14,076 research outputs found
On control of discrete-time state-dependent jump linear systems with probabilistic constraints: A receding horizon approach
In this article, we consider a receding horizon control of discrete-time
state-dependent jump linear systems, particular kind of stochastic switching
systems, subject to possibly unbounded random disturbances and probabilistic
state constraints. Due to a nature of the dynamical system and the constraints,
we consider a one-step receding horizon. Using inverse cumulative distribution
function, we convert the probabilistic state constraints to deterministic
constraints, and obtain a tractable deterministic receding horizon control
problem. We consider the receding control law to have a linear state-feedback
and an admissible offset term. We ensure mean square boundedness of the state
variable via solving linear matrix inequalities off-line, and solve the
receding horizon control problem on-line with control offset terms. We
illustrate the overall approach applied on a macroeconomic system
Performance-oriented model learning for data-driven MPC design
Model Predictive Control (MPC) is an enabling technology in applications
requiring controlling physical processes in an optimized way under constraints
on inputs and outputs. However, in MPC closed-loop performance is pushed to the
limits only if the plant under control is accurately modeled; otherwise, robust
architectures need to be employed, at the price of reduced performance due to
worst-case conservative assumptions. In this paper, instead of adapting the
controller to handle uncertainty, we adapt the learning procedure so that the
prediction model is selected to provide the best closed-loop performance. More
specifically, we apply for the first time the above "identification for
control" rationale to hierarchical MPC using data-driven methods and Bayesian
optimization.Comment: Accepted for publication in the IEEE Control Systems Letters (L-CSS
Bayesian Updating, Model Class Selection and Robust Stochastic Predictions of Structural Response
A fundamental issue when predicting structural response by using mathematical models is how to treat both modeling and excitation uncertainty. A general framework for this is presented which uses probability as a multi-valued
conditional logic for quantitative plausible reasoning in the presence of uncertainty due to incomplete information. The
fundamental probability models that represent the structure’s uncertain behavior are specified by the choice of a stochastic
system model class: a set of input-output probability models for the structure and a prior probability distribution over this set
that quantifies the relative plausibility of each model. A model class can be constructed from a parameterized deterministic
structural model by stochastic embedding utilizing Jaynes’ Principle of Maximum Information Entropy. Robust predictive
analyses use the entire model class with the probabilistic predictions of each model being weighted by its prior probability, or if
structural response data is available, by its posterior probability from Bayes’ Theorem for the model class. Additional robustness
to modeling uncertainty comes from combining the robust predictions of each model class in a set of competing candidates
weighted by the prior or posterior probability of the model class, the latter being computed from Bayes’ Theorem. This higherlevel application of Bayes’ Theorem automatically applies a quantitative Ockham razor that penalizes the data-fit of more
complex model classes that extract more information from the data. Robust predictive analyses involve integrals over highdimensional spaces that usually must be evaluated numerically. Published applications have used Laplace's method of
asymptotic approximation or Markov Chain Monte Carlo algorithms
A MPC Strategy for the Optimal Management of Microgrids Based on Evolutionary Optimization
In this paper, a novel model predictive control strategy, with a 24-h prediction horizon, is
proposed to reduce the operational cost of microgrids. To overcome the complexity of the optimization
problems arising from the operation of the microgrid at each step, an adaptive evolutionary strategy
with a satisfactory trade-off between exploration and exploitation capabilities was added to the
model predictive control. The proposed strategy was evaluated using a representative microgrid that
includes a wind turbine, a photovoltaic plant, a microturbine, a diesel engine, and an energy storage
system. The achieved results demonstrate the validity of the proposed approach, outperforming
a global scheduling planner-based on a genetic algorithm by 14.2% in terms of operational cost.
In addition, the proposed approach also better manages the use of the energy storage system.Ministerio de Economía y Competitividad DPI2016-75294-C2-2-RUnión Europea (Programa Horizonte 2020) 76409
Data-driven Economic NMPC using Reinforcement Learning
Reinforcement Learning (RL) is a powerful tool to perform data-driven optimal
control without relying on a model of the system. However, RL struggles to
provide hard guarantees on the behavior of the resulting control scheme. In
contrast, Nonlinear Model Predictive Control (NMPC) and Economic NMPC (ENMPC)
are standard tools for the closed-loop optimal control of complex systems with
constraints and limitations, and benefit from a rich theory to assess their
closed-loop behavior. Unfortunately, the performance of (E)NMPC hinges on the
quality of the model underlying the control scheme. In this paper, we show that
an (E)NMPC scheme can be tuned to deliver the optimal policy of the real system
even when using a wrong model. This result also holds for real systems having
stochastic dynamics. This entails that ENMPC can be used as a new type of
function approximator within RL. Furthermore, we investigate our results in the
context of ENMPC and formally connect them to the concept of dissipativity,
which is central for the ENMPC stability. Finally, we detail how these results
can be used to deploy classic RL tools for tuning (E)NMPC schemes. We apply
these tools on both a classical linear MPC setting and a standard nonlinear
example from the ENMPC literature
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