## Abstract

In view of the problem that notable flexible displacement will occur for parallel manipulators when operating at high speed, the composite controller based on the integral manifold and high-gain observer is proposed for trajectory tracking and the 3RRR parallel manipulator is taken as the object. Based on the stiffness matrix, the small variable is introduced to decompose the rigid-flexible coupling dynamic model into slow and fast subsystem. For the slow subsystem, the backstepping control is applied for rigid motion tracking. In order to account for the links’ flexible displacement the corrective torque is deduced, and the compensation for the flexible displacement is realized. For the fast subsystem, the sliding mode control is utilized to suppress the vibration. The high-gain observer is designed to avoid the measurement of the curvature rate of flexible links. Also, the stability of the overall system is proven with the Lyapunov stability theorem and the upper bound of the small variable is obtained. At last, the proposed composite controller together with the singular perturbation control and the rigid body model-based backstepping control are simulated, and vibration suppression and tracking performances are compared to validate the proposed control scheme.

### Keywords

- parallel manipulator
- integral manifold
- high-gain observer
- composite control
- sliding mode control
- backstepping control
- vibration suppression

## 1. Introduction

Parallel manipulators (PMs) possess advantages of high precision, high stiffness, and large load-to-weight ratio; they have attracted wide attention and have been widely used in industries such as high-speed handling, motion simulation, and electronic manufacturing [1]. However, in order to increase efficiency, PMs are increasingly used in high-speed and heavy-duty operations. In order to reduce costs and energy consumption, the lightweight design of the mechanical body will be the inevitable choice. However, in the high-speed or heavy-duty application, the lightweight mechanical body will produce significant elastic deformation and vibration. Therefore, the end-effector’s movement consists of the rigid-body motion and the elastic displacement caused by elastic deformation and vibration. Using conventional control methods for rigid-body manipulators will not guarantee good tracking accuracy of flexible manipulator’s end-effector. Therefore, it is of great significance to improve the tracking accuracy of high-speed lightweight PMs by considering the flexibility of members to establish the dynamic model for rigid-flexible coupling and carrying out research on high-precision control algorithms.

Many scholars have conducted extensive and in-depth studies on modeling methods for manipulators with flexible links. Dwivedy et al. [2] reviewed the dynamic modeling of robots with flexible links. Due to the presence of link flexibility, the system will exhibit nonminimum phase characteristics when selecting the end-effector of the manipulator as the output. The literature [3, 4, 5] redefines the output of the manipulator’s end position by taking the link elasticity into account, and uses the control algorithm for the rigid-body manipulator to control the new output; however, this method can only realize the point-to-point position control and cannot guarantee tracking control of the end trajectory [6]. The singular perturbation method is another effective method to deal with the nonminimum phase characteristics of manipulators with elastic links. The small parameters are introduced to reduce the order of rigid-flexible coupling models, which are decomposed into two subsystems, the fast and the slow, and two subcontrollers are designed using compound control algorithm. The controller of the system realizes the control of the rigid body motion and the rapid suppression of the elastic vibration. However, as the deformation increases, the singularity perturbation algorithm shows a deficiency and the algorithm cannot compensate for the elastic displacement [7, 8, 9]. Khorasani [10] proposed an integral manifold method by high-order approximation of fast subsystem variables, which greatly improved the vibration suppression effect. By introducing the elastic displacement into the end of the manipulator and designing the corrective torque, Moallem et al. [11] realized the trajectory tracking precision control and vibration suppression of the two-degree-of-freedom serial robot. Based on the above method, Fotouhi et al. [12, 13, 14, 15, 16] studied the trajectory tracking control of the flexible joint robot, the flexible robot with the single link, the rigid-flexible hybrid robot, and the two-bar flexible robot by simplifying the selection of correction moments, and show good results.

Due to the existence of the closed-chain structure, the dynamic model of PMs is complex when considering the flexibility of the links. Therefore, the research on the vibration suppression and trajectory tracking control is very limited. Zhang et al. [7] used assumption mode method and Lagrange equation to model 3PRR PMs with flexible passive links, and adopted singular perturbation compound control to suppress vibration. However, the influence of the elastic displacement of the links on the moving platform is not considered in the model, and the elastic displacement compensation and the rate of change of the elastic links are not processed when the algorithm is designed. Therefore, the trajectory tracking effect needs to be improved. Existing research has not yet been found for the above issues. In the research of trajectory tracking control based on integral manifold, no relevant research has been found for PMs. The control algorithms for the slow subsystem in the existing research are feedback linearization methods, and the fast subsystem is PD control or pole placement. In order to taking into accounts of the elastic deformation and vibration of high-speed PMs due to the flexibility of links and improve the tracking accuracy and dynamic performance, this chapter introduces the integral manifold based on the rigid-flexible coupling model of the 3RRR PM, the hypothesis of small deformation and the velocity mapping in the previous paper [17], and the high-order rigid-flexible coupling model is transformed into two subsystems, then a composite control algorithm based on sliding mode variable structure control and backstepping control is proposed. At the same time, a high-gain observer is introduced to the curvature rate caused by the flexibility. Finally, simulation studies are conducted to verify the feasibility of the algorithm.

