机械毕业设计英文外文翻译气动肌肉伺服并联机构位姿自适应鲁棒控

徐州工程学院毕业设计

外文翻译

学生姓名 学院名称 专业名称 指导教师

邹仲清 机电工程学院 机械设计制造及其自动化

陈跃

2011年

5月

27日

英文原文

Adaptive robust posture control of a parallel manipulator driven by pneumatic

muscles

Keywords:

Pneumatic muscle Parallel manipulator Adaptive control Nonlinear robust control

Abstract

Rather severe parametric uncertainties and uncertain nonlinearities exist in the dynamic modeling of a parallel manipulator driven by pneumatic muscles. Those uncertainties not only come from the time-varying friction forces and the static force modeling errors of pneumatic muscles but also from the inherent complex

nonlinearities and unknown disturbances of the parallel manipulator. In this paper, a discontinuous projection-based adaptive robust control strategy is adopted to compensate for both the parametric uncertainties and uncertain nonlinearities of a three-pneumatic-muscles-driven parallel manipulator to achieve precise posture trajectory tracking control. The resulting controller effectively handles the effects of various parameter variations and the hard-to-model nonlinearities such as the friction forces of the pneumatic muscles. Simulation and experimental results are obtained to illustrate the effectiveness of the proposed adaptive robust controller.

? 2008 Elsevier Ltd. All rights reserved.

1. Introduction

Pneumatic muscle is a new type of flexible actuator similar to human muscle. It is usually made up of a rubber tube and crossweave sheath material. Pneumatic muscles have the advantages of cleanness, cheapness, light-weight, easy maintenance,

and the higher power/weight and power/volume ratios when compared with pneumatic cylinders (Ahn, Thanh, & Yang, 2004). The basic working principle of a pneumatic muscle is as follows: when the rubber tube is inflated, the cross-weave sheath experiences lateral expansion, resulting in axial contractive force and the movement of the end point position of the pneumatic muscle. Thus, the position or force control of a pneumatic muscle along its axial direction can be realized by regulating the inner pressure of its rubber tube. The parallel manipulator driven by pneumatic muscles (PMDPM) studied in this paper is a new application of pneumatic muscles.It consists of three pneumatic muscles connecting the moving arm of the parallel manipulator to its base platform as shown in Fig. 1. By controlling the lengths of three pneumatic muscles,three degrees-of-freedom (DOF) rotation motion of the parallel manipulator can be realized. Such a parallel manipulator combines the advantages of compact structure of parallel mechanisms with the adjustable stiffness and high power/volume ratio of pneumatic muscles, which will have promising wide applications in robotics,industrial automation, and bionic devices.

Many researchers have investigated the precise position control of pneumatic muscles during the past several years.Most of them dealt with the control of single or antagonistic pneumatic muscles. Specifically, Bowler, Caldwell, and Medrano-Cerda (1996), employed an adaptive pole-placement scheme to control a bi-directional pneumatic muscle actuator system, for use on a 7-DOF anthropomorphic robot arm. Cai and Yamaura (1996)presented a sliding mode controller for a manipulator driven by artificial muscle actuators. Kimura, Hara, Fujita, and Kagawa(1997), applied the feedback linearization method to the position control of a single-input pneumatic system with a third-order dynamics including the pressure dynamics. Kimoto and Ito (2003)added nonlinear robust compensations to a linear controller in order to stabilize the system globally and achieve robustness to uncertain nonlinearities. Carbonell, Jiang, and Repperger (2001),Chan, Lilly, Repperger, and Berlin (2003), Repperger, Johnson, andPhillips (1998) and Repperger, Phillips, and Krier (1999), proposed several methods such as fuzzy backstepping, gain-scheduling,variable structure and fuzzy PD+I for a SISO pneumatic muscle system with a second-order dynamics to achieve asymptotic position tracking. Lilly (2003), Lilly and Quesada (2004) and Lilly and Yang (2005), applied the sliding mode control technique with boundary layer to control pneumatic muscle actuators arranged in bicep and tricep

configurations. Tian, Ding, Yang, and Lin(2004), adopted the RPE algorithm to train

neural networks for modeling and controlling an artificial muscle system. Hildebrandt,Sawodny, Neumann, and Hartmann (2002); Sawodny, Neumann,and Hartmann (2005), presented a cascade controller for a twoaxis planar articulated robotic arm driven by four pneumatic muscles.

