Optimal deployment of the spoke of a largesized transformable reflector according to the hierarchy of criteria

The article discusses the process of controlling the angular motion of the spoke of a large-sized space-based reflector, taking into account bending vibrations. Currently, large antennas are actively used for receiving and transmitting data. When launching large structures into space, the problem arises of reliably deployment the spokes, since they are packed in a small volume to be able to be installed in a launch vehicle. Due to the possibility of various abnormal situations, such as jamming of elements, engagement of the net, it is necessary to re-deployment the antenna. Therefore, it is important to develop control algorithms that can reliably solve the problems of direct and reverse motion. In the process of deployment and bringing together the elements of the reflector, various deformations appear in the structure. When the antenna spokes are brought together, lateral oscillations make the largest contribution to the oscillatory of the transient process. Currently, elastically deformed elements are used to deployment large-sized reflectors, and a control program is also used. This prevents the control from being adjusted when the deployment conditions change. The paper investigates the possibility of minimizing the vibrations of a structure during its deployment by using optimal control algorithms in real time. The forward and reverse motion of the antenna elements is performed by means of a two-criteria hierarchy optimization. The results of numerical simulation of the optimal rotation of the reflector spoke are presented. The proposed algorithm allows you to choose the optimal control in emergency situations for various types of large reflectors.

Theoretical Study of the Unconstrained and Constrained Nonlinear Optimal Discrete Time State Feedback Control

Majority of the optimal control techniques can only be applied successfully if the model of the controlled process is known and it is linear. If the system model is nonlinear, then this nonlinear model can be approximated with different simple, linear models. However, these models are valid only in the neighbourhood of the operating points. The success of the control algorithms is highly dependent on the used linearization methods. The aim of the paper is to compare different optimal control algorithms and linearization methods. The presented optimal control algorithms have been also tested in constrained and unconstrained versions.

Towards an intelligent HVAC system automation using Reinforcement Learning

HVAC systems are among the biggest energy consumers in buildings and therefore in the focus of optimal control research. In practice, rule-based control and PID controllers are typically used and implemented at the beginning of the building operation. Since this approach neither guarantees optimal or even good control, optimal control algorithms (which can be predictive and adaptive) are in the focus of research. The problem with most of the approaches is that a model of the system is often needed which comes with high engineering efforts. Further, the required computing power can quickly exceed the capacities, even in modern buildings. Therefore, in this paper we investigate the application of a state-of-the-art Reinforcement Learning (RL) algorithm, as a self-calibrating valve controller for two water-air heat exchangers of a real-world air handling unit. We choose a generic problem formulation to pre-train the algorithm with a simulation of an admixing heater and use it to control an injection heater and a throttle cooler. Our results show that after only 70 hours, the control quality significantly increases. Therefore, it seems evident that with pre-trained RL algorithms, a self-improving HVAC automation can be realized with little hardware requirements and without extensive modelling of the system dynamics.

Trajectory planning with mid-air collision avoidance for quadrotor unmanned aerial vehicles

Flight collision between unmanned aerial vehicles (UAVs) in mid-air poses a potential risk to flight safety in low-altitude airspace. This article transforms the problem of collision avoidance between quadrotor UAVs into a trajectory-planning problem using optimal control algorithms, therefore achieving both robustness and efficiency. Specifically, the pseudospectral method is introduced to solve the raised optimal control problem, while the generated optimal trajectory is precisely followed by a feedback controller. It is worth noting that the contributions of this article also include the introduction of the normalized relative coordinate, so that UAVs can obtain collision-free trajectories more conveniently in real time. The collision-free trajectories for a classical scenario of collision avoidance between two UAVs are given in the simulation part by both solving the optimal control problem and querying the prior results. The scalability of the proposed method is also verified in the simulation part by solving a collision avoidance problem among multiple UAVs.

Decentralized and Centralized Optimal Control of HVAC Systems

Utility service providers are often challenged with the synchronization of thermostatically controlled loads. Load synchronization, resulting from naturally occurring or demand response events, can damage power distribution equipment and reduce the grid's efficiency. Because thermostatically controlled loads constitute most of the power consumed by the grid at any given time, the proper control of such devices can lead to significant energy savings and improved grid stability. The contribution of this thesis is developing optimal control algorithms for both single-stage and variable-speed heat pump HVAC systems. Our control architecture allows for regulating home temperatures through selective peer-to-peer communication while simultaneously minimizing aggregate power consumption and aggregate load volatility. For comparison purposes, various low-level and centralized optimal controllers are explored and compared against their decentralized counterparts.

Development of intelligent electric power systems and optimal control algorithms for distributed energy problems

Решается актуальная научно-практическая задача современной энергетики, нацеленная на разработку универсальных гибридных электроэнергетических комплексов на основе нетрадиционных и возобновляемых источников энергии разных типов.