A new Proportional-Integral (PI) tuning method based on Linear Matrix Inequalities (LMIs) is presented. In particular, an LMI-based optimal control problem is solved to obtain a sparse feedback that provides the PI tuning. The ASCE Test Canal 1 is used as a case study. Using a linearised model of the canal, different tunings for the design of the PI controller are developed and tested using the software Sobek. Furthermore, the proposed method is also compared with other tunings proposed for the same canal available in the literature. Our results show that the proposed method reduces by half the maximum errors with respect to other assessed alternatives and minimizes undesired mutual interactions between canal pools. Also, our method improves the optimality degree of the PI tuning by 30%. Therefore, it is concluded that the LMI based PI controllers lead to satisfactory performance in regulating water levels and canal flows/structure outflows, outperforming other tested alternatives, thus becoming a useful tool for irrigation canal control.
Under the networked control paradigm, controllers, sensors, and actuators are different devices that communicate via a communication network. This might represent a source of vulnerability because the loss of data packets may endanger both system performance and stability. Therefore, this is a major concern in cybersecurity. For example, jamming attacks can be performed by malicious entities with the goal of disrupting the system. To deal with this issue, this paper proposes a model predictive control (MPC) scheme in which the controller computes a tree of control actions tailored to different packet loss patterns so that additional robustness can be gained in these situations. This work uses a case study to illustrate its advantages with respect to standard MPC alternatives.
Software rejuvenation was born to fix operating system faults by periodically refreshing the run-time code and data. This mechanism has been extended to protect control systems from cyber-attacks. This work proposes a software rejuvenation design method in discretetime where invariant sets for the safety and mission controllers are designed to schedule the timing of software refreshes. To compute a minimal robust positively invariant (min-RPI) set and the bounded time between software refreshes to ensure system safety, an LP based approach is proposed for stable and unstable systems. Finally, the designed approach is illustrated by the case study of a simulated lab-scale microgrid.
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