328 research outputs found

    A Model Predictive Capture Point Control Framework for Robust Humanoid Balancing via Ankle, Hip, and Stepping Strategies

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    The robust balancing capability of humanoid robots against disturbances has been considered as one of the crucial requirements for their practical mobility in real-world environments. In particular, many studies have been devoted to the efficient implementation of the three balance strategies, inspired by human balance strategies involving ankle, hip, and stepping strategies, to endow humanoid robots with human-level balancing capability. In this paper, a robust balance control framework for humanoid robots is proposed. Firstly, a novel Model Predictive Control (MPC) framework is proposed for Capture Point (CP) tracking control, enabling the integration of ankle, hip, and stepping strategies within a single framework. Additionally, a variable weighting method is introduced that adjusts the weighting parameters of the Centroidal Angular Momentum (CAM) damping control over the time horizon of MPC to improve the balancing performance. Secondly, a hierarchical structure of the MPC and a stepping controller was proposed, allowing for the step time optimization. The robust balancing performance of the proposed method is validated through extensive simulations and real robot experiments. Furthermore, a superior balancing performance is demonstrated, particularly in the presence of disturbances, compared to a state-of-the-art Quadratic Programming (QP)-based CP controller that employs the ankle, hip, and stepping strategies. The supplementary video is available at https://youtu.be/CrD75UbYzdcComment: 19 pages,13 figure

    ZMP Constraint Restriction for Robust Gait Generation in Humanoids

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    We present an extension of our previously proposed IS-MPC method for humanoid gait generation aimed at obtaining robust performance in the presence of disturbances. The considered disturbance signals vary in a range of known amplitude around a mid-range value that can change at each sampling time, but whose current value is assumed to be available. The method consists in modifying the stability constraint that is at the core of IS-MPC by incorporating the current mid-range disturbance, and performing an appropriate restriction of the ZMP constraint in the control horizon on the basis of the range amplitude of the disturbance. We derive explicit conditions for recursive feasibility and internal stability of the IS-MPC method with constraint modification. Finally, we illustrate its superior performance with respect to the nominal version by performing dynamic simulations on the NAO robot

    Review of Anthropomorphic Head Stabilisation and Verticality Estimation in Robots

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    International audienceIn many walking, running, flying, and swimming animals, including mammals, reptiles, and birds, the vestibular system plays a central role for verticality estimation and is often associated with a head sta-bilisation (in rotation) behaviour. Head stabilisation, in turn, subserves gaze stabilisation, postural control, visual-vestibular information fusion and spatial awareness via the active establishment of a quasi-inertial frame of reference. Head stabilisation helps animals to cope with the computational consequences of angular movements that complicate the reliable estimation of the vertical direction. We suggest that this strategy could also benefit free-moving robotic systems, such as locomoting humanoid robots, which are typically equipped with inertial measurements units. Free-moving robotic systems could gain the full benefits of inertial measurements if the measurement units are placed on independently orientable platforms, such as a human-like heads. We illustrate these benefits by analysing recent humanoid robots design and control approaches

    Benchmarking Dynamic Balancing Controllers for Humanoid Robots

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    This paper presents a comparison study of three control design approaches for humanoid balancing based on the Center of Mass (CoM) stabilization and body posture adjustment. The comparison was carried out under controlled circumstances allowing other researchers to replicate and compare our results with their own. The feedback control from state space design is based on simple models and provides sufficient robustness to control complex and high Degrees of Freedom (DoFs) systems, such as humanoids. The implemented strategies allow compliant behavior of the robot in reaction to impulsive or periodical disturbances, resulting in a smooth and human-like response while considering constraints. In this respect, we implemented two balancing strategies to compensate for the CoM deviation. The first one uses the robotโ€™s capture point as a stability principle and the second one uses the Force/Torque sensors at the ankles to define a CoM reference that stabilizes the robot. In addition, was implemented a third strategy based on upper body orientation to absorb external disturbances and counterbalance them. Even though the balancing strategies are implemented independently, they can be merged to further increase balancing performance. The proposed strategies were previously applied on different humanoid bipedal platforms, however, their performance could not be properly benchmarked before. With this concern, this paper focuses on benchmarking in controlled scenarios to help the community in comparing different balance techniques. The key performance indicators (KPIs) used in our comparison are the CoM deviation, the settling time, the maximum measured orientation, passive gait measure, measured ankles torques, and reconstructed Center of Pressure (CoP). The benchmarking experiments were carried out in simulations and using the facility at Istituto Italiano di Tecnologia on the REEM-C humanoid robot provided by PAL robotics inside the EU H2020 project EUROBENCH framework

