Control Programming Research Articles

Programmable Encryption Patterns from Phosphorescent Polymers Via Laser Controlling Water‐Quenching Effect

AbstractOrganic room temperature phosphorescence (RTP) from polymers holds significant promise for information encryption and anti‐counterfeiting applications. However, conventional patterning techniques limit the development of advanced RTP encryption, highlighting the need for an efficient method that achieves encryption processes quickly and conveniently. In this study, the UV laser marking machine is applied to create traceless phosphorescent patterns by leveraging the photothermal effect of the laser and the water‐absorbent quenching RTP property of polyacrylamide. A series of biaryl derivative phosphors with low π‐electron numbers are designed to limit UV absorption to below 355 nm, thereby preventing polymer sintering by the UV laser (355 nm). Additionally, two phosphorescent polymers, PB2COOH (phosphorescence lifetime τP = 2.34 s; phosphorescence quantum yield ΦP = 13.59%) and PBN (τP = 1.46 s; ΦP = 20.33%), possessing relatively excellent phosphorescent properties, are obtained. The controllable program and non‐contact processing of the marking machine enable the rapid realization of complex patterns, such as the Twelve Chinese Zodiac Signs, effectively overcoming the constraints of traditional techniques. Based on this laser technology, applications such as dual‐locking QR (quick response) codes, misleading double QR codes, labeling, and other advanced encryption methods are developed.

Intelligent Numerical Control Programming System Based on Knowledge Graph

With the wide application of computer-aided manufacturing (CAM) software, manufacturing enterprises have accumulated a wealth of numerical control (NC) programming data, providing valuable knowledge resources for new products’ development. Efficiently acquiring and reusing existing NC knowledge is essential for enhancing programming efficiency, improving product quality, and shortening manufacturing cycles. This study proposes an intelligent NC programming method based on knowledge graph. Firstly, the relevant knowledge in the NC programming domain is analyzed, and CAM knowledge elements are constructed to reduce the granularity of knowledge. Then, the ontology layer and data layer are constructed to achieve the development of the knowledge graph. Next, knowledge reasoning is performed on the knowledge graph through entity alignment and semantic rule-based reasoning. Furthermore, to address the issues of low reliability, limited applicability and need for frequent manual modifications in NC programming templates guided by the CAM knowledge graph, a CAM knowledge graph completion method based on neighborhood aggregation and semantic enhancement is proposed. Finally, an intelligent NC programming system based on knowledge graph is developed, and comparative experiments with mainstream algorithms on public datasets for few-shot knowledge graph completion are conducted, validating the effectiveness of the proposed method by experimenting with the key components of marine diesel engines.

Adaptive Dynamic Programming for Robust Event-Driven Tracking Control of Nonlinear Systems With Asymmetric Input Constraints.

This article considers the robust dynamic event-driven tracking control problem of nonlinear systems having mismatched disturbances and asymmetric input constraints. Initially, to tackle the asymmetric constraints, a novel nonquadratic value function is constructed for the original system. This makes the asymmetrically constrained tracking control problem transformed into an unconstrained optimal regulation problem. Then, a dynamic event-driven mechanism is proposed. Meanwhile, the event-driven Hamilton-Jacobi-Bellman equation (ED-HJBE) is developed for the optimal regulation problem in order to acquire the optimal control with distinctly decreased computational burden. To solve the ED-HJBE, a single critic neural network (CNN) is designed in the adaptive dynamic programming framework. Meanwhile, the gradient descent method is employed to update the CNN's weights. After that, both the weight estimation error and the tracking error are proved to be uniformly ultimately bounded via Lyapunov's direct method. Finally, simulations of the spring-mass-damper system and the pendulum plant are separately utilized to validate the established theoretical claims.

Colorectal cancer research priorities in Uganda: perspectives from local key experts and stakeholders.

