Model-free adaptive filter to mitigate actuator wear.

The present article introduces an adaptive filter of statistical basis developed for closed-loop control applications, whose goal is to reduce actuator wear while ensuring a similar control performance regarding the original closed-loop system. The main idea is to avoid the rapid change of the filtered signal when the system output has a derivative not statistically significant regarding the expected measurement noise. The adaptation law of the time constant of the filter is model-free, and the only required information is the variance of the additive noise that the measurements are subjected to. The performance of the proposed adaptive method is illustrated through a combined numerical and experimental study, in addition to its application in an operational oil plant. The results indicate that our formulation holds promise for extending the life of actuators and is easy to implement in most programmable logic controllers.

LMI-based consensus of linear multi-agent systems by reduced-order dynamic output feedback.

This work proposes new conditions for consensus of homogeneous multi-agent systems subjected to exogenous disturbances in directed communication graphs by dynamic output feedback protocols. The agents under investigation are described as linear dynamics, and the communication network is such that each agent receives as information only the output of neighbor agents. The synchronization problem is rewritten as an output feedback stabilization without requiring the Laplacian matrix to be diagonalizable. As the main appeal, we propose new necessary and sufficient conditions for the design of dynamic output feedback controllers of arbitrary order - including static output feedback as a particular case - and sufficient Linear Matrix Inequalities (LMIs) for H∞ consensus. Numerical experiments illustrate the effectiveness of the proposed approach.