Today's world is changing rapidly due to advancements in information technology,
computation and communication. Actuation, communication, sensing, and control are …

Controlling biomimetic underwater robots in unknown flow fields remains a challenge due to
the strong nonlinearity of the fluid. This article investigates the attitude holding task of a …

Time-distributed optimization is an implementation strategy that can significantly reduce the
computational burden of model predictive control. When using this strategy, optimization …

Mobile medical robots have been widely used in various structured scenarios, such as
hospital drug delivery, public area disinfection, and medical examinations. Considering the …

In this paper, a varying-parameter complementary neural network (VPCNN) is designed and
combined with model predictive control (MPC) to solve the multi-robot tracking and formation …

A common problem affecting neural network (NN) approximations of model predictive
control (MPC) policies is the lack of analytical tools to assess the stability of the closed-loop …

This paper proposes a Distributed Model Predictive Control (DMPC) approach for a family of
discrete-time linear systems with local (uncoupled) and global (coupled) constraints. The …

We present a distributed model predictive control method, which enables a group of agents
to compute their control inputs locally while communicating with their neighbors over a …

This article introduces an approach for reducing the computational cost of implementing
linear quadratic model predictive control (MPC) for set-point tracking subject to pointwise-in …

Time-distributed optimization is a suboptimal implementation strategy for reducing the
computational effort required to implement Model Predictive Control (MPC). Time-distributed …