CART-based wide-area damping controller for inter-area oscillations in bulk power system consisting of WAMS data.

The paper proposes a WADC approach using CART technique to dampen inter-area oscillations (IAOs) in bulk power systems. In this case, PMU data are filtered to estimate inter-area dynamics in which using pade approximation, a pole-zero IAO compensation block is designed. An online random decrement technique is also developed to identify the coherent groups and damping ratios to activate the WADC for oscillation damping. An offline process is provided to identify 200 critical IAO contingencies and tunes WADC gains using PSO for training CARTs via a set of 200 input inter-area signals and assigning output controlling gains pre-trained data and evaluating the CART estimations through online operation. The WADC approach is validated for oscillation damping on a 39-bus system and a realistic 561-generator Iranian grid. Simulations show 98 % accuracy in achieving sufficient damping ratios (>0.6) across various operating conditions.

Intuitionistic Fuzzy Biofeedback Control of Implanted Dual-Sensor Cardiac Pacemakers.

Cardiac pacemakers are used for handling bradycardia, which is a cardiac rhythm of usually less than 60 beats per minute. Therapeutic dual-sensor pacemakers aim to preserve or restore the normal electromechanical activity of the cardiac muscle. In this article, a novel intelligent controller has been developed for implanted dual-sensor cardiac pacemakers. The developed controller is mainly based on intuitionistic fuzzy logic (IFL). The main advantage of the developed IFL controller is its ability to merge the qualitative expert knowledge of cardiologists in the proposed design of controlled pacemakers. Additionally, the implication of non-membership functions with the uncertainty term plays a key role in the developed fuzzy controller for improving the performance of a cardiac pacemaker over other fuzzy control schemes in previous studies. Moreover, the proposed pacemaker control system is efficient for managing all health-status conditions and constraints during the different daily activities of cardiac patients. Consequently, the healthcare of patients with implanted dual-sensor pacemakers can be efficiently improved intuitively.

Intelligent control of self-driving vehicles based on adaptive sampling supervised actor-critic and human driving experience.

Due to the complexity of the driving environment and the dynamics of the behavior of traffic participants, self-driving in dense traffic flow is very challenging. Traditional methods usually rely on predefined rules, which are difficult to adapt to various driving scenarios. Deep reinforcement learning (DRL) shows advantages over rule-based methods in complex self-driving environments, demonstrating the great potential of intelligent decision-making. However, one of the problems of DRL is the inefficiency of exploration; typically, it requires a lot of trial and error to learn the optimal policy, which leads to its slow learning rate and makes it difficult for the agent to learn well-performing decision-making policies in self-driving scenarios. Inspired by the outstanding performance of supervised learning in classification tasks, we propose a self-driving intelligent control method that combines human driving experience and adaptive sampling supervised actor-critic algorithm. Unlike traditional DRL, we modified the learning process of the policy network by combining supervised learning and DRL and adding human driving experience to the learning samples to better guide the self-driving vehicle to learn the optimal policy through human driving experience and real-time human guidance. In addition, in order to make the agent learn more efficiently, we introduced real-time human guidance in its learning process, and an adaptive balanced sampling method was designed for improving the sampling performance. We also designed the reward function in detail for different evaluation indexes such as traffic efficiency, which further guides the agent to learn the self-driving intelligent control policy in a better way. The experimental results show that the method is able to control vehicles in complex traffic environments for self-driving tasks and exhibits better performance than other DRL methods.

Innate Orientating Behavior of a Multi-Legged Robot Driven by the Neural Circuits of C. elegans.

The objective of this research is to achieve biologically autonomous control by utilizing a whole-brain network model, drawing inspiration from biological neural networks to enhance the development of bionic intelligence. Here, we constructed a whole-brain neural network model of Caenorhabditis elegans (C. elegans), which characterizes the electrochemical processes at the level of the cellular synapses. The neural network simulation integrates computational programming and the visualization of the neurons and synapse connections of C. elegans, containing the specific controllable circuits and their dynamic characteristics. To illustrate the biological neural network (BNN)'s particular intelligent control capability, we introduced an innovative methodology for applying the BNN model to a 12-legged robot's movement control. Two methods were designed, one involving orientation control and the other involving locomotion generation, to demonstrate the intelligent control performance of the BNN. Both the simulation and experimental results indicate that the robot exhibits more autonomy and a more intelligent movement performance under BNN control. The systematic approach of employing the whole-brain BNN for robot control provides biomimetic research with a framework that has been substantiated by innovative methodologies and validated through the observed positive outcomes. This method is established as follows: (1) two integrated dynamic models of the C. elegans' whole-brain network and the robot moving dynamics are built, and all of the controllable circuits are discovered and verified; (2) real-time communication is achieved between the BNN model and the robot's dynamical model, both in the simulation and the experiments, including applicable encoding and decoding algorithms, facilitating their collaborative operation; (3) the designed mechanisms using the BNN model to control the robot are shown to be effective through numerical and experimental tests, focusing on 'foraging' behavior control and locomotion control.

