1,179 research outputs found

    Control strategies for robotic manipulators

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    This survey is aimed at presenting the major robust control strategies for rigid robot manipulators. The techniques discussed are feedback linearization/Computed torque control, Variable structure compensator, Passivity based approach and Disturbance observer based control. The first one is based on complete dynamic model of a robot. It results in simple linear control which offers guaranteed stability. Variable structure compensator uses a switching/relay action to overcome dynamic uncertainties and disturbances. Passivity based controller make use of passive structure of a robot. If passivity of a feedback system is proved, nonlinearities and uncertainties will not affect the stability. Disturbance observer based controllers estimate disturbances, which can be cancelled out to achieve a nominal model, for which a simple controller can then be designed. This paper, after explaining each control strategy in detail, finally compares these strategies for their pros and cons. Possible solutions to cope with the drawbacks have also been presented in tabular form. ยฉ 2012 IEEE

    Modeling and Control of Flexible Link Manipulators

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    Autonomous maritime navigation and offshore operations have gained wide attention with the aim of reducing operational costs and increasing reliability and safety. Offshore operations, such as wind farm inspection, sea farm cleaning, and ship mooring, could be carried out autonomously or semi-autonomously by mounting one or more long-reach robots on the ship/vessel. In addition to offshore applications, long-reach manipulators can be used in many other engineering applications such as construction automation, aerospace industry, and space research. Some applications require the design of long and slender mechanical structures, which possess some degrees of flexibility and deflections because of the material used and the length of the links. The link elasticity causes deflection leading to problems in precise position control of the end-effector. So, it is necessary to compensate for the deflection of the long-reach arm to fully utilize the long-reach lightweight flexible manipulators. This thesis aims at presenting a unified understanding of modeling, control, and application of long-reach flexible manipulators. State-of-the-art dynamic modeling techniques and control schemes of the flexible link manipulators (FLMs) are discussed along with their merits, limitations, and challenges. The kinematics and dynamics of a planar multi-link flexible manipulator are presented. The effects of robot configuration and payload on the mode shapes and eigenfrequencies of the flexible links are discussed. A method to estimate and compensate for the static deflection of the multi-link flexible manipulators under gravity is proposed and experimentally validated. The redundant degree of freedom of the planar multi-link flexible manipulator is exploited to minimize vibrations. The application of a long-reach arm in autonomous mooring operation based on sensor fusion using camera and light detection and ranging (LiDAR) data is proposed.publishedVersio

    A field programmable gate array based modular motion control platform

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    The expectations from motion control systems have been rising day by day. As the systems become more complex, conventional motion control systems can not achieve to meet all the specifications with optimized results. This creates the necessity of fundamental changes in the infrastructure of the system. Field programmable gate array (FPGA) technology enables the reconfiguration of the digital hardware, thus dissolving the necessity of infrastructural changes for minor manipulations in the hardware even if the system is deployed. An FPGA based hardware system shrinks the size of the hardware hence the cost. FPGAs also provide better power ratings for the systems as well as a more reliable system with improved performance. As a trade off, the development is rather more difficult than software based systems, which also affects the research and development time of the overall system. In this paper a level of abstraction is introduced in order to diminish the requirement of advanced hardware description language (HDL) knowledge for implementing motion control systems thoroughly on an FPGA. The intellectual property library consists of synthesizable hardware modules specifically implemented for motion control purposes. Other parts of a motion control system, like user interface and trajectory generation, are implemented as software functions in order to protect the modularity of the system. There are also several external hardware designs for interfacing and driving various types of actuators

