Disturbance observer–based neural flight control for aircraft with switched time-varying distributed delays

In this article, the high angle of attack (AOA) maneuver control problem is studied under multiple disturbances and uncertainties. For the first time, the switched distributed delay is constructed to characterize the unsteady aerodynamics. Based on neural networks (NNs) and hyperbolic tangent function, the disturbance observer technique is extended to the nonstrict-feedback system control. To handle the switching problem, time-delay problem, and nonstrict-feedback problem caused by switched distributed delay terms, the Lyapunov–Krasovskii (LK) functional method and a variable separation method are cleverly combined. The proposed LK function can relax the constraints on time-varying delay. Finally, a disturbance observer–based neural finite-time prescribed performance flight control law is developed to improve the flight performance at high AOA, and its effectiveness has been verified through rigorous theoretical analysis and simulation experiments.

Output-driven feedback system control platform optimizes combinatorial therapy of tuberculosis using a macrophage cell culture model

Tuberculosis (TB) remains a major global public health problem, and improved treatments are needed to shorten duration of therapy, decrease disease burden, improve compliance, and combat emergence of drug resistance. Ideally, the most effective regimen would be identified by a systematic and comprehensive combinatorial search of large numbers of TB drugs. However, optimization of regimens by standard methods is challenging, especially as the number of drugs increases, because of the extremely large number of drug–dose combinations requiring testing. Herein, we used an optimization platform, feedback system control (FSC) methodology, to identify improved drug–dose combinations for TB treatment using a fluorescence-based human macrophage cell culture model of TB, in which macrophages are infected with isopropyl β-D-1-thiogalactopyranoside (IPTG)-inducible green fluorescent protein (GFP)-expressing Mycobacterium tuberculosis (Mtb). On the basis of only a single screening test and three iterations, we identified highly efficacious three- and four-drug combinations. To verify the efficacy of these combinations, we further evaluated them using a methodologically independent assay for intramacrophage killing of Mtb; the optimized combinations showed greater efficacy than the current standard TB drug regimen. Surprisingly, all top three- and four-drug optimized regimens included the third-line drug clofazimine, and none included the first-line drugs isoniazid and rifampin, which had insignificant or antagonistic impacts on efficacy. Because top regimens also did not include a fluoroquinolone or aminoglycoside, they are potentially of use for treating many cases of multidrug- and extensively drug-resistant TB. Our study shows the power of an FSC platform to identify promising previously unidentified drug–dose combinations for treatment of TB.

Use of feedback system control in optimizing chemical combinations to synthesize nanoparticles with desired characteristics

Defining desired chemical combinations through the FSC method.