On-line direct control design for nonlinear systems

An approach to design a feedback controller for nonlinear systems directly from experimental data is presented. Improving over a recently proposed technique, which employs exclusively a batch of experimental data collected in a preliminary experiment, here the control law is updated and rened during real-time operation, hence enabling an on-line learning capability. The theoretical properties of the described approach, in particular closed-loop stability and tracking accuracy, are discussed. Finally, the experimental results obtained with a water tank laboratory setup are presented

Fractional CO₂ vaginal laser for the genitourinary syndrome of menopause in breast cancer survivors.

Adjuvant chemotherapy and endocrine therapy can induce early iatrogenic menopause or worsen pre-existing menopausal symptoms in breast cancer survivors (BCS). The second most frequent menopausal symptom after hot flushes is the genitourinary syndrome (GSM). Since hormone replacement therapy is contraindicated in BCS, vaginal laser might represent a new nonhormonal option for GSM. This study aims at evaluating the effectiveness of the fractional CO₂ vaginal laser for GSM in BCS compared with healthy women. This is a retrospective study on 135 postmenopausal women (45 BCS and 90 healthy women) who underwent fractional CO2 laser for GSM. Objective (VHI and VVHI) and subjective outcomes (VAS for dyspareunia and vaginal dryness and a pain questionnaire) were evaluated at baseline visit and at every follow-up visit. Subjective and objective parameters improved significantly in both groups after laser therapy. The improvement was progressive and long-lasting up to 12 months after the end of the treatment. No severe adverse events were observed during the treatment. Fractional CO₂ vaginal laser induces a significant and long-lasting improvement on GSM symptoms in BCS. However, this improvement is slower than in healthy women undergoing the same treatment. Laser therapy turns out to be safe and well-tolerated

Changes in renal function after nephroureterectomy for upper urinary tract carcinoma: analysis of a large multicenter cohort (Radical Nephroureterectomy Outcomes (RaNeO) Research Consortium)

Purpose To investigate prevalence and predictors of renal function variation in a multicenter cohort treated with radical nephroureterectomy (RNU) for upper tract urothelial carcinoma (UTUC). Methods Patients from 17 tertiary centers were included. Renal function variation was evaluated at postoperative day (POD)-1, 6 and 12 months. Timepoints differences were Delta 1 = POD-1 eGFR - baseline eGFR; Delta 2 = 6 months eGFR - POD-1 eGFR; Delta 3 = 12 months eGFR - 6 months eGFR. We defined POD-1 acute kidney injury (AKI) as an increase in serum creatinine by >= 0.3 mg/dl or a 1.5 1.9-fold from baseline. Additionally, a cutoff of 60 ml/min in eGFR was considered to define renal function decline at 6 and 12 months. Logistic regression (LR) and linear mixed (LM) models were used to evaluate the association between clinical factors and eGFR decline and their interaction with follow-up. Results A total of 576 were included, of these 409(71.0%) and 403(70.0%) had an eGFR < 60 ml/min at 6 and 12 months, respectively, and 239(41.5%) developed POD-1 AKI. In multivariable LR analysis, age (Odds Ratio, OR 1.05, p < 0.001), male gender (OR 0.44, p = 0.003), POD-1 AKI (OR 2.88, p < 0.001) and preoperative eGFR < 60 ml/min (OR 7.58, p < 0.001) were predictors of renal function decline at 6 months. Age (OR 1.06, p < 0.001), coronary artery disease (OR 2.68, p = 0.007), POD-1 AKI (OR 1.83, p = 0.02), and preoperative eGFR < 60 ml/min (OR 7.80, p < 0.001) were predictors of renal function decline at 12 months. In LM models, age (p = 0.019), hydronephrosis (p < 0.001), POD-1 AKI (p < 0.001) and pT-stage (p = 0.001) influenced renal function variation (ss 9.2 +/- 0.7, p < 0.001) during follow-up. Conclusion Age, preoperative eGFR and POD-1 AKI are independent predictors of 6 and 12 months renal function decline after RNU for UTUC

Learning a nonlinear controller from data: theory and computation

In this paper, we consider the problem of learning a nonlinear controller directly from experimental data. We assume that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operation. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a $\ell_{1}$-norm regularized learning algorithm that achieves the stability condition as the number of data points tends to infinity. The approach is completely based on convex optimization

Learning a Nonlinear Controller from Data: Theory, Computation, and Experimental Results

The problem of learning a nonlinear controller directly from experimental data is considered. It is assumed that an existing, unknown controller, able to stabilize the plant, is available, and that input-output measurements can be collected during closed loop operation. A theoretical analysis shows that the error between the input issued by the existing controller and the input given by the learned one shall have low variability in order to achieve closed loop stability. This result is exploited to derive a `1-norm regularized learning algorithm that achieves the stability condition for a finite number of data points. The approach is completely based on convex optimization. The presented technique is finally tested in real-world experiments to control the flight of a tethered flexible wing, which is characterized by highly nonlinear, unstable and uncertain dynamics subject to external disturbances