1,721,110 research outputs found
Direct data-driven design of switching controllers
No full textSwitching linear models can be used to represent the behavior of hybrid, time-varying, and nonlinear systems, while generally providing a satisfactory trade-off between accuracy and complexity. Although several control design techniques are available for such models, the effect of modeling errors on the closed-loop performance has not been formally evaluated yet. In this paper, a data-driven synthesis scheme is thus introduced to design optimal switching controllers directly from data, without needing a model of the plant. In particular, the theory will be developed for piecewise affine controllers, which have proven to be effective in many real-world engineering applications. The performance of the proposed approach is illustrated on some benchmark simulation case studies
Proper closed-loop specifications for data-driven model-reference control
No full textIn control applications where finding a model of the plant is costly and time consuming, direct data-driven approaches represent a valid alternative for the design of model reference controllers. However, the selection of a proper reference model within a model-free setting is known to be a critical task, as such a model typically plays the role of a hyperparameter. In this work, we extend the existing theory so as to compute both a reference model and the corresponding optimal controller parameters from data to satisfy given behavioral bounds on the desired closed-loop performance. The effectiveness of the proposed approach is illustrated 011 a benchmark simulation example
Control-oriented regularization for linear system identification
No full textIn this paper, we develop a novel theoretical framework for control-oriented identification, based on a Bayesian perspective on modeling. Specifically, we show that closed-loop specifications can be incorporated within the identification procedure as a prior of the model probability distribution via suitable regularization. The corresponding kernel varies according to the additional penalty term and provides a new insight on control-oriented identification. As a secondary contribution, we derive a Bayesian robust control design approach exploiting all the information coming from the above modeling procedure, including the estimate of the uncertainty set The effectiveness of the proposed strategy against state-of-the-art regularized identification is illustrated on a benchmark example for digital control system design
A comparison between structured low-rank approximation and correlation approach for data-driven output tracking
No full textData-driven control is an alternative to the classical model-based control paradigm. The main idea is that a model of the plant is not explicitly identified prior to designing the control signal. Two recently proposed methods for data-driven control a method based on correlation analysis and a method based on structured matrix low-rank approximation and completion solve identical control problems. The aim of this paper is to compare the methods, both theoretically and via a numerical case study. The main conclusion of the comparison is that there is no universally best method: the two approaches have complementary advantages and disadvantages. Future work will aim to combine the two methods into a more effective unified approach for data-driven output tracking. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved
Model predictive control for multi-period portfolio optimization: a trading-oriented learning approach
No full textPortfolio optimization aims at finding the optimal allocation of a given wealth among different assets to maximize an investor's utility function. A major critical issue concerns the prediction of future asset returns to forecast market evolution, due to the non-stationarity and volatility of asset prices. In this work, a learning-based Model Predictive Control (MPC) strategy for multi-period portfolio optimization is proposed, where the return prediction model is estimated via a novel trading-oriented learning paradigm. According to such a perspective, the model parameters are not the ones minimizing the prediction error but those that directly maximize the investor's utility. An extensive experimental study carried out on real-market data shows the potential improvements introduced by the proposed methodology compared to benchmark financial strategies
Process noise covariance estimation via stochastic approximation
Full text linkKalman filtering for linear systems is known to provide the minimum variance estimation error, under the assumption that the model dynamics is known. While many system identification tools are available for computing the system matrices from experimental data, estimating the statistics of the output and process noises is still an open problem. Correlation-based approaches are very fast and sufficiently accurate, but there are typically restrictions on the number of noise covariance elements that can be estimated. On the other hand, maximum likelihood methods estimate all elements with high accuracy, but they are computationally expensive, and they require the use of external optimization solvers. In this paper, we propose an alternative solution, tailored for process noise covariance estimation and based on stochastic approximation and gradient-free optimization, that provides a good trade-off in terms of performance and computational load, and is also easy to implement. The effectiveness of the method as compared to the state of the art is shown on a number of recently proposed benchmark examples
Data-driven clamping force control for an Electric Parking Brake without speed measurement
No full textThis paper addresses the control of the clamping force provided by an Electric Parking Brake (EPB). A simple on-off strategy is implemented: the device is actuated until the actual force reaches the target value maintaining the vehicle in a steady position. The effectiveness of the control is then highly depending on the quality of the clamping force estimation. The proposed estimator relies on the sole DC motor current and voltage signals and does not require the knowledge of any physical parameters nor the measurement of the DC motor angular displacement. Extensive eperimental tests show the robustness of the proposed strategy with respect to different operating and aging conditions
Hourly operation of a regulated lake via Model Predictive Control
No full textThe optimal operation of regulated lakes is a challenging task involving conflicting objectives, ranging from controlling lake levels to avoid floods and low levels to water supply downstream. The traditional approach to operation policy design is based on an offline optimization, where a feedback control rule mapping lake storage into daily release decisions is identified over a set of observational data. In this paper, we propose a receding-horizon policy for a more frequent, online regulation of the lake level, and we discuss its tuning as compared to benchmark approaches. As side contributions, we provide a daily alternative based on the same rationale, and we show that this is still valid under some assumptions on the water inflow. Numerical simulations are used to show the effectiveness of the proposed approach. We demonstrate the approach on the regulated lake Como, Italy. Copyright (C) 2022 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
On-line model-based wheel speed filtering for geometrical error compensation
No full textWheel speed measurements provided by incremental encoders in road vehicles are usually affected by a significant periodic noise. Unavoidable geometrical or misalignment errors in the structure of the encoder are here regarded as possible causes of the measurement disturbance. Such disturbances are commonly rejected using simple solutions, like low-pass or notch filters. However, such methods may not be adequate in some applications, as the signal information is canceled jointly with the disturbances, thus jeopardizing the overall system performance. This paper presents an online filtering procedure, based on the geometrical model of the sensor and recursive constrained least squares estimation, aimed at rejecting only the periodic noise. Such a procedure will result into a speed measurement processing that is most suited for advanced vehicle applications. Experimental data are used to show the effectiveness of the proposed approach considering two different vehicles: a bicycle - where the proposed method is shown to be effective for cycling cadence estimation - and a sport car - where the speed variable is of primary importance, e.g., for braking and stability control
Explicit online least costly energy management for hybrid electric vehicles
No full textIn this paper, a numerical solution to the online least costly energy management problem for hybrid electric vehicles is illustrated. The key idea is that the optimal control problem corresponding to the least costly energy management task is here solved for different steady-state working points, so that explicit quasi-optimal power split maps suitable for realtime vehicle operation can be derived. The effectiveness of the proposed approach is shown within a simulation environment for the control of an extended range electric bus
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