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Integration of modern defence weapons into ship power systems poses a challenge in terms of meeting the high ramp rate requirements of those loads. It might be demanding for the generators to meet the ramp rates of these loads. Failure to meet so, might lead to stability issues. This is addressed by conglomeration of generators and energy storage elements to handle the required power demand posed by loads. This paper proposes an energy management strategy based on model predictive control that incorporates the uncertainty in the load prediction. The proposed controller optimally coordinates the power split between the generators and energy storage elements to guarantee that the power demand is met taking into considerations the ramp rate limitations and the load uncertainty. A low bandwidth model consisting of a single generator and a single energy storage element is used to validate the results of the proposed energy management strategy. The results demonstrate the robustness of the controller under load prediction uncertainty and demonstrate the effect of load uncertainty on battery capacity loss.

In this paper, we present the concept of boosting the resiliency of optimization-based observers for cyber-physical systems (CPS) using auxiliary sources of information. Due to the tight coupling of physics, communication and computation, a malicious agent can exploit multiple inherent vulnerabilities in order to inject stealthy signals into the measurement process. The problem setting considers the scenario in which an attacker strategically corrupts portions of the data in order to force wrong state estimates which could have catastrophic consequences. The goal of the proposed observer is to compute the true states inspite of the adversarial corruption. In the formulation, we use a measurement prior distribution generated by the auxiliary model to refine the feasible region of a traditional compressive sensingbased regression problem. A constrained optimization-based observer is developed using l1-minimization scheme. Numerical experiments show that the solution of the resulting problem recovers the true states of the system. The developed algorithm is evaluated through a numerical simulation example of the IEEE 14-bus system.