Intelligent Control Strategy Research Articles

Lithology identification of coal-bearing strata based on data-driven dual-channel relevance networks in coal mine roadway drilling process

The accurate prediction of coal seam thickness and the identification of the lithology of coal-bearing strata represent a crucial aspect of the intelligent perception of the stratigraphic environment. These technologies represent a fundamental aspect of intelligent mining operations with respect to coal. Accordingly, this paper concentrates on the investigation of lithology identification in coal-bearing strata through the utilization of data-driven dual-channel relevance networks in the context of coal mine roadway drilling operations. In consideration of the impact of coal seam thickness fluctuations on lithology identification, the region in question is initially delineated into two distinct categories: those comprising relatively stable coal seams and those comprising unstable coal seams. This is achieved through the application of coal seam thickness prediction and stability evaluation techniques. Subsequently, in consideration of the varying challenges posed by the region's coal seams, which can be either relatively stable or unstable, distinct lithology identification schemes are employed in different regions. In regions where the coal seam is relatively stable, there is a significant demand for classification of soft and hard coal seams. Additionally, the non-coal seam samples belong to a limited number of classes, necessitating the use of the SMOTE-GBDT algorithm to classify the coal-bearing strata into soft coal seam, hard coal seam, and non-coal seam. In the case of an unstable coal seam, the ability to distinguish between different seams is of paramount importance for lithology identification purposes. To this end, a double-layer binary classification RVM approach is employed to categorize coal-bearing strata into weak interlayer, coal seam and mudstone layer. The proposed methodology entails the utilization of diverse techniques in accordance with the specific characteristics of varying coal seam stability regions, with the objective of meticulously delineating the targets of lithology identification. This approach facilitates the generation of a comprehensive understanding of the coal-bearing strata environment, which can subsequently inform the implementation of intelligent adaptive control strategies.

Application to novel smart techniques for decarbonization of commercial building heating and cooling through optimal energy management

The present article proposes a novel smart building energy system utilizing deep geothermal resources through naturally-driven borehole thermal energy storage interacting with the district heating network. It includes an intelligent control strategy for lowering operational costs, making better use of renewables, and avoiding CO2 emissions by eliminating heat pumps and cooling machines to address the heating and cooling demands of a commercial building in Uppsala, a city near Stockholm, Sweden. After comprehensively conducting techno-environmental and economic assessments, the system is fine-tuned using artificial neural networks (ANN) for optimization. The study aims to determine which ANN design and training procedure is the most efficient in terms of accuracy and computing speed. It also assesses well-known optimization algorithms using the TOPSIS decision-making technique to find the best trade-off among various indicators. According to the parametric results, deeper boreholes can collect more geothermal energy and reduce CO2 emissions. However, deep drilling becomes more expensive overall, suggesting the need for multi-objective optimization to balance costs and techno-environmental benefits. The results indicate that Levenberg-Marquardt algorithms offer the optimum trade-off between computation time and error minimization. From a TOPSIS perspective, while the dragonfly algorithm is not ideal for optimizing the suggested system, the non-dominated sorting genetic algorithm is the most efficient since it yields more ideal points rated below 100. The optimization yields a higher energy production of 120 kWh/m2, as well as a decreased levelized cost of energy of 57 $/MWh, a shorter payback period of two years, and a reduced CO2 index of 1.90 kg/MWh. The analysis reveals that despite the high investment costs of 382.50 USD/m2, the system is financially beneficial in the long run due to a short payback period of around eight years, which aligns with the goals of future smart energy systems: reduce pollution and increase cost-effectiveness.

Intelligent Control Strategy for Optimal Utilization of Energy in Smart Grid

Amidst the relentless surge in global energy demand, the optimal utilization of intelligent grid energy has emerged as a pivotal avenue for energy conservation, emission reduction, and sustainable development. The deployment of sophisticated control strategies within intelligent grids aims to elevate energy utilization rates and mitigate power losses. Drawing upon an extensive analysis of data, this study delves into the impact and significance of intelligent control strategies in the optimal utilization of smart grid energy. Our data analysis reveals that the implementation of intelligent control strategies enhances the energy utilization rate of smart grids by approximately 25%, significantly mitigating energy waste compared to traditional grids. Furthermore, power losses are effectively curbed and reduced by approximately 18%. This implies that, while fulfilling the same power demand, smart grids can reduce substantial energy losses and conserve vast resources for society. Furthermore, the intelligent control strategy boasts the capability to carry out real-time scheduling and control, tailored to the evolving energy demand and supply scenarios. This approach ensures the stability and reliability of power supply, subsequently elevating both the operational efficiency of the power system and the quality of power service delivered. Ultimately, the intelligent control strategy occupies a crucial position in optimizing the utilization of intelligent grid energy, serving as a catalyst for enhanced efficiency and sustainability within the global energy landscape. Our data analysis unequivocally demonstrates the remarkable impact achieved by sophisticated control strategies. Looking ahead, with the continuous advancement and refinement of technology, smart grids are poised to play an even more significant role in energy optimization, contributing meaningfully towards global energy conservation, emission reduction, and sustainable development.

