This monograph presents a new framework for learning-based control synthesis of
continuous-time dynamical systems with unknown dynamics. The new design paradigm …

In this paper, we present an overview of adaptive control by contrasting model‐based
approaches with data‐driven approaches. Indeed, we propose to classify adaptive …

The idea of optimization can be regarded as an important basis of many disciplines and
hence is extremely useful for a large number of research fields, particularly for artificial …

This paper presents a method to compute an approximate explicit model predictive control
(MPC) law using neural networks. The optimal MPC control law for constrained linear …

This article considers a problem of tracking, convergence of disturbance observer (DO)
based optimal control design for uncertain surface vessels (SVs) with external disturbance …

In this paper, a novel off-policy interleaved Q-learning algorithm is presented for solving
optimal control problem of affine nonlinear discrete-time (DT) systems, using only the …

In this article, the data-based two-player zero-sum game problem is considered for linear
discrete-time systems. This problem theoretically depends on solving the discrete-time game …

In this paper, an optimal motion/force hybrid control strategy based on adaptive
reinforcement learning (ARL) is proposed for cooperating manipulator systems. The optimal …

A revolution in network technology has been ushered in by software defined networking
(SDN), which makes it possible to control the network from a central location and provides …

We introduce a framework for eliminating the computation of controller gain functions in PDE
control. We learn the nonlinear operator from the plant parameters to the control gains with a …