Mathematical Problems in Engineering

Mathematical Problems in Engineering / 2015 / Article
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Advanced Control of Complex Dynamical Systems with Applications

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Research Article | Open Access

Volume 2015 |Article ID 175342 | 9 pages | https://doi.org/10.1155/2015/175342

End-Effector Trajectory Tracking Control of Space Robot with Gain Performance

Academic Editor: Xinggang Yan
Received17 Apr 2015
Accepted06 Sep 2015
Published01 Oct 2015

Abstract

This paper presents a novel solution to the control problem of end-effector robust trajectory tracking for space robot. External disturbance and system uncertainties are addressed. For the considered robot operating in free-floating mode, a Chebyshev neural network is introduced to estimate system uncertainties and external disturbances. An adaptive controller is then proposed. The closed-loop system is guaranteed to be ultimately uniformly bounded. The key feature of this proposed approach is that, by choosing appropriate control gains, it can achieve any given small level of gain disturbance attenuation from external disturbance to system output. The tracking performance is evaluated through a numerical example.

1. Introduction

With the development and launch of spacecraft, the function of spacecrafts is becoming more and more complex. As a result, any component failure will deteriorate spacecraft’s performance and sometimes even make the planned mission totally terminate. Aiming to decrease economic loss induced by spacecraft failures, on-orbit servicing has received considerable attentions. However, due to the harsh operating environment such as high temperature, it is very difficult for astronauts to accomplish orbital works. This makes space robot become the best option to accomplish orbital repair. Additionally, the space robot can also perform other on-orbit servicing missions such as repair, assembly, refueling, and/or upgrade of spacecraft. This leads to development of space robot techniques [16].

For space robot, the end-effector control in the presence of uncertain kinematics and dynamics is becoming one of the challenges that need to be addressed. In [7], the problem of uncertain kinematics in space robot’s end-effector was investigated. In [8], a feedback control approach was presented to accomplish position and attitude control maneuver. The uncertainties in end-effector were addressed, and experimental results were given to verify the effectiveness of the proposed controller. Taking control input saturation of end-effector’s actuator into consideration, an adaptive controller was developed to perform trajectories tracking [9]. The tracking error was governed to be semiglobally asymptotic stable. On the other hand, on the standpoint of tracking control in mission space, many researchers have developed many effective control algorithms. In [10], an adaptive controller was presented to achieve tracking control of end-effector, and uncertain kinematics and dynamics were solved. The controller was able to guarantee the asymptotic stability of the closed-loop system, and ground test was also conducted to demonstrate its effectiveness. In [11], an adaptive control scheme without velocity measurements was developed. The demand of decreasing numbers of measurement sensors was satisfied, and the closed-loop tracking system was governed to be stable. In [12, 13], another novel adaptive controller was also synthesized in the presence of the dynamics of actuators, uncertain kinematics, and dynamics.

The preceding approaches were proposed based on the assumption that the dynamic model can be linearized. However, this assumption would not be satisfied for space robot. As a result, the above control methodologies were not applicable to space robot. Moreover, in the above nonlinear controller design, the developed controllers can only ensure the stability of the resulted system. They were not able to achieve disturbance attenuation. It greatly limits the application of those schemes. To achieve tracking control with disturbance attenuation, gain control approach is one of the most applied techniques [1418].

Inspired by the great performance of gain control, this work will investigate the problem of trajectory tracking control of space robot end-effector. Uncertain kinematics and dynamics will be addressed. To solve those uncertainties, Chebyshev neural network will be used to approximate those uncertainties, and an adaptive controller will be developed to compensate for these uncertainties. The main contribution of this work is that the desired trajectories can be followed with high accuracy, and gain performance is achieved in the presence of external disturbances.

This paper is organized as follows. In Section 2 we recall some necessary notation, definitions, preliminary results, and the mathematical model used to investigate space robot end-effector trajectory tracking control problem. The control solution with gain performance is presented in Section 3. Section 4 demonstrates the application of the proposed control scheme to a space robot. Conclusions are given in Section 5.

2. Preliminaries and Problem Formulation

The notation adopted in this paper is fairly standard. Let (resp., ) denote the set of real (resp., positive real) numbers. The set of by real matrices is denoted as . For a given vector, denotes the vector Euclidean norm; for a given matrix, represents its induced Euclidean norm, and denotes the matrix Frobenius norm. denotes the trace operator.

2.1. Definition

Our main results relay on the following stability definitions for a given nonlinear system:where are locally Lipschitz and piecewise continuous in and is an exogenous disturbance, while is the system output. We denote by the solution to the nonlinear system (1) with the initial state and initial time .

