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In this work, we focus on iterative distributed model predictive control (DMPC) of large-scale nonlinear systems subject to delayed state feedback. The motivation for studying this control problem is the presence of delayed measurements in feedback control of large-scale chemical processes and the potential use of sensors and actuators in industrial applications to improve closed-loop performance...
A moving-target-enclosing problem is investigated in the paper, where the velocity of the target is unknown and the neighbor topologies may change over time. Each robot only uses the relative position information of the target and its neighbors that may dynamically change over time. An adaptive scheme is proposed to estimate the velocity of the target. Then a distributed control law for each robot...
This work focuses on a general class of nonlinear process systems controlled by a two-tier networked control system integrating a local control system using continuous sensing/actuation with a networked control system using asynchronous sensing/actuation. To deal with control actuator faults that may occur in the closed-loop system and eliminate the ability of the two-tier networked control system...
In this work, we focus on distributed model predictive control of nonlinear systems subject to delayed measurements. Delayed measurements arise naturally in process control applications when, for example, species concentrations or particle size distributions are measured. We design distributed Lyapunov-based model predictive controllers that coordinate their actions and take delayed measurements explicitly...
In this work, we introduce a distributed Lyapunov-based model predictive control method for nonlinear systems with input constraints. The class of systems considered arises naturally when new sensors, actuators and controllers are added to already operating control loops to improve closed-loop performance, taking advantage from the latest advances in sensor/actuator network technology. Assuming that...
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