PDE-Constrained Optimization: Optimal control with L 1-Regularization, State and Control Box Constraints

Author(s):  
Ivo Dravins ◽  
Maya Neytcheva
1996 ◽  
Vol 118 (3) ◽  
pp. 482-488 ◽  
Author(s):  
Sergio Bittanti ◽  
Fabrizio Lorito ◽  
Silvia Strada

In this paper, Linear Quadratic (LQ) optimal control concepts are applied for the active control of vibrations in helicopters. The study is based on an identified dynamic model of the rotor. The vibration effect is captured by suitably augmenting the state vector of the rotor model. Then, Kalman filtering concepts can be used to obtain a real-time estimate of the vibration, which is then fed back to form a suitable compensation signal. This design rationale is derived here starting from a rigorous problem position in an optimal control context. Among other things, this calls for a suitable definition of the performance index, of nonstandard type. The application of these ideas to a test helicopter, by means of computer simulations, shows good performances both in terms of disturbance rejection effectiveness and control effort limitation. The performance of the obtained controller is compared with the one achievable by the so called Higher Harmonic Control (HHC) approach, well known within the helicopter community.


2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Seung Jae Kim ◽  
Oh. Deog Kwon ◽  
Kyung-Soo Kim

Abstract Background This study aimed to investigate the prevalence, awareness, treatment, and control rates of dyslipidemia and identify the predictors of optimal control (low-density lipoprotein cholesterol < 100 mg/dL) among patients with diabetes mellitus (DM). Methods A cross-sectional study was conducted using the representative Korea National Health and Nutrition Examination Survey (2014–2018). Overall, 4311 patients with DM, aged ≥19 years, and without cardiovascular diseases were selected, and the prevalence, awareness, treatment, and control rates of dyslipidemia were calculated. Univariate and multivariate logistic regression analyses were conducted to evaluate the factors influencing the optimal control of dyslipidemia. Results Dyslipidemia was prevalent in 83.3% of patients with DM, but the awareness and treatment rates were 36.5 and 26.9%, respectively. The control rate among all patients with dyslipidemia was 18.8%, whereas it was 61.1% among those being treated. Prevalence and awareness rates were also significantly higher in women than in men. Dyslipidemia was most prevalent in those aged 19–39 years, but the rates of awareness, treatment, and control among all patients with dyslipidemia in this age group were significantly lower than those in other age groups. The predictors of optimal control were age ≥ 40 years [range 40–49 years: adjusted odds ratio (aOR) 3.73, 95% confidence interval (CI) 1.43–9.72; 50–59 years: aOR 6.25, 95% CI 2.50–15.65; 60–69 years: aOR 6.96, 95% CI 2.77–17.44; 70–79 years: aOR 9.21, 95% CI 3.58–23.74; and ≥ 80 years: aOR 4.43, 95% CI 1.60–12.27]; urban living (aOR 1.44, 95% CI 1.15–1.80); higher body mass index (aOR 1.27, 95% CI 1.13–1.42); lower glycated hemoglobin levels (aOR 0.71, 95% CI 0.67–0.76); hypertension (aOR 1.53, 95% CI 1.22–1.92); poorer self-rated health status (aOR 0.72, 95% CI 0.62–0.84); and receiving regular health check-ups (aOR 1.58, 95% CI 1.25–2.00). Conclusions Most patients with DM were diagnosed with dyslipidemia, but many were unaware of or untreated for their condition. Therefore, their control rate was suboptimal. Thus, by understanding factors influencing optimal control of dyslipidemia, physicians should make more effort to encourage patients to undergo treatment and thus, adequately control their dyslipidemia.


2020 ◽  
Vol 23 (6) ◽  
pp. 1783-1796
Author(s):  
Neelam Singha

Abstract In this article, we aim to analyze a mathematical model of tumor growth as a problem of fractional optimal control. The considered fractional-order model describes the interaction of effector-immune cells and tumor cells, including combined chemo-immunotherapy. We deduce the necessary optimality conditions together with implementing the Adomian decomposition method on the suggested fractional-order optimal control problem. The key motive is to perform numerical simulations that shall facilitate us in understanding the behavior of state and control variables. Further, the graphical interpretation of solutions effectively validates the applicability of the present analysis to investigate the growth of cancer cells in the presence of medical treatment.


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