Definition 1 (see [18]). Let be a given constant; then system (1) is said to be achieved with gain disturbance attenuation level of from external disturbance to output , if the following inequality holds:where is a Lyapunov candidate function to be chosen.

2.2. System Description of Space Robot

Consider -link space robot with each joint driven by a dedicated, armature-controlled dc motor and operating in a free-floating mode. Define () as the end-effector positive and attitude vector; then the space robot kinematics and dynamics can be described as where denotes the generalized velocity vector of the end-effector; here and are the velocity and the angular velocity of the end-effector, respectively. The vector is the generalized coordinates. The term denotes the generalized but known/nominal Jacobian matrix, while is the uncertain Jacobian matrix. is the nominal inertia matrix; is the uncertain inertia. is the nominal vector of Coriolis and centrifugal forces, and denotes its uncertain part. is the vector of control torque, and is the vector of external disturbance.

To control the plant (3)-(4) successfully, the following assumption is assumed to be valid throughout this paper.

Assumption 2. The nominal Jacobian matrix and the matrix are bounded. There exist two positive scalars and such that and , respectively.

2.3. Chebyshev Neural Network

The Chebyshev neural network (CNN) [19] has been shown to be capable of universally approximating any well-defined functions over a compact set to any degree of accuracy. Therefore, CNN will be used to estimate the uncertain terms in the space robot dynamics. The CNN structure employed in this paper is with single layer and the Chebyshev polynomial basis function. This basis function is a set of Chebyshev differential equations and generated by the following two-term recursive formula:In this paper, is chosen. Define ; then the Chebyshev polynomial equation can be described aswhere is the order of Chebyshev polynomial chosen and is called the Chebyshev polynomial basis function.

As a result, for any continuous nonlinear function vector , it can be approximated by CNN aswhere is the bounded CNN approximation error, is an optimal weight matrix, and is the Chebyshev polynomial basis function.

Assumption 3. The optimal weight matrix is bounded. That is, there exists a positive constant such that .

2.4. Problem Statement

The objective of the proposed design methodology is to construct a control input function such that the end-effector trajectory state of the controlled system is capable of tracking a desired reference trajectory in spite of the existence of system uncertainties and external disturbances.

3. End-Effector Trajectory Tracking Control Design with Uncertain Kinematics and Dynamics

Because the system dynamics described in (3)-(4) cannot be linearized, CNN will be applied in this section to approximate the unknown system dynamics which can be not linearized in the system. Then, an adaptive backstepping control law will be presented to achieve trajectory tracking control for the space robot end-effector. Moreover, the tracking performance is evaluated by gain from external disturbance/system uncertainties to the system outputs of the robot and desired trajectories.

3.1. Control Law Design with Gain Performance

In the controller design, it is assumed that the trajectory of the space robot’s end-effector is always within the Path Independent Workspace (PIW). All the points in the PIW are guaranteed not to have dynamic singularities. As a result, it can ensure that will be always invertible.

Define the trajectory tracking error asCombining with the dynamics (3), it leads to the time derivative of as where denotes the uncertain kinematics.

To remove the effect of the above uncertain kinematics, CNN is used to approximate ; that is,where is the bounded CNN approximation error, is an optimal weight matrix, and is the Chebyshev polynomial basis function.

To accomplish controller design, a virtual control input iswhere is a constant and is the estimate of the term in (11).

Additionally, define an error vector for and ; that is,From the dynamics (4), one haswhere denotes the uncertain dynamics. As the same technique applied to handle with uncertain kinematics, CNN will also be applied to approximate . It thus follows thatwhere is the bounded CNN approximation error, is an optimal weight matrix, and is the Chebyshev polynomial basis function.

Introduce two new variables and Then, it leads to , where is the estimate of the optimal weight matrix and is the estimate error, .

Theorem 4. Consider the space robot system described by (3)-(4) with external disturbance and system uncertainties; design as Let be updated bywhere and are two control gains and , , , and are parameters for the adaptive laws. Suppose that the control parameters are chosen such thatwhere , , and are positive constants. Then, the closed-loop attitude tracking system is guaranteed to be ultimately uniformly bounded. The gain disturbance attenuation level is achieved. Moreover, when there is no external disturbance, the closed-loop system is asymptotically stable.

3.2. Stability Analysis

For the introduced variables, applying (12) and (15)–(17), the dynamics for and can be rewritten as

Proof of Theorem 4. Choose a Lyapunov candidate function asCalculating the time derivative of yields According to the properties of matrix trace, one hasApplying (12) and (15), it can be obtained thathere is used.
Based on Assumption 2, it leaves (23) as follows: From Assumption 3, inequality (26) can be simplified as where and .
Define lumped disturbance as and system output as ; thenWith the choice of and the control gains in (19)-(20), it results inThen, it can be obtained from (28) thatBy integrating inequality (30) from 0 to , it can be shown thatApplying Definition 1, it can be concluded that the trajectory tracking is performed with gain disturbance attenuation level, and the closed-loop system is ultimately uniformly bounded. Thereby the proof is completed here.

