Journal Papers:
1) M. Palladino and R. B. Vinter, Minimizers that are not also Relaxed Minimizers, SIAM J. Control and Optim., 52 (2014), no. 4, pgs. 2164 - 2179.
2) M. Palladino and R. B. Vinter, When are Minimizing Controls also Minimizing Relaxed Controls?, Discrete Contin. Dyn. Syst. Series A, 35 (2015), no. 9, pgs. 4573 - 4592.
3) M. Palladino and R. B. Vinter, Regularity of the Hamiltonian along Optimal Trajectories, SIAM J. Control and Optim., 53 (2015), no. 4, 1892 - 1919.
4) G. Colombo and M. Palladino, The Minimum Time Function for the Controlled Moreau's Sweeping Process, SIAM J. Control and Optim., 54 (2016), no. 4, pgs. 2036 - 2072.
5) M. Palladino, Necessary Conditions for Adverse Control Problems expressed by Relaxed Derivatives, Set-Valued and Var. Anal., 24 (2016), no. 4, pgs. 659-683.
6) A. Bressan, M. Palladino and W. Shen, Growth Models for Tree Stems and Vines, Journal of Differential Equations, Volume 263, Issue 4, (2017), Pages 2280-2316.
7) A. Bressan, A. Marigonda, K. T. Nguyen and M. Palladino, Stochastic Model of Optimal Debt Management and Bankruptcy, SIAM Journal of Financial Mathematics, 8(1), pgs. 841–873.
8) A. Bressan, M. Palladino, Well-posedness of a Model for the Growth of Tree Stems and Vines, Discrete Contin. Dyn. Syst. Series A., April 2018, 38(4): pgs. 2047-2064
9) R. W. Murray, M. Palladino, A model for system uncertainty in reinforcement learning, Systems and Control Letters, Volume 122, December 2018, pgs. 24-31
10) A. Bressan, M. Palladino, Q. Sun, Variational Problems for Tree Roots and Branches, Calc. Var. & PDEs, Vol. 59, Issue 1, February 2020
11) F. Tedone, E. Del Dottore, M. Palladino, B. Mazzolai, P. Marcati, Optimal control of plant root tip dynamics in soil, Bioinspiration & Biomimetics, vol. 15, n. 5, July 2020.
12) A. Porat, F. Tedone, M. Palladino, P. Marcati, Y. Meroz, A general 3D model for growth dynamics of Sensory-Growth Systems: from Plants to Robotics, Front. Robot. AI, August 2020.
13) M. Palladino, F. Rampazzo, A geometrically based criterion to avoid infimum-gaps in Optimal Control, Journal of Differential Equations, Vol. 269, Issue 11, 15 November 2020, pgs. 10107 - 10142.
14) A. Pesare, M. Palladino, M. Falcone, A convergent approximation of the Linear Quadratic Optimal Control problem for Reinforcement Learning, Mathematics of Control, Signals and Systems, vol. 33, Issue 3, September 2021, pgs. 379-411.
15) F. Tedone, M. Palladino, Hamilton-Jacobi-Bellman Equations for Control Systems with Friction, IEEE Transactions on Automatic Control, Vol. 66, Issue 12, December 2021, pgs. 5651-5664.
16) M. Zanon, S. Gros, M. Palladino, Stability-Constrained Markov Decision Processes Using MPC, Automatica, Volume 143, September 2022, 110399.
17) M. Motta, M. Palladino and F. Rampazzo, Unbounded Control, Infimum Gaps and Higher Order Normality, SIAM Control and Optimization, vol 60, issue 3, pgs. 1436-1462.
18) C. Hermosilla, M. Palladino, Optimal Control of the Sweeping Process with a Non-Smooth Moving Set, SIAM Control and Optimization, Vol. 60, Iss. 5 (2022), pgs 2811-2834.
19) G. Vecchiato, T. Hattermann, M. Palladino, F. Tedone, P. Heuret, N. P. Rowe, P. Marcati, A 2D model to study how secondary growth affects the self-supporting behaviour of climbing plants, PLOS Computational Biology, October 2023,
20) A. Alla, A. Pacifico, M. Palladino, A. Pesare, Online identification and control of PDEs via reinforcement learning methods, Advances in Computational Mathematics, vol. 50, n. 85, 2024
21) C. Hermosilla, M. Palladino, E. Vilches, Hamilton-Jacobi-Bellman Approach for Optimal Control Problems of
Sweeping Processes, Applied Mathematics and Optimization, Vol. 90, n. 33, (2024)
22) L. Nasti, G. Vecchiato, P. Heuret, N. P. Rowe, M. Palladino, P. Marcati, A Reinforcement Learning approach
to study climbing plant behaviour, Scientific Reports, 14(1), n. 18222, (2024)
23) F. Angrisani, M. Palladino, F. Rampazzo, On Quasi Differential Quotients, Set-Valued and Variational
Analysis, vol. 32, n. 34, (2024)
24) M. Palladino, A. Pesare, T. Scarinci, Convergence Results for Control Problems with Unknown Dynamics and
Applications to Reinforcement Learning, to appear on Mathematical Control and Related Fields.
25) M. S. Aronna, M. Palladino, O. Sierra, Dynamic Programming Principle and Hamilton-Jacobi-Bellman Equation
for Optimal Control Problems with Uncertainty, under revision.
Peer-reviewed Proceedings and Chapter Contributions:
1) M. Palladino and R. B. Vinter, When does relaxation reduce the minimum cost of an optimal control problem?, IEEE 52nd Annual Conference on Decision and Control (CDC), 10-13 Dec. 2013.
2) M. Palladino, Relaxation in Optimal Control, Chapter for the book: "Optimal Control: Novel Directions and Applications", Springer Lecture Notes, 2017.
3) R. Murray, M. Palladino, Modelling Uncertainty in Reinforcement Learning, IEEE 58th Annual Conference on Decision and Control (CDC), 11-13 Dec. 2019.
4) M. Palladino, F. Rampazzo, A No Infimum-Gap Criterion, IEEE 58th Annual Conference on Decision and Control (CDC), 11-13 Dec. 2019.
5) A. Pesare, M. Palladino, M. Falcone, Convergence of the Value Function in Optimal Control problems with unknown dynamics, European Control Conference (ECC), 29 June - 2 July, 2021.
Works in Progress: