Jonathan M. Palma

Jonathan M. Palma, Leonardo de P. Carvalho, Alim P. C. Goncalves

The present work proposes a new approach to energy effiency for filtering of a system interconnected by a Multi-Hop network. The minimization of the energy cost per time unit is obtained through limiting the number of packets retransmission in the Hop-by-Hop mechanism. We explore a trade-Off between system performance, measured by estimation error H-inf norm and energy consumption. The proposal is validated using a theoretical model for energy consumption of MICA2 transceivers.

Francisco J. Uribe, Cecília F. Morais, Jonathan M. Palma

The present work analyzes the feasibility of obtaining a single controller (robust), with theoretical guarantees of stability and performance, valid for a total set of network configurations in designed the controller for an uncertain success probability obtain the protocol for Energy-Efficiency in Networked Control System NCS. In particular, this work investigates the performance degradation, in terms of the $\mathcal{H}_{\infty}$ guaranteed cost, between optimal controller design (precisely known probability) and the sub-optimal controller design (robust to probability uncertainties). The feasibility of the proposed methodology is validated by a numerical example.

Leonardo de P. Carvalho, Jonathan M. Palma, Lucas P. Moreira et al.

In this work, we present an analysis of the Burst failure effect in the $H_\infty$ norm. We present a procedure to perform an analysis between different Markov Chain models and a numerical example. In the numerical example the results obtained pointed out that the burst failure effect in the performance does not exceed 6.3%. However, this work is an introduction for a wider and more extensive analysis in this subject.

Hugo O. Garcés, Alejandro J. Rojas, Bernardo A. Hernández et al.

This paper compares the performance of model-free controllers on a nonlinear system under cyberattacks, including false data injection and denial-of-service attacks. Four RL reward types are analyzed for accuracy, cost, and resilience. Results show that the Lyapunov reward offers the best resilience with low tracking error. Exponential mode also provides good trade-offs with acceptable resilience under moderate training conditions. Progressive and linear rewards converge faster but are less robust. RL-MPCs show strong steady-state resilience but require longer training times; RL-PID controllers are faster with significantly less training time. Proximal Policy Optimization outperforms Deep Deterministic Policy Gradient with a significant reduction in KPI variance. This study serves to highlight how well-designed RL rewards can improve performance and resilience against cyber threats.