This paper investigates the oxygen starvation and parasitic loss prevention problem of the adaptive oxygen excess ratio (OER) control system of nonlinear proton exchange membrane fuel cells (PEMFCs). Asymmetric OER constraints are considered to avoid oxygen starvation and parasitic loss in the air supply system of PEMFCs. An approximation-based adaptive control strategy is established to ensure robust regulation of the OER while not violating the OER constraints, regardless of unknown system parameters, nonlinearities, and the abrupt changes of the load current. A dynamic surface design technique using an asymmetric barrier Lyapunov function is employed for a recursive control design. Compared with existing control approaches for uncertain nonlinear air supply systems of PEMFCs, this paper first considers the oxygen starvation and parasitic loss prevention problem for the regulation of optimal OER in the control field of nonlinear PEMFCs. Using the Lyapunov stability theorem, the boundedness of all closed-loop signals and the convergence of the output tracking error to the vicinity of zero are proved. INDEX TERMSAdaptive control, oxygen excess ratio (OER) constraints, neural networks, proton exchange membrane fuel cells (PEMFCs).
Stockpiling of liquid assets in cash decreases the possibility of a firm's falling into financial distress and becoming technically insolvent. Such stockpiling provides incentives for firms to increase their leverage because cash holdings decrease potential financial distress costs and increase target debt‐equity ratios. This paper examines whether firms' excessive cash holdings enhance an explanatory power of the marginal tax rate for the change in leverage. The results show that high‐taxed firms with excess cash are more likely to increase their leverage. The increase in leverage is not to discipline the entrenched managers' discretionary use of free cash, and the excess cash is not simply for planned future investments either. We also find that the results still hold even when the financial crisis periods are excluded and that the debt financing behavior changes with the level of statutory tax rate. These findings provide evidence that firms with sufficient cash are more likely to take advantage of interest tax shields.
This paper addresses an adaptive temperature control problem for preventing the membrane dehydration and electrode flooding of nonlinear proton exchange membrane fuel cells (PEMFCs). Compared with the previous thermal control results of PEMFC temperature systems, the main contributions of this paper are two-fold: (i) nonlinear thermal management systems with nonlinear coolant circuit dynamics are firstly adopted in the temperature control field of PEMFCs and (ii) temperature constraints are considered to avoid the membrane dehydration and electrode flooding phenomena of PEMFCs. It is assumed that all system parameters and nonlinearities of thermal management systems including nonlinear coolant circuit dynamic are unknown. A recursive control design methodology is presented to guarantee the robust regulation and constraint satisfaction of the stack temperature. From the Lyapunov theorem, the stability of the resulting closed-loop system is analyzed.INDEX TERMS Thermal management systems, nonlinear coolant circuit dynamics, temperature constraints, adaptive control, proton exchange membrane fuel cells (PEMFCs).
This paper addresses an approximation-based quantized state feedback tracking problem of multiple-input multiple-output (MIMO) nonlinear systems with quantized input saturation. A uniform quantizer is adopted to quantize state variables and control inputs of MIMO nonlinear systems. The primary features in the current development are that (i) an adaptive neural network tracker using quantized states is developed for MIMO nonlinear systems and (ii) a compensation mechanism of quantized input saturation is designed by constructing an auxiliary system. An adaptive neural tracker design with the compensation of quantized input saturation is developed by deriving an augmented error surface using quantized states. It is shown that closed-loop stability analysis and tracking error convergence are conducted based on Lyapunov theory. Finally, we give simulation and experimental results of the 2-degrees-of-freedom (2-DOF) helicopter system for verifying to the validity of the proposed methodology where the tracking performance of pitch and yaw angles is measured with the mean squared errors of 0.1044 and 0.0435 for simulation results, and those of 0.0656 and 0.0523 for experimental results.
This article presents a distributed adaptive containment control strategy using quantized state feedback and communication for uncertain multiple‐input‐multiple‐output pure‐feedback nonlinear multi‐agent systems with state quantization. It is assumed that quantized state variables of multiple followers and quantized outputs of multiple dynamic leaders are only available for constructing local adaptive feedback controllers of followers under a directed network. Compared with existing containment control approaches, the primary contribution of this study is dealing with discontinuously quantized state feedback and communication problems for containment control design in the presence of unknown nonaffine nonlinearities. Based on quantized state and communication information, local adaptive neural network trackers of followers and their adaptive tuning laws are developed to guarantee that the trajectories of all followers converge to the dynamic convex hull spanned by the multiple leaders regardless of unknown pure‐feedback nonlinearities. By analyzing the stability of the local quantization errors between quantized signals and original signals, we prove the boundedness of all closed‐loop signals and the convergence of the containment errors to a sufficiently small domain around the origin. Simulation examples involving flexible‐joint manipulators are presented to validate the proposed quantized feedback scheme.
We present an adaptive quantized state feedback tracking methodology for a class of uncertain multiple-input multiple-output (MIMO) nonlinear block-triangular pure-feedback systems with state quantizers. Uniform quantizers are considered to quantize all measurable state variables for feedback. Compared with the existing tracking approaches of MIMO lower-triangular nonlinear systems, the main contributions of the proposed strategy are developing (1) a quantized-state-feedback-based adaptive tracker in the presence of nonaffine interaction of states and control variables of MIMO systems and (2) an analysis strategy for quantized feedback stability using adaptive compensation terms to derive bounded quantization errors. In addition, the stability of the closed-loop system with quantized state feedback is analyzed based on the Lyapunov stability theorem. Finally, simulation examples, including interconnected inverted pendulums, are presented to validate the effectiveness of the proposed control strategy. INDEX TERMSQuantized state feedback control, neural networks, state quantization, MIMO nonlinear systems, pure-feedback form. BYUNG MO KIM received the B.S. and M.S. degrees from the School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, South Korea, in 2019 and 2021, respectively. His current research interests include non-linear adaptive control and intelligent control using neural networks. SUNG JIN YOO (Member, IEEE) received the B.S., M.S., and Ph.D. degrees in electrical and electronic engineering from
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.