Search

The work reported in this paper aims at developing an algorithm for system operators to solve a robust security-constrained AC optimal power flow (R-SC-ACOPF) problem, which also optimizes the settings of discrete controllers including load tap changers (LTCs) and shunt capacitor banks (SCBs). An iterative algorithm involving a master problem and a number of sub-problems is proposed. The master problem selects the optimal discrete controllers’ settings by taking into account primal SC-ACOPF constraints based on the single-component worst contingencies identified by the sub-problems. Each sub-problem is a bi-level max-min problem used to find the current worst contingency. The overall objective is to minimize the total cost, including generation and load shedding cost, while satisfying all relevant constraints. The master problem and sub-problems allow representing LTC tap positions, SCB status, and contingencies using binary variables. Two case studies are presented as applications of this novel technique. [ABSTRACT FROM AUTHOR]

Pesticides are essential for removing plant pests and sustaining good yields on agricultural land. Excessive use has detrimental repercussions, such as the depletion of soil fertility and the proliferation of immune insect species, such as Nilaparvata lunges and Nezara viridula. Unmanned aerial vehicle (UAV) variable-rate spraying offers a precise and adaptable alternative strategy for overcoming these challenges. This study explores research trends in the application of semi-automatic approaches and land-specific platforms for precision spraying. The employment of an autonomous control system, together with a selection of hardware such as microcontrollers, sensors, pumps, and nozzles, yields the performance necessary to accomplish spraying precision, UAV performance efficacy, and flexibility in meeting plant pesticide requirements. This paper discusses the implications of ongoing and developing research. The comparison of hardware, control system approaches, and data acquisition from the parameters of each study is presented to facilitate future research. Future research is incentivized to continue the precision performance of the variable rate development by combining it with cropland mapping to determine the need for pesticides, although strict limits on the amount of spraying make it difficult to achieve the same, even though the quality is very beneficial.

The purpose of the paper is to propose a new implementation of the PID (proportional–integral–derivative) algorithm in digital hardware. The proposed structure is optimized for cost. It follows a serialized, rather than parallel, scheme. It uses only one arithmetic block, performing the multiply-and-add operation. The calculations are carried out in a sequentially cyclic manner. The proposed circuit operates on standard single-precision (32-bit) floating-point numbers. It implements an extended PID formula, containing a non-ideal derivative component, and weighting coefficients, which enable reducing the influence of setpoint changes in the proportional and derivative components. The circuit was implemented in a Cyclone V FPGA (Field-Programmable Gate Array) device from Intel, Santa Clara, CA, USA. The proper operation of the circuit was verified in a simulation. For the specific implementation, which is reported in the paper, the sampling period of 516 ns was obtained, which means that the proposed solution is comparable in terms of speed with other hardware implementations of the PID algorithm operating on single-precision floating-point numbers. However, the presented solution is much more efficient in terms of cost. It uses 1173 LUT (Look-up Table) blocks, 1026 registers, and 1 DSP (Digital Signal Processing) block, i.e., about 30% of logic resources required by comparable solutions. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Robotics is currently the most cutting-edge international science and technology, as well as a highvalue-added core technology. Robots are widely used in a variety of industrial fields, as a new direction in the development of robotics, and play an important role in solving the current employment problems in China. OBJECTIVES: This paper combines its research results, introduces the machine learning method in the robot control system, and establishes the employment index system in the robot working environment by combining the employment factors with the environmental relationship analysis. METHODS: This paper combines its research results, introduces the machine learning method in the robot control system, and establishes the employment index system in the robot working environment by combining the employment factors with the environmental relationship analysis. RESULTS: The study found that the willingness of university students to choose a job gradually increases as their education level rises; the lower the level of education, the weaker their willingness to look for a job; the higher the level of education the more sensitive they are to the quality of education and educational specialities, the higher their willingness to work. CONCLUSION: Based on the robot control system design the factors that have an impact on the environment in real economic activities (e.g., age, gender, occupation, education level, etc.) play a role in promoting the future application and development of robotics in China. [ABSTRACT FROM AUTHOR]

When adequately maintained, the mechanical system of rewinders and similar machines in paper industry may have a significantly longer service life than its electrical and control systems. Over the lifetime of the facility, a rewinder may experience several major maintenance cycles, and these generally include significant upgrades to the electrical drive and control system. These upgrades present an opportunity to modify the design of the control system to eliminate some of the sensors or to provide signal redundancy by using state estimation. During such major reconstruction of a paper-board rewinder, it was noticed that the shipping-roll radius and the web tension force can be accurately estimated from the measurements available within the drive's control system. This paper presents the implementation of the two estimators on a commercial production rewinder. Test results from the equipment in service show that the measured signals can be supplemented with the estimated ones to provide an alternative in the event of sensor malfunction or to enable trending of accuracy of calibration. Both applications have an entitlement to reduce the downtime of the rewinder, as the replacement of the faulted sensor can be scheduled for the next planned idle period of the machine. [ABSTRACT FROM AUTHOR]

While using multirotor UAVs for transport of suspended payloads, there is a need for stability along the desired path, in addition to avoidance of any excessive payload oscillations, and a good level of precision in maintaining the desired path of the vehicle. However, due to the nonlinear and underactuated nature of the system, in addition to the presence of mismatched uncertainties, the development of a control system for this application poses an interesting research problem. This paper proposes a control architecture for a multirotor slung load system by integrating a Multi-Surface Sliding Mode Control, aided by a Radial Basis Function Neural Network, with a Deep Q-Network Reinforcement Learning agent. The former will be used to ensure asymptotic tracking stability, while the latter will be used to suppress payload oscillations. First, we will present the dynamics of a multirotor slung load system, represented here as a quadrotor with a single pendulum load suspended from it. We will then propose a control method in which a multi-surface sliding mode controller, based on an adaptive RBF Neural Network for trajectory tracking of the quadrotor, works in tandem with a Deep Q-Network Reinforcement Learning agent whose reward function aims to suppress the oscillations of the single pendulum slung load. Simulation results demonstrate the effectiveness and potential of the proposed approach in achieving precise and reliable control of multirotor slung load systems. [ABSTRACT FROM AUTHOR]