The incidence of colorectal cancer (CRC) is increasing in Uganda but there is limited local research to guide policy and programming for CRC prevention and control. A stakeholder engagement workshop took place in Kampala on 19March 2024 to identify challenges and opportunities for CRC prevention and control in Uganda. A total of 30 stakeholders with expertise in CRC primary and secondary prevention, diagnosis, treatment, palliative care as well as cancer survivors participated in the workshop. Key challenges for primary prevention included low knowledge/awareness of CRC among the general population and health workers, and rising prevalence of CRC related risk factors. Limited CRC screening, diagnostic facilities and specialists were identified as barriers to diagnosis. Treatment related challenges included limited accessibility to surgical services and drugs, late-stage presentation leading to poor treatment response, treatment abandonment and drug related toxicity. Lack of universal health coverage policies, limited community-based cancer awareness programs, and lack of national cancer registries were cited as policy and economics challenges. Opportunities to address these challenges were discussed. Our findings highlight areas for further research and prioritization to address Uganda's growing CRC burden and may be applicable to other low-resource settings.

A-199 Use of R programming for monthly quality control (QC) data review in a university hospital laboratory

Abstract Background Our laboratory information system (LIS) provides only cursory capability for monthly review of quality control measurements. We streamlined the major portion of this process using customized programming in R, as described below. Methods Month-long QC data were obtained using the LIS archive’s data retrieval program (QLIK). Data were read and processed using our R program (QC-R.R), for each analyte and QC level, to produce both Levey-Jennings (LJ) plots (for evaluation of possible temporal trends in QC data) as well as cumulative distribution (CD) plots (to evaluate whether QC data are normally distributed). Plots were sent to graphics files whereby they could be reviewed sequentially by a single forward-arrow click. Results Our laboratory utilizes two QC measurements for each of 157 analytes. Thus, monthly review involves evaluation of 314 datasets for QC results. With the R program outputs, results can be reviewed visually in rapid fashion, to determine whether there exist discrepancies between fixed limits and current data. An example CD plot is shown in Figure. In this example, QC data (points) were, appropriately, normally distributed (theoretical curved line based on data mean and SD). The currently operative fixed mean±2SD limits (vertical dashed lines) were, however, disparate from those of data (vertical solid lines). This set of results was thus marked for updating of mean and SD, with reference to a master list of results of calculations generated by the QC-R.R program. Time for review of monthly QC results was dramatically reduced, from more than 12 man-hours to less than 4 man-hours per month. Conclusions Limitations of our LIS for monthly QC review were bypassed using R-based programming to automate a significant fraction of the process. This led to a significant savings in labor attending to this important task. The use of CD plots is helpful for rapid data interpretation.

Dynamic Programming, Neuro-Dynamic Programming, Rollout Method and Model Predictive Control to Optimal Control of a Fermentation Process

This work presents the use of dynamic programming (DP), neuro-dynamic programming (NDP), rollout algorithm (RA) and model predictive control (MPC) for optimal control of batch cultivation of the yeast Kluyveromyces marxianus var. lactis MC5. DP is a widespread method for solving problems related to optimization and optimal process control. To reduce the “curse of dimensionality”, NDP has been implemented as an alternative. In NDP, a neural network is used to solve the dimensionality problem. A simpler NDP method, called RA, is used to approximate the optimal cost through the cost of a relatively good suboptimal policy, called the baseline policy. RA is a suboptimal method for deterministic and stochastic problems that can be solved by DP. In this paper we also present off-line MPC technique for tracking of constrained fermentation systems and it overcomes the problem by off-line optimizations prior to implementation. MPC is used to provide perturbation feedback and it is developed theoretical on base a controller as an illustration how we can avoid disturbances in the process optimisation. The developed control algorithm-combined NDP and MPC ensures maximum biomass production at the end of the process and feedback during disturbances and process stability and shows that robust stability can be ensured.

Adaptive neural network dynamic surface optimal saturation control for single‐phase grid‐connected photovoltaic systems

AbstractAn adaptive neural network (NN) based optimal saturation control scheme is investigated for single‐phase grid‐connected photovoltaic (PV) systems by incorporating dynamic surface control (DSC) and adaptive dynamic programming (ADP) based on the backstepping control design framework. For each backstepping step, a critic‐actor architecture is constructed via reinforcement learning (RL), and the PV system is optimized according to the cost function in the architecture. Due to the nonlinearity, it is difficult to solve the Hamilton–Jacobi–Bellman (HJB) equation. The neural networks (NNs) are employed to approximate the solution of the HJB equation such that the optimal virtual control and the actual controller are obtained. By considering control input symmetric saturation nonlinearity link, constraints on pulse width modulation (PWM) are ensured. On this basis, the combination of backstepping control design and dynamic surface technique is used to overcome the shortcomings of “differential explosion” and simplify calculations. Based on the Lyapunov method, the stability analysis proves that all signals of the closed‐loop PV systems are semiglobally uniformly ultimately bounded (SGUUB). Simulation experiments and comparative results are given to verify the efficacy of the studied control strategy.

