Distributed model predictive control: A tutorial review and future research directions
In this paper, we provide a tutorial review of recent results in the design of distributed model
predictive control systems. Our goal is to not only conceptually review the results in this area …
predictive control systems. Our goal is to not only conceptually review the results in this area …
Reinforcement learning for control: Performance, stability, and deep approximators
Reinforcement learning (RL) offers powerful algorithms to search for optimal controllers of
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …
systems with nonlinear, possibly stochastic dynamics that are unknown or highly uncertain …
Data-driven model predictive control with stability and robustness guarantees
We propose a robust data-driven model predictive control (MPC) scheme to control linear
time-invariant systems. The scheme uses an implicit model description based on behavioral …
time-invariant systems. The scheme uses an implicit model description based on behavioral …
Resilient reinforcement learning and robust output regulation under denial-of-service attacks
In this paper, we have proposed a novel resilient reinforcement learning approach for
solving robust optimal output regulation problems of a class of partially linear systems under …
solving robust optimal output regulation problems of a class of partially linear systems under …
Economic nonlinear model predictive control
Abstract In recent years, Economic Model Predictive Control (EMPC) has received
considerable attention of many research groups. The present tutorial survey summarizes …
considerable attention of many research groups. The present tutorial survey summarizes …
State and output feedback nonlinear model predictive control: An overview
The purpose of this paper is twofold. In the first part, we give a review on the current state of
nonlinear model predictive control (NMPC). After a brief presentation of the basic principle of …
nonlinear model predictive control (NMPC). After a brief presentation of the basic principle of …
[HTML][HTML] Analysis and design of model predictive control frameworks for dynamic operation—An overview
This article provides an overview of model predictive control (MPC) frameworks for dynamic
operation of nonlinear constrained systems. Dynamic operation is often an integral part of …
operation of nonlinear constrained systems. Dynamic operation is often an integral part of …
Data-driven self-triggered control via trajectory prediction
Self-triggered control, a well-documented technique for reducing the communication
overhead while ensuring desired system performance, is gaining increasing popularity …
overhead while ensuring desired system performance, is gaining increasing popularity …
Nominal stability of real-time iteration scheme for nonlinear model predictive control
A Newton-type method is investigated for online optimisation in nonlinear model predictive
control, the so-called real-time iteration scheme. Only one Newton-type iteration is …
control, the so-called real-time iteration scheme. Only one Newton-type iteration is …
Safe deep reinforcement learning for building energy management
The optimization of building energy systems poses a complex challenge due to the dynamic
nature of building environments and the need for ensuring both energy efficiency and …
nature of building environments and the need for ensuring both energy efficiency and …