Explore the vast range of titles available.

This study addresses the adaptive tracking control problem for a class of time-delay systems in strict-feedback form with unknown control gains and uncertain actuator non-linearity. The actuator non-linearity can be either backlash or dead zone, and the proposed approach does not require the knowledge of the bounds of non-linearity parameters. By applying an appropriate Lyapunov–Krasovskii functional and utilising the property of the well-defined trigonometric functions, the problems of time delay and controller singularity are avoided. The feasibility of using a static neural network to attenuate the effect of actuator non-linearity is proved with the aid of intermediate value theorem. Furthermore, it is proved that all closed-loop signals are bounded and the tracking error converges to a small residual set asymptotically. Two simulation examples are provided to demonstrate the effectiveness of the designed method.

In this study, a neuro-controller with adaptive deadzone compensation for a class of unknown SISO non-linear systems in a Brunovsky form with uncertain deadzone input is presented. Based on a proper smooth parameterisation of the deadzone, the unknown dynamics is identified by using a continuous time recurrent neural network whose weights are adjusted on-line by stable differential learning laws. On the basis of this neural model so obtained, a feedback linearisation controller is developed in order to follow a bounded reference trajectory specified. By means of Lyapunov analysis, the boundedness of all the closed-loop signals as well as the weights and deadzone parameter estimations is rigorously proven. Besides, the exponential convergence of the actual tracking error to a bounded zone is guaranteed. The effectiveness of this scheme is illustrated by a numerical simulation.

In this study, we deal with the output tracking control problem for a class of switched non-linear systems in lower triangular form subject to constraints on the states. To prevent states from transgressing the constraints, we employ a barrier Lyapunov function (BLF), which grows to infinity when its arguments approach domain boundaries. Based on the simultaneous domination assumption, we design a continuous feedback controller for the switched system. Furthermore, we show that asymptotic tracking is achieved without violation of the constraints and all closed-loop signals remain bounded, when a mild requirement on the initial values is imposed. Finally, the effectiveness of the proposed results is demonstrated using two simulation examples.

In this paper, an indirect adaptive fuzzy switching control scheme with average dwell-time method is proposed for switched strict-feedback nonlinear systems. By employing fuzzy logic systems to approximate unknown nonlinear functions and combing with the indirect adaptive fuzzy switching controller design procedure, the proposed control scheme can guarantee that all the closed-loop signals are boundedness, and the output of the systems converges to a small neighborhood of the desired trajectory. Finally, simulation results have been conducted to demonstrate the effectiveness of the proposed controllers. It has also been shown that the convergence of parameters and output tracking errors are smoother and faster with the proposed scheme, compared with the conventional adaptive fuzzy control methods.

Many practical non-linear systems can be described by non-linear auto-regressive moving average (NARMA) system models, whose stabilisation problem is challenging in the presence of large parametric uncertainties and non-parametric uncertainties. In this work, to address this challenging problem for a wide class of discrete-time NARMA systems, in which there are uncertain periodic parameters as well as uncertain non-linear part with unknown periodic time delays, we develop adaptive predictive control laws using the key ideas of ‘future outputs prediction’ and ‘nearest-neighbour compensation’, among which the former is carried out to overcome the non-causalness problem and the latter novel idea is proposed to completely compensate for the effect of non-linear uncertainties as well as unknown time delays. To achieve the desired asymptotic tracking performance in the presence of semi-parametric uncertainties with time delays, an ‘n-step parameter update law’ is first designed, based on which an ‘one-step update law’ is then elaborately constructed to obtain smoother closed-loop signals. This study in general develops a systematic adaptive control framework for periodic NARMA systems with guaranteed boundedness stability and asymptotic tracking performance, which are established by rigorous theoretic proof and verified by simulation studies.

