The method of sparsity has been attracting a lot of attention in the fields related not only to
signal processing, machine learning, and statistics, but also systems and control. The …

This work proposes a framework to design, formulate and implement a path tracker for self‐
driving cars (SDC) based on a nonlinear model‐predictive‐control (NMPC) approach. The …

In this paper, we propose a method for the reconstruction of a complex discrete-valued
vector from its linear measurements. In particular, we mainly focus on the underdetermined …

In this survey paper, we review recent advances of compressed sensing applied to systems
and control. Compressed sensing has been actively researched in the field of signal …

In this paper, we analyze the asymptotic performance of convex optimization-based discrete-
valued vector reconstruction from linear measurements. We firstly propose a box …

An iterative model predictive control (MPC) scheme for constrained nonlinear systems is
presented. The idea of the method is to detour from the solution of a non‐convex …

This work proposes a framework to design, formulate and implement a path tracker for self-
driving cars (SDC) based on nonlinear model-predictive-control (NMPC) approach. The …

This paper presents a fault-tolerant control scheme for a class of nonlinear stochastic
distribution collaborative control systems, which are composed of two nonlinear subsystems …

Different road conditions and dynamic environment bring significant challenges to the
control system of autonomous driving vehicle (ADV). As is known, historical data collected …

In this article, we discuss mathematical properties of maximum hands-off control. Maximum
hands-off control is the L 0 optimal control, that is the sparsest control among all feasible …