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Nikolas Sacchi
Nikolas Sacchi
PhD Student, University of Pavia
Verified email at universitadipavia.it
Title
Cited by
Cited by
Year
Neural network-based practical/ideal integral sliding mode control
N Sacchi, GP Incremona, A Ferrara
IEEE Control Systems Letters 6, 3140-3145, 2022
92022
Deep reinforcement learning of robotic prosthesis for gait symmetry in trans-femoral amputated patients
N Sacchi, GP Incremona, A Ferrara
2021 29th Mediterranean Conference on Control and Automation (MED), 723-728, 2021
62021
Sliding mode based fault diagnosis with deep reinforcement learning add‐ons for intrinsically redundant manipulators
N Sacchi, GP Incremona, A Ferrara
International Journal of Robust and Nonlinear Control 33 (15), 9109-9127, 2023
42023
Experimental assessment of deep reinforcement learning for robot obstacle avoidance: a lpv control perspective
GP Incremona, N Sacchi, B Sangiovanni, A Ferrara
IFAC-PapersOnLine 54 (8), 89-94, 2021
42021
Design of a deep neural network-based integral sliding mode control for nonlinear systems under fully unknown dynamics
E Vacchini, N Sacchi, GP Incremona, A Ferrara
IEEE Control Systems Letters, 2023
32023
Analysis of hybrid cable-thruster actuated ROV in heavy lifting interventions
N Sacchi, E Simetti, G Antonelli, G Indiveri, V Creuze, M Gouttefarde
2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022
22022
A configuration space reference generation approach for real-time collision avoidance of industrial robot manipulators
N Sacchi, B Sangiovanni, GP Incremona, A Ferrara
2021 European Control Conference (ECC), 1316-1321, 2021
22021
Neural network based integral sliding mode control of systems with time-varying state constraints
N Sacchi, E Vacchini, A Ferrara
2023 31st Mediterranean Conference on Control and Automation (MED), 624-629, 2023
2023
Integral Sliding Modes Generation via DRL-Assisted Lyapunov-Based Control for Robot Manipulators
N Sacchi, GP Incremona, A Ferrara
2023 European Control Conference (ECC), 1-6, 2023
2023
Actuator Fault Diagnosis With Neural Network-Integral Sliding Mode Based Unknown Input Observers
N Sacchi, GP Incremona, A Ferrara
IFAC-PapersOnLine 56 (2), 773-778, 2023
2023
Scenario-Based Collision Avoidance Control with Deep Q-Networks for Industrial Robot Manipulators
N Sacchi, B Sangiovanni, GP Incremona, A Ferrara
2021 60th IEEE Conference on Decision and Control (CDC), 4388-4393, 2021
2021
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