In order to further improve the vibration energy harvesting efficiency of piezoelectric energy harvester under low frequency environmental excitation, this paper, based on the traditional magnetic tri-stable piezoelectric energy collector model, proposes a tri-stable piezoelectric energy harvester (TPEH+DEM) model with two elastic amplifiers which are installed between the U-shaped frame and the base and between the fixed end of the piezoelectric cantilever beam and the U-shaped frame respectively. Based on Hamilton principle, the motion equation of electromechanical coupling of TPEH+DEM system is established, and the analytical solutions of displacement, output voltage and power of the system are obtained by harmonic balance method. The effects of the mass of elastic amplifier, spring stiffness, magnet spacing and load resistance on the dynamic characteristics of energy harvesting of TPEH+DEM system are analyzed. The result shows that there are two peaks in the response output power of TPEH+DEM system in the operating frequency range. By adjusting the mass and stiffness of the elastic amplifier reasonably, the system can move into the inter-well motion under low external excitation intensity, and produce high output power. Compared with the traditional model which only has an elastic amplifier on the base of piezoelectric energy harvester, TPEH+DEM model has better energy harvesting performance under low frequency and low intensity external excitation.

Objective of this study is to develop a novel, effective, and robust Sliding Mode Control (SMC) method for quadcopters (also called quadrotors) based on Adaptive Neuro-Fuzzy Inference System (ANFIS) for the purposes of enhancing trajectory tracking performance and realizing safe and reliable flight. In the paper, the ANFIS was combined with SMC technology to propose a scheme of adaptive robust controller, which is composed of three sub-controllers, x position controller, y position controller, and z position (altitude) controller. The proposed method can realize position tracking control of quadcopters in the presence of external disturbances. With the help of ANFIS, an adjustable gain rather than a fixed gain was established for the SMC controller, the optimal output could be attained based on a set of rules, and the position control gain was updated by ANFIS, enabling the SMC to adapt to environmental changes. Through modelling, simulation and comparison, experimental data verified that the proposed ANFIS-SMC controller outperformed conventional SMC controller in terms of convergence speed, robustness, accuracy, and stability with a maximum mean error of 0.125 meters in trajectory tracking. Research findings of this paper could contribute to the development of robust and responsive control strategies for Unmanned aerial vehicles (UAVs) trajectory tracking by providing valuable insights into the design of more effective and efficient control systems for UAVs, particularly in the context of dynamic environmental conditions.

Nowadays more and more devices and appliances are operated with electricity, thus the electrical crisis is increasing exponentially day by day. In order to avoid the occurrence of electricity crisis, various power generation resources are used at the utility side to enhance the power generation to meet the consumers’ demand for electricity. Hence, a suitable control scheme has to be implemented at the microgrid to reduce the electrical fluctuation, power loss and manage the power quality. The Adaptive Proportional Integral Voltage Controller (APIVC) and hysteresis current controller (HCC) are integrated to enhance the quality of power generated. The electrical fluctuation is reduced by the proposed efficient hybrid parallel source controller model for DC Microgrid. The proposed model exerts decentralized control, which is an advanced droop control where communication is not required. The outer voltage control loop and inner current control loop provide faster control to maintain the grid voltage constant. The grid voltage is set as the reference value and the actual value is sensed to generate error value, which sets the reference value of current. The error signal is processed to provide switching signals for the converters. The performance analysis and simulation results show that the proposed mechanism performed better than the conventional methods such as Hysteresis Band Current Controller (HBCC) with Pulse Width Modulation (PWM) and Proportional Integral Voltage Controller (PIVC) with Hysteresis Current Controller (HCC), in terms of the electrical fluctuation, power loss and manage the power quality in the microgrid.

Due to simple structure, easy maintenance and low cost, induction motors (IMs) are widely applied in various industries, accounting for 60-80% alternating current (AC) motors used in industry. However, the efficiency of IMs is very low, and even small improvement can result in significant energy saving. For instance, 1% efficiency increase saves billions of kilowatt hours. Therefore, this paper aimed to improve the efficiency of IMs, thus reducing energy consumption and greenhouse gas emissions. For an IM with 7.5kW rated power and IE3 energy efficiency, the efficiency is improved by making various changes. Sequential quadratic algorithm and fmincon function are proposed to change the rotor slot and teeth structures, realizing nearly 91% motor efficiency, which is a significant improvement over the original efficiency. It is worth noting that improving the efficiency of IMs saves a lot of energy, especially in cases where IMs account for a large proportion of AC motors.

Creating a fair replenishment strategy is one of the most significant instruments in the inventory management for automotive spare parts. It is also crucial to controlling the enterprise's inventory level. This study considers the significance of retailers' demand forecasting at the conclusion of the sales period to build a lateral transfer inventory optimization scheme with high scientific rigor, aiming to ensure the correctness and logic of the replenishment strategy. To provide a more scientific direction for the inventory management of an automotive spare parts company, this research constructs an upgraded particle swarm optimization (PSO)-backpropagation (BP) neural network prediction model, and a lateral transfer inventory optimization method based on demand forecasting. Finally, 26 retailers of Company B in Central China's Hunan Province were taken as examples to confirm the model's efficacy. The outcomes demonstrate an improvement in the lateral transfer's applicability in Company B.