    From walking to running: robust and 3D humanoid gait generation via MPC

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    Humanoid robots are platforms that can succeed in tasks conceived for humans. From locomotion in unstructured environments, to driving cars, or working in industrial plants, these robots have a potential that is yet to be disclosed in systematic every-day-life applications. Such a perspective, however, is opposed by the need of solving complex engineering problems under the hardware and software point of view. In this thesis, we focus on the software side of the problem, and in particular on locomotion control. The operativity of a legged humanoid is subordinate to its capability of realizing a reliable locomotion. In many settings, perturbations may undermine the balance and make the robot fall. Moreover, complex and dynamic motions might be required by the context, as for instance it could be needed to start running or climbing stairs to achieve a certain location in the shortest time. We present gait generation schemes based on Model Predictive Control (MPC) that tackle both the problem of robustness and tridimensional dynamic motions. The proposed control schemes adopt the typical paradigm of centroidal MPC for reference motion generation, enforcing dynamic balance through the Zero Moment Point condition, plus a whole-body controller that maps the generated trajectories to joint commands. Each of the described predictive controllers also feature a so-called stability constraint, preventing the generation of diverging Center of Mass trajectories with respect to the Zero Moment Point. Robustness is addressed by modeling the humanoid as a Linear Inverted Pendulum and devising two types of strategies. For persistent perturbations, a way to use a disturbance observer and a technique for constraint tightening (to ensure robust constraint satisfaction) are presented. In the case of impulsive pushes instead, techniques for footstep and timing adaptation are introduced. The underlying approach is to interpret robustness as a MPC feasibility problem, thus aiming at ensuring the existence of a solution for the constrained optimization problem to be solved at each iteration in spite of the perturbations. This perspective allows to devise simple solutions to complex problems, favoring a reliable real-time implementation. For the tridimensional locomotion, on the other hand, the humanoid is modeled as a Variable Height Inverted Pendulum. Based on it, a two stage MPC is introduced with particular emphasis on the implementation of the stability constraint. The overall result is a gait generation scheme that allows the robot to overcome relatively complex environments constituted by a non-flat terrain, with also the capability of realizing running gaits. The proposed methods are validated in different settings: from conceptual simulations in Matlab to validations in the DART dynamic environment, up to experimental tests on the NAO and the OP3 platforms

    ์™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•

    Active Disturbance Rejection Control based on Generalized Proportional Integral Observer to Control a Bipedal Robot with Five Degrees of Freedom

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    An Active Disturbance Rejection Control based on Generalized Proportional Integral observer (ADRC with GPI observer) was developed to control the gait of a bipedal robot with five degrees of freedom. The bipedal robot used is a passive point feet which produces an underactuated dynamic walking. A virtual holonomic constraint is imposed to generate online smooth trajectories which were used as references of the control system. The proposed control strategy is tested through numerical simulation on a task of forward walking with the robot exposed to external disturbances. The performance of ADRC with GPI observer strategy is compared with a feedback linearization with proportional-derivative control. A stability test consisting on analyzing the existence of limit cycles using the Poincareฬ's method revealed that asymptotically stable walking was achieved. The proposed control strategy effectively rejects the external disturbances and keeps the robot in a stable dynamic walking
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