Control Theory Research Articles

СИНТЕЗ ЭЛЕКТРОПРИВОДА КОМПРЕССОРНОЙ УСТАНОВКИ ЭЛЕКТРОВОЗА

The study objective is to improve the reliability and energy efficiency of the electric locomotive compressor drive. To achieve this, the required power of the electric motor was calculated the performance of the compressor unit, the volume of the main tanks and the selected electric motor were checked to start and adjust the capacity at low supply voltage. The task to which the paper is devoted is to design an electric drive for a compressor motor. A vector model of an asynchronous motor was made. The process of direct start-up and starting an asynchronous electric motor with the use of a scalar control system and IR compensation were modeled. Research methods: systematic approach; provisions of the theory of automatic control and electrical engineering; numerical PC simulation using Matlab software package. The novelty of the work is in the use of a frequency-controlled electric drive to start the asynchronous electric motor of the compressor unit instead of the normally installed starting capacitors. This design solution will significantly reduce the amplitude of the starting currents, which in turn will reduce the wear of the winding insulation. The study results are presented, based on the results of the constructed models, as graphs of the dependencies of speed and current during direct start of the electric motor and with the control system. Conclusions: the scalar control system, thanks to the introduction of an intensity setter, as well as IR compensation diagram, allow extending the transition time and controlling the value of the inrush current in acceptable values. Such an approach to make an electric drive compressor motor is able to significantly increase its reliability and service life, as well as to increase the overall energy efficiency of the system.

Intelligent in-process enhancement technique for machining efficiency in CNC machine tools based on spindle power

In the realm of mechanical machining, adaptive machining techniques offer an efficient method. However, existing adaptive machining technologies cannot automatically identify the type of workpiece to be machined, nor can they set different control targets based on tool load-bearing capacity. This often requires manual and cumbersome operations, making the application of adaptive machining technology difficult to popularize. To address these challenges, this paper proposes an intelligent and efficient adaptive machining method. Specifically, a workpiece automatic classification algorithm based on machine tool spindle power is introduced. This algorithm classifies the machined workpieces according to the collected spindle power data, enhancing the convenience of adaptive machining technology. Also based on spindle power data, a time-series segmentation method is proposed, dividing spindle power into different subsequences according to the tools used in machining, which enhances the accuracy of adaptive machining technology. Furthermore, an adaptive feed rate control algorithm is designed based on fuzzy control theory, realizing intelligent and efficient adaptive machining of workpieces to improve machining efficiency. Finally, experiments are conducted to validate the effectiveness of the intelligent and efficient adaptive machining method. The proposed method effectively addresses the drawbacks of existing adaptive machining method, which require complex parameter settings. It simplifies the operational process, enhances the intelligence, and improves the practical applicability of adaptive machining techniques. This represents an innovative contribution to the field of adaptive machining techniques.

Impact of warranty and green level of the product with nonlinear demand via optimal control theory and Artificial Hummingbird Algorithm

Due to the current environmental situation and human health, a green manufacturing system is very essential in the manufacturing world. Several researchers have developed various types of green manufacturing models by considering green products, green investments, carbon emission taxes, etc. Motivated by this topic, a green production model is formulated by considering selling price, time, warranty period and green level dependent demand with a carbon emission tax policy. Also, the production rate of the system is an unknown function of time. Per unit production cost of the products is taken as increasing function of production rate and green level of the products. In our proposed model, carbon emission rate is taken as linear function of time. Then, an optimization problem of the production model is constructed. To validate of our proposed model, a numerical example is considered and solved it by AHA. Further, other five metaheuristics algorithms (AEFA, FA, GWOA, WOA and EOA) are taken to compare the results obtained from AHA. Also, concavity of the average profit function and convergence graph of different metaheuristics algorithms are presented. Finally, a sensitivity analysis is carried out to investigate the impact of different system parameters on our optimal policy and reach a fruitful conclusion from this study.

