On-line Control Research Articles

Recent Trends in the Pre-Drying, Drying, and Post-Drying Processes for Cassava Tuber: A Review.

Cassava tuber is an essential staple crop in tropical regions with versatile applications in the food, feed, and industrial sectors. However, its high moisture content and perishable nature necessitate efficient preservation methods to extend its shelf life and enhance its value. Pre-drying, drying, and post-drying processes play pivotal roles in maintaining the quality and usability of cassava products. This review comprehensively examines the current status and future directions in the pre-drying, drying, and post-drying processes of cassava tuber. Various pre-drying or pretreatment methods and drying techniques are evaluated for their impacts on drying kinetics and product quality. Additionally, challenges and limitations in achieving high-quality processing of cassava flour are identified. Future directions in cassava drying methods emphasize the integration of combined pre-drying and drying techniques to optimize resource utilization and processing efficiency. Furthermore, the adoption of advanced online measurement and control technologies in drying equipment is highlighted for real-time monitoring and optimization of drying parameters. The importance of optimizing existing processes to establish a comprehensive cassava industrial chain and foster the development of the cassava deep-processing industry is emphasized. This review provides valuable insights into the current trends and future prospects in cassava drying technologies, aiming to facilitate sustainable and efficient utilization of cassava resources for various applications.

DETECTION OF PROCESS ERRORS OF ADDITIVE MANUFACTURING

Additive manufacturing has the potential to revolutionize the production of complex and adapted parts, but is prone to manufacturing errors, ranging from minor inaccuracies and mechanical failures to complete failures associated with the production of rejects. It is therefore necessary to detect these deficiencies in a timely manner, to analyses and apply appropriate settings to minimize the possible occurrence of manufacturing errors. This article describes the proposed deep-learning monitoring system, which allows for a system of monitoring the quality of the printout during the process of additive manufacturing. The system identifies whether an error occurs during the printing process and may notify the operator if something went wrong. The combination of additive manufacturing, artificial intelligence, Raspberry Pi and online controls may create a comprehensive system for monitoring, managing and predicting process errors.

Online control for pressure regulation of oxygen mask based on neural network

Online control for pressure regulation of oxygen mask based on neural network

Pars Opercularis Underlies Efferent Predictions and Successful Auditory Feedback Processing in Speech: Evidence From Left-Hemisphere Stroke.

Hearing one's own speech allows for acoustic self-monitoring in real time. Left-hemisphere motor planning regions are thought to give rise to efferent predictions that can be compared to true feedback in sensory cortices, resulting in neural suppression commensurate with the degree of overlap between predicted and actual sensations. Sensory prediction errors thus serve as a possible mechanism of detection of deviant speech sounds, which can then feed back into corrective action, allowing for online control of speech acoustics. The goal of this study was to assess the integrity of this detection-correction circuit in persons with aphasia (PWA) whose left-hemisphere lesions may limit their ability to control variability in speech output. We recorded magnetoencephalography (MEG) while 15 PWA and age-matched controls spoke monosyllabic words and listened to playback of their utterances. From this, we measured speaking-induced suppression of the M100 neural response and related it to lesion profiles and speech behavior. Both speaking-induced suppression and cortical sensitivity to deviance were preserved at the group level in PWA. PWA with more spared tissue in pars opercularis had greater left-hemisphere neural suppression and greater behavioral correction of acoustically deviant pronunciations, whereas sparing of superior temporal gyrus was not related to neural suppression or acoustic behavior. In turn, PWA who made greater corrections had fewer overt speech errors in the MEG task. Thus, the motor planning regions that generate the efferent prediction are integral to performing corrections when that prediction is violated.

Trajectory planning and control of spacecraft avoiding dynamic debris swarm

The continuous accumulation of space debris poses a significant threat to spacecraft on orbit. This paper introduces a dynamic trajectory planning and control framework aimed at ensuring the safe traversal of spacecraft through space debris swarms. Initially, a threat model is established, considering the movement of the debris swarm and the constraints of spacecraft detection range. This model utilizes a dynamically updated, redefined Gaussian mixture model to characterize the threat posed by the debris swarm to spacecraft, with proximity to debris correlating to increased threat levels. Building upon this threat model, four guidance rules are defined, including target guidance, minimal threat, threat threshold, and local path optimization, to facilitate the generation of the shortest collision-free local trajectory for spacecraft. Combining the concept of receding horizon control, an online real-time planning and control framework is formulated for spacecraft navigating through debris swarms. Finally, numerical and semi-physical simulations verify that the proposed framework can stably generate spacecraft safe trajectories and control commands under the threat of numerous dynamic space debris, showing the characteristics of optimal planning trajectory, rapid computation speed and robust safety features.

