We develop an approach to improve the learning capabilities of robotic systems by
combining learned predictive models with experience-based state-action policy map**s …

In this paper, we use the calculus of variations to derive a sensitivity analysis for ordinary
differential equations with events. This swee** gradient method (SGM) requires a forward …

We present a decentralized ergodic control policy for time-varying area coverage problems
for multiple agents with nonlinear dynamics. Ergodic control allows us to specify distributions …

In this paper, we consider an optimal control problem of switched systems with input and
state constraints. It is difficult to numerically integrate the switched system with the initial …

Recently, battery powered electric vehicles (EV), such as the Tesla Model S, have reached
commercialization. Future EVs will likely pair the battery with a supercapacitor to extend …

This paper concerns optimal mode-scheduling in autonomous switched-mode hybrid
dynamical systems, where the objective is to minimize a cost-performance functional defined …

In this article, a hybrid intelligent dynamic optimization method integrating of particle swarm
optimization (PSO) and gradient-based optimization (GBO) is proposed for optimal control of …

Practical industrial process is usually a dynamic process including uncertainty. Stochastic
constraints can be used for industrial process modeling, when system sate and/or control …

This article presents new mathematical results for exact and approximate relaxations of
nonconvex optimal control problems defined on disconnected control sets. The exact …

We present a theoretical formulation, and a corresponding numerical algorithm, that can find
Pontryagin-optimal inputs for general dynamical systems by using a direct method. Optimal …