Adaptive dynamic programming for containment control with robustness analysis to iterative error: A global Nash equilibrium solution

Global Nash equilibrium is an optimal solution for each player in a graphical game. This paper proposes an iterative adaptive dynamic programming-based algorithm to solve the global Nash equilibrium solution for optimal containment control problem with robustness analysis to the iterative error. The containment control problem is transferred into the graphical game formulation. Sufficient conditions are given to decouple the Hamilton–Jacobi equations, which guarantee the solvability of the global Nash equilibrium solution. The iterative algorithm is designed to obtain the solution without any knowledge of system dynamics. Conditions of iterative error for global stability are given with rigorous proof. Compared with existing works, the design procedures of control gain and coupling strength are separated, which avoids trivial cases in the design procedure. The robustness analysis exactly quantifies the effect of the iterative error caused by various sources in engineering practice. The theoretical results are validated by two numerical examples with marginally stable and unstable dynamics of the leader.

Differentiable programming for gradient-based control and optimization in physical systems

This paper presents an exploration of the application of control theory, particularly utilizing a gradient-based algorithm, to automate and optimize the operation of photovoltaic panels and refrigeration systems in warehouse environments. The study emphasizes achieving coordination between energy generation and consumption, specifically harnessing surplus solar energy for efficient refrigeration. The complex interplay between fluctuating solar irradiance, thermal dynamics of the warehouse, and refrigeration needs underscores the significance of control theory in designing algorithms to dynamically adjust PV panel output and refrigeration system operation. The paper discusses foundational control theory principles, proposes a tailored framework for warehouse operations, and highlights the potential for sustainable energy practices. This paper explores the use of data-driven approaches based on NeuralODEs vs classical ones using physics equations.

Energy-efficient battery thermal management strategy for range extended electric vehicles based on model predictive control and dynamic programming

Battery thermal management system is important for improving the efficiency, lifespan, and safety of new energy vehicle batteries. An energy-efficient model predictive control algorithm based on dynamic programming solver is proposed for battery thermal management strategy. A control-oriented nonlinear battery thermal model is established for predicting temperature changes in thermal management system. The dynamic programming algorithm is utilized to solve the nonlinear optimal control problem, which includes state boundary calculation and optimal control sequence backward calculation. The effects of the weighting parameters and other hyperparameters in the proposed control strategy on the control performance are investigated to achieve the optimal trade-off between thermal equilibrium temperature control, computational efficiency, and energy savings. The simulation results show that the proposed strategy can achieve considerable energy savings in high and low environment temperatures, as well as under standard and real driving conditions. Compared to the on-off based strategy and proportional control-based strategy, the proposed strategy saves up to 8.94 % and 8.33 % of actuator energy at an environment temperature of −20 °C, and up to 77.83 % and 99.83 % of actuator energy at an environment temperature of 40 °C, utilizing the energy consumption of the proposed strategy as a baseline.

Development of Nutrient Control System in Plant Factory based on Growth Phases of Romaine Lettuce (Lactuca sativa var. longifolia)

Nutrient is one of the conditions controlled in a plant factory. Nutrient control must be based on the plant growth phase, but previous research has shown shortcomings in this area. This study aims to develop a nutrient control system in a plant factory based on the plant growth phase, specifically using Romaine lettuce. The research method used is the experimental method, with research stages including system design (controller modification, programming, and TDS sensor calibration testing) and system performance testing. The TDS sensor calibration test results showed an R² of 0.9999, indicating that the TDS sensor readings are very close to those of the TDS meter. The system performance test results showed that the developed system works well in controlling nutrient levels based on the growth phase of Romaine lettuce. The planting process lasted 30 days, from October 13, 2023, to November 11, 2023, with a total of 2,697 data points recorded by Antares. Nutrient requirements were adjusted per the growth phase of Romaine lettuce, with set points in the first to fourth weeks being 500 PPM, 600 PPM, 700 PPM, and 800 PPM, respectively. The average nutrient levels recorded in the first week were 518 PPM, 623 PPM in the second week, 721 PPM in the third week, and 820 PPM in the fourth week. The TDS sensor accuracy test results showed an R² of 0.9998, indicating that the TDS sensor performed well in controlling nutrient levels during the system testing, with an average TDS sensor reading error of only 1.17%. During the planting process, the total amount of nutrients added for each of nutrients A and B was 210.10 ml, with the total operating time of the control components being 314.34 seconds for each peristaltic pump of nutrients A and B.