Survey of Advanced Nonlinear Control Strategies for UAVs: Integration of Sensors and Hybrid Techniques.

This survey paper explores advanced nonlinear control strategies for Unmanned Aerial Vehicles (UAVs), including systems such as the Twin Rotor MIMO system (TRMS) and quadrotors. UAVs, with their high nonlinearity and significant coupling effects, serve as crucial benchmarks for testing control algorithms. Integration of sophisticated sensors enhances UAV versatility, making traditional linear control techniques less effective. Advanced nonlinear strategies, including sensor-based adaptive controls and AI, are increasingly essential. Recent years have seen the development of diverse sliding surface-based, sensor-driven, and hybrid control strategies for UAVs, offering superior performance over linear methods. This paper reviews the significance of these strategies, emphasizing their role in addressing UAV complexities and outlining future research directions.

Intelligent technologies powering clean incineration of municipal solid waste: A system review.

Cleanliness has been paramount for municipal solid waste incineration (MSWI) systems. In recent years, the rapid advancement of intelligent technologies has fostered unprecedented opportunities for enhancing the cleanliness of MSWI systems. This paper offers a review and analysis of cutting-edge intelligent technologies in MSWI, which include process monitoring, intelligent algorithms, combustion control, flue gas treatment, and particulate control. The objective is to summarize current applications of these techniques and to forecast future directions. Regarding process monitoring, intelligent image analysis has facilitated real-time tracking of combustion conditions. For intelligent algorithms, machine learning models have shown advantages in accurately forecasting key process parameters and pollutant concentrations. In terms of combustion control, intelligent systems have achieved consistent prediction and regulation of temperature, oxygen content, and other parameters. Intelligent monitoring and forecasting of carbon monoxide and dioxins for flue gas treatment have exhibited satisfactory performance. Concerning particulate control, multi-objective optimization facilitates the sustainable utilization of fly ash. Despite remarkable progress, challenges remain in improving process stability and monitoring instrumentation of intelligent MSWI technologies. By systematically summarizing current applications, this timely review offers valuable insights into the future upgrade of intelligent MSWI systems.

Monitoring multistage healthcare processes using state space models and a machine learning based framework.

Monitoring healthcare processes, such as surgical outcomes, with a keen focus on detecting changes and unnatural conditions at an early stage is crucial for healthcare professionals and administrators. In line with this goal, control charts, which are the most popular tool in the field of Statistical Process Monitoring, are widely employed to monitor therapeutic processes. Healthcare processes are often characterized by a multistage structure in which several components, states or stages form the final products or outcomes. In such complex scenarios, Multistage Process Monitoring (MPM) techniques become invaluable for monitoring distinct states of the process over time. However, the healthcare sector has seen limited studies employing MPM. This study aims to fill this gap by developing an MPM control chart tailored for healthcare data to promote early detection, confirmation, and patient safety. As it is important to detect unnatural conditions in healthcare processes at an early stage, the statistical control charts are combined with machine learning techniques (i.e., we deal with Intelligent Control Charting, ICC) to enhance detection ability. Through Monte Carlo simulations, our method demonstrates better performance compared to its statistical counterparts. To underline the practical application of the proposed ICC framework, real data from a two-stage thyroid cancer surgery is utilized. This real-world case serves as a compelling illustration of the effectiveness of the developed MPM control chart in a healthcare setting.

Experimental study of a new technique for minimally invasive percutaneous nephrolithotomy: intelligent pressure-controlled minimally invasive percutaneous nephrolithotomy.

