Intelligent Optimal Control Research Articles

Research on Low-Voltage Ride-Through and Intelligent Optimization Control of Wind Turbines Based on Hybrid Power Prediction Models

This paper proposes a dual-loop back-to-back converter coordination control scheme with a DC-side voltage as the primary control target, along with a CROW unloading control strategy for low voltage ride-through (LVRT) capability enhancement. The feasibility and effectiveness of the proposed system topology and control strategy are verified through MATLAB/Simulink simulations. Furthermore, a hybrid short-term wind power prediction model based on data-driven and deep learning techniques (CEEMDAN-CNN-Transformer-XGBoost) is introduced in the wind turbine control system. The coordination control strategy seamlessly integrates wind power prediction, pitch angle adjustment, and the control system, embodying a predictive-driven intelligent optimization control approach. This method significantly improves prediction accuracy and stability, theoretically reduces unnecessary pitch angle adjustments, lowers mechanical stress, and enhances system adaptability in complex operating conditions. The research findings provide a valuable theoretical foundation and technical reference for the intelligent and efficient operation of wind power generation systems.

Research on intelligent optimal allocation control strategy for stability and braking of heavy tyre blowout vehicle

The traditional control strategy mainly compensates and corrects the driving state of heavy vehicles with a tyre blowout, and rarely considers the need for emergency braking. It is also difficult to effectively coordinate the problems of lateral yaw, slip and tailspin, and longitudinal braking performance degradation. For this, an intelligent optimization allocation control strategy combining stability control and braking control is innovatively designed in this article. The joint simulation model for heavy tyre blowout vehicles is built based on the Dugoff tyre blowout model and the TruckSim vehicle model. A linear quadratic form regulator stable control based on the particle swarm optimization algorithm optimization is used to dynamic equilibrium of the additional yaw moment generated by the vehicle tyre blowout. A logic threshold braking control based on adaptive adjustment is adopted to achieve an ideal braking effect for the vehicle. In order to simultaneously consider the stability and braking performance of vehicles with blowout tyre, a braking force distribution strategy and a fuzzy optimization allocation algorithm are proposed to achieve coordination of lateral and longitudinal control strategies. The results indicate that intelligent optimized allocation control can effectively correct the deviation trajectory of a heavy tyre blowout vehicle, enabling it to achieve rapid braking while maintaining stability.

Intelligent Control Framework for Improving Energy System Stability Through Deep Learning-Based Modal Optimization Scheme

Ensuring the stability of power systems is essential to promote energy sustainability. The integrated operation of these systems is critical in sustaining modern societies and economies, responding to the increasing demand for electricity and curbing environmental consequences. This study focuses on the optimization of energy system stability through the coordination of power system stabilizers (PSSs) and power oscillation dampers (PODs) in a single-machine infinite bus energy grid configuration that has flexible AC alternating current transmission system (FACTS) devices. Intelligent control strategies using PSS and POD techniques are suggested to increase power system stability and generate supplementary control signals for both the generator excitation system and FACTS device switching control. An intelligent optimal modal control framework equipped with deep learning methods is introduced to control the generator excitation system and thyristor-controlled series capacitor (TCSC). By optimally choosing the weighting matrix Q and implementing close-loop pole shifting, an optimal modal control approach is formulated. To harness its adaptive potential in fine-tuning controller parameters, an auxiliary deep learning-based optimization algorithm with actor–critic architecture is implemented. This comprehensive technique provides a promising path to effectively reduce electromechanical oscillations, thereby enhancing voltage regulation and transient stability in power systems.

Intelligent optimal control model of selection pressure for rapid culture of aerobic granular sludge based on machine learning and simulated annealing algorithm

Aerobic Granular Sludge (AGS) has advantages over Activated sludge (AS) but faces challenges with long granulation periods. In this study, a novel grey-box model is devised to optimize the cultivation of AGS to shorten the formation time. This model is based on an existing white-box model. The modeling process starts with the application of four sensitivity analysis methods to assess the 12 model metrics selected. Subsequently, 12 prediction models were constructed by combining the six Machine learning (ML) algorithms and integrated algorithms, with the best performance selected (R2 = 0.98). Finally, an AGS selection pressure planning model was designed in conjunction with a simulated annealing (SA) algorithm to guide AGS training. The results demonstrate that AGS formation could be achieved within four days under the model’s optimal control. Therefore, the establishment of this model provides a new technique for the cultivation of AGS.

