2,750 research outputs found
Load frequency controllers considering renewable energy integration in power system
Abstract: Load frequency control or automatic generation control is one of the main operations that take place daily in a modern power system. The objectives of load frequency control are to maintain power balance between interconnected areas and to control the power flow in the tie-lines. Electric power cannot be stored in large quantity that is why its production must be equal to the consumption in each time. This equation constitutes the key for a good management of any power system and introduces the need of more controllers when taking into account the integration of renewable energy sources into the traditional power system. There are many controllers presented in the literature and this work reviews the traditional load frequency controllers and those, which combined the traditional controller and artificial intelligence algorithms for controlling the load frequency
A Learning-based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
Vehicle to Vehicle (V2V) communication has a great potential to improve
reaction accuracy of different driver assistance systems in critical driving
situations. Cooperative Adaptive Cruise Control (CACC), which is an automated
application, provides drivers with extra benefits such as traffic throughput
maximization and collision avoidance. CACC systems must be designed in a way
that are sufficiently robust against all special maneuvers such as cutting-into
the CACC platoons by interfering vehicles or hard braking by leading cars. To
address this problem, a Neural- Network (NN)-based cut-in detection and
trajectory prediction scheme is proposed in the first part of this paper. Next,
a probabilistic framework is developed in which the cut-in probability is
calculated based on the output of the mentioned cut-in prediction block.
Finally, a specific Stochastic Model Predictive Controller (SMPC) is designed
which incorporates this cut-in probability to enhance its reaction against the
detected dangerous cut-in maneuver. The overall system is implemented and its
performance is evaluated using realistic driving scenarios from Safety Pilot
Model Deployment (SPMD).Comment: 10 pages, Submitted as a journal paper at T-I
On generalized terminal state constraints for model predictive control
This manuscript contains technical results related to a particular approach
for the design of Model Predictive Control (MPC) laws. The approach, named
"generalized" terminal state constraint, induces the recursive feasibility of
the underlying optimization problem and recursive satisfaction of state and
input constraints, and it can be used for both tracking MPC (i.e. when the
objective is to track a given steady state) and economic MPC (i.e. when the
objective is to minimize a cost function which does not necessarily attains its
minimum at a steady state). It is shown that the proposed technique provides,
in general, a larger feasibility set with respect to existing approaches, given
the same computational complexity. Moreover, a new receding horizon strategy is
introduced, exploiting the generalized terminal state constraint. Under mild
assumptions, the new strategy is guaranteed to converge in finite time, with
arbitrarily good accuracy, to an MPC law with an optimally-chosen terminal
state constraint, while still enjoying a larger feasibility set. The features
of the new technique are illustrated by three examples.Comment: Part of the material in this manuscript is contained in a paper
accepted for publication on Automatica and it is subject to Elsevier
copyright. The copy of record is available on http://www.sciencedirect.com
Formal Synthesis of Control Strategies for Positive Monotone Systems
We design controllers from formal specifications for positive discrete-time
monotone systems that are subject to bounded disturbances. Such systems are
widely used to model the dynamics of transportation and biological networks.
The specifications are described using signal temporal logic (STL), which can
express a broad range of temporal properties. We formulate the problem as a
mixed-integer linear program (MILP) and show that under the assumptions made in
this paper, which are not restrictive for traffic applications, the existence
of open-loop control policies is sufficient and almost necessary to ensure the
satisfaction of STL formulas. We establish a relation between satisfaction of
STL formulas in infinite time and set-invariance theories and provide an
efficient method to compute robust control invariant sets in high dimensions.
We also develop a robust model predictive framework to plan controls optimally
while ensuring the satisfaction of the specification. Illustrative examples and
a traffic management case study are included.Comment: To appear in IEEE Transactions on Automatic Control (TAC) (2018), 16
pages, double colum
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