    ์™ธ๋ž€ ๋ฐ ํ† ํฌ ๋Œ€์—ญํญ ์ œํ•œ์„ ๊ณ ๋ คํ•œ ํ† ํฌ ๊ธฐ๋ฐ˜์˜ ์ž‘์—… ๊ณต๊ฐ„ ์ œ์–ด

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ์œตํ•ฉ๊ณผํ•™๋ถ€(์ง€๋Šฅํ˜•์œตํ•ฉ์‹œ์Šคํ…œ์ „๊ณต), 2021.8. ๋ฐ•์žฌํฅ.The thesis aims to improve the control performance of the torque-based operational space controller under disturbance and torque bandwidth limitation. Torque-based robot controllers command the desired torque as an input signal to the actuator. Since the torque is at force-level, the torque-controlled robot is more compliant to external forces from the environment or people than the position-controlled robot. Therefore, it can be used effectively for the tasks involving contact such as legged locomotion or human-robot interaction. Operational space control strengthens this advantage for redundant robots due to the inherent compliance in the null space of given tasks. However, high-level torque-based controllers have not been widely used for transitional robots such as industrial manipulators due to the low performance of precise control. One of the reasons is the uncertainty or disturbance in the kinematic and dynamic properties of the robot model. It leads to the inaccurate computation of the desired torque, deteriorating the control stability and performance. To estimate and compensate the disturbance using only proprioceptive sensors, the disturbance observer has been developed using inverse dynamics. It requires the joint acceleration information, which is noisy due to the numerical error in the second-order derivative of the joint position. In this work, a contact-consistent disturbance observer for a floating-base robot is proposed. The method uses the fixed contact position of the supporting foot as the kinematic constraints to estimate the joint acceleration error. It is incorporated into the dynamics model to reduce its effect on the disturbance torque solution, by which the observer becomes less dependent on the low-pass filter design. Another reason for the low performance of precise control is torque bandwidth limitation. Torque bandwidth is determined by the relationship between the input torque commanded to the actuator and the torque actually transmitted into the link. It can be regulated by various factors such as inner torque feedback loop, actuator dynamics, and joint elasticity, which deteriorates