It should be stressed that the smaller the value of is, the better the disturbance attenuation capability will be obtained. To evaluate the gain disturbance attenuation capability, the following index is defined:From (32), it is known that smaller will lead to better disturbance attenuation performance.

Additionally, because the desired trajectory , the CNN approximation error , , and the external disturbance are bounded, there will exist a positive constant such that . Using the inequality , it yields Then, one has and . It can thus obtain that and are bounded. More specifically, when , it follows that and . As a result, the end-effector trajectory will asymptotically follow the desired trajectory.

4. Numerical Example

To test the proposed controller, a two-link space robot operating in a free-floating mode is numerically simulated. The trajectory tracking control for its end-effector is performed. The main physical parameters, control gains, and external disturbances are listed in Table 1. The desired trajectory is a circle in plane with its radius equal to 1m.


Physical nameValue

Space robot link, , ,
, , ,  
, ,

Control gains, , ,  
, , ,

The order of CNN

The initial value of optimal weight matrix

External disturbance
,

4.1. Response by Using Different Control Gains

With application of the proposed approach, Figure 1 shows that the controller successfully accomplishes the trajectory following mission of the space robot end-effector. As the position tracking error shown in Figure 2, good steady-state performance is guaranteed with minor overshoot. The velocity tracking error of end-effector is shown in Figure 3. Vibration with high-frequency is seen. That is induced by external disturbances. The corresponding estimates of the optimal weight matrix when using CNN to handle system uncertainties are illustrated in Figure 4. It is got to know that those estimates of CNN are all bounded.

As summarized in Theorem 4, the trajectory tracking performance is dependent on the control gains. Hence, simulation by using different control gains is further carried out. Figures 5, 6, and 7 show the trajectory tracking error by using , ; , ; and , , respectively. From Figures 5~7, it is seen that larger value of will lead to fast convergence rate of the tracking error. Figure 8 shows the control performance by using and . It is obtained from those results that larger value of cannot increase the response rate of the system when has a fixed value. Therefore, to ensure that the actual trajectory of the end-effector can follow the desired trajectory in a faster rate, the designer should choose , to satisfy (17) and (20), respectively. At this time, choosing larger will result in that the desired trajectory will be followed in a shorter time. However, the maximum control effort of actuator should be taken into account when choosing .

4.2. Performance in the Absence of External Disturbances

In this case, an ideal condition is considered. That is, there are no external disturbances acting on the space robot. By using the proposed control law, the control performance is shown in Figures 9~12. Those results demonstrate the conclusion in Theorem 4 that an asymptotic tracking can be guaranteed in the absence of external disturbances. Comparing Figures 2~4 with Figures 10~12, respectively, fewer overshoots are obtained in the absence of disturbances compared to those in the presence of external disturbances.

5. Conclusions

The problem of end-effector trajectory tracking control was investigated for a space robot working in free-floating mode by incorporating the criterion of a tracking performance given by gain constraint in controller synthesis. External disturbance and system uncertainties were addressed. The proposed adaptive control approach was able to achieve high tracking performance even in the presence of uncertain kinematics and dynamics. The closed-loop tracking system was ensured to be global uniform ultimate bounded stable with the gain less than any given small level. Moreover, when the space robot was not under the effect of any disturbance, the desired trajectory can be asymptotically followed. It should be pointed out that actuators are assumed to run normally when implementing the proposed approach. However, this assumption may not be satisfied in practice. As one of future works, trajectory tracking control with fault tolerant capability should be carried out for space robot’s end-effector.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work was supported partially by the National Natural Science Foundation of China (Project nos. 61503035 and 61573071) and the Foundation of the National Key Laboratory of Science and Technology on Space Intelligent Control (Project no. 9140C590202140C59015). The authors highly appreciate the preceding financial supports. The authors would also like to thank the reviewers and the editor for their valuable comments and constructive suggestions that helped to improve the paper significantly.