BACKGROUND: To test the reliability and safety of a newly invented technique for minimally invasive percutaneous nephrolithotomy, intelligent pressure-controlled minimally invasive percutaneous nephrolithotomy (IPC-MPCNL). METHODS: Eighteen kidneys of nine female pigs were randomly divided into three groups. Those in Groups A and B underwent IPC-MPCNL through the new system composed of a pressure-measuring MPCNL suctioning sheath and an irrigation and suctioning platform with pressure feedback control. The infusion flow rate was 500 ml/min in Group A and 750 ml/min in Group B. Those in Group C underwent MPCNL at an infusion flow rate of 500 ml/min. The renal pelvic pressure (RPP) monitored by a ureteral catheter and that monitored by the pressure-measuring sheath in Groups A and B were compared. The RPP in Group C was monitored by a ureteral catheter. RESULTS: The RPP measured by the pressure-measuring sheath and that measured by the ureteral catheter in Group A was - 5.59 ± 1.95 mmHg and 4.46 ± 2.08 mmHg, respectively. The RPP measured by the pressure-measuring sheath and that measured by the ureteral catheter in Group B was - 4.00 ± 2.01 mmHg and 5.92 ± 2.05 mmHg, respectively. Hence, the RPPs measured by the pressure-measuring sheath in Groups A and B were consistent with those measured by the ureteral catheter. The RPP in Group C was 27.75 ± 5.98 mmHg (large fluctuations). CONCLUSIONS: IPC-MPCNL can be used to accurately monitor the RPP and maintain it within a preset safe range via suction. The new technique and the new system are safe and reliable.

Retrograde intrarenal surgery with intelligent control of renal pelvic pressure for staghorn calculi: a case report.

Percutaneous nephrolithotomy is the gold standard treatment for staghorn calculi. However, this study reviews a case of an almost complete removal of staghorn calculi following one session of retrograde intrarenal surgery with intelligent control of renal pelvic pressure (RIRS-ICP). A 45 years-old female patient with an 8.3 × 4.5 cm complete staghorn stone was infected with Proteus mirabilis. Two sensitive antibiotics, piperacillin tazobactam and etimicin, were administered for 3 days. Semirigid 7/8.4 Fr ureteroscope was used to treat the renal pelvis and upper calyceal calculi for 57 min. A 550 µm holmium laser fiber with 2.0 J × 30 Hz was set. Next, a disposable flexible ureteroscope of 8.4 Fr was used to address residual middle and lower calyx stones for 94 min. A 200 µm holmium laser fiber with 1.0 J × 30 Hz was set. The renal pelvis pressure was controlled within 15 mmHg. A 2 mm CT scan on the first postoperative day showed inferior caliceal residue of approximately 1.0 × 0.6 cm. No complications occurred. This suggests that RIRS-ICP is a safe and effective treatment for staghorn calculi.

Humidity Adaptive Antifreeze Hydrogel Sensor for Intelligent Control and Human-Computer Interaction.

Conductive hydrogels have emerged as ideal candidate materials for strain sensors due to their signal transduction capability and tissue-like flexibility, resembling human tissues. However, due to the presence of water molecules, hydrogels can experience dehydration and low-temperature freezing, which greatly limits the application scope as sensors. In this study, an ionic co-hybrid hydrogel called PBLL is proposed, which utilizes the amphoteric ion betaine hydrochloride (BH) in conjunction with hydrated lithium chloride (LiCl) thereby achieving the function of humidity adaptive. PBLL hydrogel retains water at low humidity (<50%) and absorbs water from air at high humidity (>50%) over the 17 days of testing. Remarkably, the PBLL hydrogel also exhibits strong anti-freezing properties (-80 °C), high conductivity (8.18 S m-1 at room temperature, 1.9 S m-1 at -80 °C), high gauge factor (GF approaching 5.1). Additionally, PBLL hydrogels exhibit strong inhibitory effects against Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), as well as biocompatibility. By synergistically integrating PBLL hydrogel with wireless transmission and Internet of Things (IoT) technologies, this study has accomplished real-time human-computer interaction systems for sports training and rehabilitation evaluation. PBLL hydrogel exhibits significant potential in the fields of medical rehabilitation, artificial intelligence (AI), and the Internet of Things (IoT).