the control stability and performance. Operational space control is especially prone to this problem, since the limited bandwidth of a single actuator can reduce the performance of all related tasks simultaneously. In this work, an intuitive way to penalize low performance actuators is proposed for the operational space controller. The basic concept is to add joint torques only to high performance actuators recursively, which has the physical meaning of the joint-weighted torque solution considering each actuator performance. By penalizing the low performance actuators, the torque transmission error is reduced and the task performance is significantly improved. In addition, the joint trajectory is not required, which allows compliance in redundancy. The results of the thesis were verified by experiments using the 12-DOF biped robot DYROS-RED and the 7-DOF robot manipulator Franka Emika Panda.๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์€ ์™ธ๋ž€๊ณผ ํ† ํฌ ๋Œ€์—ญํญ ์ œํ•œ์ด ์กด์žฌํ•  ๋•Œ ํ† ํฌ ๊ธฐ๋ฐ˜ ์ž‘์—… ๊ณต๊ฐ„ ์ œ์–ด๊ธฐ์˜ ์ œ์–ด ์„ฑ๋Šฅ์„ ๋†’์ด๋Š” ๊ฒƒ์„ ๋ชฉํ‘œ๋กœ ํ•œ๋‹ค. ํ† ํฌ ๊ธฐ๋ฐ˜์˜ ๋กœ๋ด‡ ์ œ์–ด๊ธฐ๋Š” ๋ชฉํ‘œ ํ† ํฌ๋ฅผ ์ž…๋ ฅ ์‹ ํ˜ธ๋กœ์„œ ๊ตฌ๋™๊ธฐ์— ์ „๋‹ฌํ•œ๋‹ค. ํ† ํฌ๋Š” ํž˜ ๋ ˆ๋ฒจ์ด๊ธฐ ๋•Œ๋ฌธ์—, ํ† ํฌ ์ œ์–ด ๋กœ๋ด‡์€ ์œ„์น˜ ์ œ์–ด ๋กœ๋ด‡์— ๋น„ํ•ด ์™ธ๋ถ€ ํ™˜๊ฒฝ์ด๋‚˜ ์‚ฌ๋žŒ์œผ๋กœ๋ถ€ํ„ฐ ๊ฐ€ํ•ด์ง€๋Š” ์™ธ๋ ฅ์— ๋” ์œ ์—ฐํ•˜๊ฒŒ ๋Œ€์‘ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ํ† ํฌ ์ œ์–ด๋Š” ๋ณดํ–‰์ด๋‚˜ ์ธ๊ฐ„-๋กœ๋ด‡ ์ƒํ˜ธ์ž‘์šฉ๊ณผ ๊ฐ™์€ ์ ‘์ด‰์„ ํฌํ•จํ•˜๋Š” ์ž‘์—…์„ ์œ„ํ•ด ํšจ๊ณผ์ ์œผ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ์ž‘์—… ๊ณต๊ฐ„ ์ œ์–ด๋Š” ์ด๋Ÿฌํ•œ ํ† ํฌ ์ œ์–ด์˜ ์žฅ์ ์„ ๋” ๊ฐ•ํ™”์‹œํ‚ฌ ์ˆ˜ ์žˆ๋Š”๋ฐ, ๋กœ๋ด‡์ด ์—ฌ์œ  ์ž์œ ๋„๊ฐ€ ์žˆ์„ ๋•Œ ์ž‘์—…์˜ ์˜๊ณต๊ฐ„์—์„œ ์กด์žฌํ•˜๋Š” ๋ชจ์…˜๋“ค์ด ๋‚ด์žฌ์ ์œผ๋กœ ์œ ์—ฐํ•˜๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์žฅ์ ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ํ† ํฌ ๊ธฐ๋ฐ˜์˜ ๋กœ๋ด‡ ์ œ์–ด๊ธฐ๋Š” ์ •๋ฐ€ ์ œ์–ด ์„ฑ๋Šฅ์ด ๋–จ์–ด์ง€๊ธฐ ๋•Œ๋ฌธ์— ์‚ฐ์—…์šฉ ๋กœ๋ด‡ ํŒ”๊ณผ ๊ฐ™์€ ์ „ํ†ต์ ์ธ ๋กœ๋ด‡์—๋Š” ๋„๋ฆฌ ์‚ฌ์šฉ๋˜์ง€ ๋ชปํ–ˆ๋‹ค. ๊ทธ ์ด์œ  ์ค‘ ํ•œ ๊ฐ€์ง€๋Š” ๋กœ๋ด‡ ๋ชจ๋ธ์˜ ๊ธฐ๊ตฌํ•™ ๋ฐ ๋™์—ญํ•™ ๋ฌผ์„ฑ์น˜์— ์กด์žฌํ•˜๋Š” ์™ธ๋ž€์ด๋‹ค. ๋ชจ๋ธ ์˜ค์ฐจ๋Š” ๋ชฉํ‘œ ํ† ํฌ๋ฅผ ๊ณ„์‚ฐํ•  ๋•Œ ์˜ค์ฐจ๋ฅผ ์œ ๋ฐœํ•˜๋ฉฐ, ์ด๊ฒƒ์ด ์ œ์–ด ์•ˆ์ •์„ฑ๊ณผ ์„ฑ๋Šฅ์„ ์•ฝํ™”์‹œํ‚ค๊ฒŒ ๋œ๋‹ค. ์™ธ๋ž€์„ ๋‚ด์žฌ ์„ผ์„œ๋งŒ์„ ์ด์šฉํ•˜์—ฌ ์ถ”์ • ๋ฐ ๋ณด์ƒํ•˜๊ธฐ ์œ„ํ•ด ์—ญ๋™์—ญํ•™ ๊ธฐ๋ฐ˜์˜ ์™ธ๋ž€ ๊ด€์ธก๊ธฐ๊ฐ€ ๊ฐœ๋ฐœ๋˜์–ด ์™”๋‹ค. ์™ธ๋ž€ ๊ด€์ธก๊ธฐ๋Š” ์—ญ๋™์—ญํ•™ ๊ณ„์‚ฐ์„ ์œ„ํ•ด ๊ด€์ ˆ ๊ฐ๊ฐ€์†๋„ ์ •๋ณด๊ฐ€ ํ•„์š”ํ•œ๋ฐ, ์ด ๊ฐ’์ด ๊ด€์ ˆ ์œ„์น˜๋ฅผ ๋‘ ๋ฒˆ ๋ฏธ๋ถ„ํ•œ ๊ฐ’์ด๊ธฐ ๋•Œ๋ฌธ์— ์ˆ˜์น˜์ ์ธ ์˜ค์ฐจ๋กœ ๋…ธ์ด์ฆˆํ•ด์ง€๋Š” ๋ฌธ์ œ๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋ถ€์œ ํ˜• ๊ธฐ์ € ๋กœ๋ด‡์„ ์œ„ํ•œ ์ ‘์ด‰ ์กฐ๊ฑด์ด ๊ณ ๋ ค๋œ ์™ธ๋ž€ ๊ด€์ธก๊ธฐ๊ฐ€ ์ œ์•ˆ๋˜์—ˆ๋‹ค. ์ œ์•ˆ๋œ ๋ฐฉ๋ฒ•์€ ๋กœ๋ด‡์˜ ๊ณ ์ •๋œ ์ ‘์ด‰ ์ง€์ ์— ๋Œ€ํ•œ ๊ธฐ๊ตฌํ•™์ ์ธ ๊ตฌ์† ์กฐ๊ฑด์„ ์ด์šฉํ•˜์—ฌ ๊ด€์ ˆ ๊ฐ๊ฐ€์†๋„ ์˜ค์ฐจ๋ฅผ ์ถ”์ •ํ•œ๋‹ค. ์ถ”์ •๋œ ์˜ค์ฐจ๋ฅผ ๋™์—ญํ•™ ๋ชจ๋ธ์— ๋ฐ˜์˜ํ•˜์—ฌ ์™ธ๋ž€ ํ† ํฌ๋ฅผ ๊ณ„์‚ฐํ•จ์œผ๋กœ์จ ์ €์—ญ ํ†ต๊ณผ ํ•„ํ„ฐ ์„ฑ๋Šฅ์— ๋Œ€ํ•œ ์˜์กด๋„๋ฅผ ์ค„์ผ ์ˆ˜ ์žˆ๋‹ค. ํ† ํฌ ๊ธฐ๋ฐ˜ ์ œ์–ด์˜ ์ •๋ฐ€ ์ œ์–ด ์„ฑ๋Šฅ์ด ๋–จ์–ด์ง€๋Š” ๋˜ ๋‹ค๋ฅธ ์ด์œ  ์ค‘ ํ•˜๋‚˜๋Š” ํ† ํฌ ๋Œ€์—ญํญ ์ œํ•œ์ด๋‹ค. ํ† ํฌ ๋Œ€์—ญํญ์€ ๊ตฌ๋™๊ธฐ์— ์ „๋‹ฌ๋˜๋Š” ์ž…๋ ฅ ํ† ํฌ์™€ ์‹ค์ œ ๋งํฌ์— ์ „๋‹ฌ๋˜๋Š” ํ† ํฌ์™€์˜ ๊ด€๊ณ„๋กœ ๊ฒฐ์ •๋œ๋‹ค. ํ† ํฌ ๋Œ€์—ญํญ์€ ๊ตฌ๋™๊ธฐ ๋‚ด๋ถ€์˜ ํ† ํฌ ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„, ๊ตฌ๋™๊ธฐ ๋™์—ญํ•™, ๊ด€์ ˆ ํƒ„์„ฑ ๋“ฑ์˜ ์š”์ธ๋“ค์— ์˜ํ•ด ์ œํ•œ๋  ์ˆ˜ ์žˆ๋Š”๋ฐ ์ด๊ฒƒ์ด ์ œ์–ด ์•ˆ์ •์„ฑ ๋ฐ ์„ฑ๋Šฅ์„ ๊ฐ์†Œ์‹œํ‚จ๋‹ค. ์ž‘์—… ๊ณต๊ฐ„ ์ œ์–ด๋Š” ํŠนํžˆ ์ด ๋ฌธ์ œ์— ์ทจ์•ฝํ•œ๋ฐ, ๋Œ€์—ญํญ์ด ์ œํ•œ๋œ ๊ตฌ๋™๊ธฐ ํ•˜๋‚˜๊ฐ€ ๊ทธ์™€ ์—ฐ๊ด€๋œ ๋ชจ๋“  ์ž‘์—… ๊ณต๊ฐ„์˜ ์ œ์–ด ์„ฑ๋Šฅ์„ ๊ฐ์†Œ์‹œํ‚ฌ ์ˆ˜ ์žˆ๊ธฐ ๋•Œ๋ฌธ์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ž‘์—… ๊ณต๊ฐ„ ์ œ์–ด๊ธฐ์—์„œ ์„ฑ๋Šฅ์ด ๋‚ฎ์€ ๊ตฌ๋™๊ธฐ์˜ ์‚ฌ์šฉ์„ ์ œํ•œํ•˜๊ธฐ ์œ„ํ•œ ์ง๊ด€์ ์ธ ์ „๋žต์ด ์ œ์•ˆ๋˜์—ˆ๋‹ค. ๊ธฐ๋ณธ ์ปจ์…‰์€ ์ž‘์—… ์ œ์–ด๋ฅผ ์œ„ํ•œ ํ† ํฌ ์†”๋ฃจ์…˜์— ์„ฑ๋Šฅ์ด ์ข‹์€ ๊ด€์ ˆ์—๋งŒ ์ถ”๊ฐ€์ ์œผ๋กœ ํ† ํฌ ์†”๋ฃจ์…˜์„ ๋”ํ•ด๋‚˜๊ฐ€๋Š” ๊ฒƒ์œผ๋กœ, ์ด๊ฒƒ์€ ๊ฐ ๊ด€์ ˆ์˜ ๊ฐ€์ค‘์น˜๊ฐ€ ๊ณ ๋ ค๋œ ํ† ํฌ ์†”๋ฃจ์…˜์ด ๋˜๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•œ๋‹ค. ์„ฑ๋Šฅ์ด ๋‚ฎ์€ ๊ตฌ๋™๊ธฐ์˜ ์‚ฌ์šฉ์„ ์ œํ•œํ•จ์œผ๋กœ์จ ํ† ํฌ ์ „๋‹ฌ ์˜ค์ฐจ๊ฐ€ ์ค„์–ด๋“ค๊ณ  ์ž‘์—… ์„ฑ๋Šฅ์ด ํฌ๊ฒŒ ํ–ฅ์ƒ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์˜ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋“ค์€ 12์ž์œ ๋„ ์ด์กฑ ๋ณดํ–‰ ๋กœ๋ด‡ DYROS-RED์™€ 7์ž์œ ๋„ ๋กœ๋ด‡ ํŒ” Franka Emika Panda๋ฅผ ์ด์šฉํ•œ ์‹คํ—˜์„ ํ†ตํ•ด ๊ฒ€์ฆ๋˜์—ˆ๋‹ค.1 INTRODUCTION 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Contributions of Thesis . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Overview of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 BACKGROUNDS 6 2.1 Operational Space Control . . . . . . . . . . . . . . . . . . . . . . 