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Found 17 papers, 6 papers with code
Integrated Optimal Control for Fast Charging and Active Thermal Management of Lithium-Ion Batteries in Extreme Ambient Temperatures
A state-feedback model predictive control algorithm is then developed for integrated fast charging and active thermal management.
Distributed Optimization via Kernelized Multi-armed Bandits
In the proposed approach, the agents sample their individual local functions in a way that benefits the whole network by utilizing a running consensus to estimate the upper confidence bound on the global function.
Adaptive Policy Learning to Additional Tasks
This paper develops a policy learning method for tuning a pre-trained policy to adapt to additional tasks without altering the original task.
Policy Learning based on Deep Koopman Representation
This paper proposes a policy learning algorithm based on the Koopman operator theory and policy gradient approach, which seeks to approximate an unknown dynamical system and search for optimal policy simultaneously, using the observations gathered through interaction with the environment.
A Data-Driven Approach for Inverse Optimal Control
By assuming the objective function to be learned is parameterized as a linear combination of features with unknown weights, the proposed approach for IOC is able to achieve a Koopman representation of the unknown dynamics and the unknown weights in objective function together.
Variable Sampling MPC via Differentiable Time-Warping Function
Designing control inputs for a system that involves dynamical responses in multiple timescales is nontrivial.
Deep Koopman Learning of Nonlinear Time-Varying Systems
This paper presents a data-driven approach to approximate the dynamics of a nonlinear time-varying system (NTVS) by a linear time-varying system (LTVS), which is resulted from the Koopman operator and deep neural networks.
Cooperative Tuning of Multi-Agent Optimal Control Systems
Different from classical techniques for tuning parameters in a controller, we allow tunable parameters appearing in both the system dynamics and the objective functions of each agent.
Resilience for Distributed Consensus with Constraints
By applying this new device to multi-agent systems, we introduce network and constraint redundancy conditions under which resilient constrained consensus can be achieved with an exponential convergence rate.
Safe Pontryagin Differentiable Programming
We propose a Safe Pontryagin Differentiable Programming (Safe PDP) methodology, which establishes a theoretical and algorithmic framework to solve a broad class of safety-critical learning and control tasks -- problems that require the guarantee of safety constraint satisfaction at any stage of the learning and control progress.
Towards Resilience for Multi-Agent $QD$-Learning
This paper considers the multi-agent reinforcement learning (MARL) problem for a networked (peer-to-peer) system in the presence of Byzantine agents.
Learning from Human Directional Corrections
This paper proposes a novel approach that enables a robot to learn an objective function incrementally from human directional corrections.
Learning Objective Functions Incrementally by Inverse Optimal Control
This paper proposes an inverse optimal control method which enables a robot to incrementally learn a control objective function from a collection of trajectory segments.
Robotics
Learning from Sparse Demonstrations
The time stamps of the keyframes can be different from the time of the robot's actual execution.
Neural Certificates for Safe Control Policies
This paper develops an approach to learn a policy of a dynamical system that is guaranteed to be both provably safe and goal-reaching.
Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework
This paper develops a Pontryagin Differentiable Programming (PDP) methodology, which establishes a unified framework to solve a broad class of learning and control tasks.
Inverse Optimal Control with Incomplete Observations
We handle the problem by proposing the recovery matrix, which establishes a relationship between available observations of the trajectory and weights of given candidate features.
Robotics Systems and Control