Discussion on the Construction and Operational Experience of Intelligent Control Systems for Laboratory Animal Facilities： Shenzhen Institute for Drug Control as an Example / 实验动物与比较医学

Intelligent control systems can effectively assist in the construction and management of laboratory animal facilities, improving operational efficiency, ensuring the reliability of animal experimental results, and significantly saving human resources. The intelligent control system for laboratory animal facilities at Shenzhen Institute for Drug Control was completed in April 2021. It includes an intelligent management platform and an information management system for animal laboratories. The intelligent management platform regulates room environment parameters such as temperature, humidity, and pressure through building equipment management system, controlling devices such as the Venturi valve, electric air valve, electric water valve, and steam humidification valve. At the same time, various environmental parameters are monitored online through the environmental monitoring system. The laboratory’s intelligence is further enhanced by systems such as automatic lighting control, full HD video monitoring, automatic access control and door system, independent ventilation and feeding, automatic cleaning, automatic exhaust gas treatment, centralized gas supply, and real-time instrument parameter monitoring. The information management system for animal laboratories integrates inspection, instrument and equipment, personnel, documents, standard substances, reagents, inspection standards, books, records, scientific research management, relevant applications, quality management, and query statistics. For animal experimentation, a management module has been developed to achieve a comprehensive digitization of animal management. Furthermore, real-time collection and recording of data such as balance calibration, sample quality, and animal weight are facilitated through electronic experimental recording. In summary, the Animal Laboratory of Shenzhen Institute for Drug Control has extensively utilized intelligent systems to achieve real-time online control and monitoring, improve efficiency, ensure high-quality facility operation, and meet standard requirements. Smooth execution of all inspection and research activities has been achieved over the past three years. This paper provides insights into the construction, management, and operation of laboratory animal facilities at Shenzhen Institute for Drug Control, offering guidance for the implementation of intelligent control in similar facilities across China.

Machine learning framework for intelligent aeration control in wastewater treatment plants: Automatic feature engineering based on variation sliding layer.

Intelligent control of wastewater treatment plants (WWTPs) has the potential to reduce energy consumption and greenhouse gas emissions significantly. Machine learning (ML) provides a promising solution to handle the increasing amount and complexity of generated data. However, relationships between the features of wastewater datasets are generally inconspicuous, which hinders the application of artificial intelligence (AI) in WWTPs intelligent control. In this study, we develop an automatic framework of feature engineering based on variation sliding layer (VSL) to control the air demand precisely. Results demonstrated that using VSL in classic machine learning, deep learning, and ensemble learning could significantly improve the efficiency of aeration intelligent control in WWTPs. Bayesian regression and ensemble learning achieved the highest accuracy for predicting air demand. The developed models with VSL-ML models were also successfully implemented under the full-scale wastewater treatment plant, showing a 16.12 % reduction in demand compared to conventional aeration control of preset dissolved oxygen (DO) and feedback to the blower. The VSL-ML models showed great potential to be applied for the precision air demand prediction and control. The package as a tripartite library of Python is called wwtpai, which is freely accessible on GitHub and CSDN to remove technical barriers to the application of AI technology in WWTPs.

Delay-Informed Intelligent Formation Control for UAV-Assisted IoT Application.

Multiple unmanned aerial vehicles (UAVs) have a greater potential to be widely used in UAV-assisted IoT applications. UAV formation, as an effective way to improve surveillance and security, has been extensively of concern. The leader-follower approach is efficient for UAV formation, as the whole formation system needs to find only the leader's trajectory. This paper studies the leader-follower surveillance system. Owing to different scenarios and assignments, the leading velocity is dynamic. The inevitable communication time delays resulting from information sending, communicating and receiving process bring challenges in the design of real-time UAV formation control. In this paper, the design of UAV formation tracking based on deep reinforcement learning (DRL) is investigated for high mobility scenarios in the presence of communication delay. To be more specific, the optimization UAV formation problem is firstly formulated to be a state error minimization problem by using the quadratic cost function when the communication delay is considered. Then, the delay-informed Markov decision process (DIMDP) is developed by including the previous actions in order to compensate the performance degradation induced by the time delay. Subsequently, an extended-delay informed deep deterministic policy gradient (DIDDPG) algorithm is proposed. Finally, some issues, such as computational complexity analysis and the effect of the time delay are discussed, and then the proposed intelligent algorithm is further extended to the arbitrary communication delay case. Numerical experiments demonstrate that the proposed DIDDPG algorithm can significantly alleviate the performance degradation caused by time delays.

Self-Organizing Interval Type-2 Fuzzy Neural Network Compensation Control Based on Real-Time Data Information Entropy and Its Application in n-DOF Manipulator.