Fluidic control programming for 3D magnetic soft metamaterials with reconfigurable mechanical behaviors

Fluidic control programming for 3D magnetic soft metamaterials with reconfigurable mechanical behaviors

Synthesis of surface-engineered SrFe2O4 for efficient catalytic partial oxidation of methane

In this study, a series of platinum (Pt)-doped strontium iron oxide (SrFe2O4) catalysts with varying particle sizes were synthesized through the four different catalysis synthesis methods such as solution combustion synthesis (SCS), co-precipitation (Co-PPT), oxalic acid assisted sol-gel (OXA) and, hydrothermal (HT). The objective was to investigate the impact of particle size on the catalytic activity and long-term stability of these four catalysts. The XRD and Raman results confirmed the formation of the SrFe2O4 perovskite structure. HRTEM, SEM, and other characterizations revealed a clear correlation between the synthesis conditions and the resulting particle sizes. The highest%CH4 conversion was around 95 % for the catalyst prepared through Solution combustion synthesis and the catalyst was found to be thermally stable up to. 100 h at 800 °C with a negligible variation of conversion while maintaining the H2/CO ratio at 2.0. To gain insight into catalytic activity, stability, and selectivity of catalysts we have performed Temperature-programmed surface reaction (TPSR) at a controlled temperature ramping program. This study also includes the study of coke deposition on the spent catalysts through different characterization techniques. Furthermore, we have performed a kinetic study to find the initial rate of the reaction and the activation energy of the Pt-doped SrFe2O4 catalyst and it has been found that activation energy was 35 KJ/mol for the catalyst Pt/SrFe2O4 synthesis through the solution combustion method.

Sequential Convex Programming for Nonlinear Optimal Control in UAV Trajectory Planning

In this paper, an algorithm is proposed to solve the non-convex optimization problem using sequential convex programming. An approximation method was used to solve the collision avoidance constraint. An iterative approach was utilized to estimate the non-convex constraints, replacing them with their linear approximations. Through the simulation, we can see that this method allows for quadcopters to take off from a given initial position and fly to the desired final position within a specified flight time. It is guaranteed that the quadcopters will not collide with each other in different scenarios.

Event-Triggered Adaptive Dynamic Programming for Hierarchical Sliding-Mode Surface-Based Optimal Control of Switched Nonlinear Systems

Event-Triggered Adaptive Dynamic Programming for Hierarchical Sliding-Mode Surface-Based Optimal Control of Switched Nonlinear Systems

Utilization of Programmable Logic Controller (PLC) with System Control Lights On Large Buildings

Enhancement need for energy electricity in buildings or large buildings​​ will increase waste energy and improve the expenditure of funds. For That need given solution How method saves electricity with cheap and efficient equipment.​ The PLC ( Programmable Logic Controller ) program is A tool operating electronics​ digitally for​ replace Suite a row of relays in a conventional process control system. To carry out its function then the PLC has sensor elements, controllers, and output devices so that capable do task control sequential, control sophisticated, and control supervision. Research results This shows that the application of PLC in buildings or big buildings​ will increase high efficiency​ by utilizing: a) elements of the system control light involving appropriate sensors, controllers (servers), and actuators; b) put sensor devices with appropriate; c) count wide room with lighting appropriate; and d) combine usage halogen lamp as replacement ray sun and TL lamps as lighting normal.

