ISA transactions最新文献

As global interest grows in renewable energy sources, the impact of combined Electric Vehicle (EV) and PhotoVoltaic (PV) penetration on the power grid stability requires renewed attention, to incorporate new technologies to maintain the power quality under operational constraints. Energy-saving techniques such as Conservation Voltage Reduction (CVR) allow the power utilities to transmit voltage at a lower operation limit, increasing the generation margin to absorb the peak load demands. Increased reverse PV penetration results in grid overvoltage while EV charging absorbs the reactive power causing grid instability. Both overvoltage and loss of reactive power in the grid can be reduced by using CVR and reactive power injection techniques. A power electronic secondary var controller (SVC) can dynamically inject reactive power into selected grid buses. This work compares the voltage stability of an IEEE 33 bus system operating with and without CVR. The simulation studies analyzed the effects of EV penetration level, and PV hosting capacity with SVC compensation paired with and without conservation voltage reduction technique. The analysis results demonstrate that tandem usage of CVR and SVC maintains the grid voltage under operational limits, meets load and EV demand, and increases power efficiency and PV penetration.

Excavators, a type of human-operated construction machinery, suffer from poor hydraulic load braking stability, which seriously affects operator comfort. To address this challenge, this study investigates load braking laws through model analysis and designs an open-loop control algorithm called command reshaping, which can prolong the small-opening time of the main valve by segmentally adjusting the joystick command during load braking and then actively adjusting the key parameters reflecting the system's kinetic-potential energy state, thereby suppressing braking oscillations. The experimental results based on a 1.5-ton excavator indicated that the proposed algorithm can significantly enhance braking stability with a small stop-position deviation, the maximum amplitude of the working pressure of the boom cylinder was reduced by at least 40 %, and the maximum amplitude of the swing motor speed was reduced by at least 54 %. Furthermore, the algorithm is easily implemented, cost-free, and suitable for the performance tuning of excavators.

This paper focuses on the issue of global practical tracking control by output feedback for uncertain nonlinear systems with unknown control coefficients and unknown reference signal. Unlike other tracking works, the upper and lower bounds of the unknown control coefficients in the studied nonlinear system are not required to be known, while the nonlinearities are bounded by the unmeasured states multiplying an unknown constant, the polynomial-of-output and the polynomial-of-input. Inspired by related works, an adaptive tracking controller based on a new dynamic high gain has been successfully constructed by combining the universal control idea and the concept of dead-zone with backstepping technique, which effectively handles the impacts of multiple uncertainties. The designed adaptive controller ensures that the state of the closed-loop system is globally bounded, and the tracking error of the system converges to any arbitrarily small range of the origin after a finite time. Finally, two numerical examples are provided to illustrate the effectiveness of the theoretical results.

This article proposes using the extended Kalman filter (EKF) for recurrent neural network (RNN) training and fault estimation within a parabolic-trough solar plant. The initial step involves employing an RNN to model the system. Given the challenge of fault discernibility in the collectors, parallel EKFs are employed to reconstruct the parameters of the faults. The parameters are used independently to estimate the system output, and the type of fault is isolated based on the estimation errors using another feedforward neural network. To evaluate the effectiveness of the methodology, simulations are conducted on a loop of the ACUREX plant with irradiances from sunny and cloudy days. The results reveal a fault classification accuracy of approximately 90% and a fault reconstruction error below 3%, with even better accuracies in the cloudy dataset than in the sunny dataset.

Early and highly accurate detection of rapidly damaging deadly disease like Acute Lymphoblastic Leukemia (ALL) is essential for providing appropriate treatment to save valuable lives. Recent development in deep learning, particularly transfer learning, is gaining a preferred trend of research in medical image processing because of their admirable performance, even with small datasets. It inspires us to develop a novel deep learning-based leukemia detection system in which an efficient and lightweight MobileNetV2 is used in conjunction with ShuffleNet to boost discrimination ability and enhance the receptive field via convolution layer succession. More importantly, the suggested weight factor and an optimal threshold value (which is experimentally selected) is responsible for maintaining a healthy balance between computational efficiency and classification performance. Hence, the benefits of inverted residual bottleneck structure, depthwise separable convolution, tunable hyperparameters, pointwise group convolution, and channel shuffling are integrated to improve the feature discrimination ability and make the proposed system faster and more accurate. The experimental results convey that the proposed framework outperforms others with the best detection performances. It achieves superior performance to its competitors with the best accuracy (99.07%), precision(98.00%), sensitivity (100%), specificity (98.31%), and F1 score (0.9899) in ALLIDB1 dataset. Similarly, it outperforms others with 98.46% accuracy, 98.46% precision, 98.46% specificity, 98.46% sensitivity, and 0.9846 F1 Score in ALLIDB2 dataset.

