1,721,006 research outputs found
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
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
Shrinkage Strategies for Structure Selection and Identification of Piecewise Affine Models
No full textWe propose two optimization-based heuristics for structure selection and identification of PieceWise Affine (PWA) models with exogenous inputs. The first method determines the number of affine sub-models assuming known model order of the sub-models, while the second approach estimates the model order for a given number of affine sub-models. Both approaches rely on the use of regularization-based shrinking strategies, that are exploited within a coordinate-descent identification algorithm. This allows us to estimate the structure of the PWA models along with its model parameters. Starting from an overparameterized model, the key idea is to alternate between an identification step and structure refinement. The performance of the presented strategies is assessed over two benchmark examples
Recursive Bias-Correction Method for Identification of Piecewise Affine Output-Error Models
No full textLearning PieceWise Affine Output-Error (PWA-OE) models from data requires to estimate a finite set of affine output-error sub-models as well as a partition of the regressors space over which the sub-models are defined. For an output-error type noise structure, the algorithms based on ordinary least squares (LS) fail to compute a consistent estimate of the sub-model parameters. On the other hand, the prediction error methods (PEMs) provide a consistent parameter estimate, however, they require to solve a non-convex optimization problem for which the numerical algorithms may get trapped in a local minimum, leading to inaccurate estimates. In this letter, we propose a recursive bias-correction scheme for identifying PWA-OE models, retaining the computational efficiency of the standard LS algorithms while providing a consistent estimate of the sub-model parameters, under suitable assumptions. The proposed approach allows one to recursively update the estimates of the sub-models parameters and to cluster the regressors. Linear multi-category techniques are then employed to estimate a partition of the regressor space based on the estimated clusters. The performance of the proposed algorithm is demonstrated via an academic example
Estimation of jump Box–Jenkins models
No full textJump Box–Jenkins (BJ) models are a collection of a finite set of linear dynamical submodels in BJ form that switch over time, according to a Markov chain. This paper addresses the problem of maximum-a-posteriori estimation of jump BJ models from a given training input/output dataset. The proposed solution method estimates the coefficients of the BJ submodels, the state transition probabilities of the Markov chain regulating the switching of operating modes, and the corresponding mode sequence hidden in the dataset. In particular, the posterior distribution of all the unknown variables characterizing the jump BJ model is derived and then maximized using a coordinate ascent algorithm. The resulting estimation algorithm alternates between Gauss–Newton optimization of the coefficients of the BJ submodels, a method derived based on an instance of prediction error methods tailored to BJ models with switching coefficients, and approximated dynamic programming for optimization of the sequence of active modes. The quality of the proposed estimation approach is evaluated on a numerical example based on synthetic data and in a case study related to segmentation of honeybee dances
Data-driven design of explicit predictive controllers with structural priors
Full text linkIn this letter, we propose a data-driven approach to derive explicit predictive control laws. The key idea of the presented strategy is to exploit the prior knowledge that the optimal solution is a piece-wise affine controller. As the proposed method allows us to automatically retrieve also a model of the closed-loop system, we show that we can apply classical Lyapunov techniques to perform a prior stability check for safe controller deployment. The effectiveness of the proposed strategy is assessed on a benchmark simulation example, through which we also discuss the use of regularization and preprocessing techniques to handle the presence of noise.</p
Cloud-aided collaborative estimation by ADMM-RLS algorithms for connected diagnostics and prognostics
No full textAs the connectivity of consumer devices is rapidly growing and cloud computing technologies are becoming more widespread, cloud-aided algorithms for parameter estimation can be developed to exploit the theoretically unlimited storage memory and computational power of the 'cloud', while relying on information provided by multiple sources. With the ultimate goal of developing monitoring, diagnostic and prognostic strategies, this paper focuses on the design of a Recursive Least-Squares (RLS) based estimator for identification over a multitude of similar devices (such as a mass production) connected to the cloud. The proposed approach, that relies on Node-to-Cloud-to-Node (N2C2N) transmissions, is designed so that: (i) estimates of the unknown parameters are computed locally and (ii) the local estimates are refined on the cloud by exploiting the additional information that the devices have similar characteristics. The proposed approach requires minimal changes to local (pre-existing) RLS estimators
Cooperative constrained parameter estimation by ADMM-RLS
No full textWith recent advances in cloud computing, resources with customizable computational power and memory can be exploited to store and analyze data collected from large sets of devices. Although one can exploit the connection to the cloud to perform all the desired tasks on the cloud itself, in many applications it is also desirable to retrieve and process information locally. In this paper, we present a collection of cloud-aided consensus-based Recursive Least-Squares (RLS) estimators. The approaches are tailored to handle linear and nonlinear consensus constraints and limitations on parameter ranges. All the methods are designed so that raw measurements collected at the device level are processed by the device itself, requiring minimal changes to (possibly pre-existing) RLS estimators. The local estimates are then recursively refined and fused on the cloud to reach consensus among the devices
Direct data-driven design of switching controllers for constrained systems
No full textThis paper presents a hierarchical structure to directly design controllers for (possibly nonlinear) constrained systems. The proposed architecture combines the advantages of an inner data-driven switching controller designed to achieve a predefined closed-loop behavior and an outer model predictive controller, which is used as a reference governor. These design choices enable us to avoid the identification step typical of model-based approaches while exploiting the ability of model predictive controllers to handle constraints and optimize the closed-loop performance. As a proof of concept, a benchmark simulation example is used to demonstrate the effectiveness of the proposed strategy
Piecewise affine regression via recursive multiple least squares and multicategory discrimination
No full textIn nonlinear regression choosing an adequate model structure is often a challenging problem. While simple models (such as linear functions) may not be able to capture the underlying relationship among the variables, over-parametrized models described by a large set of nonlinear basis functions tend to overfit the training data, leading to poor generalization on unseen data. Piecewise-affine (PWA) models can describe nonlinear and possible discontinuous relationships while maintaining simple local affine regressor-to-output mappings, with extreme flexibility when the polyhedral partitioning of the regressor space is learned from data rather than fixed a priori. In this paper, we propose a novel and numerically very efficient two-stage approach for PWA regression based on a combined use of (i) recursive multi-model least-squares techniques for clustering and fitting linear functions to data, and (ii) linear multi-category discrimination, either offline (batch) via a Newton-like algorithm for computing a solution of unconstrained optimization problems with objective functions having a piecewise smooth gradient, or online (recursive) via averaged stochastic gradient descent
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