From the 1 of 6 linked papers with an AI index.
6 papers
Modeling and Control of Deep Sign-Definite Dynamics with Application to Hybrid Powertrain Control
Teruki Kato, Ryotaro Shima, Kenji Kashima
The paper proposes enforcing sign constraints on the Jacobian of deep neural network models to ensure physical properties like monotonicity and positivity, enabling convex model pr…
Regularized Model Predictive Control via Contractivity and Implicit Lur'e Analysis
Ryotaro Shima, Anand Gokhale, Alexander Davydov +1
This paper develops a contraction-based stability analysis for regularized model predictive control (MPC), whose feedback law is defined implicitly by a finite-horizon optimal cont…
Convex Chance-Constrained Stochastic Control under Uncertain Specifications with Application to Learning-Based Hybrid Powertrain Control
Teruki Kato, Ryotaro Shima, Kenji Kashima
This paper presents a strictly convex chance-constrained stochastic control framework that accounts for uncertainty in control specifications such as reference trajectories and ope…
Contractivity Analysis and Control Design for Lur'e Systems: Lipschitz, Incrementally Sector Bounded, and Monotone Nonlinearities
Ryotaro Shima, Alexander Davydov, Francesco Bullo
In this paper, we study the contractivity of Lur'e dynamical systems whose nonlinearity is either Lipschitz, incrementally sector bounded, or monotone. We consider both the discret…
Contraction Analysis of Continuation Method for Suboptimal Model Predictive Control
Ryotaro Shima, Yuji Ito, Tatsuya Miyano
This letter analyzes the contraction property of the nonlinear systems controlled by suboptimal model predictive control (MPC) using the continuation method. We propose a contracti…
Continuation Method for Nonsmooth Model Predictive Control Using Proximal Technique
Ryotaro Shima, Ryuta Moriyasu, Teruki Kato
This paper presents a novel framework for the continuation method of model predictive control based on optimal control problem with a nonsmooth regularizer. Via the proximal operat…