Iterative Learning Control for Compliant Underactuated Arms

Pierallini, Michele; Angelini, Franco; Mengacci, Riccardo; Palleschi, Alessandro; Bicchi, Antonio; Garabini, Manolo

doi:10.1109/tsmc.2023.3234403

Cited by 11 publications

(9 citation statements)

References 43 publications

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“…τ ext ∈ X 6 q collects the external forces (if present), i.e., sup t∈[0,t f ] {∥τ ext ∥} ≤ b τ ext < ∞,and ξ (•) : [0,t f ] → X n q such as sup t {||ξ (t)||} ≤ b ξ < ∞ embeds the model artifacts, and it will described in Sec. VI, [14], [21]. u(•) : [0,t f ] → X m q is the control input (Sec.…”

Section: B Dynamics and Output Functionmentioning

confidence: 99%

“…Assumption 1. The system (3)-(4) has relative degree r = 2 for almost all q, q ∈ X n q , (see, e.g., [14]). Assumption 1 guarantees an inertial coupling between all the variables, which is very common for any Lagrangian system [14], [22].…”

Section: Dynamic Model Propertiesmentioning

confidence: 99%

“…The system (3)-(4) has relative degree r = 2 for almost all q, q ∈ X n q , (see, e.g., [14]). Assumption 1 guarantees an inertial coupling between all the variables, which is very common for any Lagrangian system [14], [22]. In practice, it means that the controller inertially acts on the output function (4) and its time derivatives.…”

Section: Dynamic Model Propertiesmentioning

confidence: 99%

“…The main contribution of the paper is a pure feedforward torque-based control strategy for soft robots. The algorithm belongs to the class of iterative learning controller (ILC) [14] Fig. 1.…”

mentioning

confidence: 99%

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A Provably Stable Iterative Learning Controller for Continuum Soft Robots

Pierallini,

Stella,

Angelini

et al. 2023

IEEE Robot. Autom. Lett.

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Fully exploiting soft robots' capabilities requires devising strategies that can accurately control their movements with the limited amount of control sources available. This task is challenging for reasons including the hard-to-model dynamics, the system's underactuation, and the need of using a prominent feedforward control action to preserve the soft and safe robot behavior. To tackle this challenge, this letter proposes a purely feedforward iterative learning control algorithm that refines the torque action by leveraging both the knowledge of the model and data obtained from past experience. After presenting a 3D polynomial description of soft robots, we study their intrinsic properties, e.g., input-to-state stability, and we prove the convergence of the controller coping with locally Lipschitz nonlinearities. Finally, we validate the proposed approach through simulations and experiments involving multiple systems, trajectories, and in the case of external disturbances and model mismatches.

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Section: B Dynamics and Output Functionmentioning

confidence: 99%

Section: Dynamic Model Propertiesmentioning

confidence: 99%

Section: Dynamic Model Propertiesmentioning

confidence: 99%

mentioning

confidence: 99%

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A Provably Stable Iterative Learning Controller for Continuum Soft Robots

Pierallini,

Stella,

Angelini

et al. 2023

IEEE Robot. Autom. Lett.

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“…Zhang [28] proposed combining data‐driven and iterative learning to balance the speed and accuracy of nano‐positioning tracking. Pierallini [29] added adaptive gain‐based ILC to tandem elastic robot control to solve the trajectory tracking problem for underdriven systems.…”

Section: Introductionmentioning

confidence: 99%

Design of a trajectory contour controller for a dual‐axis precision motion stage based on improved iterative learning

Zhang

Wang

Fang

et al. 2023

IET Control Theory & Appl

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To solve the problem of contour error (CE) arising from the dual‐axis coupling of the X–Y coordinate planar motor stage under reciprocating motion conditions in integrated circuit (IC) manufacturing, the following two perspectives are considered: indirect intervention from a single‐axis servo tracking error (TE) control and direct intervention from a dual‐axis cross‐coupling control (CCC). An improved motion controller is designed using the iterative learning control (ILC) principle in conjunction with the motion characteristics of the dual‐axis mechanism. Specifically, the variable gain iterative learning controller with a variable forgetting factor (VFF‐VGILC) is designed to address the characteristics of single‐axis trajectory repetition and variable velocity planning in dual‐axis coupling. The significance of the VFF‐VGILC is to increase the servo control bandwidth of a single axis. On the other hand, the cross‐coupled iterative learning controller (CCILC) is designed on the dual‐axis cross‐coupling, while a pre‐compensation strategy is introduced in the CCC structure to form the pre‐CCILC. After experimental designing and testing are performed, the X–Y coordinate planar motor stage can effectively reduce the CE under various trajectory movements compared with the traditional controller. The trajectory curves drawn can meet the motion control requirements better and effectively enhance the motion control capability of the mechanism.

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Fractional-order iterative learning control for fractional-order systems with initialization non-repeatability

Xu,

Chen,

2023

ISA Transactions

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Iterative Learning Control for Compliant Underactuated Arms

Cited by 11 publications

References 43 publications

A Provably Stable Iterative Learning Controller for Continuum Soft Robots

A Provably Stable Iterative Learning Controller for Continuum Soft Robots

Design of a trajectory contour controller for a dual‐axis precision motion stage based on improved iterative learning

Fractional-order iterative learning control for fractional-order systems with initialization non-repeatability

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