Accurate prediction and intelligent control of COD and other parameters removal from pharmaceutical wastewater using electrocoagulation coupled with catalytic ozonation process.

In this study, we employed the response surface method (RSM) and the long short-term memory (LSTM) model to optimize operational parameters and predict chemical oxygen demand (COD) removal in the electrocoagulation-catalytic ozonation process (ECOP) for pharmaceutical wastewater treatment. Through RSM simulation, we quantified the effects of reaction time, ozone dose, current density, and catalyst packed rate on COD removal. Then, the optimal conditions for achieving a COD removal efficiency exceeding 50% were identified. After evaluating ECOP performance under optimized conditions, LSTM predicted COD removal (56.4%), close to real results (54.6%) with a 0.2% error. LSTM outperformed RSM in predictive capacity for COD removal. In response to the initial COD concentration and effluent discharge standards, intelligent adjustment of operating parameters becomes feasible, facilitating precise control of the ECOP performance based on this LSTM model. This intelligent control strategy holds promise for enhancing the efficiency of ECOP in real pharmaceutical wastewater treatment scenarios. PRACTITIONER POINTS: This study utilized the response surface method (RSM) and the long short-term memory (LSTM) model for pharmaceutical wastewater treatment optimization. LSTM predicted COD removal (56.4%) closely matched experimental results (54.6%), with a minimal error of 0.2%. LSTM demonstrated superior predictive capacity, enabling intelligent parameter adjustments for enhanced process control. Intelligent control strategy based on LSTM holds promise for improving electrocoagulation-catalytic ozonation process efficiency in pharmaceutical wastewater treatment.

A Deep-Learning-Based Data-Management Scheme for Intelligent Control of Wastewater Treatment Processes Under Resource-Constrained IoT Systems

A Deep-Learning-Based Data-Management Scheme for Intelligent Control of Wastewater Treatment Processes Under Resource-Constrained IoT Systems

Comparison between an Adaptive Gain Scheduling Control Strategy and a Fuzzy Multimodel Intelligent Control Applied to the Speed Control of Non-Holonomic Robots

The main objective of this work is to address problems related to the speed control of mobile robots with non-holonomic constraints and differential traction—specifically, robots for football games in the VSS (Very Small Size) category. To achieve this objective, an implementation and comparison is carried out between two control strategies: an adaptive control strategy by gain scheduling and a fuzzy multimodel intelligent control strategy. The mathematical models of the wheel motors for each operating range are approximated by a first-order system since data acquisition is performed using the step response. Tuning of the proportional and integral gains of the local controllers is carried out using the root locus technique in discrete time. For each mathematical model obtained for an operating range, a local controller is tuned. Finally, with the local controllers in hand, the implementation of and comparison between the gain scheduling adaptive control strategy and the fuzzy multimodel intelligent control strategy are carried out, in which the control strategies are programmed into the low-level code of a non-holonomic robot with a differential drive to verify the performance of the speed tracking dynamics imposed on the wheel motors to improve robot navigation during a robot football match.

Control of D-Statcom in Appropriation Framework Involving ANFIS for Further Developing Power Quality

This paper introduces an intelligent controller designed for the optimal operation of a D-STATCOM (Distribution Static Compensator) utilizing the ANFIS. In primary focus is improve the power quality within a 3 phase, four wire system. And distribution of fortifying and overall consistency. Then the ANFIS controller in-corporates fuzzy logic’s, devices are applied in “adaptive network framework”. To use the ANFIS method are established and using the MATLAB Simulink software. D-STATCOM, featuring a VSI and a DC side capacitance, excels in reactive power compensation and harmonic mitigation, even when subjected to dynamic load conditions and advice of the DC link capacitor V, the proposed ANFIS controller generates a compensation current reference. This reference adequately addresses the reactive power requirements imposed by nonlinear loads, ensuring the maintenance of DC voltage regulation. Through comprehensive simulation results, it is evident that the ANFIS controller surpasses in providing superior harmonic compensation and robust DC link voltage regulation under varying operating conditions. This research contributes to the advancement of intelligent control strategies for enhancing power quality and reliability in distribution systems.

