We study wireless networked control systems (WNCSs), where controllers (CLs), controlled objects (COs), and other devices are connected through wireless networks. In WNCSs, COs can become unstable due to bursty packet losses and random delays on wireless networks. To reduce these network-induced effects, we utilize the packetized predictive control (PPC) method, where future control vectors to compensate bursty packet losses are generated in the receiving horizon manner, and they are packed into packets and transferred to a CO unit. In this paper, we extend the PPC method so as to compensate random delays as well as bursty packet losses. In the extended PPC method, generating many control vectors improves the robustness against both problems while it increases traffic on wireless networks. Therefore, we consider control vector selection to improve the robustness effectively under the constraint of single packet transmission. We first reconsider the input strategy of control vectors received by COs and propose a control vector selection scheme suitable for the strategy. In our selection scheme, control vectors are selected based on the estimated average and variance of round-trip delays. Moreover, we solve the problem that the CL may misconceive the CO's state due to insufficient information for state estimation. Simulation results show that our selection scheme achieves the higher robustness against both bursty packet losses and delays in terms of the 2-norm of the CO's state.

Event-triggered control is a method that the control input is updated only when a certain triggering condition is satisfied. In networked control systems, quantization errors via A/D conversion should be considered. In this paper, a new method for quantized event-triggered control with switching triggering conditions is proposed. For a discrete-time linear system, we consider the problem of finding a state-feedback controller such that the closed-loop system is uniformly ultimately bounded in a certain ellipsoid. This problem is reduced to an LMI (Linear Matrix Inequality) optimization problem. The volume of the ellipsoid may be adjusted. The effectiveness of the proposed method is presented by a numerical example.

In this paper, we consider a networked control system where bounded network delays and packet dropouts exist in the network. The physical plant is abstracted by a transition system whose states are quantized states of the plant measured by a sensor, and a control specification for the abstracted plant is given by a transition system when no network disturbance occurs. Then, we design a prediction-based controller that determines a control input by predicting a set of all feasible abstracted states at time when the actuator receives the delayed input. It is proved that the prediction-based controller suppresses effects of network delays and packet dropouts and that the controlled plant still achieves the specification in spite of the existence of network delays and packet dropouts.

Event-triggered and self-triggered control methods are an important control strategy in networked control systems. Event-triggered control is a method that the measured signal is sent to the controller (i.e., the control input is recomputed) only when a certain condition is satisfied. Self-triggered control is a method that the control input and the (non-uniform) sampling interval are computed simultaneously. In this paper, we propose new methods of event-triggered control and self-triggered control from the viewpoint of online optimization (i.e., model predictive control). In self-triggered control, the control input and the sampling interval are obtained by solving a pair of a quadratic programming (QP) problem and a mixed integer linear programming (MILP) problem. In event-triggered control, whether the control input is updated or not is determined by solving two QP problems. The effectiveness of the proposed methods is presented by numerical examples.

Self-triggered control is a control method that the control input and the sampling period are computed simultaneously in sampled-data control systems, and is studied in the field of networked control systems. In this paper, a new approach for self-triggered control is proposed based on the model predictive control (MPC) method. First, self-triggered MPC with delay compensation in which the delay-compensation input is introduced is newly formulated. Next, in order to efficiently solve this MPC problem, the optimal control problem with horizon one is formulated, and an approximate solution method is derived. Finally, the effectiveness of the proposed approach is shown by a numerical example.

This paper provides an overview on the recent research on networked control with an emphasis on the tight relation between the two fields of control and communication. In particular, we present several results focusing on data rate constraints in networked control systems, which can be modeled as quantization of control-related signals. The motivation is to reduce the amount of data rate as much as possible in obtaining control objectives such as stabilization and control performance under certain measures. We also discuss some approaches towards control problems based on techniques from signal processing and information theory.

In this paper, we study the decentralized coverage control problem for an environment using a group of autonomous mobile robots with nonholonomic kinematic and dynamic constraints. In comparison with standard coverage control procedures, we develop a combined controller for Voronoi-based coverage approach in which kinematic and dynamic constraints of the actual mobile sensing robots are incorporated into the controller design. Furthermore, a collision avoidance component is added in the kinematic controller in order to guarantee a collision free coverage of the area. The convergence of the network to the optimal sensing configuration is proven with a Lyapunov-type analysis. Numerical simulations are provided approving the effectiveness of the proposed method through several experimental scenarios.