Robust Control Synthesis and Verification for Wire-Borne Underactuated Brachiating Robots Using Sum-of-Squares Optimization

Farzan, Siavash; Hu, Ai-Ping; Rogers, Jonathan

doi:10.1109/iros45743.2020.9341348

Cited by 8 publications

(5 citation statements)

References 34 publications

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“…2 presents our proposed low-fidelity model for a wire-borne underactuated brachiating robot. Using this model, the dynamic effects of the flexible cable are captured as a time-varying force F c applied to the pivot gripper [9]:…”

Section: Robot-cable Model and Dynamics A Low-fidelity Dynamic Model ...mentioning

confidence: 99%

“…To overcome these challenges, the cable dynamics effects and the resulting uncertainties are required to be estimated, and the discrepancies between the approximated and actual model need to be compensated. The former objective motivates the use of an adaptive control scheme, while the latter can be dealt with by using a robust control design [8], [9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cable Estimation-Based Control for Wire-Borne Underactuated Brachiating Robots: A Combined Direct-Indirect Adaptive Robust Approach

Farzan¹,

Azimi²,

Hu³

et al. 2020

Preprint

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In this paper, we present an online adaptive robust control framework for underactuated brachiating robots traversing flexible cables. Since the dynamic model of a flexible body is unknown in practice, we propose an indirect adaptive estimation scheme to approximate the unknown dynamic effects of the flexible cable as an external force with parametric uncertainties. A boundary layer-based sliding mode control is then designed to compensate for the residual unmodeled dynamics and time-varying disturbances, in which the control gain is updated by an auxiliary direct adaptive control mechanism. Stability analysis and derivation of adaptation laws are carried out through a Lyapunov approach, which formally guarantees the stability and tracking performance of the robot-cable system. Simulation experiments and comparison with a baseline controller show that the combined direct-indirect adaptive robust control framework achieves reliable tracking performance and adaptive system identification, enabling the robot to traverse flexible cables in the presence of unmodeled dynamics, parametric uncertainties and unstructured disturbances.

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Section: Robot-cable Model and Dynamics A Low-fidelity Dynamic Model ...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cable Estimation-Based Control for Wire-Borne Underactuated Brachiating Robots: A Combined Direct-Indirect Adaptive Robust Approach

Farzan¹,

Azimi²,

Hu³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Machine learning and heuristic methods are also popular, providing a model-free approach to learn brachiation behavior [13] [12] [14]. Most recent research however focuses on model-based and energy-optimal control approaches, since energy-optimal formulations bring the advantage of robustness against uncertainties and can be incorporated in behavior generation [10] [3], behavior control [15], or both [5]. Table I summarizes the brachiation robot literature in categories of system design, behavior generation, and control approaches.…”

Section: Introductionmentioning

confidence: 99%

AcroMonk: A Minimalist Underactuated Brachiating Robot

Javadi

Harnack

Stocco

et al. 2023

IEEE Robot. Autom. Lett.

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Brachiation is a dynamic, coordinated swinging maneuver of body and arms used by monkeys and apes to move between branches. As a unique underactuated mode of locomotion, it is interesting to study from a robotics perspective since it can broaden the deployment scenarios for humanoids and animaloids. While several brachiating robots of varying complexity have been proposed in the past, this paper presents the simplest possible prototype of a brachiation robot, using only a single actuator and unactuated grippers. The novel passive gripper design allows it to snap on and release from monkey bars, while guaranteeing well defined start and end poses of the swing. The brachiation behavior is realized in three different ways, using trajectory optimization via direct collocation and stabilization by a model-based time-varying linear quadratic regulator (TVLQR) or model-free proportional derivative (PD) control, as well as by a reinforcement learning (RL) based control policy. The three control schemes are compared in terms of robustness to disturbances, mass uncertainty, and energy consumption. The system design and controllers have been open-sourced 1 . Due to its minimal and open design, the system can serve as a canonical underactuated platform for education and research.

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“…This is, in part, due to their broad applications such as manipulators operating on dynamic platforms, 1 aircraft, 2 bipedal and quadruped walking robots, 3,4 and brachiating robots. 5,6 While there has been a large body of work on developing control strategies for fully-actuated systems, [7][8][9][10] the current techniques in the literature for fully-actuated systems do not apply to underactuated systems. This fact, coupled with the recent growing interest in developing multi-objective control schemes for underactuated robots, motivates a theoretical study of control techniques for such systems.…”

Section: Introductionmentioning

confidence: 99%

“…In the last two decades, there has been increased emphasis on designing control algorithms specific to underactuated systems; herein underactuated systems refer to systems with fewer degrees of actuation (DoA) than degrees of freedom (DoF). This is, in part, due to their broad applications such as manipulators operating on dynamic platforms, 1 aircraft, 2 bipedal and quadruped walking robots, 3,4 and brachiating robots 5,6 . While there has been a large body of work on developing control strategies for fully‐actuated systems, 7‐10 the current techniques in the literature for fully‐actuated systems do not apply to underactuated systems.…”

Section: Introductionmentioning

confidence: 99%

Active space control of underactuated robots: From adaptation to robustness to optimality

Azimi

Farzan

Ames

et al. 2022

Adaptive Control & Signal

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Summary This article develops a controller for the active space control of underactuated robotic systems with the ability to adapt to unknown parameters, be robust to unmodeled dynamics and disturbances, and generate optimal control policies in a pointwise fashion. To achieve the set forth objectives, the model dynamics are first partitioned into active and passive spaces. As a remedy for nonlinear coupling between these spaces, the system's acceleration is estimated for use in the control algorithm. The modeling uncertainty associated with both unknown system parameters and unknown control map is then estimated by proposing a concurrent learning adaptive approach. An online quadratic program (QP) is synthesized utilizing an intelligent robust control Lyapunov function (IRCLF) constraint to ensure the system stability with minimal control effort while using the estimates of the unknown dynamics. The proposed IRCLF scheme automatically compensate for acceleration estimation errors, unmodeled dynamics, and disturbances without the knowledge of their bounds a priori. The uniform ultimate boundedness of all system signals is proven using the Lyapunov stability argument and Barbalat's lemma. The performance of the proposed QP‐AIR‐CLF control framework is validated on two different underactuated systems: a foot‐leg model on a deformable ground, and an overhead crane system. Simulation results show the benefits of the proposed controller over the baseline QP‐RCLF and an adaptive QP‐RCLF in terms of the control optimality, tracking accuracy, dynamic estimation performance, and robustness to disturbances.

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Robust Control Synthesis and Verification for Wire-Borne Underactuated Brachiating Robots Using Sum-of-Squares Optimization

Cited by 8 publications

References 34 publications

Cable Estimation-Based Control for Wire-Borne Underactuated Brachiating Robots: A Combined Direct-Indirect Adaptive Robust Approach

Cable Estimation-Based Control for Wire-Borne Underactuated Brachiating Robots: A Combined Direct-Indirect Adaptive Robust Approach

AcroMonk: A Minimalist Underactuated Brachiating Robot

Active space control of underactuated robots: From adaptation to robustness to optimality

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