Intelligent Control Strategy for Robotic Manta via CPG and Deep Reinforcement Learning

The robotic manta has attracted significant interest for its exceptional maneuverability, swimming efficiency, and stealthiness. However, achieving efficient autonomous swimming in complex underwater environments presents a significant challenge. To address this issue, this study integrates Deep Deterministic Policy Gradient (DDPG) with Central Pattern Generators (CPGs) and proposes a CPG-based DDPG control strategy. First, we designed a CPG control strategy that can more precisely mimic the swimming behavior of the manta. Then, we implemented the DDPG algorithm as a high-level controller that adaptively modifies the CPG’s control parameters based on the real-time state information of the robotic manta. This adjustment allows for the regulation of swimming modes to fulfill specific tasks. The proposed strategy underwent initial training and testing in a simulated environment before deployment on a robotic manta prototype for field trials. Both further simulation and experimental results validate the effectiveness and practicality of the proposed control strategy.

Comparative analysis of time series neural network methods for three-way catalyst modeling

Relative Oxygen Level of the Three-Way Catalyst is an important parameter that affects the conversion efficiency of pollutants. ROL is a time-varying hidden state variable that is difficult to directly observe in practice. Therefore, it is common to use a method of clearing oxygen storage to simplify control in vehicles. However, this method negates the positive effects of ROL on pollutant treatment. ROL can be indirectly observed through modeling methods. Chemical modeling methods involve extensive computational requirements that cannot meet the demands of practical control. In contrast, time-series neural networks offer computational speed advantages when dealing with similar problems. Therefore, the ROL observation models using both NARX and LSTM neural networks are developed and compared in this study. The results indicate that the NARX neural network exhibits higher precision with a smaller number of neurons and time steps. The LSTM neural network demonstrates greater stability when dealing with data error fluctuations. In practical applications, the ROL model can monitor the TWC operating status and assist in the development of intelligent pollutant aftertreatment control strategies.

Development of Intelligent Control Strategy for an Anesthesia System Based on Radial Basis Function Neural Network Like PID Controller

Development of Intelligent Control Strategy for an Anesthesia System Based on Radial Basis Function Neural Network Like PID Controller

Advances in dissolved oxygen prediction and control methods in aquaculture: a review

Abstract In the aquaculture industry, maintaining stable levels of dissolved oxygen (DO) is crucial for ensuring the health of aquatic organisms and enhancing farming efficiency. This article delves into the challenges faced in predicting and controlling DO levels, such as the need for real-time monitoring and response, the complexity of systems, and limitations in technology and resources. The paper comprehensively reviews various methods for DO prediction and control, including mechanistic modeling prediction, machine learning techniques, and both classical and intelligent control strategies. It analyzes their advantages, limitations, and applicability in aquaculture environments. Through this review and analysis, the article provides more comprehensive insights and guidance for future research directions in DO prediction and control in aquaculture.

Hierarchical intelligent energy-saving control strategy for fuel cell hybrid electric buses based on traffic flow predictions

Vehicles' perception capability of environmental situations has been enhanced in the connected environment. However, the utilization of abundant and diverse traffic information poses a challenge in the formulation of energy-efficient strategies for current connected vehicles. To address this challenge, a hierarchical intelligent energy-saving control strategy for fuel cell hybrid buses based on traffic flow prediction is proposed. Diverging from traditional approaches that rely on surrounding vehicles status information, a more macroscopic perspective is introduced by incorporating traffic flow prediction information into the formulation of energy-saving control strategies for the first time, which enhances the adaptability of strategies. At the upper layer, a multi-objective intelligent eco-driving control strategy is designed, encompassing driving safety, energy consumption costs, traffic efficiency, and ride comfort. At the lower layer, an intelligent energy management strategy is developed to reduce hydrogen consumption and maintain stable battery state of charge. Simultaneously, action variables serve as the information bridge between the upper and lower layer strategies, and comprehensive analyses and validations of strategic performance are conducted from various perspectives. The research findings indicate that the introduction of traffic flow information enhances the cognitive capabilities of the intelligent system towards the traffic environment with little impact on the convergence process. The model excels in energy efficiency, driving smoothness, and passenger comfort compared to the baseline model, while also having the opportunity to surpass the traffic efficiency of the IDM model. The developed energy management strategy demonstrates an energy-saving benefit of 92.07 % of the offline optimal benchmark, and closely approaches the theoretical optimal performance of MPC with a prediction horizon of 5s. Additionally, the proposed strategy demonstrates high computational efficiency and effectively mitigates the degradation of the vehicle lifespan, making it conducive to real-time online applications.