Risk Management Techniques on Financial Performance of State-Owned Sugar Companies in Kenya

Financial performance of sugar companies in Kenya is generally poor and has been fluctuating over a long period. The state-owned sugar companies are worse compared to the privately owned companies. The sugar sector has been orbiting around sugar scarcity, inefficiencies, failure to pay farmers, failure to renovate and modernize production unit, failure to battle with imported sugar, recurrent losses and political interference. The main objective of this study was to evaluate the effect of risk management techniques on financial performance of state-owned sugar companies in Kenya. The specific objectives guiding the study were; to establish the effect of risk avoidance on financial performance of state-owned sugar companies in Kenya, and to assess the effect of risk acceptance on financial performance of state-owned sugar companies in Kenya. The study was anchored on the risk management theory, model risk management and the theory of financial control. The study adopted a descriptive research design with a target population of fifty four respondents being employees from the six state-owned sugar companies in Kenya. Questionnaires were the main data collection tool. Data was analyzed with the help of statistical package for social science version 28, Analysis of Variances, Correlation and regression analysis tools were used to establish the relationship between the variables. The findings of the study revealed a statistically significant regression effect and indicative of accomplished prediction of financial performance of state-owned sugar factories through the F-calculated value which showed that the model was significant. Risk avoidance explained 53.9% of financial performance and risk acceptance explained 61.9% of financial performance of state-owned sugar factories in western Kenya. The findings of the study will provide an insight to finance practitioners on the risk management techniques and their relationship with organizational financial performance as well as contributing to the scholarly literature on risk management techniques in other sectors of the economy. Keywords: Risk, Risk Avoidance, Risk Acceptance, Financial Performance, Risk management Techniques

Autonomous distributed braking and driving force control architecture based on broadcast control for vehicles with in-wheel motors on four wheels

In this paper, we investigate an architecture for autonomous distributed control of braking and driving force on each wheel of a vehicle equipped with in-wheel motors on four wheels. Specifically, in a scenario where the driving force of some wheels is reduced due to low-friction road or motor failure during going straight ahead, the remaining wheels that can generate driving force autonomously control the yaw rate caused by the unbalanced driving force and driving force reduction to maintain the vehicle behavior. To realize autonomous distributed control, we focus on broadcast control from the control theory of multi-agent systems, which deals with the control of systems composed of many elements. We verify by simulation that the proposed architecture performs as desired for the assumed scenario.

Analysis and Control of Partially Observed Discrete-Event Systems via Positively Constructed Formulas

This paper establishes a connection between control theory for partially observed discrete-event systems (DESs) and automated theorem proving (ATP) in the calculus of positively constructed formulas (PCFs). The language of PCFs is a complete first-order language providing a powerful tool for qualitative analysis of dynamical systems. Based on ATP in the PCF calculus, a new technique is suggested for checking observability as a property of formal languages, which is necessary for the existence of supervisory control of DESs. In the case of violation of observability, words causing a conflict can also be extracted with the help of a specially designed PCF. With an example of the problem of path planning by a robot in an unknown environment, we show the application of our approach at one of the levels of a robot control system. The prover Bootfrost developed to facilitate PCF refutation is also presented. The tests show positive results and perspectives for the presented approach.

Study on SEAI Model of COVID-19 Based on Asymptomatic Infection

In this paper, an SEAI epidemic model with asymptomatic infection is studied under the background of mass transmission of COVID-19. First, we use the next-generation matrix method to obtain the basic reproductive number R0 and calculate the equilibrium point. Secondly, when R0<1, the local asymptotic stability of the disease-free equilibrium is proved by Hurwitz criterion, and the global asymptotic stability of the disease-free equilibrium is proved by constructing the Lyapunov function. When R0>1, the system has a unique endemic equilibrium point and is locally asymptotically stable, and it is also proved that the system is uniformly persistent. Then, the application of optimal control theory is carried out, and the expression of the optimal control solution is obtained. Finally, in order to verify the correctness of the theory, the stability of the equilibrium point is numerically simulated and the sensitivity of the parameters of R0 is analyzed. We also simulated the comparison of the number of asymptomatic infected people and symptomatic infected people before and after adopting the optimal control strategy. This shows that the infection of asymptomatic people cannot be underestimated in the spread of COVID-19 virus, and an isolation strategy should be adopted to control the spread speed of the disease.