This chapter contains sections titled: Introduction Grid Synchronization Techniques for Single‐Phase Systems Phase Detection Based on In‐Quadrature Signals Some PLLs Based on In‐Quadrature Signal Generation Some PLLs Based on Adaptive Filtering The SOGI Frequency‐Locked Loop Summary References

The design of modern industrial products is further improved through the hardware-in-the-loop (HIL) simulation. Realistic simulation is enabled by the closed loop between the hardware under test (HUT) and real-time simulation. Such a system involves a field programmable gate array (FPGA) and digital signal processor (DSP). An HIL model can bypass serious damage to the real object, reduce debugging cost, and, finally, reduce the comprehensive effort during the testing. This paper provides a historical overview of HIL simulations through different engineering challenges, i.e., within automotive, power electronics systems, and different industrial drives. Various platforms, such as National Instruments, dSPACE, Typhoon HIL, or MATLAB Simulink Real-Time toolboxes and Speedgoat hardware systems, offer a powerful tool for efficient and successful investigations in different fields. Therefore, HIL simulation practice must begin already during the university’s education process to prepare the students for professional engagements in the industry, which was also verified experimentally at the end of the paper.

Heat shock protein 90 (Hsp90) is an ATP-dependent molecular chaperone which is essential in eukaryotes. It is required for the activation and stabilization of a wide variety of client proteins and many of them are involved in important cellular pathways. Since Hsp90 affects numerous physiological processes such as signal transduction, intracellular transport, and protein degradation, it became an interesting target for cancer therapy. Structurally, Hsp90 is a flexible dimeric protein composed of three different domains which adopt structurally distinct conformations. ATP binding triggers directionality in these conformational changes and leads to a more compact state. To achieve its function, Hsp90 works together with a large group of cofactors, termed co-chaperones. Co-chaperones form defined binary or ternary complexes with Hsp90, which facilitate the maturation of client proteins. In addition, posttranslational modifications of Hsp90, such as phosphorylation and acetylation, provide another level of regulation. They influence the conformational cycle, co-chaperone interaction, and inter-domain communications. In this review, we discuss the recent progress made in understanding the Hsp90 machinery.

‘Smart’ bandages based on multimodal wearable devices could enable real-time physiological monitoring and active intervention to promote healing of chronic wounds. However, there has been limited development in incorporation of both sensors and stimulators for the current smart bandage technologies. Additionally, while adhesive electrodes are essential for robust signal transduction, detachment of existing adhesive dressings can lead to secondary damage to delicate wound tissues without switchable adhesion. Here we overcome these issues by developing a flexible bioelectronic system consisting of wirelessly powered, closed-loop sensing and stimulation circuits with skin-interfacing hydrogel electrodes capable of on-demand adhesion and detachment. In mice, we demonstrate that our wound care system can continuously monitor skin impedance and temperature and deliver electrical stimulation in response to the wound environment. Across preclinical wound models, the treatment group healed ~25% more rapidly and with ~50% enhancement in dermal remodeling compared with control. Further, we observed activation of proregenerative genes in monocyte and macrophage cell populations, which may enhance tissue regeneration, neovascularization and dermal recovery. A wireless ‘smart’ bandage stimulates wound healing.

With the increasing demand for high-sensitivity signal tracking in complex environments, GNSS receiver technologies have continuously evolved in both architecture and algorithmic design. This paper presents a systematic review of the development of high-sensitivity tracking techniques, with a focus on the transition from closed-loop to open-loop, hybrid, and deeply integrated architectures. Key strategies—such as coherent integration time extension, discriminator and loop filter optimization, vector tracking (VT), and Direct Position Estimation (DPE) are evaluated in the context of weak signal scenarios. To address the limitations of existing methods, including strong dependence on filter models, oscillator instability, and computational complexity, this study also outlines future research directions, including (1) integrating deep learning techniques into filter structures to enhance adaptability and robustness; (2) developing multi-channel collaborative estimation schemes to mitigate oscillator noise in weak signal environments; and (3) designing low-complexity open-loop tracking approximations to align with hardware resource constraints. Thus, enhancing the necessity of navigation continuity and robustness under low-signal-to-noise-ratio (SNR) conditions.