaching.

Figure 15.

Lead-through teaching trajectory comparison under adaptive control scheme: (a) shows the comparison between the actual drag trajectory and the target trajectory, and (b)-(e) shows a partially enlarged view.