References

  1. A. Flores-Abad, O. Ma, K. Pham, and S. Ulrich, “A review of space robotics technologies for on-orbit servicing,” Progress in Aerospace Sciences, vol. 68, pp. 1–26, 2014. View at: Publisher Site | Google Scholar
  2. W. Xu, B. Liang, and Y. Xu, “Survey of modeling, planning, and ground verification of space robotic systems,” Acta Astronautica, vol. 68, no. 11-12, pp. 1629–1649, 2011. View at: Publisher Site | Google Scholar
  3. D. L. Akin, J. C. Lanef, B. J. Roberts, and S. R. Weisman, “Robotic capabilities for complex space operations,” in Proceedings of the AIAA Space Conference and Exposition, pp. 1–11, Albuquerque, NM, USA, August 2001. View at: Google Scholar
  4. L. M. Capisani, A. Ferrara, A. F. de Loza, and L. M. Fridman, “Manipulator fault diagnosis via higher order sliding-mode observers,” IEEE Transactions on Industrial Electronics, vol. 59, no. 10, pp. 3979–3986, 2012. View at: Publisher Site | Google Scholar
  5. S. Kim, A. Shukla, and A. Billard, “Catching objects in flight,” IEEE Transactions on Robotics, vol. 30, no. 5, pp. 1049–1065, 2014. View at: Publisher Site | Google Scholar
  6. S. Kim and A. Billard, “Estimating the non-linear dynamics of free-flying objects,” Robotics and Autonomous Systems, vol. 60, no. 9, pp. 1108–1122, 2012. View at: Publisher Site | Google Scholar
  7. C. C. Cheah, S. Kawamura, and S. Arimoto, “Feedback control for robotic manipulator with uncertain kinematics and dynamics,” in Proceedings of the IEEE International Conference on Robotics and Automation, vol. 4, pp. 3607–3612, Leuven, Belgium, May 1998. View at: Publisher Site | Google Scholar
  8. C. C. Cheah, M. Hirano, S. Kawamura, and S. Arimoto, “Approximate Jacobian control for robots with uncertain kinematics and dynamics,” IEEE Transactions on Robotics and Automation, vol. 19, no. 4, pp. 692–702, 2003. View at: Publisher Site | Google Scholar
  9. W. E. Dixon, “Adaptive regulation of amplitude limited robot manipulators with uncertain kinematics and dynamics,” in Proceedings of the American Control Conference (AAC '04), pp. 3839–3844, Boston, Mass, USA, July 2004. View at: Publisher Site | Google Scholar
  10. C. C. Cheah, C. Liu, and J. J. E. Slotine, “Approximate Jacobian adaptive control for robot manipulators,” in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '04), vol. 3, pp. 3075–3080, IEEE, New Orleans, La, USA, April-May 2004. View at: Publisher Site | Google Scholar
  11. X. Liang, X. Huang, M. Wang, and X. Zeng, “Adaptive task-space tracking control of robots without task-space- and joint-space-velocity measurements,” IEEE Transactions on Robotics, vol. 26, no. 4, pp. 733–742, 2010. View at: Publisher Site | Google Scholar
  12. C. C. Cheah, C. Liu, and J. J. E. Slotine, “Adaptive Jacobian tracking control of robots with uncertainties in kinematic, dynamic and actuator models,” IEEE Transactions on Automatic Control, vol. 51, no. 6, pp. 1024–1029, 2006. View at: Publisher Site | Google Scholar
  13. L. Cheng, Z.-G. Hou, and M. Tan, “Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model,” Automatica, vol. 45, no. 10, pp. 2312–2318, 2009. View at: Publisher Site | Google Scholar | MathSciNet
  14. A. J. van der Schaft, “L2-gain analysis of nonlinear systems and nonlinear state-feedback H control,” IEEE Transactions on Automatic Control, vol. 37, no. 6, pp. 770–784, 1992. View at: Publisher Site | Google Scholar
  15. M. Fujita, H. Kawai, and M. W. Spong, “Passivity-based dynamic visual feedback control for three-dimensional target tracking: stability and L2-gain performance analysis,” IEEE Transactions on Control Systems Technology, vol. 15, no. 1, pp. 40–52, 2007. View at: Publisher Site | Google Scholar
  16. C. Ishii, T. Shen, and Z. Qu, “Lyapunov recursive design of robust adaptive tracking control with L2-gain performance for electrically-driven robot manipulators,” International Journal of Control, vol. 74, no. 8, pp. 811–828, 2001. View at: Publisher Site | Google Scholar | MathSciNet
  17. H. K. Khalil and J. Grizzle, Nonlinear Systems, Macmillan, New York, NY, USA, 1992. View at: MathSciNet
  18. A. van der Schaft, L2-Gain and Passivity Techniques in Nonlinear Control, Communications and Control Engineering, Springer, London, UK, 2nd edition, 2000. View at: Publisher Site | MathSciNet
  19. A.-M. Zou and K. D. Kumar, “Adaptive attitude control of spacecraft without velocity measurements using Chebyshev neural network,” Acta Astronautica, vol. 66, no. 5-6, pp. 769–779, 2010. View at: Publisher Site | Google Scholar

Copyright © 2015 Haibo Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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