Hydrogen production by wind and solar hybrid power generation is an important means to solve the strong randomness and high volatility of wind and solar power generation. In this paper, the permanent magnet direct-drive wind turbine, photovoltaic power generation unit, battery pack, and electrolyzer are assembled in the AC bus, and the mathematical model of the wind-solar hydrogen storage coupled power generation system and the simulation model in PSCAD/EMTDC are established. An energy coordination control strategy is designed. After simulation, the proposed control strategy can effectively reduce the rate of curtailment of wind and solar power, and stabilize the fluctuation of wind and solar power generation. It verifies that the established model is correct and the control strategy is effective and feasible.

For a class of system with nonlinear hysteresis, this paper presents an adaptive hybrid controller based on the hybrid backstepping-sliding mode, and describes the controller analytically by the LuGre model. Both backstepping and the sliding mode techniques are based on the Lyapunov theory. Drawing on this common point, the authors developed a new controller combining the two control techniques with a recursive design. The design aims to achieve two effects: assuring the stability of the closed loop system, and improving the continuous performance of the tracking position trajectory. The performance of the proposed hybrid controller was verified by implementing the identified Piezo model. The results show that our controller can track the system output desirably with the reference trajectory.

The overhead crane is a typical underactuated system with complicated dynamics and strong couplings. It is widely employed to transport heavy cargoes in many industrial fields. Due to the complexity of working environments, however, cranes often encounter various unfavorable factors, which may degrade the transportation efficiency. To enhance control performance and anti-disturbance ability, this paper proposes an active disturbance rejection control approach based on differential flatness for double pendulum cranes with variable rope lengths. The proposed approach can position the trolley accurately, regulate rope length, and suppress the swing angles of the payload and the hook simultaneously. During the controller design, flat outputs were constructed based on differential flatness technique to deal with system couplings, and the results prove that double pendulum crane system is differentially flat. After that, model uncertainties and external disturbances were estimated by the designed extended state observer. On this basis, a controller was developed based on the feedback control technique. Finally, a series of simulations were carried out to show that the control scheme is effective and robust.

The rising power demand has forced power systems all over the world to operate very close to their stability limits. When power systems are overloaded, faulty, or in lack of reactive power, voltage collapses would ensue. The capacity of a power system to keep the voltage of every bus constant under disturbances is called voltage stability. This dynamic phenomenon hinges on the load features. It is commonly known that flexible AC transmission systems (FACTS) can improve voltage stability. This paper puts forward a load flow model with the unified power flow controller (UPFC), and relies on the model to investigate the voltage stability of a power system through continuation power flow (CPF) method. The validity of the model was verified through a simulation, using the power system analysis toolbox (PSAT) in MATLAB/Simulink environment.

This study designs a reconfigurable multi-cloud storage server architecture for dynamic and secure data sharing has been designed, improves the security of unstructured data using cryptographic index-based data slicing (CIBDS), and reduces the malicious insider through data encryption using a third data encryption algorithm (3DEA). Focusing on multi-cloud storage server (MCSS) and data life cycle which includes three stages (i.e., data input, transition and utilization), the authors determined the efficiency of reconfigurable data file slicing, standard format, privacy and trustworthiness of the customers, in contrast to existing methods. Every part of a data file was encrypted using 3DEA, and Rivest Shamir Adleman (RSA) was employed to produce the private key to secure the unstructured data. The results show that the proposed framework effectively searches the data files in MCSS based on tags, such as input file names and private keys. The performance of the framework was measured by the security level, uploading/downloading latency time between our method and conventional methods, under different data sizes in (MB). Overall, our method reduces the malicious insider to 0.23% using 3DEA and RSA, during data encryption in the existing USDS-MC, shortens the uploading/downloading latency time (s) by 10% and 12%, compared to USDS-MC, and enhances the unstructured data security by 12% in comparison with that method. In this way, the authors managed to improve the self-protection of reconfigurable and secure unstructured data files in huge cloud infrastructure. This research optimizes the data security and privacy of encryption, decryption and cryptography technologies, and helps with the online process and its security maintenance during cloud storage.

This paper mainly explores the system identification and control of an automatic car pedal pressing system. Specifically, the system identification was achieved using an artificial neural network, with the help of MATLAB’s System Identification Toolbox. The proportional-integral-derivative (PID) controller and fuzzy logic controller were designed, and normalized with membership functions. These functions were scaled with a gain as a scaling factor. The controller gains were tuned by a metaheuristic algorithm named particle swarm optimization (PSO). On this basis, the two controllers were compared with a number of performance indices, including integral squared error (ISE), integral absolute error (IAE), integral time absolute error (ITAE), and mean squared error (MSE). The car pedal pressing performance was measured at different speed levels for each controller.