Abstracts:Growing agricultural demands for the global population are unlocking the path to developing innovative solutions for efficient water management. Herein, an intelligent variable rate irrigation system (fuzzy-VRI) is proposed for decision-making to achieve optimized irrigation in various delimited zones. The proposed system automatically creates irrigation maps for a center pivot irrigation system for a variable rate application of water. Primary inputs are satellite imagery on remotely sensed soil moisture (SSM), soil-adjusted vegetation index (SAVI), canopy temperature (CT), and nitrogen content (NI). The system relates these inputs to set reference values for the rotation speed controllers and individual openings of each central pivot sprinkler valve. The results showed that the system can detect and characterize the spatial variability of the crop and further, the fuzzy logic solved the uncertainties of an irrigation system and defined a control model for high-precision irrigation. The proposed approach is validated through the comparison between the recommended irrigation and actual irrigation at two field sites, and the results showed that the developed approach gives an accurate estimation of irrigation with a reduction in the volume of irrigated water of up to 27% in some cases. Future research should implement the fuzzy-VRI real-time during field trials in order to quantify its effect on irrigation use, yield, and water use efficiency. Note to Practitioners—This work is motivated by the objective of managing irrigation more efficiently. It will be a site-specific irrigation management tool and we proposed a theoretical framework that aims an artificial intelligence approach to automatically create optimal control maps for a center pivot irrigation system. At the heart of this system will be the fuzzy logic, which will define the reference values for the rotation speed controllers and the individual opening of each center pivot sprinkler valve. Currently, there is a lack of these types of systems which ends up generating an increase in demand for more intelligent, automated, and accurate systems. The proposed system will be based on decision-making - whether to apply more or less water - and will use remote sensing data, therefore, the innovative irrigation system will efficiently describe the spatial variability of the crop. The results indicate that edaphoclimatic variables, when well combined with fuzzy logic, can resolve uncertainties and nonlinearities of an irrigation system and define a control model for high precision irrigation. However, it will not always be possible to reduce water consumption, but this technology has many uses to increase farm profitability.

Abstracts:This paper presents a comprehensive model of a DC/DC boost converter interfaced with all types of local loads. As known, the presence of constant power loads (CPLs) may cause stability-related issues. To mitigate such destructive effects, an active disturbance rejection control (ADRC) technique is employed in this paper to improve the boost converter performance from the stability point of view and to tackle the voltage tracking control problem during load variations. External disturbances on the controlled objects are estimated by an extended state observer (ESO), and subsequently compensated by a state error feedback (SEF) in the presence of a tracking differentiator (TD) in the feedforward direction of the control loop. The closed-loop stability of the tracking error system with the ESO is also proved using Lyapunov theory. To evaluate the system’s performance, the root locus method is utilized, investigating the impacts of each load type on the system stability (1 DOF uncertainty). The Kharitonov theorem alongside the zero exclusion condition (ZEC) is also applied to evaluate the system’s robust stability in case of multi-parameter uncertainties. Since the implementation of the proposed ADRC becomes challenging as the order of the system increases, a well-known model order reduction (MOR) method is introduced to lessen the computational complexity. Indeed, the full-order model (FOM) is replaced by the reduced-order model (ROM) here using the iterative rational Krylov algorithm (IRKA) method based on the moment matching concept and Krylov subspaces. A comparison between the proposed control method, the traditional PI controller, and optimal control approaches is also provided. The numerical results carried out in MATLAB/SIMULINK software confirm the effectiveness of the suggested compensator. Note to Practitioners—For the sake of clean energy and as a remedy for environmental issues such as global warming, renewable energy sources (RESs) have been widely used recently to prove that they are a well-fitted substitute for traditional fossil fuels. Besides, DC/DC converter-based distributed generations integrated with RESs can compensate for the excessive load demand that the grid for any reason is unable to provide. The reason for choosing DC systems is that available loads are mainly of DC type. However, instability of DC microgrids as a result of negative incremental effect of constant power loads must be considered a crucial challenge in designing such modern power systems. After solving this, the voltage regulation problem must be dealt with using a proper control technique considering the system structure. The designed control system must be able to tackle the available challenges and resolve the impacts of uncertainties and external disturbances. Uncertainties in RESs, uncertain loads, voltage tracking problem, probable faults in the system configuration, etc are considered challenges in the voltage control of DC microgrids that all tried to be resolved and dealt with in this study.

Abstracts:Non-ferrous metals, as important basic raw materials, are the strategic supports for national economic development. For non-ferrous metal smelting enterprises, raw material procurement is the focal and most important session. Due to the fluctuation of production volumes and the future changes in raw-material prices, the procurement cost of raw materials is high and with a high risk of shortage. In this paper, we propose a multi-period rolling robust procurement model considering price and demand uncertainties. In particular, we design a data-driven method to construct the budget-based uncertainty sets and derive the robust counterpart of the robust procurement model. Comparative experiments on the real data with classic and advanced procurement policies show that our proposed solution approach achieves the lowest cost under the premise of continuous supply of raw materials. Interestingly, we observe that limited capital and warehouse capacity can effectively restrain unreasonable behavior and thus not to cause big losses in uncertain environments. In addition, a relatively long planning horizon can be counterproductive. These valuable and actionable insights can well guide practical decision-making. Note to Practitioners—For the raw material procurement of non-ferrous metal smelter, this article proposes a multi-period rolling robust procurement model considering price and demand uncertainties. Taking account of the dynamic characteristics of raw-material prices and the seasonal characteristics of raw-material demands, a data-driven method to construct budget-based uncertainty sets is designed. In particular, we derive the solvable robust counterpart of the robust procurement model. The proposed approach can reduce costs ensuring the continuous supply of raw materials. Some interesting and actionable managerial insights are obtained that can well guide practical decision-making, and the proposed data-driven approach is realizable.

