A Robust Iterative Learning Control for Continuous-Time Nonlinear Systems With Disturbances

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

doi:10.1109/access.2021.3124014

Cited by 5 publications

(5 citation statements)

References 34 publications

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“…The operator hits/stops the robot during iterations j = 2 and j = 4 creating an external disturbance. This experiment validates the learning capabilities in the case of state non-repetitive disturbances [21]. Fig.…”

Section: Experiments Resultssupporting

confidence: 79%

“…External disturbances are state nonrepetitive while loads are state repetitive. Noise or artifacts are time nonrepetitive [21].…”

Section: Experiments Resultsmentioning

confidence: 99%

“…We tested the controller with and without an unmodeled load of 200g attached to the robot tip, i.e., the M test. The load represents a state-repetitive disturbance in the ILC framework [21]. Fig.…”

Section: Experiments Resultsmentioning

confidence: 99%

“…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%

“…External disturbances J ⊤ (q)τ ext (state nonrepetitive disturbances) and unmodeled model mismatches (state repetitive disturbances) can be learned by the iterative law (24) leading to b e = 0 in (28), [21]. Conversely, noise or actuator artifacts ξ j (•) (time non-repetitive disturbances) imply a bounded convergence (28) [21]. If ξ j (•) ≡ 0, ∀t ∈ [0,t f ], ∀ j, Theorem 1 guarantees b e = 0, i.e., perfect tracking.…”

Section: A Proposed Algorithmmentioning

confidence: 99%

See 4 more Smart Citations

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: Experiments Resultssupporting

confidence: 79%

“…External disturbances are state nonrepetitive while loads are state repetitive. Noise or artifacts are time nonrepetitive [21].…”

Section: Experiments Resultsmentioning

confidence: 99%

Section: Experiments Resultsmentioning

confidence: 99%

Section: B Dynamics and Output Functionmentioning

confidence: 99%