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This paper presents a practical discrete-time fractional order terminal sliding mode (DFOTSM) control strategy for high-precision tracking tasks based on a linear motor. In particular, the practical parametric uncertainties involving sliding friction, uncertain payload, and disturbance in tracking tasks are considered in this paper. Combining Grünwald–Letnikov fractional order definition and terminal sliding mode technique, the proposed method synthesizes a novel DFOTSM control law to drive the sliding mode dynamics into the stable region in finite steps theoretically, even though the system is suffering from uncertainties and disturbances, and the motion on the surface can guarantee higher tracking precision than the conventional discrete-time terminal sliding surface by selecting suitable controller parameters. The theoretical analyses give out the guideline of parameter selection, and experiments are carried out on the linear-motor-based test platform to demonstrate that the proposed controller is easily implemented and can achieve high-precision tracking, fast response, and considerable robustness to uncertainties. [ABSTRACT FROM AUTHOR]

Recently, it has been verified by simulation results that the ability of recognizing and classifying patterns by artificial neural network can be successfully used for anti-islanding protection of distributed synchronous generators. However, since this type of protection differs from the available commercial relays, it is important to test the algorithm performance implemented in hardware. So, this paper presents a performance evaluation of the hardware implementation of an ANN-based algorithm for anti-islanding protection of distributed generators. For the evaluation process, a complete digital relay architecture is developed in LabVIEW, thus allowing its implementation in the commercial real-time platform cRIO-9025/cRIO-9018. The proposed scheme is tested under real conditions by means of the CMC-256-plus test set, as performed for conventional intelligent electronic devices. The proposed algorithm implemented in hardware shows high effectiveness, detecting all islanding conditions within 1 s, sharing the same good performance verified by computational simulations. The algorithm implementation shows robustness, as it remains blocked for other power grid disturbances, such as frequency variation in the grid utility, massive connection or load relief. [ABSTRACT FROM PUBLISHER]

This paper investigates the data-based optimal control for a class of networked industrial processes with a double-layer architecture. Without knowing the dynamics of subsystems at the device layer, the index prediction function is constructed via the input/output signals, and radial basis function neural networks. The tuning laws for the index prediction function are obtained through the optimal control strategy. Then, by treating the network-induced phenomenon as random round-trip time delay and introducing the predictive algorithm, the compensation scheme is designed at the operation layer to dynamically decompose the setpoints. Finally, two simulation examples are given to further illustrate the effectiveness of the proposed compensation strategy. [ABSTRACT FROM AUTHOR]

This paper presents a control strategy for the position and posture control of a planar four-link underactuated manipulator with a passive second link based on a back-propagation neural network (BPNN) model. First, using the differential evolution (DE) optimization algorithm, the target angles of all links are calculated. The third and fourth links are then controlled from their initial angles to zero. Next, the control process is divided into the following two steps: the control objective of the passive link is implemented in the first step, and the control objectives of the active links are implemented in the second step. In the first step, the system is reduced to a planar virtual Pendubot, a BPNN model based on the DE algorithm is trained to obtain the coupling relationship between the links of this Pendubot. This model gives the speed of the first link to move the passive link to approach the target angle. Then, a fuzzy controller is designed to indirectly control the passive link to its target angle. In the second step, the position mode of the servo controller is adopted to control all active links to their target angles. Experimental results verified the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

Traditional bipolar differential amplifiers have only a \pm 5\text-\textmV operational range with nonlinear distortion below 0.1 dB. In this paper, a linearization technique based on neural-network training algorithm is proposed to expand this 0.1-dB linear region to a much wider \pm 200\-\textmV range. Compared with the traditional and recent state-of-the-art techniques for linearization, where gain or noise performance is always being tradeoff for high linearity, the proposed technique leads to the increase in the amplifier’s gain with low noise. Another advantage of the proposed approach is that it is implemented using a simple, paralleled structure that leads to a smaller area and less power consumption than other linearization schemes. The design procedure also enables a completely customizable solution based on required linear range, ripples, and the number of available transistors. The experimental results show that the proposed linearized amplifier has a very good linearity and is capable of wide bandwidth, good gain, and low-noise performance. [ABSTRACT FROM PUBLISHER]

The temperature distribution of a lithium-ion battery (LIB) belongs to a nonlinearly distributed parameter system (DPS), which is infinite dimensional in the space direction. Modeling of such systems often leads to the following challenges: time/space-coupled dynamics; time-varying dynamics; and spatial nonlinearity. For the first two challenges, Karhunen–Loève (KL) decomposition based data-driven spatiotemporal models have been widely researched and successfully applied to industrial thermal processes. However, this method is a global linear model reduction method that ignores the nonlinear properties of measurements. This will prohibit the model performance of a strong nonlinear DPS, which has strong spatial nonlinearity refers to the aforementioned third challenge. To address this problem, a local-properties-embedding-based modeling method is developed for the nonlinear DPS. First, the finite-dimensional nonlinear spatial basis functions that can be the representative of the nonlinear feature of the original space are learned by the local-properties-embedding-based method. Then, the low-dimensional representative can be derived using the time/space separation and the unknown temporal coefficients can be identified through the traditional neural learning algorithm. Finally, the spatiotemporal temperature distribution can be reconstructed by the time/space synthesis. Since the nonlinear structure feature of the spatiotemporal datasets has been considered, the proposed modeling method can be more effective than the traditional KL-based method for modeling the nonlinear DPS. Numerical simulations on the LIB showed that the proposed methods are more robust than the KL-based method in both the time direction and the space direction. [ABSTRACT FROM AUTHOR]