This paper investigates the optimal fixed-time tracking control problem for a class of nonstrict-feedback large-scale nonlinear systems with prescribed performance. In the process of optimal control design, the new critic and actor neural network updating laws are proposed by adopting the fixed-time technique and the simplified reinforcement learning algorithm, which both guarantee the simplified optimal control algorithm and accelerate the convergence rate. Furthermore, the prescribed performance method is contemplated simultaneously, which ensures tracking errors can converge within the prescribed performance bounds in fixed time. The minimum parameter method is utilized to reduce the number of parameters designed in the adaptive laws for large-scale systems. Meanwhile, the proposed control strategy can guarantee that all closed-loop signals are bounded within a fixed time interval. Finally, simulation examples are provided to validate the effectiveness of the proposed control strategy.

For tolerant containment control of multi-agent systems, considering the challenges in modeling and the impact of actuator faults on system security and reliability, a finite index dynamic event-triggered policy iteration algorithm is proposed. This algorithm only requires input and output data, without relying on system models, and simultaneously considers the faults and energy consumption issues to improve the system reliability and save energy consumption. The conditions are provided to demonstrate the convergence and optimality of the algorithm, including a convergence speed, that is, the number of iterations required for convergence is finite. For the convenience of practical implementation, an actor-critic structure is adopted and an actor network weight tuning law with actuator fault factors is designed to more accurately approximate the control protocol in policy iteration algorithm. In addition, an event-triggered mechanism is employed in saving computational resources. Finally, simulation results verify the efficiency of the designed algorithm.

As a typically complex mechanical system, robotic manipulator has the characteristics of complex structure, parameter uncertainty and vulnerability to external interference. From the perspective of second-order servo constraints, this paper proposes a robust constraint following control algorithm, which can meet the control requirements of robotic manipulator system under system uncertainty, which is described by fuzzy set theory. It also guarantees the deterministic performance of uniform boundedness (UB) and uniform ultimate boundedness (UUB). The system performance index function based on fuzzy information is established, and by minimizing the system performance index function, the optimization design problem of the controller gain parameter is solved. Numerical simulations and experimental results verify the efficacy of the robust controller and control parameter optimization method.

Hand-held robotic instruments enhance precision in microsurgery by mitigating physiological tremor in real time. Current tremor filtering algorithms in these instruments often employ nonlinear phase prefilters to isolate the tremor signal. However, these filters introduce phase distortion in the filtered tremor, compromising accuracy. Although improved variants of recursive singular spectrum analysis (RSSA) have addressed the issue of phase distortion, they still face challenges such as reduced generalization performance, large sample delays, and longer computational times. To address these issues, we integrate an accurate and fast random vector functional link (RVFL) with RSSA, referred to as RSSA-RVFL. The proposed approach consists of two main steps: estimation using RSSA and prediction with RVFL. Additionally, we introduce two moving window variants of RSSA-RVFL for real-time implementation. These variants significantly reduce computational costs while delivering the same performance. Experimental results on real tremor data show that our proposed approach achieves an average accuracy of 79.03%, surpassing the benchmark of 70.40%, with a nine-sample delay.

In this paper, the state estimation problem is investigated for a general class of nonlinear networked systems subject to both external disturbances and stochastic deception attacks. In the presence of deception attacks, a novel hybrid stubborn extended state observer (ESO) is established to estimate the states and total disturbances, simultaneously. In addition, the event-triggered mechanism (ETM) is introduced utilizing the estimation errors to relieve the burden of the transmission networks. Then, an inter-trigger output predictor is adopted based on the estimation state of the hybrid stubborn ESO to predict the information between two consecutive triggering moments. To overcome the disadvantage of the outliers induced by the deception attack, the saturation nonlinearity is adopted as the gain function of the hybrid stubborn ESO. Sufficient conditions are established to ensure that the estimation error dynamics is locally mean-square bounded in a domain of attraction, and then an iterative linear matrix inequality (ILMI) approach is employed to design the desired hybrid stubborn ESO. Moreover, the Zeno behavior can be avoided when the designed ETM is utilized. Some numerical simulations are conducted to demonstrate the validity of the proposed methodology.