Data-driven multi-objective intelligent optimal control of municipal solid waste incineration process

The municipal solid waste incineration (MSWI) process has become the primary technology for municipal solid waste (MSW) treatment worldwide due to its advantages of harmlessness, reduction, and resource recovery. However, the automatic combustion control (ACC) system developed in developed countries are often not effectively applicable to other countries, particularly China, due to regional differences in MSW composition. To achieve intelligent development of MSWI plants, it is crucial to develop intelligent optimal control technology tailored to national conditions to reduce comprehensive pollutant emission concentration (CPEC) and improve combustion efficiency (CE). This article proposes a data-driven multi-objective intelligent optimal control strategy. Firstly, based on the analysis of the whole process, the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm is used to establish a MSWI whole process model, incorporating serial controlled objects and parallel pollutant indicators, to support a multi-objective optimization model. Then, leveraging the ACC system and expert experience, a multi-input multi-output loop controller is established using the single neuron adaptive PID (SNA-PID) algorithm to achieve stable control of key controlled variables. Next, a mutation crossover strategy and termination condition are integrated with the multi-objective particle swarm optimization (MOPSO) algorithm to solve the multi-objective optimization model, and domain expert rules are applied to determine the optimal setpoints for key controlled variables in the Pareto front, aiming to reduce CPEC and improve CE. Finally, the effectiveness of the proposed intelligent optimal control strategy is validated using actual data. The experimental results demonstrate that this strategy not only maintains the stability of key controlled variables but also decreases CPEC by 1.82% and increases CE by 2.38%, laying a foundation for further research into intelligent optimization control of the MSWI process. Further, the software system based on the proposed strategy is developed and validated on a hardware-in-loop simulation platform.

Intelligent optimal control of furnace temperature for the municipal solid waste incineration process using multi-loop controller and particle swarm optimization

Furnace temperature (FT) is a key variable in municipal solid waste incineration (MSWI) processes, influenced by many manipulated variables and directly impacting pollutant concentrations in exhaust gas. Domain experts cannot achieve the optimal FT setpoint value under manual control, leading to abnormal pollutant emission concentrations. To address this, we propose an intelligent optimal control framework for FT aiming to minimize pollutant emission concentration. First, the FT controlled object model is established using the Tikhonov regularization-least regression decision tree (TR-LRDT) algorithm. Then, based on the experience of domain experts, a multi-loop controller is developed using an improved single neuron adaptive PID (ISNA-PID) algorithm to stabilize FT. Next, after establishing NOx and CO2 indicator models, the particle swarm optimization (PSO) algorithm is employed to determine the FT setpoint value in terms of minimum pollutant emission concentration. Finally, the FT intelligent optimal control framework is verified. Experimental results indicate that the optimal FT setpoint value can reduce NOx and CO2 emission concentrations by 19.93 % and 6.99 %, respectively.

Development of data-knowledge-driven predictive model and multi-objective optimization for intelligent optimal control of aluminum electrolysis process

Operational optimization of the Hall-Héroult cell is essential for achieving high efficiency and cost-effectiveness in the aluminum electrolysis process. Due to the complicated mechanism and variable working conditions, manual operational decision-making is extensively used in practice. They challenge the reliable and optimal operation of aluminum electrolysis process. In this paper, we develop a data-knowledge-driven decision-making support system (DMSS) to achieve operational optimization for the aluminum electrolysis process. DMSS consists of a prediction model, a multi-objective optimizer, and a knowledge-guided decision-making module. Specifically, we propose a working-conditions-based attention with the exogeneous inputs auto-regressive neural network (WCA-NARX) to construct a data-driven heat balance indicator (HBI) prediction model, where the working condition-related variables serve as covariates to enhance predictability. In addition, the designed structure of introducing working condition information through an attention mechanism can decouple covariates from operational variables and autoregressive variables, facilitating subsequent operational optimization. Then, a novel knowledge-assigned reference vector evolutionary algorithm (KRVEA) is designed to solve the multi-objective optimization problem of the aluminum electrolysis process, in which Pareto front solutions can be solved in the preferred region. Finally, we utilize the knowledge base that stores historical optimization cases to make decisions regarding the selection of a practical-requirement-based control scheme from the Pareto set. Real-world industrial experiments demonstrate that DMSS can effectively enhance control performance and achieve superior results compared to other competitive methods. The source code is available at https://github.com/wjiecsu/WCA-NARX.