## 2. The dynamic model of the 3RRR PM

The structure of the 3RRR parallel manipulator was shown in Figure 1, which consists of three branches, and each branch composed of one active link and passive link, the end of which is the moving platform. The coordinates and the parameters are given in Figure 2,

According to our previously published paper [17], the flexibility of passive links can be neglected, so only the deformation of active links is considered here, which can be expressed as * k*th point in the

*th active link, respectively, where*i

where * i* are the link’s stiffness and the reduction ratio, respectively,

_{g}

## 3. Integral manifold-based model reduction of the high-speed PM

From the dynamic model (1), the state variables are defined as below [15],

where

where

For Eq. (4), the integral manifold is defined as [15, 18],

Eq. (5) can be interpreted that if the fast subsystem variables arrive at the integral manifold trajectory at the moment * t*, then for the moment

^{*}

Due to the small variable

where * p* should be at least 2 when the elastic displacement can be accounted in the end trajectory, the

*is selected 2 here.*p

The inverse matrix of the mass matrix, the Coriolis force and the centrifugal force terms are functions of the small variable

The centrifugal and inertial force after the expansion of Eq. (1) can be expressed as,

Substituting Eqs. (6) through (8) into Eq. (4), we can obtain,

When the flexibility of the links is ignored, the small variable

where

According to the integral manifold, the deviation of the fast subsystem variable can be expressed as,

Multiply the Eq. (11) with

For the slow and fast subsystems represented by Eqs. (10) and (12), the composite control algorithm is designed as shown in Figure 3. For the slow subsystem, the backstepping control is used to achieve the tracking control of the rigid body motion. At the same time, according to the velocity mapping relationship, the mapping relationship between the elastic deformation of the links and the elastic displacement of the moving platform is established. The motion of the moving platform is obtained according to the rigid-body motion and the elastic displacement, and the elastic torque compensation is realized by designing the correction torque

## 4. The backstepping algorithm-based slow subsystem control

The backstepping control is a recursive control algorithm for complex nonlinear systems. The original system is decomposed into subsystems that do not exceed the system order. The control design is realized by establishing Lyapunov functions step by step for each subsystem, and the stability of the system is ensured [19]. First, define the position error as,

where

where

Based on the position error, define the Lyapunov function as,

Deriving Eq. (16) can be obtained,

According to the velocity error (15) in conjunction with Eq. (17), the Lyapunov function is defined as,

Deriving the above formula and substituting the relevant parameters, the derivative of the Lyapunov function can be expressed as,

According to Eq. (19), the control torque of the slow subsystem is

where

Therefore, according to the Lyapunov stability principle, the slow subsystem is stable with the torque

where * G* of the moving platform induced by the elastic deformation and vibration of the links, which is the elastic displacement of the end-effector of the moving platform.

According to the velocity mapping relationship, the acceleration of the moving platform generated by the elastic motion can be expressed as,

where

The flexibility examined in this chapter is within a small deformation range, and the elastic displacement

Make the second derivative of Eq. (22), when considering the rigid-flexible coupling motion, the acceleration of the end-effector of the moving platform can be expressed as,

Defining the position error

According to Eq. (26), define the Lyapunov function,

Derivative of Eq. (27) with respect to time can be obtained as,

Let the coefficient of

At this time,

## 5. Sliding mode variable structure-based fast subsystem control

Define a new time scale

The latter two terms of the second equation contain small parameter

Due to the existence of the disturbance term, the fast subsystem adopts sliding mode variable structure control, and the sliding mode surface is selected as,

where

According to the sliding surface, the Lyapunov function is defined as,

Derivative of the above equation with respect to time can be obtained as,

According to Eq. (35), the fast subsystem control law designed as,

where

Therefore, according to the Lyapunov stability principle, the fast subsystem is convergent with torque of (36). The symbolic function will cause jitter to the system. To reduce the generation of jitter, the saturation function