Nonlinear model-based predictive control of a yeast fermentation bioreactor using new bee colony-WNN modelling structure

This paper concentrates on the application of a newly developed model for the nonlinear control of a yeast fermentation bioreactor. The primary control objectives are the maintenance of temperature and product concentration within the desired range. So, having an accurate model in control strategy is not ignorable and this model influences on close-loop controllability. In the context of online control, the parameters are changed (the system is time varying) and precision of extracted model is more important. Moreover, there is a demand for a computationally efficient model, and the convergence speed during the training of the neural network is significant. To overcome the challenges associated with slow convergence, the risk of being trapped in local minima, and the imperative of achieving computational efficiency, this study introduces a novel combination of a pruned Bee Colony Wavelet Neural Network for the online modeling of the bioreactor. The method's superiority lies in its ability to conduct searches in multiple sections independently, thereby avoiding local minima. The hybrid model controllability is further investigated using Dynamic Matrix Control (DMC) and Generalized Matrix Control (GPC) techniques. Subsequently, the paper explores the application of Nonlinear Model Predictive Control (NMPC) by solving a Gauss-Newton quadratic programming problem online, followed by a comparative analysis of the results. It is demonstrated that the precision of the model closely influences profitability and enhances product yields.

Online learning deep neural network fuzzy control of structures under earthquake motions: numerical and experimental test

Online learning deep neural network fuzzy control of structures under earthquake motions: numerical and experimental test

Online Control of Adaptive Large Neighborhood Search Using Deep Reinforcement Learning

Online Control of Adaptive Large Neighborhood Search Using Deep Reinforcement Learning

Control of overflows in interception wells based on water quantity and quality in rainwater pipelines for reducing pollution to rivers

ABSTRACT The separate sewer system has led to severe river pollution because of domestic sewage infiltrations and initial rainfall pollution. Therefore, interception wells (IWs) are installed to divert sewage to the wastewater treatment plant but similarly contribute to overflow-induced river pollution on rainy days. The aim of the present study is to propose a control strategy for the regulation of IWs’ overflow based on the online monitoring data, control measures, and software simulation. The results indicate that during light rain, overflow from the IWs is minimal, requiring no control measures. However during heavy rainfall, the overflow of sewage leads to an increase in the river’s concentration of NH3-N from 0.25 to 3.04 mg/L. Finally, Simulations demonstrate a reduction in river NH3-N levels from 2.27 to 1.33 mg/L after implementation. The results indicated the substantial effectiveness of measures in mitigating the pollution originating from IWs’ overflows during rainfall events.

An Online Energy-Saving Control Allocation Strategy Based on Self-Updating Loss Estimation for Multi-Motor Drive Systems

In this paper, an online energy-saving control allocation strategy based on self-updating loss estimation for multi-motor drive systems is proposed, where the impact of variations in motor parameters and distribution coefficients is considered. Firstly, a drive system model for multi-motor drive systems incorporating iron loss in permanent magnet synchronous motor (PMSM) is established. Then, a self-updating PMSM loss estimation method based on dynamic torque–current mapping is proposed. The torque–current mapping is initially identified based on the conv-fusion curve, and iteratively updated by optimal estimation. Subsequently, an online control allocation method based on line search is proposed, which mitigates the adverse effects caused by variations in distribution coefficients and reduces the total motor loss. Finally, the effectiveness of the proposed strategy is verified on the hardware-in-the-loop (HIL)-based platform. The results demonstrate that the strategy effectively enhances energy efficiency while maintaining the original control performance of the system.

An online model-free adaptive learning control solution for robotic arms

This paper focuses on the online control of a class of nonlinear dynamical systems, specifically robotic manipulators. Solutions utilizing Proportional–Integral–Derivative (PID) control schemes are employed to control the joints of robotic manipulators. However, the existing control strategies utilize fixed gains, which do not fully account for the inherent nonlinearity of the dynamical structure or the dynamics of reference-tracking error. Additionally, the individual joint’s dynamic performance is optimized independently from the performance of other joints. This work introduces an adaptive integral Reinforcement Learning algorithm to control a four-DoF robotic arm in real time. This is done using a model-free Value Iteration process implemented in a continuous-time mode. The solution does not assume any knowledge of the dynamics of the robot arm and does not require any initial admissible control strategy to proceed with the adaptive learning solution. The self-learning algorithm provides adaptable strategies to control the turntable, forearm, bicep, and wrist joints of the robotic arm. The performance of the adaptive learning solution is compared with those of Proportional–Integral–Derivative and high-order model-free adaptive control schemes to highlight its effectiveness.

