Low-Cycle Fatigue Behavior and the Combined Cyclic Hardening Material Model of Plate-Shaped Zn-22Al Alloy for Seismic Dampers.

This study investigates the potential of the plate-shaped Zn-22 wt.% Al (Zn-22Al) alloy as an innovative energy dissipation material for seismic damping devices, since plate-shaped material is more suitable to fabricate large-scale devices for building structures. The research begins with the synthesis of Zn-22Al alloy, given its unavailability in the commercial market. Monotonic tensile tests and low-cycle fatigue tests are performed to analyze material properties and fatigue performance of plate-shaped specimens. Monotonic tensile curves and cyclic stress-strain curves, along with SEM micrographs for microstructure and fracture surface analysis, are acquired. The combined cyclic hardening material model is calibrated to facilitate finite element analysis. Experimental results reveal exceptional ductility in Zn-22Al alloy, achieving a fracture strain of 200.37% (1.11 fracture strain). Fatigue life ranges from 1126 to 189 cycles with increasing strain amplitude (±0.8% to ±2.5%), surpassing mild steel by at least 6 times. The cyclic strain-life relationships align well with the Basquin-Coffin-Manson relationship. The combined kinematic/isotropic hardening model in ABAQUS accurately predicts the hysteretic behavior of the material, showcasing the promising potential of Zn-22Al alloy for seismic damping applications.

Dynamic Event-Triggered Control for GSES of Memristive Neural Networks Under Multiple Cyber-Attacks.

In this article, the dynamic event-triggered control problem of memristive neural networks (MNNs) under multiple cyber-attacks is considered. A novel dynamic event-triggering scheme (DETS) and the corresponding event-triggered controller are proposed by taking into consideration both denial-of-service and deception attacks (DoS-DAs). Then, a key lemma is established to show that the dynamic event-triggered controller can be used to solve the globally stochastically exponential stability (GSES) issue of concerned MNN under multiple cyber-attacks. Meanwhile, a novel Lyapunov functional is proposed based on the actual sampling pattern. It is shown that under our proposed dynamic event-triggered controller and Lyapunov functional, the concerned MNN can achieve GSES in the presence of DoS-DAs. In addition, our results include relevant results on event-triggered control of MNN with static event-triggering scheme (SETS) or without cyber-attacks as special cases. The effectiveness of the proposed event-triggered controller under multiple cyber-attacks is illustrated by a simulation example.

Alloying Iron into Palladium Nanoparticles for an Efficient Catalyst in Acetylene Dicarbonylation.

Motivated by the prominent catalytic performance and durability of nanoalloy catalysts, the Pd-based bimetallic nanoalloy catalysts were prepared using an aqueous reduction method. The Fe-Pd bimetallic nanoalloy catalyst (nano-Fe/Pd) demonstrated 98.4% yield and 99.7% selectivity for the unsaturated 1,4-dicarboxylic acid diesters. Moreover, the inductively coupled plasma (ICP) analysis shows that the Pd leaching of the catalyst can be effectively suppressed by alloying Fe atoms into the Pd crystal lattice for acetylene dicarbonylation. The detailed catalyst structure and morphology characterization demonstrate that introducing Fe into the Pd nanoparticles tunes the electronic-geometrical properties of the catalyst. Theoretical calculations indicate that the electrons of Fe transfer to Pd in the nano-Fe/Pd catalyst, enhancing activation of the C≡C bond in acetylene and weakening CO absorption capacity on catalyst surfaces. Alloying Fe into the Pd nanocatalyst effectively inhibits active metal leaching and improves catalyst activity and stability under high-pressure CO reactions.

Adaptive Neural Control for MIMO Pure-Feedback Nonlinear Systems With Periodic Disturbances.

In this paper, an adaptive neural control design method is presented for a class of multiple-input-multiple-output (MIMO) pure-feedback nonlinear systems with periodically time-varying disturbances appearing nonlinearly in unknown nonaffine functions. The nonaffine functions do not need to be differentiable, and the bounded condition of unknown nonaffine functions is relaxed such that only a more general semibounded assumption is required as the controllability condition of the considered MIMO pure-feedback system. To facilitate the control design, the gain functions are designed to be continuous and positive with the bounds being unknown functions. Furthermore, for handling with the difficulty caused by these unknown bounds, several appropriate compact sets are defined to obtain the bounds of gain functions. By utilizing Lyapunov analysis, all the variables of the resulting closed-loop system are proven to be semiglobally uniformly ultimately bounded, and the tracking error can converge to an arbitrarily small neighborhood around zero by choosing design parameters appropriately. The effectiveness of the proposed control algorithm is demonstrated by two simulations.

A Novel Error-Compensation Control for a Class of High-Order Nonlinear Systems With Input Delay.

A novel tracking error-compensation-based adaptive neural control scheme is proposed for a class of high-order nonlinear systems with completely unknown nonlinearities and input delay. In the tracking errors of existing papers, there exist the following difficulties: first, output curve always lags behind the desired trajectory, second, some big peak errors cause a decrease in tracking precision, and third, a big initial value of the modified tracking error can make the closed-loop system unstable. To tackle them, three corresponding error-compensation terms are constructed, including a prediction and compensation term, an auxiliary signal produced by the constructed auxiliary system, and a damping term. However, inequality amplification caused by high order will weaken the effectiveness of the proposed error-compensation scheme, and the control precision will decrease under an assumption that the lower bounds of the unknown control coefficients should be exactly known. To overcome aforementioned difficulties, in the derivation of the first virtual control law, the radial basis function neural network is used to approximate a hybrid term online constructed by unknown nonlinearities, a lumped control coefficient achieved by state transformation, and the dynamic of the proposed error-compensation terms and desired signal. Meanwhile, input delay is coped with a robust compensation signal constructed based on a finite integral of the past control values. Finally, it is proven that all the closed-loop signals are semiglobally uniformly ultimately bounded. Simulation results demonstrate the effectiveness of the proposed method.

Adaptive Neural Control for a Class of Pure-Feedback Nonlinear Systems via Dynamic Surface Technique.

This brief addresses the adaptive control problem for a class of pure-feedback systems with nonaffine functions possibly being nondifferentiable. Without using the mean value theorem, the difficulty of the control design for pure-feedback systems is overcome by modeling the nonaffine functions appropriately. With the help of neural network approximators, an adaptive neural controller is developed by combining the dynamic surface control (DSC) and minimal learning parameter (MLP) techniques. The key features of our approach are that, first, the restrictive assumptions on the partial derivative of nonaffine functions are removed, second, the DSC technique is used to avoid "the explosion of complexity" in the backstepping design, and the number of adaptive parameters is reduced significantly using the MLP technique, third, smooth robust compensators are employed to circumvent the influences of approximation errors and disturbances. Furthermore, it is proved that all the signals in the closed-loop system are semiglobal uniformly ultimately bounded. Finally, the simulation results are provided to demonstrate the effectiveness of the designed method.