where

## 6. The high-gain observer for the curvature change rate

The curvature can be obtained by strain gage measurement of the stress of the links, and the change rate of curvature is directly related to the rate of change of stress, and generally cannot be directly measured. In order to avoid direct measurement of the change rate of curvature, this chapter will design a high-gain observer to observe the curvature change rate by measuring the curvature. It can be known from Eq. (11) that the fast subsystem variable

where

To prove the stability of the system, new variables of error are defined as,

Substitute the above equation into (39), the state observer can be expressed as,

The Eq. (42) can be rewritten as,

where

where

Since

According to Eq. (47), when

Therefore, according to Eq. (48), the upper bound of the small parameter can be obtained, and the fast subsystem torque can be expressed as,

where

where

According to Eq. (50), the Lyapunov function can be defined as:

where

Since

According to the Rayleigh-Ritz inequality,

where

According to Eq. (56), when

## 7. Stability proof of the system

The abovementioned integral manifold is used to reduce the rigid-flexible coupling system of high-speed PM, and the complex high-order system is decomposed into a slow subsystem describing the rigid body motion and a fast subsystem of elastic deformation, and the backstepping control and sliding mode variable structure control are adopted for two subsystems, respectively, and designed a high-gain observer to solve the problem that the elastic displacement change rate is difficult to measure, and proved the stability of each subsystem. However, the stability of each subsystem does not guarantee the stability of the overall system. Therefore, it is necessary to synthesize the subsystems to prove the stability of the overall system. Substituting Eqs. (9), (20), and (29) into kinetic Eq. (3), the systematic error equation can obtained,

where

According to the error equation, define the Lyapunov function that contains the overall system as,

where

Since

According to Eqs. (53) and (61),

According to the Rayleigh-Ritz inequality, we can obtain,

where

The condition that the closed-loop system is asymptotically stable is

Ignoring the influence of high-order terms of

According to Eq. (67), when the value of

## 8. Algorithm simulations

When the Taylor expanding order

where the running time

To describe the control performance of the end-effector, an average error is introduced, and is defined as,

where

According to Eq. (24), the elastic displacement ^{−4} rad from backstepping control and singular perturbation drops to 6.872 × 10^{−4} rad of the composite control. Compared with the above algorithm, the elastic displacement of the composite control decreases by more than 14% in both directions. Compared with the backstepping control, when the composite control and the singular perturbation algorithm are used, the residual vibration is greatly reduced, and both algorithms are close to zero.

The tracking error is the difference between the actual output and the desired output of the end of the moving platform. * X* direction decreased by 85.56 and 91.41%, and the

*direction decreased by 57.55 and 90.57%, while the rotation direction decreased by 53.34 and 61.5%, respectively. For the average tracking error, the translation direction decreased by 88.2 and 92.62%, the rotational direction decreased by 37.26 and 49.57%, respectively; in the tracking error of the end point, the*Y

*direction decreased by 92.8 and 72.34%, and the Y direction decreased by 89.73 and 83.62%, respectively, while the rotational direction decreased by 85.96 and 70.85%, respectively. For the tracking error at the end point, the singular perturbation method is significantly worse than the backstepping controller in all directions. This is mainly because the singular perturbation algorithm only considers the vibration suppression, and the cost of the vibration suppression is at the cost of sacrificing the trajectory tracking due to the delay of the adjustment. It can be seen from the above analysis that in the aspect of trajectory tracking accuracy, the composite control based on integral manifold and observer has significant advantages.*X

## 9. Conclusions

Decompose the rigid-flexible coupling dynamic model into fast and slow subsystems based on the integral manifold, and employ the sliding mode control and backstepping control to design the fast and slow subsystem controllers, respectively, and compensate the elastic displacement at the end of the manipulator. A high-gain observer estimates the rate of change of curvature, which in turn enables trajectory tracking control of high-speed PM.

The Lyapunov function is selected to prove the asymptotic stability of the slow subsystem, fast subsystem, high-gain observer, and the overall system. The conditions for selecting the integral manifold and the small parameters of the observer are given.

Apply MATLAB-SIMULINK to establish a comparison simulation to verify the performance of the proposed compound control algorithm. The simulation results show that the composite control algorithm has obvious advantages in vibration suppression and trajectory tracking.

## Acknowledgments

This research was supported in part by the Natural Science Foundation of Zhejiang under Grant No. LY18E050019 and the Excellent Talent Cultivation Foundation under Grant No. ZSTUME02B09.

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