Reinforcement learning for the optimization and online control of emulsion polymerization reactors: Particle morphology

Online control of emulsion polymerization reactors is challenging due to the complex nature of the polymerization process, which causes difficulties not only for online measurement, but also for real-time prediction and optimization of future trajectories. In this work, the use of reinforcement learning (RL) to overcome these challenges is explored for the case of online control of particle morphology. It is shown in silico that assuming knowledge of the system dynamics in the form of a mathematical model, reinforcement learning can be used to train a neural network that can select optimal control actions in real-time to target a desired final polymer product. Moreover, it is shown that a second neural network model can be utilized as a state estimator to allow for full online control of particle morphology, which cannot be directly measured online. This work highlights the potential of machine learning for optimization and control in challenging polymerization systems.

Discrete Dynamic System Modeling for Simulated Moving Bed Processes

Although the simulated moving bed (SMB) process boasts advantages such as high productivity and low consumption, the cost of obtaining optimized parameters through practical experiments to control the separation process can be enormous due to its complex nonlinear characteristics. Consequently, the successful transformation of the SMB separation process into a mathematical dynamic model for computer simulation would greatly reduce the research costs associated with experimental studies. In this study, the Crank–Nicolson method was employed to discretize and dynamize the SMB process, enabling the simulation of processes under both linear and Langmuir isotherms. The results of the simulation experiments demonstrated the feasibility and high efficiency of this approach, thereby establishing a solid foundation for further advancements in online control strategies.

Erroneous Compensation for Long-Latency Feedback Delays as Origin of Essential Tremor.

Essential tremor (ET), a movement disorder characterized by involuntary oscillations of the limbs during movement, remains to date not well understood. It has been recently suggested that the tremor originates from impaired delay compensation, affecting movement representation and online control. Here we tested this hypothesis directly with 24 ET patients (14 female; 10 male) and 28 neurologically intact (NI) human volunteers (17 female; 11 male) in an upper limb postural perturbation task. After maintaining their hand in a visual target, participants experienced perturbations of unpredictable direction and magnitude and were instructed to counter the perturbation and steer their hand back to the starting position. In comparison with NI volunteers, ET patients' early muscular responses (short and long-latency responses, 20-50 and 50-100 ms, respectively) were preserved or even slightly increased. However, they exhibited perturbation-dependent deficits when stopping and stabilizing their hand in the final target supporting the hypothesis that the tremor was generated by the feedback controller. We show in a computational model that errors in delay compensation accumulating over time produced the same small increase in initial feedback response followed by oscillations that scaled with the perturbation magnitude as observed in ET population. Our experimental results therefore validate the computational hypothesis that inaccurate delay compensation in long-latency pathways could be the origin of the tremor.

Utilizing two models of the converter to eliminate the partial shading on solar panel: a comparative scheme between classical offline and advanced online control strategies

Utilizing two models of the converter to eliminate the partial shading on solar panel: a comparative scheme between classical offline and advanced online control strategies

Optimal control strategies for stormwater detention ponds

Stormwater detention ponds are essential stormwater management solutions that regulate the urban catchment discharge towards streams. Their purposes are to reduce the hydraulic load to avoid stream erosion, as well as to minimize the degradation of the natural waterbody by direct discharge of pollutants. Currently, static controllers are widely implemented for detention pond outflow regulation in engineering practice, i.e., the outflow discharge is capped at a fixed value. Such a passive discharge setting fails to exploit the full potential of the overall water system, hence further improvements are needed. We apply formal methods to synthesize (i.e., derive automatically) optimal active controllers. We model the stormwater detention pond, including the urban catchment area and the rain forecasts with its uncertainty, as hybrid Markov decision processes. Subsequently, we use the tool UppaalStratego to synthesize using Q-learning a control strategy maximizing the retention time for pollutant sedimentation (optimality) while also minimizing the duration of emergency overflow in the detention pond (safety). These strategies are synthesized for both an off-line and on-line settings. Simulation results for an existing pond show that UppaalStratego can learn optimal strategies that significantly reduce emergency overflows. For off-line controllers, a scenario with low rain periods shows a 26% improvement of pollutant sedimentation with respect to static control, and a scenario with high rain periods shows a reduction of overflow probability of 10%–19% for static control to lower than 5%, while pollutant sedimentation has only declined by 7% compared to static-control. For on-line controllers, one scenario with heavy rain shows a 95% overflow duration reduction and a 29% pollutant sedimentation improvement compared to static control.

