3 research outputs found
LQR Control with Sparse Adversarial Disturbances
Recent developments in cyber-physical systems and event-triggered control
have led to an increased interest in the impact of sparse disturbances on
dynamical processes. We study Linear Quadratic Regulator (LQR) control under
sparse disturbances by analyzing three distinct policies: the blind online
policy, the disturbance-aware policy, and the optimal offline policy. We derive
the two-dimensional recurrence structure of the optimal disturbance-aware
policy, under the assumption that the controller has information about future
disturbance values with only a probabilistic model of their locations in time.
Under mild conditions, we show that the disturbance-aware policy converges to
the blind online policy if the number of disturbances grows sublinearly in the
time horizon. Finally, we provide a finite-horizon regret bound between the
blind online policy and optimal offline policy, which is proven to be quadratic
in the number of disturbances and in their magnitude. This provides a useful
characterization of the suboptimality of a standard LQR controller when
confronted with unexpected sparse perturbations.Comment: 61st IEEE Conference on Decision and Contro
Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021
The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above