In order to solve the high-precision motion control problem of the n-degree-of-freedom (n-DOF) manipulator driven by large amount of real-time data, a motion control algorithm based on self-organizing interval type-2 fuzzy neural network error compensation (SOT2-FNNEC) is proposed. The proposed control framework can effectively suppress various types of interference such as base jitter, signal interference, time delay, etc., during the movement of the manipulator. The fuzzy neural network structure and self-organization method are used to realize the online self-organization of fuzzy rules based on control data. The stability of the closed-loop control systems are proved by Lyapunov stability theory. Simulations show that the algorithm is superior to a self-organizing fuzzy error compensation network and conventional sliding mode variable structure control methods in control performance.

LiDAR-Based Maintenance of a Safe Distance between a Human and a Robot Arm.

This paper demonstrates the capabilities of three-dimensional (3D) LiDAR scanners in supporting a safe distance maintenance functionality in human-robot collaborative applications. The use of such sensors is severely under-utilised in collaborative work with heavy-duty robots. However, even with a relatively modest proprietary 3D sensor prototype, a respectable level of safety has been achieved, which should encourage the development of such applications in the future. Its associated intelligent control system (ICS) is presented, as well as the sensor's technical characteristics. It acquires the positions of the robot and the human periodically, predicts their positions in the near future optionally, and adjusts the robot's speed to keep its distance from the human above the protective separation distance. The main novelty is the possibility to load an instance of the robot programme into the ICS, which then precomputes the future position and pose of the robot. Higher accuracy and safety are provided, in comparison to traditional predictions from known real-time and near-past positions and poses. The use of a 3D LiDAR scanner in a speed and separation monitoring application and, particularly, its specific placing, are also innovative and advantageous. The system was validated by analysing videos taken by the reference validation camera visually, which confirmed its safe operation in reasonably limited ranges of robot and human speeds.

[Analysis and Research on Intelligent Management and Control System of Reusable Medical Devices].

Focusing on the influencing factors of the operation and processing process of reusable medical devices, the management problems of reusable medical devices are analyzed from the processing processes of device assembly, packaging, handover, inventory and information recording. In the specific practice of designing the intelligent management and control system of reusable medical devices, the medical processes in different periods from device addition, packaging, disinfection, transfer, transportation, distribution, recycling to scrapping are integrated into the intelligent service system. Through the changes of medical device treatment, this study comprehensively explores the innovative ideas and specific problems in the construction of intelligent process system of hospital disinfection supply center.

Fuzzy Control Application to an Irrigation System of Hydroponic Crops under Greenhouse: Case Cultivation of Strawberries (Fragaria Vesca).

Hydroponics refers to a modern set of agricultural techniques that do not require the use of natural soil for plant germination and development. These types of crops use artificial irrigation systems that, together with fuzzy control methods, allow plants to be provided with the exact amount of nutrients for optimal growth. The diffuse control begins with the sensorization of the agricultural variables that intervene in the hydroponic ecosystem, such as the environmental temperature, electrical conductivity of the nutrient solution and the temperature, humidity, and pH of the substrate. Based on this knowledge, these variables can be controlled to be within the ranges required for optimal plant growth, reducing the risk of a negative impact on the crop. This research takes, as a case study, the application of fuzzy control methods to hydroponic strawberry crops (Fragaria vesca). It is shown that, under this scheme, a greater foliage of the plants and a larger size of the fruits are obtained in comparison with natural cultivation systems in which irrigation and fertilization are carried out by default, without considering the alterations in the aforementioned variables. It is concluded that the combination of modern agricultural techniques such as hydroponics and diffuse control allow us to improve the quality of the crops and the optimization of the required resources.

A Survey on Reinforcement Learning Methods in Bionic Underwater Robots.

Bionic robots possess inherent advantages for underwater operations, and research on motion control and intelligent decision making has expanded their application scope. In recent years, the application of reinforcement learning algorithms in the field of bionic underwater robots has gained considerable attention, and continues to grow. In this paper, we present a comprehensive survey of the accomplishments of reinforcement learning algorithms in the field of bionic underwater robots. Firstly, we classify existing reinforcement learning methods and introduce control tasks and decision making tasks based on the composition of bionic underwater robots. We further discuss the advantages and challenges of reinforcement learning for bionic robots in underwater environments. Secondly, we review the establishment of existing reinforcement learning algorithms for bionic underwater robots from different task perspectives. Thirdly, we explore the existing training and deployment solutions of reinforcement learning algorithms for bionic underwater robots, focusing on the challenges posed by complex underwater environments and underactuated bionic robots. Finally, the limitations and future development directions of reinforcement learning in the field of bionic underwater robots are discussed. This survey provides a foundation for exploring reinforcement learning control and decision making methods for bionic underwater robots, and provides insights for future research.