To more efficiently harness the renewable energy sources, advanced power converters
have become an indispensable part in real time implementation. Multilevel inverters (MLI) …

This paper develops a Pontryagin differentiable programming (PDP) methodology, which
establishes a unified framework to solve a broad class of learning and control tasks. The …

Abstract We propose a Safe Pontryagin Differentiable Programming (Safe PDP)
methodology, which establishes a theoretical and algorithmic framework to solve a broad …

Researchers in biomechanics, neuroscience, human–machine interaction and other fields
are interested in inferring human intentions and objectives from observed actions. The …

The industry needs controllers that allow smooth and natural physical Human-Robot
Interaction (pHRI) to make production scenarios more flexible and user-friendly. Within this …

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 results from the …

This article develops a methodology that enables learning an objective function of an
optimal control system from incomplete trajectory observations. The objective function is …

This paper proposes a data-driven model-free inverse reinforcement learning (IRL)
algorithm tailored for solving an inverse control problem. In the problem, both an expert and …

This paper presents a novel sequential inverse optimal control (SIOC) method for discrete-
time systems, which calculates the unknown weight vectors of the cost function in real time …

In this article, we develop the method of continuous Pontryagin differentiable programming
(Continuous PDP), which enables a robot to learn an objective function from a few sparsely …