Electrical Engineering and Intelligent Control Strategies in Renewable Energy Integration: Enhancing Energy Sustainability

This paper discusses in detail the important role and application value of electrical engineering and intelligent control strategies in the integration of renewable energy, and proposes the viewpoints of improving the utilization rate of renewable energy and ensuring the stability of energy systems through electrical engineering and intelligent control strategies. At the same time, it also looks forward to the future development trend.

Research on the Control Strategy of the Power Shift System of a Cotton Picker Based on a Fuzzy Algorithm

The control strategy of a power shift system has a significant impact on the driving stability and comfort of cotton pickers. To prevent the cotton picker from stopping to shift and reduce the jerks while shifting, in this study, the power shift system of a four-speed cotton picker was taken as the research object, and the working principles of the power shift transmission of the cotton picker and the hydrostatic transmission were analyzed. Firstly, the intelligent fuzzy control strategy of the variable pump and dual variable motor displacement of the cotton picker power shift system was designed using a fuzzy algorithm, and two-input and three-output fuzzy controllers were constructed with the engine rotational speed and vehicle speed as the input parameters, and the variable pump displacement, front motor displacement, and rear motor displacement as the outputs. Secondly, the model of the whole vehicle travel drive system of the cotton picker was constructed using co-simulation of Amesim and Simulink. Finally, the influence of the fuzzy shift control strategy and conventional manual shift method on the speed and driving distance of the cotton picker was compared and analyzed. The analysis results show that the fuzzy algorithm-based control strategy of the power shift system of the cotton picker can ensure that the cotton picker travels stably according to the target speed, and effectively reduces the speed fluctuation and jerks while shifting. The results of the study are of great significance to realize the non-stop shifting of the cotton picker as well as improve the stability and smoothness of the shift while driving.

The Design of an Intelligent Aquarium Management System

The comprehensive intelligent aquarium management system designed and implemented in this paper meets the diverse needs of aquarium management. The system includes functions such as automatic water change, temperature control, automatic water replenishment, light adjustment, timed feeding, oxygen supply, manual control, data display, and remote monitoring, providing users with comprehensive and convenient management capabilities. Through intelligent control strategies and a WIFI module, the system achieves comprehensive monitoring and precise control of the aquarium environment, allowing users to remotely monitor and control via a smartphone application at any time. Strict experimental verification ensures the stability and reliability of the system, meeting the practical needs and application scenarios of aquarium farming.

Intelligent Control of Pre-Chamber Pressure Based on Working Condition Identification for the Coke Dry Quenching Process

The pre-chamber pressure is an important control parameter that affects the coke dry quenching process. It often fluctuates violently and is detrimental for the safe operation of the coke dry quenching process. This study proposes an intelligent control method for the pre-chamber pressure based on working condition identification for the coke dry quenching process to realize stable control of the pre-chamber pressure. First, by describing the coke dry quenching process and analyzing the factors affecting the pre-chamber pressure, an intelligent control strategy was developed. Then, the K-means clustering algorithm was used to identify the working conditions of pre-chamber, and the working conditions were divided into two categories: stable and fluctuating. For stable conditions, a fuzzy proportional-integral-derivative controller was designed to improve the pressure control accuracy. For fluctuating conditions, an expert controller was designed to rapidly adjust the pressure. Finally, experiments based on actual data were performed and the results showed that the proposed method can effectively improve the control accuracy of pressure under different conditions. This satisfies the requirements for a continuous coke dry quenching process.

