Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …

Advances in optical manipulation and observation of neural activity have set the stage for
widespread implementation of closed-loop and activity-guided optical control of neural …

Common approaches for direct model predictive control (MPC) for current reference tracking
in power electronics suffer from the high computational complexity encountered when …

This paper presents a novel method of global adaptive dynamic programming (ADP) for the
adaptive optimal control of nonlinear polynomial systems. The strategy consists of relaxing …

Many control policies used in applications compute the input or action by solving a convex
optimization problem that depends on the current state and some parameters. Common …

Starting in the late 1970s, optimization-based control has built up an impressive track record
of successful industrial applications, in particular in the petrochemical and process …

Recent advances in sensing, communication, and computing technologies have enabled
the use of multirobot systems for practical applications such as surveillance, area map** …

We consider dynamic trading of a portfolio of assets in discrete periods over a finite time
horizon, with arbitrary time-varying distribution of asset returns. The goal is to maximize the …

This chapter aims to give a concise overview of numerical methods and algorithms for
implementing robust model predictive control (MPC). We introduce the mathematical …

In this work we illustrate how approximate dynamic programing can be utilized to address
problems of stochastic reachability in infinite state and control spaces. In particular we focus …