A novel output-feedback controller for uncertain SISO nonaffine nonlinear systems is proposed using high-order sliding mode (HOSM) observer which is a robust exact finite-time convergent differentiator. The proposed controller utilizes (n+1)th-order HOSM observer to cancel the uncertainty and disturbance where $n$ is the relative degree of the controlled system. As a result, the control law has an extremely simple form of linear in the differentiator states. It is required no separate sliding-mode controller or universal approximators such as neural networks or fuzzy logic systems that are adaptively tuned online. The proposed controller guarantees finite time (FT) stability of the output tracking error.

A novel differentiator-based approximation-free output-feedback controller for uncertain nonautonomous nonlinear pure-feedback systems is proposed. Using high-order sliding mode observer, which is a finite-time exact differentiator, the time-derivatives of the signal generated using tracking error and filtered input are directly estimated. As a result, the proposed non-backstepping control law and stability analysis are drastically simple. The tracking error vector is guaranteed to be exponentially stable in finite time regardless of the nonautonomous property in the considered system. It does not require neural networks or fuzzy logic systems, which are typically adopted to capture unstructured uncertainties intrinsic in the controlled system. As far as the authors know, there are no research results on the output-feedback controller for the uncertain nonautonomous pure-feedback nonlinear systems. The results of the simulation show clearly the performance and compactness of the control scheme proposed.

A novel switching-differentiator (SD) that can asymptotically track the time derivative of time-varying signal was previously proposed. This paper extends the previous SD to estimation of higher-order time derivatives. This study shows that higher-order time-derivatives can be estimated by connecting multiple SDs in cascade form. By successive applying the generalized Barbalat’s lemma, all higher-order tracking errors also approach zeros asymptotically. To illustrate the performance of the proposed higher-order switching differentiator, simulations were performed for estimating higher-order time-derivatives of a signal.

In this paper, we proposed a novel saddle type FinFET (S-FinFET) to effectively solve problems occurring under the capacitor node of a dynamic random-access memory (DRAM) cell and showed how its structure was superior to conventional S-FinFETs in terms of short channel effect (SCE), subthreshold slope (SS), and gate-induced drain leakage (GIDL). The proposed FinFET exhibited four times lower I_off than modified S-FinFET, called RFinFET, with more improved drain-induced barrier lowering (DIBL) characteristics, while minimizing I_on reduction compared to RFinFET. Our results also confirmed that the proposed device showed improved drain-induced barrier lowering (DIBL) and I_off characteristics as gate channel length decreased.

Offshore wind power has attracted significant attention due to its high potential, capability for large-scale farms, and high capacity factor. However, it faces high investment costs and issues with subsea power transmission. Conventional high-voltage AC (HVAC) methods are limited by charging current, while high-voltage DC (HVDC) methods suffer from the high cost of power conversion stations. The low-frequency AC (LFAC) method mitigates the charging current through low-frequency operation and can reduce power conversion station costs. This paper aims to identify the economically optimal frequency by comparing the investment costs of LFAC systems at various frequencies. The components of LFAC, including transformers, offshore platforms, and cables, exhibit frequency-dependent characteristics. Lower frequencies result in an increased size and volume of transformers, leading to higher investment costs for offshore platforms. In contrast, cable charging currents and losses are proportional to frequency, causing the total cost to reach a minimum at a specific frequency. To determine the optimal frequency, simulations of investment costs for varying capacities and distances were conducted.