Intelligent optimal control of nonlinear diabetic population dynamics system using a genetic algorithm

Diabetes is a chronic disease affecting millions of people worldwide. Several studies have been carried out to control the diabetes problem, involving both linear and non-linear models. However, the complexity of linear models makes it impossible to describe the diabetic population dynamic in depth. To capture more detail about this dynamic, non-linear terms were introduced into the mathematical models, resulting in more complicated models strongly consistent with reality (capable of re-producing observable data). The most commonly used methods for control estimation are Pantryagain’s maximum principle and Gumel’s numerical method. However, these methods lead to a costly strategy regarding material and human resources; in addition, diabetologists cannot use the formulas implemented by the proposed controls. In this paper, the authors propose a straightforward and well-performing strategy based on non-linear models and genetic algorithms (GA) that consists of three steps: 1) discretization of the considered non-linear model using classical numerical methods (trapezoidal rule and Euler–Cauchy algorithm); 2) estimation of the optimal control, in several points, based on GA with appropriate fitness function and suitable genetic operators (mutation, crossover, and selection); 3) construction of the optimal control using an interpolation model (splines). The results show that the use of the GA for non-linear models was successfully solved, resulting in a control approach that shows a significant decrease in the number of diabetes cases and diabetics with complications. Remarkably, this result is achieved using less than 70% of available resources.

Control and Intelligent Optimization of a Photovoltaic (PV) Inverter System: A Review

PV power generation is developing fast in both centralized and distributed forms under the background of constructing a new power system with high penetration of renewable sources. However, the control performance and stability of the PV system is seriously affected by the interaction between PV internal control loops and the external power grid. The impact of the PV system on the reliability, stability, and power quality of power systems has restricted them to further participate in power supplies with a large capacity. Traditional control methods have become ineffective at dealing with these problems as the PV system becomes increasingly complex and nonlinear. Intelligent control as a more advanced technology has been integrated into the PV system to improve system control performance and stability. However, intelligent control for the PV system is still in the early stages due to the extensive calculation and intricate implementation of intelligent algorithms. Further investigations should be carried out to effectively combine intelligent control with the PV system to constitute an intelligent PV power system with multiple functions, high stability, and high-performance. This paper provides a systematic classification and detailed introduction of various intelligent optimization methods in a PV inverter system based on the traditional structure and typical control. The future trends and research topics are given to provide a reference for the intelligent optimization control in the PV system.

Intelligent monitoring and optimal control of HVAC system and its cloud-edge implementation

In this work, the solution of the intelligent monitoring and optimal control of a real-world Heating, Ventilation, and Air Conditioning (HVAC) system is presented with its cloud-edge implementation. The main contribution and novelty of this work are three-fold: (1) an improved just-in-time-learning aided canonical correlation analysis (JITL-CCA) based health monitoring method is proposed; (2) a hybrid particle swarm-based optimization control method to improve the energy efficiency of HVAC systems is proposed; (3) a cloud-edge platform is developed for its implementation. Particularly, this platform allows online verifying and implementing the monitoring and optimal control methods of real HVAC systems via Internet, which is significantly different from the simulation-based and the test rig-based verification ways. Such a verification is available at: www.acain.cn.

A Proposed Controller for Pitch Angle of Wind Turbine

Wind turbines are complicated non-linear systems with certain random disruptions. The pitch control system is a commonly employed method for regulating the electricity generated by a wind turbine. Many researchers have observed developments in the pitch control field during the last few decades. Traditional PID controllers have the drawback of being slow or imprecise when wind and pitch angles suddenly change. These drawbacks can be solved with artificial intelligent algorithms. However, the algorithms' design and implementation are highly complex. A new pitch-regulated variable-speed control strategy for wind turbines to address their nonlinear properties is presented. To manage the pitch system's control mechanisms with disturbances, this research evolved a mathematical model that illustrates HAWT's pitch angle control system and applied a proposed Simple Optimal Intelligent PID Controller (SOI-PID). Under various operating conditions, the proposed SOI-PID controller was tested with the Traditional PID, Fuzzy Logic Controller (FLC), and Fuzzy-Adaptive-PID controller. For system simulation, the MATLAB/Simulink software was used. According to simulation results, compared to PID, FLC, and Fuzzy-Adaptive-PID controllers, the proposed SOI-PID controller responds faster and has a better rise and settling time. Other benefits of the SOI-PID controller are its simplicity of implementation and design, distinguishing it from other intelligent algorithms.