6 2.2 Dynamics Formulation . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Fixed-Base Dynamics . . . . . . . . . . . . . . . . . . . . 9 2.2.1.1 Joint Space Formulation . . . . . . . . . . . . . 9 2.2.1.2 Operational Space Formulation . . . . . . . . . . 11 2.2.2 Floating-Base Dynamics . . . . . . . . . . . . . . . . . . . 12 2.2.2.1 Joint Space Formulation . . . . . . . . . . . . . 12 2.2.2.2 Operational Space Formulation . . . . . . . . . . 14 2.3 Position Tracking via PD Control . . . . . . . . . . . . . . . . . . 17 2.3.1 Torque Solution . . . . . . . . . . . . . . . . . . . . . . . 17 2.3.2 Orientation Control . . . . . . . . . . . . . . . . . . . . . 19 3 CONTACT-CONSISTENT DISTURBANCE OBSERVER FOR FLOATING-BASE ROBOTS 22 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.2 Momentum-Based Disturbance Observer . . . . . . . . . . . . . . 24 3.3 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . 25 3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.4.2 External Force Estimation . . . . . . . . . . . . . . . . . . 33 3.4.3 Internal Disturbance Rejection . . . . . . . . . . . . . . . 35 3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4 OPERATIONAL SPACE CONTROL UNDER ACTUATOR BANDWIDTH LIMITATION 40 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.2 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.1 General Concepts . . . . . . . . . . . . . . . . . . . . . . . 43 4.2.2 OSF-Based Torque Solution . . . . . . . . . . . . . . . . . 45 4.2.3 Comparison With a Typical Method . . . . . . . . . . . . 47 4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 4.4 Comparison With Other Approaches . . . . . . . . . . . . . . . . 61 4.4.1 Controller Formulation . . . . . . . . . . . . . . . . . . . . 62 4.4.1.1 The Proposed Method . . . . . . . . . . . . . . . 62 4.4.1.2 The OSF Controller . . . . . . . . . . . . . . . . 62 4.4.1.3 The OSF-Filter Controller . . . . . . . . . . . . 62 4.4.1.4 The OSF-Joint Controller . . . . . . . . . . . . . 67 4.4.1.5 The Joint Controller . . . . . . . . . . . . . . . . 68 4.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.5 Frequency Response of Joint Torque . . . . . . . . . . . . . . . . 72 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5 CONCLUSION 85 Abstract (In Korean) 100๋ฐ•