Abstracts:This paper investigates adaptive neural network filtering control for uncertain nonlinear systems with general model state and input quantization. The plants under consideration contain quantized states, quantized input, and unknown nonlinear system functions. A universal quantizer is established for both system states and control input. In the control design process, neural networks and the command filter are used to approximate the unknown nonlinear system functions and overcome the discontinuities of virtual control signals, respectively. A new command filtering-based control strategy is proposed using the backstepping design technique. It is testified that the proposed control approach can guarantee that the closed-loop signals are semi-global uniform ultimate boundedness. A simulation example is presented to further demonstrate our proposed scheme’s effectiveness. Note to Practitioners—This work is motivated by the quantized control problem for a class of nonlinear systems with state and input quantization. In modern control engineering applications, quantization plays a crucial role due to the prevalent use of digital processors that operate with finite precision arithmetic. It is valuable and inevitable to minimize information flow, reduce communication burden, and improve system security. However, quantization will introduce significant discontinuous characteristics and strong nonlinearity, which may decrease the system’s performance and even drive the closed-loop system to instability. This paper demonstrates how to use backstepping and adaptive control methods with command filter to complete controller design and deal with the quantization effects. Therefore, it provides a feasible approach for engineering applications.

Abstracts:This paper presents a system integration approach for a 6-DoF (Degree of Freedom) collaborative robot to operate a pipette for liquid dispensing. Its technical development is three-fold. First, we designed an end-effector for holding and triggering manual pipettes. Second, we took advantage of direct teaching to specify global labware poses and planned robotic motion based on them. Third, we leveraged hand-mounted cameras and visual classifiers to predict and correct positioning errors, which allowed precisely attaching pipettes and tips without calibration. Through experiments and analysis, we confirmed that the developed system, especially the planning and visual recognition methods, could help secure high-precision and flexible liquid dispensing. The developed system is suitable for low-frequency, high-repetition biochemical liquid dispensing tasks. We expect it to promote the deployment of collaborative robots for laboratory automation and thus improve the experimental efficiency without significantly customizing a laboratory environment. Note to Practitioners—The proposed system helps to automate low-frequency, high-repetition biochemical experiments using a vertical articulated robot with uncalibrated hand-mounted cameras and TCP (Tool Center Point). It can be quickly deployed in tight lab spaces or beside existing automation instruments for experiments. Implementing the system does not require high-quality cameras or precise manufacturing. The system relies on software, particularly vision compensation, and the flexibility provided by unfixed racks to successfully perform pipetting tasks. We particularly recommend using collaborative robots to implement the proposed system. Collaborative robots typically meet safety requirements and do not need to be enclosed in a cage. This allows them to be deployed in the same workspace as human researchers, enabling efficient lab experiments. Human researchers can prepare tips and microwell plates and place them in front of the robot with a certain degree of freedom, which is less burdensome than traditional automatic devices that require precise placement of tips and plates in specified positions. Practitioners can view an example of the robot working with a plant phenotyping system for screening chemicals in the supplementary video.

Abstracts:Deep neural networks (DNNs) are increasingly utilized in robotic tasks. However, resource-constrained mobile robots often do not have sufficient onboard computing resources or power reserves to run the most accurate and state-of-the-art DNNs. Cloud robotics has the benefit of enabling robots to offload DNNs to cloud servers, which is considered a promising technology to address the issue. However, comprehensive issues exist, including flexibility, convenience, offloading policy, and especially network robustness in its implementations and deployments. Although it is essential to promote cloud robotics to be practical, a cloud robotic system that addresses these issues comprehensively has never been proposed. Accordingly, in this work, we present RoboEC2, a novel cloud robotic system with dynamic network offloading implemented assisted by Amazon EC2. To realize the goal, we present a cloud-edge cooperation framework based on ROS and Amazon Web Services (AWS) and a network offloading approach with a dynamic splitting way. RoboEC2 is capable of executing its network offloading program in any conditions, including disconnected. We model the DNN offloading problem in RoboEC2 to a specific multi-objective optimization problem and address it by proposing the Spotlight Criteria Algorithm (SCA). RoboEC2 is flexible, convenient, and robust. It is the first cloud robotic system with no constraints on time, location, or computing power. Finally, We demonstrate RoboEC2 with analyses and experiments that it performs better in comprehensive metrics compared with the state-of-the-art approach. We open-source the system at https://github.com/RoboEC2/RoboEC2. Note to Practitioners—RoboEC2 is a work that combines cloud computing and robotics. As the deep learning models are becoming larger, robots are becoming more and more difficult to run the state-of-the-art models locally. It has become one of the major problems in robotics. RoboEC2 was proposed to address this problem. It enables more robotics researchers to equip their robots with the power of cloud computing. To be honest, it is very difficult for us to complete this work that is a robotic system with cloud computing. We need to address a lot of difficulties such as network, the cloud platform, algorithms, robot platforms, and conduct various robotic tasks. We have spent more than one year on this system and overcome countless difficulties to complete it. All of what we do is to make robotics developer easier strengthen their robots with cloud. Whether you are an autonomous driving engineer, robotic arm developer, SLAM researcher, mobile robotics researcher, or any other developer working on robotics applications based on ROS and deep learning models, you can use RoboEC2 to make them perform better. You don’t need to worry about networking, because RoboEC2 has solved it perfectly. You don’t need to worry about the serious algorithms in the system, because we provide easily used interact files for you to configure. You just need to tell RoboEC2 which metrics your robotics application needs to focus on. With RoboEC2, all the robotic researchers/developers are capable of enhancing their robotic applications with cloud computing in just a few simple steps and executing them in any network conditions. So, why not?