Mitigating vulnerability of a multimodal public transit system for sustainable megacities: a real-time operational control method

The multimodal public transit system (MPTS) has been recognized as a primary mobility support system for sustainable megacities. However, it is often vulnerable to various service disruptions, and the most severe circumstance is the interdependent cascading failures interacting among urban rail transit, bus transit, and road transit networks. The vulnerability of an MPTS against this severe failure, associated with extreme performances, limits the building of future resilient megacities. In this paper, a real-time operational control method based on the system emergency capability (SEC) is developed to block the dynamic unfolding paths of this severe failure considering network topology characteristics and dynamic evolution characteristics. This method is immediately available for real-time emergency control, while previous studies on qualitative optimization strategies cannot. Remarkably, a three-stage association design process is conducted to explore the most efficient SEC loading strategy, involved with multiple intertwined influential factors, including the target loading stations, loading time-step intervals, and loading interval length and loading strength. Finally, a case simulation is undertaken to indicate the adaptability of the proposed method. This work can provide critical insights into real-world emergency resource allocation and an underlying simulator with search direction knowledge for future intelligent algorithm-based optimal control.

Dynamic Modelling and Intelligent Hybrid Optimal Controller of Hybrid Manipulator for Vibration Suppression and Tracking Control

Dynamic Modelling and Intelligent Hybrid Optimal Controller of Hybrid Manipulator for Vibration Suppression and Tracking Control

Enhancing Energy Efficiency by Improving Internet of Things Devices Security in Intelligent Buildings via Niche Genetic Algorithm-Based Control Technology

The security measures of IoT devices used in intelligent buildings are one of the ways by which energy efficiency can be accomplished. IoT devices are very important for data collecting and monitoring in intelligent buildings, but a lack of security could result in errors in energy consumption decisions that result in energy waste. To ensure the success of the control systems used for energy optimization, it is necessary to address the security of IoT devices in order to avoid illegal access, data manipulation, and disruptions. This work proposes a research idea and scheme for energy-saving optimization of intelligent buildings by assuring the security of IoT devices used in intelligent buildings. First of all, we defined several parameters that are related to IoT devices’ security, energy consumption, and occupant comfort in the intelligent building environment. Secondly, we collected data for each of these parameters by utilizing IoT devices such as actuators, sensors, and other control systems. The niche genetic algorithm (NGA) refers to a particular class of genetic algorithms that is used to tackle problems involving many optimization objectives. We focused on optimizing both energy consumption and occupants’ comfort; therefore, we used an NGA for the preprocessed data with the goal of evaluating the data for the purpose of ensuring the comfort of occupants and protection of the security of IoT devices, which eventually leads to energy optimization. Finally, the results of the proposed approach are analyzed and carefully compared with earlier work, demonstrating that our proposed approach is significantly more effective and energy-optimized than earlier approaches. The results show that the total power consumption of the intelligent building system after using our proposed model is generally reduced by more than 18% compared with that before optimization, which shows that the intelligent building system-adaptive optimization control model can effectively optimize the operating parameters of the energy-saving system and achieve the security of IoT devices.

Retracted: Intelligent Optimal Control of Sewage Treatment Based on Multiobjective Evolutionary Algorithm

Retracted: Intelligent Optimal Control of Sewage Treatment Based on Multiobjective Evolutionary Algorithm

Multi-source information fusion-based dynamic model for online prediction of rate of penetration (ROP) in drilling process

Rate of penetration (ROP) accurate prediction can effectively reduce drilling cycle time and operation cost. However, the complexity and variability of drilling process is not conducive to accurate ROP modeling. The existing ROP models focus on static prediction considering drilling data, which cannot effectively solve the ROP mutation problem caused by complex formations. Some ROP online prediction studies have great real-time capture capability, but without considering the formation lithology adequately, and can be limited in practical application. In this paper, a multi-source information fusion-based dynamic model for online prediction of ROP in drilling process is proposed, which consists of three stages (drilling data pre-processing, optimized ROP modeling, and ROP model updating). In the first stage, three drilling parameters including weight on bit, rotational speed, and torque are filtered by using the outlier removing and wavelet filtering techniques. In the second stage, a novel ROP modeling method, named as hybrid bat algorithm optimized - restricted Boltzmann machine - back propagation neural network (HBAO-RBM-BPNN) is proposed to build the ROP prediction model. In the last stage, formation drillability variation and time interval are used as update conditions, and the established ROP model is updated via a moving window strategy to realize high accuracy online prediction of ROP. Multi-source (formation and drilling) information fusion is considered in this key stage. Finally, the proposed method and seven ROP prediction methods (two offline and five online) are compared through the use of industrial data from a drilling site in Dandong area, Northeast China. The comparison results verify the effectiveness of the proposed method, which lays a foundation for intelligent optimization control of complex geological drilling process.

