Special Issue on “Distributed and Networked Control Systems”

received worldwide, and 353 papers were accepted for presentation at the invited, regular, and interactive sessions. Among these papers, we have selected nine research articles for this special issue from high-quality results presented at the conference, and they represent the latest developments in distributed and networked control systems (NCSs). The papers are consolidated to provide potential readers with a broader perspective of this currently hot research topic, as well as a comprehensive background of the state-of-the-art approaches for designing distributed and NCSs. Recent advancements in communication technologies , distributed, and NCSs have resulted in a wide range of realistic engineering applications, including environmental monitoring, traffic control, telerobotic systems , smart grids, and even space systems. Typically, these systems comprise spatially distributed plants, actu-ators, sensors, and controllers, connected together via a communication network or bus. Connection of distributed subsystems allows for sensory signals to be shared efficiently; eliminating unnecessary wiring while providing an effective means to add new sensors, actuators, and controllers in an ad-hoc fashion with very little cost and structural changes to the overall architecture. However, control of these complex systems is highly challenging due to the huge volume of data, strong het-erogeneity in different subsystems, and imperfection in the communication network. Estimating global states and achieving global objectives using localized sensing, signal processing, and control, both homogenous and heterogeneous , are also amongst the main challenges. In essence, a distributed control system is a connection of control subsystems via a communication network, where each subsystem is regulated by one or more local controllers. To ensure overall system stability, the Lyapunov function approach is widely used to drive the subsystem states to zero according to selected non-negative potential functions [1]. For optimal control, minimization of convergence speed [2] and minimization of the sum of convex cost functions for individual subsystems [3] were also studied. The distributed model predictive control of polytopic uncertain subsystems was considered in [4], and based on an adaptive protocol, the multi-agent consensus problem considering high-order nonlinear dynamics modeled using neural networks was addressed in [5]. Stability of NCSs is usually studied considering communication issues, such as time delays [6] and packet drops [7] due to long transmission distances and network congestion, and was handled using multirate sampling [8] and quantization [9]. Competition amongst nodes is also inevitable as the communication network can be shared by other control loops or data communication tasks [10]. To date, …