3,885 research outputs found
Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces
© 2007 EUCA.A novel online-computation approach to optimal control of nonlinear, noise-affected systems with continuous state and control spaces is presented. In the proposed algorithm, system noise is explicitly incorporated into the control decision. This leads to superior results compared to state-of-the-art nonlinear controllers that neglect this influence. The solution of an optimal nonlinear controller for a corresponding deterministic system is employed to find a meaningful state space restriction. This restriction is obtained by means of approximate state prediction using the noisy system equation. Within this constrained state space, an optimal closed-loop solution for a finite decision-making horizon (prediction horizon) is determined within an adaptively restricted optimization space. Interleaving stochastic dynamic programming and value function approximation yields a solution to the considered optimal control problem. The enhanced performance of the proposed discrete-time controller is illustrated by means of a scalar example system. Nonlinear model predictive control is applied to address approximate treatment of infinite-horizon problems by the finite-horizon controller
Direct data-driven control of constrained linear parameter-varying systems: A hierarchical approach
In many nonlinear control problems, the plant can be accurately described by
a linear model whose operating point depends on some measurable variables,
called scheduling signals. When such a linear parameter-varying (LPV) model of
the open-loop plant needs to be derived from a set of data, several issues
arise in terms of parameterization, estimation, and validation of the model
before designing the controller. Moreover, the way modeling errors affect the
closed-loop performance is still largely unknown in the LPV context. In this
paper, a direct data-driven control method is proposed to design LPV
controllers directly from data without deriving a model of the plant. The main
idea of the approach is to use a hierarchical control architecture, where the
inner controller is designed to match a simple and a-priori specified
closed-loop behavior. Then, an outer model predictive controller is synthesized
to handle input/output constraints and to enhance the performance of the inner
loop. The effectiveness of the approach is illustrated by means of a simulation
and an experimental example. Practical implementation issues are also
discussed.Comment: Preliminary version of the paper "Direct data-driven control of
constrained systems" published in the IEEE Transactions on Control Systems
Technolog
Learning-based predictive control for linear systems: a unitary approach
A comprehensive approach addressing identification and control for
learningbased Model Predictive Control (MPC) for linear systems is presented.
The design technique yields a data-driven MPC law, based on a dataset collected
from the working plant. The method is indirect, i.e. it relies on a model
learning phase and a model-based control design one, devised in an integrated
manner. In the model learning phase, a twofold outcome is achieved: first,
different optimal p-steps ahead prediction models are obtained, to be used in
the MPC cost function; secondly, a perturbed state-space model is derived, to
be used for robust constraint satisfaction. Resorting to Set Membership
techniques, a characterization of the bounded model uncertainties is obtained,
which is a key feature for a successful application of the robust control
algorithm. In the control design phase, a robust MPC law is proposed, able to
track piece-wise constant reference signals, with guaranteed recursive
feasibility and convergence properties. The controller embeds multistep
predictors in the cost function, it ensures robust constraints satisfaction
thanks to the learnt uncertainty model, and it can deal with possibly
unfeasible reference values. The proposed approach is finally tested in a
numerical example
Closed-Loop Statistical Verification of Stochastic Nonlinear Systems Subject to Parametric Uncertainties
This paper proposes a statistical verification framework using Gaussian
processes (GPs) for simulation-based verification of stochastic nonlinear
systems with parametric uncertainties. Given a small number of stochastic
simulations, the proposed framework constructs a GP regression model and
predicts the system's performance over the entire set of possible
uncertainties. Included in the framework is a new metric to estimate the
confidence in those predictions based on the variance of the GP's cumulative
distribution function. This variance-based metric forms the basis of active
sampling algorithms that aim to minimize prediction error through careful
selection of simulations. In three case studies, the new active sampling
algorithms demonstrate up to a 35% improvement in prediction error over other
approaches and are able to correctly identify regions with low prediction
confidence through the variance metric.Comment: 8 pages, submitted to ACC 201
Resource-aware IoT Control: Saving Communication through Predictive Triggering
The Internet of Things (IoT) interconnects multiple physical devices in
large-scale networks. When the 'things' coordinate decisions and act
collectively on shared information, feedback is introduced between them.
Multiple feedback loops are thus closed over a shared, general-purpose network.
Traditional feedback control is unsuitable for design of IoT control because it
relies on high-rate periodic communication and is ignorant of the shared
network resource. Therefore, recent event-based estimation methods are applied
herein for resource-aware IoT control allowing agents to decide online whether
communication with other agents is needed, or not. While this can reduce
network traffic significantly, a severe limitation of typical event-based
approaches is the need for instantaneous triggering decisions that leave no
time to reallocate freed resources (e.g., communication slots), which hence
remain unused. To address this problem, novel predictive and self triggering
protocols are proposed herein. From a unified Bayesian decision framework, two
schemes are developed: self triggers that predict, at the current triggering
instant, the next one; and predictive triggers that check at every time step,
whether communication will be needed at a given prediction horizon. The
suitability of these triggers for feedback control is demonstrated in hardware
experiments on a cart-pole, and scalability is discussed with a multi-vehicle
simulation.Comment: 16 pages, 15 figures, accepted article to appear in IEEE Internet of
Things Journal. arXiv admin note: text overlap with arXiv:1609.0753
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