The digitization work of cement plant in China

This article is to introduce the digitization work of smart cement plant in China. To save power and promote the product quality, delicacy management in variety ways of digital transformation is the best tool for the new built, or existing cement plants. New technologies, such as special integrated robots, online equipment, information plant form, cement process digital applicant (SAAS), Artificial Intelligence (AI) and Advanced Process Control (APC) are implemented to approve the traditional ways of operation.This paper introduces the application of intelligent factory technology in China, and focuses on the intelligent optimization control system. The system has self-learning and global optimization functions, with strong adaptability and robustness. In combination with quality prediction, kiln condition identification and quantitative identification of fire watching TV video images, the kiln system is feed-forward controlled to solve the control problem of long time delay, large inertia and strong coupling of the burning system. The system gives the global optimal recommended value of the control target through calculation, with which to ensure the best control and operation of the system.With the application of comprehensive intelligent construction, the cement plant will usually save 2–5 % of energy and 20 % staff simplification, and make the management mode more concise and efficient. Meanwhile, the cement groups are also developing the production information plat form, by merging the Enterprise Resource Planning (ERP) data, sales business to lower down the total cost. This paper tries to introduce the digital efforts that are carried out in the smart cement plants and shows the methodology of implementation.

Optimal Intelligent Vehicle Control System for Emergency Vehicle Using Visible Light Communication VANET

Abstract: Traffic congestion is one of the significant problems in every metropolitan city. Traffic congestion occurs, when large numbers of vehicles are all together and are not able to move or move slowly, it is also known as a traffic jam. The main aim of the proposed OIVC-VLC VANET system is to improve the data transmission rate to control traffic in high density loads for emergency vehicles. Traffic congestion leads to wasting of time, road accidents, delays of trips, and reduces regional economic health and fuel consumption. Moreover, the most critical concern of traffic congestion is a delay of emergency vehicles like ambulance and police vehicles, and firefighting trucks leading to an increase the human death and loss their essential things. In addition, this paper focuses to reduce the traffic congestion and traveling time of emergency vehicles. To overcome traffic congestion issues, an Optimal Intelligent Vehicle Control System for the emergency vehicle by intelligent traffic clearance (OIVC-VLC-VANET) is proposed. Firstly an improved whale optimization (IWO) algorithm is submitted for grouping the vehicle nodes based on their behaviors. Secondly, the differential search algorithm is used to select the next forwarding node using multiple constraints received from vehicles. Thirdly, the dragonfly algorithm is submitted for avoiding extra time due to the control packets exchange process. The results show that the proposed OIVC-VLC-VANET system can perform very efficiently in terms of quality constraints than existing systems.

Energy Management Strategies for Hybrid Loaders: Classification, Comparison and Prospect

As one of the effective and crucial ways to achieve the energy saving and emission reduction of loaders, hybrid technology has attracted the attention of many scholars and manufacturers. Selecting an appropriate energy management strategy (EMS) is essential to reduce fuel consumption and emissions for hybrid loaders (HLs). In this paper, firstly, the application status of drivetrain configuration of HLs is presented. Then, the working condition characteristics of loaders are analyzed. On the basis of this, the configurations of HLs are classified, and the features and research status of each configuration are described. Next, taking the energy consumption optimization of HLs as the object, the implementation principle and research progress of the proposed rule strategy and optimization strategy are compared and analyzed, and the differences of existing EMSs and future development challenges are summarized. Finally, combining the advantages of intelligent control and optimal control, the future prospective development direction of EMSs for HLs is considered. The conclusions of the paper can be identified in two points: firstly, although the existing EMSs can effectively optimize the energy consumption of HLs, the dependence of the strategy on the mechanism model and the vehicle parameters can reduce the applicability of the strategy to heterogeneous HLs and the robustness to a complex working condition. Secondly, combining the advantages of intelligent control and optimal control, designing an intelligent EMS not depending on the vehicle analytical model will provide a new method for solving the energy consumption optimization problem of HL.

Data‐based learning control for optimization of nonlinear systems

Data‐based learning control for optimization of nonlinear systems