The online control method of independent DC microgrid based on virtual hydrogen consumption method

AbstractWith the development of distributed generation and power electronics technology, DC microgrids have made certain progress with their advantages of simple structure, economic efficiency, and easy control. Considering factors such as the geographical location of some residential areas, microgrids with fuel cells that can operate independently are also receiving increasing attention. In this paper, an online control method named virtual hydrogen consumption is proposed based on an independent DC microgrid which solves the problem of electro‐hydrogen conversion successfully. By solving the overall consumption function, the analytical solution is further obtained to realize the online control. Based on the results of RT‐LAB platform, the proposed method can maintain the output of fuel cell system in its high efficiency range with a lower volatility.

Reinforcement learning in building controls: A comparative study of algorithms considering model availability and policy representation

Reinforcement Learning (RL) has presented considerable potential as an advanced control technique in building controls to enable buildings operating more energy-efficient. As various types of RL algorithms have been studied on their performance of building controls, benchmarking these algorithms across the entire spectrum of features is essential to provide an overview and deepen the understanding of RL applications. Therefore, this study aims to compare and analyze the effectiveness of various RL algorithms, encompassing the entire RL categories featured by value-based, policy gradient, actor-critic and model-based RL considering model availability and policy representation. To provide a comprehensive analysis, in addition to the control performance quantified by the cumulative rewards based on the cost function of RL, data demand and robustness of hyperparameter tuning were investigated. The open-source Gym-Eplus framework was selected as the virtual environment to train and test different RL agents. The results showed that both model-free and model-based RL agents outperformed the baseline rule-based control in terms of energy consumption and thermal comfort, and RL agents were capable of evaluating both short-term and long-term rewards to achieve adaptive control optimization continuously along with the online control process. Model-based RL agent improved the data sampling efficiency but presented a relatively sacrificed control performance during the tested summer days.

Weight Inclusive Thinking for Fitness Spaces (WIT FITS): A Three-Month Follow-Up of a Weight Stigma Intervention for Exercise Professionals

Among the few interventions that exist to decrease weight stigma in exercise settings, none include a long-term follow-up to assess if changes are sustained. The aim of this study was to assess if a weight stigma intervention that showed success in improving fat attitudes among university recreation center professionals had sustained effects after three months, compared to a control group. Participants were 46 university recreation center professionals who took the Fat Attitudes Assessment Toolkit before completing a two-hour self-paced online intervention or control course, after course completion, and at a three-month follow-up. At follow-up, participants in the intervention group showed significant improvement in total fat attitudes, F(2, 88) = 7.94, p < .001, η2p = 0.15 and in fat acceptance, F(2, 88) = 4.25, p = .017, η2p = 0.09. Future studies should further develop interventions and assess results in larger samples and after longer periods of time.

Development of real-time brain-computer interface control system for robot

Electroencephalogram (EEG)-based brain-computer interfaces (BCI) have been considered a prevailing non-invasive method for collecting human biomedical signals by attaching electrodes to the scalp. However, it is difficult to detect and use these signals to control an online BCI robot in a real environment owing to environmental noise. In this study, a novel state recognition model is proposed to determine and improve EEG signal states. First, a Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) was designed to extract EEG features along the time sequence. During this process, errors caused by the randomness of the mind or external environmental factors may be generated. Thus, an actor-critic based decision-making model was proposed to correct these errors. The model consists of two networks that can be used to predict the final signal state based on both the current signal state probability and past signal state probabilities. Subsequently, a hybrid BCI real-time control system application is proposed to control a BCI robot. The Unicorn Hybrid Black EEG device was used to acquire brain signals. A data transmission system was constructed using OpenViBE to transfer data. An EEG classification system was built to classify the BCI commands. In this experiment, EEG data from five subjects were collected to train and test the performance and reliability of the proposed control system. The system records the time spent by the robot and the moving distance. Experimental results were provided to demonstrate the feasibility of the real-time control system. Compared to similar BCI studies, the proposed hybrid BCI real-time control system can accurately classify seven BCI commands in a more reliable and precise manner. Overall, the offline testing accuracy was 87.20%. When we apply the proposed system to control a BCI robot in a real environment, the average online control accuracy is 93.12%, and the mean information transmission rate is 67.07 bits/min, which is better than those of some state-of-the-art control systems. This shows that the proposed hybrid BCI real-time control system demonstrated higher reliability, which can be used in practical BCI control applications.