Adaptive dynamic programming for the optimal liver regeneration control

Every living organism interacts with an environment and uses that interaction for an improvement of its own adaptability, and, as a result, one’s survival and overall development. The process of evolution shows us that different species change methods of interaction with an environment with passage of time, which leads to natural selection and survival of the most adaptive ones. This learning, which based on actions, or reinforcement learning may embrace the idea of optimal behavior occurring in environmental systems. We describe mathematical formulas for reinforcement learning and the practical integration method also known as adaptive dynamic programming. That gives us the overall concept of controllers for artificial biological systems that both learn and show the optimal behavior. This paper reviews the formulation of the upper optimality problem, for which the optimal regulation strategy is guaranteed to be better or equivalent to objective regulation rules that can be observed in natural biological systems. In cases of optimal reinforcement learning algorithms the learning process itself moves from the analysis of the item take on system dynamics to the much higher level. The object of interest now is not the details of the system dynamics, but the quantity efficiency index, which clearly represents how optimally the control system works. Such scheme of reinforcement learning is learning technique of optimal behavior in order to monitor the response to non-optimal control strategies. The purpose of this article is to show the possibility of using of reinforcement learning methods, the adaptive dynamic programming (ADP) in particular, to control biological systems using feedback. This article shows the on-line methods for solving the problem of searching the upper optimality estimate with adaptive dynamic programming.

Adaptive fractional-order nonsingular terminal sliding mode control and sequential quadratic programming torque distribution for lateral stability of FWID-EVs with actuator constraints

This paper proposes a novel sliding mode control (SMC) algorithm for direct yaw moment control of four-wheel independent drive electric vehicles (FWID-EVs). The algorithm integrates adaptive law theory, fractional-order theory, and nonsingular terminal sliding mode reaching law theory to reduce chattering, handle uncertainty, and avoid singularities in the SMC system. A sequential quadratic programming (SQP) method is also proposed to optimize the yaw moment distribution under actuator constraints. The performance of the proposed algorithm is evaluated by a hardware-in-the-loop test with two driving maneuvers and compared with two existing SMC-based schemes together with the cases with the change of vehicle parameters and disturbances. The results demonstrate that the proposed algorithm can eliminate chattering and achieve the best lateral stability as compared with the existing schemes.

FABRICATION OF ORDERS BY SCARA ROBOT WITH RFID TECHNOLOGY

In today’s world of automation, recognizing and processing parcels in a particular area of zone is a crucial task. One such task is pick-and place operation, which is well- known for its challenges. The main theme of this operation is to sort products into different areas based on their representative fields. To address this issue, we have a SCARA robot that is known for its liner momentum motion of 4 degrees of freedom. This robot is well-suited for pick-and- place operations and can perform operations in two axes. Moreover, it is cost-effective and efficient. The SCARA robot is connected to the Arduino Mega, which is run by servo motors and controllers. The fabrication of an Arduino based Parcel diverter system involves the creation of a system that uses an Arduino Mega microcontroller to sort and divert parcels based on their destination. The system is designed to take input from Radio frequency identification device that detects the parcels and direct them to their appropriate destination using a series of mechanical diverters. The fabrication process includes the design and assembly of the mechanical components, the programming of the micro controller, and the integration of the sensors. The resulting system provides a reliable and efficient way to sort and direct parcels in a variety of industrial and logistical applications. Key Words: Conveyor, Arduino Mega, IR/Thermal sensors, stepper motor, micro step driver SCARA robot, RFID system, DC Vacuum pump and DC Motor.

Knowledge-shareable adaptive deep dynamic programming for hierarchical generation control of distributed high-percentage renewable energy systems

The increased proportion of distributed renewable energy sources connected to systems leads to frequency regulation difficulty, such as uncoordinated algorithms, inconsistent optimization and control objectives, and long training time. This work proposes a knowledge-shareable adaptive deep dynamic programming (KSADDP) to improve the active control accuracy and generation economy of the proposed hierarchical generation control framework, which contains two-layer control structures. In the first layer, droop control is employed to optimize the outputs of controllable generators, follow the frequency changes, reduce frequency deviation, and improve the operation economy of battery energy storage systems. In the second layer, a KSADDP is proposed to mitigate local optimum and accelerate the hybrid cooperative structure optimization by sharing the parameters of neural networks. These two layers synergistically optimize control performance and reduce generation costs. The simulation results of a three-high-percentage renewable energy system show that: with two layers of cooperation and sharing training information, the KSADDP outperforms the comparison algorithms in seven evaluation indices and reduces the generation cost by 4.85%–7.41 % compared to the comparison algorithms. The proposed KSADDP cooperates the time scales between multiple layers, cooperates the objectives between multiple layers, increases the parameter-sharing process, and reduces training time.