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Postprint (author’s final draft

Self-Triggered Model Predictive Control for Linear Systems Based on Transmission of Control Input Sequences

A networked control system (NCS) is a control system where components such as plants and controllers are connected through communication networks. Self-triggered control is well known as one of the control methods in NCSs and is a control method that for sampled-data control systems both the control input and the aperiodic sampling interval (i.e., the transmission interval) are computed simultaneously. In this paper, a self-triggered model predictive control (MPC) method for discrete-time linear systems with disturbances is proposed. In the conventional MPC method, the first one of the control input sequence obtained by solving the finite-time optimal control problem is sent and applied to the plant. In the proposed method, the first some elements of the control input sequence obtained are sent to the plant, and each element is sequentially applied to the plant. The number of elements is decided according to the effect of disturbances. In other words, transmission intervals can be controlled. Finally, the effectiveness of the proposed method is shown by numerical simulations

Performance of Self-Triggered Control Approaches

The self-triggered control produces non-periodic sampling sequences that vary depending on design factors related to stability and performance of the controlled system. Within this framework, two approaches aimed at minimizing a quadratic cost have been developed recently, considering an optimal performance and pursuing the same control objective; each approach follows a different sampling rule. One approach is based on maintaining the current control value as long as possible, while an optimal performance threshold is not passed. The other approach is based on the generation of a piecewise control signal, which approximates a continuous optimal control signal subject to certain constraints. This article presents a comparative study between the two approaches, providing a useful insight for conducting future research. Control performance and resource utilization were considered as metrics of interest and to evaluate them, the average sampling interval and the standardized cost were taken into account. It was shown that the different search space of each approach poses a challenge to design an equitable framework of comparison, and that both approaches exceed the periodic sampling

The Continuous Stream Model of Computation for Real-Time Control

This paper presents a new Model of Computation (MoC) for real-time tasks used in control systems. This new model, named continuous stream task model, relaxes some of the constraints imposed by the traditional hard and soft real-time task models. A key advantage of the model is the possibility to easily analyse the probabilistic evolution of the delays. This leads to an easy formalisation of necessary and sufficient conditions for the stochastic stability of the closed loop system producing considerable savings in the amount of CPU bandwidth required to stabilise the system. This fact is confirmed by an extensive set of simulations. © 2013 IEEE

Qualitative analysis of a one-step finite-horizon boundary for event-driven controllers

Performance optimization for networked and embedded control systems refers to the ability of minimizing controllers’ resource utilization and/or improving control performance. Event-driven control has been shown to be a promising technique for minimizing controllers’ computational demands. However, optimization of control performance for event-driven control has not been fully addressed. For LTI plants, this paper presents a boundary for event-driven controllers that determines at each job execution when the next job execution should occur in order to minimize a continuous-time quadratic cost function while minimizing controllers’ computational demand. Simulation results illustrate the qualitative shape of this boundary.Peer Reviewe

Qualitative analysis of a one-step finite-horizon boundary for event-driven controllers

Performance optimization for networked and embedded control systems refers to the ability of minimizing controllers’ resource utilization and/or improving control performance. Event-driven control has been shown to be a promising technique for minimizing controllers’ computational demands. However, optimization of control performance for event-driven control has not been fully addressed. For LTI plants, this paper presents a boundary for event-driven controllers that determines at each job execution when the next job execution should occur in order to minimize a continuous-time quadratic cost function while minimizing controllers’ computational demand. Simulation results illustrate the qualitative shape of this boundary.Peer ReviewedPostprint (published version

Qualitative analysis of a one-step finite-horizon boundary for event-driven controllers

Performance optimization for networked and embedded control systems refers to the ability of minimizing controllers’ resource utilization and/or improving control performance. Event-driven control has been shown to be a promising technique for minimizing controllers’ computational demands. However, optimization of control performance for event-driven control has not been fully addressed. For LTI plants, this paper presents a boundary for event-driven controllers that determines at each job execution when the next job execution should occur in order to minimize a continuous-time quadratic cost function while minimizing controllers’ computational demand. Simulation results illustrate the qualitative shape of this boundary.Peer Reviewe