703 research outputs found
Localized LQR Optimal Control
This paper introduces a receding horizon like control scheme for localizable
distributed systems, in which the effect of each local disturbance is limited
spatially and temporally. We characterize such systems by a set of linear
equality constraints, and show that the resulting feasibility test can be
solved in a localized and distributed way. We also show that the solution of
the local feasibility tests can be used to synthesize a receding horizon like
controller that achieves the desired closed loop response in a localized manner
as well. Finally, we formulate the Localized LQR (LLQR) optimal control problem
and derive an analytic solution for the optimal controller. Through a numerical
example, we show that the LLQR optimal controller, with its constraints on
locality, settling time, and communication delay, can achieve similar
performance as an unconstrained H2 optimal controller, but can be designed and
implemented in a localized and distributed way.Comment: Extended version for 2014 CDC submissio
Decentralized and Fault-Tolerant Control of Power Systems with High Levels of Renewables
Inter-area oscillations have been identified as a major problem faced by most power systems and stability of these oscillations are of vital concern due to the potential for equipment damage and resulting restrictions on available transmission capacity. In recent years, wide-area measurement systems (WAMSs) have been deployed that allow inter-area modes to be observed and identified.Power grids consist of interconnections of many subsystems which may interact with their neighbors and include several sensors and actuator arrays. Modern grids are spatially distributed and centralized strategies are computationally expensive and might be impractical in terms of hardware limitations such as communication speed. Hence, decentralized control strategies are more desirable.Recently, the use of HVDC links, FACTS devices and renewable sources for damping of inter-area oscillations have been discussed in the literature. However, very few such systems have been deployed in practice partly due to the high level of robustness and reliability requirements for any closed loop power system controls. For instance, weather dependent sources such as distributed winds have the ability to provide services only within a narrow range and might not always be available due to weather, maintenance or communication failures.Given this background, the motivation of this work is to ensure power grid resiliency and improve overall grid reliability. The first consideration is the design of optimal decentralized controllers where decisions are based on a subset of total information. The second consideration is to design controllers that incorporate actuator limitations to guarantee the stability and performance of the system. The third consideration is to build robust controllers to ensure resiliency to different actuator failures and availabilities. The fourth consideration is to design distributed, fault-tolerant and cooperative controllers to address above issues at the same time. Finally, stability problem of these controllers with intermittent information transmission is investigated.To validate the feasibility and demonstrate the design principles, a set of comprehensive case studies are conducted based on different power system models including 39-bus New England system and modified Western Electricity Coordinating Council (WECC) system with different operating points, renewable penetration and failures
Decentralized Control of Large Collaborative Swarms using Random Finite Set Theory
Controlling large swarms of robotic agents presents many challenges
including, but not limited to, computational complexity due to a large number
of agents, uncertainty in the functionality of each agent in the swarm, and
uncertainty in the swarm's configuration. The contribution of this work is to
decentralize Random Finite Set (RFS) control of large collaborative swarms for
control of individual agents. The RFS control formulation assumes that the
topology underlying the swarm control is complete and uses the complete graph
in a centralized manner. To generalize the control topology in a localized or
decentralized manner, sparse LQR is used to sparsify the RFS control gain
matrix obtained using iterative LQR. This allows agents to use information of
agents near each other (localized topology) or only the agent's own information
(decentralized topology) to make a control decision. Sparsity and performance
for decentralized RFS control are compared for different degrees of
localization in feedback control gains which show that the stability and
performance compared to centralized control do not degrade significantly in
providing RFS control for large collaborative swarms.Comment: arXiv admin note: text overlap with arXiv:1810.0069
Discrete Time Systems
Discrete-Time Systems comprehend an important and broad research field. The consolidation of digital-based computational means in the present, pushes a technological tool into the field with a tremendous impact in areas like Control, Signal Processing, Communications, System Modelling and related Applications. This book attempts to give a scope in the wide area of Discrete-Time Systems. Their contents are grouped conveniently in sections according to significant areas, namely Filtering, Fixed and Adaptive Control Systems, Stability Problems and Miscellaneous Applications. We think that the contribution of the book enlarges the field of the Discrete-Time Systems with signification in the present state-of-the-art. Despite the vertiginous advance in the field, we also believe that the topics described here allow us also to look through some main tendencies in the next years in the research area
Energy-Aware, Collision-Free Information Gathering for Heterogeneous Robot Teams
This paper considers the problem of safely coordinating a team of
sensor-equipped robots to reduce uncertainty about a dynamical process, where
the objective trades off information gain and energy cost. Optimizing this
trade-off is desirable, but leads to a non-monotone objective function in the
set of robot trajectories. Therefore, common multi-robot planners based on
coordinate descent lose their performance guarantees. Furthermore, methods that
handle non-monotonicity lose their performance guarantees when subject to
inter-robot collision avoidance constraints. As it is desirable to retain both
the performance guarantee and safety guarantee, this work proposes a
hierarchical approach with a distributed planner that uses local search with a
worst-case performance guarantees and a decentralized controller based on
control barrier functions that ensures safety and encourages timely arrival at
sensing locations. Via extensive simulations, hardware-in-the-loop tests and
hardware experiments, we demonstrate that the proposed approach achieves a
better trade-off between sensing and energy cost than coordinate-descent-based
algorithms.Comment: To appear in Transactions on Robotics; 18 pages and 16 figures. arXiv
admin note: text overlap with arXiv:2101.1109
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