Multi-Purpose Tracker Robot

Abstract: This paper introduces a multi-purpose tracking robot designed with Arduino, ultrasonic, infrared sensors, and motor drivers. The robot is capable of performing diverse tasks such as obstacle avoidance, line following, and object tracking. Its hardware comprises an Arduino Uno microcontroller, ultrasonic sensors for distance measurement, infrared sensors for line detection and obstacle avoidance, and motor drivers for controlling DC motors. The software, developed using Arduino IDE, includes algorithms for sensor data processing, decision-making logic, and motor control. By implementing PID control algorithms, the robot achieves enhanced stability and accuracy in navigation tasks like motor speed control and line following. Experimental results validate its effectiveness in various scenarios, showcasing its potential for advanced robotics and automation applications through intelligent sensor integration and control strategies.

Deep Q-Network Algorithm-Based Cyclic Air Braking Strategy for Heavy-Haul Trains

Cyclic air braking is a key element for ensuring safe train operation when running on a long and steep downhill railway section. In reality, the cyclic braking performance of a train is affected by its operating environment, speed and air-refilling time. Existing optimization algorithms have the problem of low learning efficiency. To solve this problem, an intelligent control method based on the deep Q-network (DQN) algorithm for heavy-haul trains running on long and steep downhill railway sections is proposed. Firstly, the environment of heavy-haul train operation is designed by considering the line characteristics, speed limits and constraints of the train pipe’s air-refilling time. Secondly, the control process of heavy-haul trains running on long and steep downhill sections is described as the reinforcement learning (RL) of a Markov decision process. By designing the critical elements of RL, a cyclic braking strategy for heavy-haul trains is established based on the reinforcement learning algorithm. Thirdly, the deep neural network and Q-learning are combined to design a neural network for approximating the action value function so that the algorithm can achieve the optimal action value function faster. Finally, simulation experiments are conducted on the actual track data pertaining to the Shuozhou–Huanghua line in China to compare the performance of the Q-learning algorithm against the DQN algorithm. Our findings revealed that the DQN-based intelligent control strategy decreased the air braking distance by 2.1% and enhanced the overall average speed by more than 7%. These experiments unequivocally demonstrate the efficacy and superiority of the DQN-based intelligent control strategy.

Enhancing Energy-Efficient Water Pumping with Intelligent Control: A Comparative Study of Advanced Fuzzy Logic and PID Controllers on Simulink/MATLAB

Water resources are crucial for social and economic development, human needs, and food production. However, with water scarcity on the rise, minimizing waste during pumping is vital. Using DC motors for pumping offers a solution for sustainable and efficient water management. This study explores intelligent control strategies to enhance energy efficiency in water pumping systems, comparing Advanced Fuzzy Logic and PID Controllers through simulation. The research aims to optimize the control approach for water pumping operations, improving energy efficiency and system performance. Simulation results indicate that both controllers effectively regulate water pumping operations. The Advanced Fuzzy Logic Controller offer better response to setpoints, with minimal overshoot 0,7%, fast settling time 0.052 Seconds, Rise Time 0.048 Seconds, Time Peak 0.05 Seconds and no steady-state error, Compared to PID Controller with Overshoot 10%, Settling Time 0.23 Seconds, Rise Time 0.048 Seconds, Time Peak 0.078 Second and no steady-state error Moreover, the Advanced Fuzzy Logic Controller performs overall better performance in terms of energy efficiency and robustness compared to the PID controller. The findings suggest that the energy efficiency of water pumping systems can be significantly increased by utilizing Advanced Fuzzy Logic Controller approaches, specifically the Advanced Fuzzy Logic Controller. This study contributes to developing control methods for sustainable water management systems with potential uses in agriculture, irrigation, and water distribution.

Research on Optimization of Intelligent Driving Vehicle Path Tracking Control Strategy Based on Backpropagation Neural Network

To enhance path tracking precision in intelligent vehicles, this study proposes a lateral–longitudinal control strategy optimized with a Backpropagation (BP) neural network. The strategy employs the BP neural network to dynamically adjust prediction and control time-domain parameters within an established Model Predictive Control (MPC) framework, effectively computing real-time front-wheel steering angles for lateral control. Simultaneously, it integrates an incremental Proportional–Integral–Derivative (PID) approach with a meticulously designed acceleration–deceleration strategy for accurate and stable longitudinal speed tracking. The strategy’s efficiency and superior performance are validated through a comprehensive CarSim(2020)/Simulink(2020b) simulation, demonstrating that the proposed controller adeptly modulates control parameters to adapt to various road adhesion coefficients and vehicle speeds. This adaptability significantly improves tracking and driving dynamics, thereby enhancing accuracy, safety, stability, and real-time responsiveness in the intelligent vehicle tracking control system.