    Force, orientation and position control in redundant manipulators in prioritized scheme with null space compliance

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    This paper addresses the problem of executing multiple prioritized tasks for robot manipulators with compliant behavior in the remaining null space. A novel controllerโ€“observer is proposed to ensure accurate accomplishment of various tasks based on a predefined hierarchy using a new priority assignment approach. Force control, position control and orientation control are considered. Moreover, a compliant behavior is imposed in the null space to handle physical interaction without using joint torque measurements. Asymptotic stability of the task space error and external torque estimation error during executing multiple tasks are shown. The performance of the proposed approach is evaluated on a 7R light weight robot arm by several case studies

    Disturbance observer based control for nonlinear systems

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    This paper presents a general framework for nonlinear systems subject to disturbances using disturbance observer based control (DOBC)techniques. A two-stage design procedure to improve disturbance attenuation ability of current linear/nonlinear controllers is proposed where the disturbance observer design is separated from the controller design. To facilitate this concept, a nonlinear disturbance observer is developed for disturbances generated by an exogenous system, and global exponential stability is established under certain condition. Furthermore, semiglobal stability condition of the composite controller consisting of a nonlinear controller and the nonlinear disturbance observer is established. The developed method is illustrated by the application to control of a two-link robotic manipulator

    Simultaneous identification, tracking control and disturbance rejection of uncertain nonlinear dynamics systems: A unified neural approach

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    Previous works of traditional zeroing neural networks (or termed Zhang neural networks, ZNN) show great success for solving specific time-variant problems of known systems in an ideal environment. However, it is still a challenging issue for the ZNN to effectively solve time-variant problems for uncertain systems without the prior knowledge. Simultaneously, the involvement of external disturbances in the neural network model makes it even hard for time-variant problem solving due to the intensively computational burden and low accuracy. In this paper, a unified neural approach of simultaneous identification, tracking control and disturbance rejection in the framework of the ZNN is proposed to address the time-variant tracking control of uncertain nonlinear dynamics systems (UNDS). The neural network model derived by the proposed approach captures hidden relations between inputs and outputs of the UNDS. The proposed model shows outstanding tracking performance even under the influences of uncertainties and disturbances. Then, the continuous-time model is discretized via Euler forward formula (EFF). The corresponding discrete algorithm and block diagram are also presented for the convenience of implementation. Theoretical analyses on the convergence property and discretization accuracy are presented to verify the performance of the neural network model. Finally, numerical studies, robot applications, performance comparisons and tests demonstrate the effectiveness and advantages of the proposed neural network model for the time-variant tracking control of UNDS
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