Direct data-driven LPV control of nonlinear systems:An experimental result

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points

Direct data-driven LPV control of nonlinear systems:An experimental result

We demonstrate that direct data-driven control of nonlinear systems can be successfully accomplished via a behavioral approach that builds on a Linear Parameter-Varying (LPV) system concept. An LPV data-driven representation is used as a surrogate LPV form of the data-driven representation of the original nonlinear system. The LPV data-driven control design that builds on this representation form uses only measurement data from the nonlinear system and a priori information on a scheduling map that can lead to an LPV embedding of the nonlinear system behavior. Efficiency of the proposed approach is demonstrated experimentally on a nonlinear unbalanced disc system showing for the first time in the literature that behavioral data-driven methods are capable to stabilize arbitrary forced equilibria of a real-world nonlinear system by the use of only 7 data points

Learning Stable and Robust Linear Parameter-Varying State-Space Models

This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models. The model parametrizations guarantee a priori that for all parameter values during training, the allowed models are stable in the contraction sense or have their Lipschitz constant bounded by a user-defined value γ . Furthermore, since the parametrizations are direct, the models can be trained using unconstrained optimization. The fact that the trained models are of the LPV-SS class makes them useful for, e.g., further convex analysis or controller design. The effectiveness of the approach is demonstrated on an LPV identification problem

Learning Stable and Robust Linear Parameter-Varying State-Space Models

This paper presents two direct parameterizations of stable and robust linear parameter-varying state-space (LPV-SS) models. The model parametrizations guarantee a priori that for all parameter values during training, the allowed models are stable in the contraction sense or have their Lipschitz constant bounded by a user-defined value γ . Furthermore, since the parametrizations are direct, the models can be trained using unconstrained optimization. The fact that the trained models are of the LPV-SS class makes them useful for, e.g., further convex analysis or controller design. The effectiveness of the approach is demonstrated on an LPV identification problem

Direct data-driven state-feedback control of general nonlinear systems

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc

Direct data-driven state-feedback control of general nonlinear systems

Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop a data-driven representation of the so-called velocity-form, i.e., the time-difference dynamics, of the NL system, which is shown to admit a direct linear parameter-varying (LPV) representation. By applying the LPV extension of the Fundamental Lemma in this velocity domain, a state-feedback controller is directly synthesized to provide asymptotic stability and dissipativity of the velocity-form. By using realization theory, the synthesized controller is realized as a NL state-feedback law for the original unknown NL system with guarantees of universal shifted stability and dissipativity, i.e., stability and dissipativity w.r.t. any (forced) equilibrium point, of the closed-loop behavior. This is achieved by the use of a single sequence of data from the system and a predefined basis function set to span the scheduling map. The applicability of the results is demonstrated on a simulation example of an unbalanced disc

Preventive healthcare use, smoking, and alcohol use among Rhode Island women experiencing intimate partner violence

OBJECTIVE: Intimate partner violence (IPV) poses major health threats to women, including increased risk for several chronic health conditions. The impact of IPV on use of preventive health services is not well understood. Although several studies indicate that female victims of IPV have higher rates of alcohol abuse, this has not been replicated in population-based studies. The association of IPV with smoking has not been a major research focus. The purpose of this study was to examine the association between physical and psychological IPV in the past 12 months and preventive healthcare use, smoking, and alcohol use among women. METHODS: Data on 1643 women aged 18-54 from the 1999 Rhode Island Behavioral Risk Factor Surveillance System were analyzed. Logistic regression, controlling for age, race, marital status, education, insurance status, and functional disability, was used to model the associations of IPV with (1) checkups, (2) clinical breast examinations (CBEs), (3) Pap smear screening, (4) cigarette smoking, and (5) high-risk alcohol use. RESULTS: Prevalence of physical IPV was 4.1%. The prevalence of psychological IPV, in the absence of physical IPV was 4.5%. Physical IPV was associated with receiving regular Pap smears odds ratio ([OR] = 2.39, 95% confidence interval [CI] 1.01-5.70), current smoking (OR = 2.07, 95% CI 1.03-4.18), and high-risk alcohol use (OR = 4.85, 95% CI 2.02-11.60). Psychological IPV was associated with high-risk alcohol use (OR = 3.22, 95% CI 1.46-7.09). CONCLUSIONS: Women experiencing IPV regularly access preventive healthcare, providing healthcare providers with opportunities to assess and counsel women for IPV in addition to smoking and high-risk alcohol use