As reviewed above, some of the researchers designed robust controllers without considering the pressure dynamics, while the effect of pressure dynamics is essential for the precise control of pneumatic muscles (Carbonell et al., 2001; Chan et al., 2003; Lilly, 2003; Lilly & Quesada, 2004; Lilly & Yang, 2005; Repperger et al., 1998,1999). Some of the researchers developed controllers with the assumption that the system model is accurate, or that model uncertainties satisfy matching condition only, while those assumptions are hard to be satisfied in practice (Hildebrandt et al., 2002, 2005; Kimura et al., 1997). For the PMDPM shown in Fig. 1, it not only has all the control difficulties associated with the pneumatic muscles, but also the added difficulties of the coupled multi-input-multi-output (MIMO) complex dynamics of the parallel manipulator and the large extent of unmatched model uncertainties of the combined overall system. In other words,there exist rather severe parametric uncertainties and uncertain nonlinearities, which are caused not only by the time-varying friction forces and static force modeling errors of pneumatic muscles but also by the inherent complex nonlinearities and unknown disturbances of the parallel manipulator. Therefore, it is very difficult to control precisely the posture of the PMDPM.

The recently proposed adaptive robust control (ARC) has been shown to be a very effective control strategy for systems with both parametric uncertainties and uncertain nonlinearities (Xu & Yao,2001; Yao, 2003; Yao, Bu, Reedy, & Chiu, 2000; Yao & Tomizuka,2001). This approach effectively integrates adaptive control with robust control through utilizing on-line parameter adaptation to reduce the extent of parametric uncertainties and employing certain robust control laws to attenuate the effects of various uncertainties. In ARC, a projection-type parameter estimation algorithm is used to solve the design conflict between adaptive control and robust control. Thus, high final tracking accuracy is achieved while guaranteeing excellent transient performance.

In this paper, the posture control of a PMDPM shown in Fig. 1 is considered in which each pneumatic muscle is controlled by two fast switching valves. The adaptive robust control strategy is applied to reduce the lumped uncertain nonlinearities and parametric uncertainties while using certain robust feedback to attenuate the effects of

uncompensated model uncertainties.In addition, pressure dynamics are explicitly considered in the proposed controller. Consequently, good tracking performance is achieved in practice as demonstrated by simulation and experimental results.

The paper is organized as follows: Section 2 gives the dynamic models of the PMDPM controlled by fast switching valves. Section3 presents the proposed adaptive robust controller, along with proofs of the stability and asymptotic output tracking of the resulting closed-loop system. Section 4 shows the advantages of the proposed adaptive robust controller over the traditional deterministic robust controller via simulation results. Section 5 details the obtained experimental results to verify the effectiveness of the proposed adaptive robust posture controller and Section 6 draws the conclusions.

2. Dynamic models

The PMDPM shown in Fig. 1 consists of a moving platform,a base platform, a central pole, and three pneumatic muscles.Pneumatic muscles are linked with the moving platform and the base platform by spherical joints Bi and Ai (i = 1, 2, 3) respectively.The joints are evenly distributed along a circle on the moving platform and the base platform respectively. The central pole is rigidly fixed with the base platform and is connected with the moving platform by a ball joint. Consider two frames, the first one,reference frame Oxyz fixed to the base platform, and the second one, moving frame O1x1y1z1 attached to the moving platform at the center (Tao, Zhu,&Cao, 2005). The posture of thePMDPMis defined through the standard Roll–Pitch–Yaw (RPY) angles: first rotate the moving frame around the fixed x-axis by the yaw angle _x, then rotate the moving frame around the fixed y-axis by the pitch angle_y, and finally rotate the moving frame around the fixed z-axis by the roll angle _z. Two fast switching valves are used to regulate the pressure inside each pneumatic muscle, and this combination is subsequently referred to as a driving unit.

Some realistic assumptions are made as follows to simplify the analysis: (a) The working medium of the pneumatic muscles satisfies the ideal gas equation, (b) Resistance and dynamics of various pipes of the pneumatic muscles are neglected, (c) The gas leakage from the pipe is neglected, and (d) The opening and closing time of the fast switching valves are also neglected.

2.1. Dynamic model of parallel manipulator

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