A resilient control strategy for networked multi-agent systems subject to covert attacks

In this paper, a resilient distributed control scheme against covert attacks for multi-agent networked systems subject to input and state constraints is developed. The idea consists in a clever deployment of predictive arguments with a twofold aim: detection of malicious agent behaviors affecting the normal system operations and consequent specific control actions implementation to mitigate as much as possible undesirable knock-on effects resulting from adversary actions. Specifically, the multi-agent system is organized in terms of a grid topology and set-theoretic receding horizon control ideas are exploited to develop a distributed algorithm capable to recognize the attacked agent. In essence, the resulting solution relies on the combined use of predictive control and set-invariance ideas that are exploited to generate redundant control sequences randomly selected on the actuator side such that the malicious agent is never aware about the effective control action indeed exploited. As a consequence, countermeasures on the sensor-to-controller channel could lead to significantly erroneous data not complying with the expected evolution of the system modeling. Finally, numerical simulations are carried out to show benefits and effectiveness of the proposed approach.

Model Predictive Control Tuning by Inverse Matching for a Wave Energy Converter

This paper investigates the application of a method to find the cost function or the weight matrices to be used in model predictive control (MPC) such that the MPC has the same performance as a predesigned linear controller in state-feedback form when constraints are not active. This is potentially useful when a successful linear controller already exists and it is necessary to incorporate the constraint-handling capabilities of MPC. This is the case for a wave energy converter (WEC), where the maximum power transfer law is well-understood. In addition to solutions based on numerical optimization, a simple analytical solution is also derived for cases with a short prediction horizon. These methods are applied for the control of an empirically-based WEC model. The results show that the MPC can be successfully tuned to follow an existing linear control law and to comply with both input and state constraints, such as actuator force and actuator stroke.