Optimal control analysis of COVID-19 stochastic model with comprehensive strategies

In this paper, we construct a stochastic SVEAIR (Susceptible-Vaccinated-Exposed -Asymptomatic-Infected-Removed) epidemic model. It aimed to investigate COVID-19 control strategies through four different control methods, which consist of prophylaxis, vaccination control, rapid screening of people in exposure categories, and people identified as infected without screening. Firstly, we study the optimal control model and obtain the optimal control strategy using stochastic control theory. Secondly, the optimal control model with control term or without control term is numerically analyzed by using the implicit Runge-Kutta approximation method. At last, The numerical simulations verify the theoretical results and it show that control could help reduce infection and improve population health.

Iterative algorithm to the generalized coupled Sylvester‐transpose matrix equations with application in robust and minimum norm observer design of linear systems

AbstractThis article consider a class of generalized coupled Sylvester‐transpose matrix equations which play an important role in control and systems theory. Based on the Jacobi iterative algorithm, the full‐row rank accelerated Jacobi gradient based iterative (RRAJGI) algorithm and the full‐column rank accelerated Jacobi gradient based iterative (CRAJGI) algorithm are proposed. By using the Frobenius norm of matrix and the trace function of matrix, the convergence of the algorithms are proved. The results show that the new algorithms are convergent for arbitrary initial matrices under the convergence number satisfies appropriate conditions. Numerical examples show that RRAJGI algorithm and CRAJGI algorithm have the advantages of faster convergence speed and higher convergence accuracy than other existing algorithms. Finally, an application example for robust and minimum norm observer design of linear systems is given.

Distributed-Drive Vehicle Lateral-Stability Coordinated Control Based on Phase-Plane Stability Region

The lateral stability control of vehicles is one of the most crucial aspects of vehicle safety. This article introduces a coordinated-control strategy designed to enhance the handling stability of distributed-drive electric vehicles. The upper controller uses active front steering and direct yaw moment-control controllers designed based on sliding-mode control theory. The lower controller optimally allocates control inputs to the upper controller, considering factors such as load transfer and tire load rate. It divides the stability region by relying on the phase plane and develops a coordinated-control strategy based on the degree of deviation of the vehicle state from the stability region. The results of the simulation experiments demonstrate that the proposed control strategy effectively improves handling stability under extreme working conditions.

A review of dynamic optimization in aquaculture production economics

AbstractDynamic optimization (DO) has been applied to aquaculture industry to determine optimal management strategies of aquaculture production systems. Optimal control of feeding, optimal stocking successions, and harvesting time among other factors has been studied. This work shows a review of the application of optimal control theory (OCT) and dynamic programming in economics and management of aquaculture. The Pontriagyn's maximum principle and the Bellman's optimality are the most commonly used mechanisms to solve control problems that optimize the producer's benefit or cost. Recently, model‐based predictive control has also been applied. Such tools have been used in aspects involving the design, planning, and monitoring of variables relevant to the optimal management of the culture system. It was found that in aquaculture there is low scientific productivity in the application of DO in bioeconomic models. In this review, an example of OCT applied to the control of feeding of farmed tilapia using a bioeconomic model is shown. DO represents a useful tool for optimal decision‐making and this review discusses the implications of the use of DO in aquaculture and recommendations for its use in the future.

Luxury Consumption Tendency: A Comparative Study Between Chinese and Portuguese Consumers

A statistical model comparing national and cultural differences in luxury consumption tendencies is presented in this paper. The objective is to investigate the factors influencing luxury consumption tendency, such as social influence, conspicuous consumption, and brand image, and their impact on consumer purchasing behavior and life satisfaction. Specifically, the Tend and Befriend Theory, the Compensatory Control Theory, and the Technology Acceptance Model are utilized to analyze the model. The quantitative analysis is based on cross sectional data with 401 valid questionnaires from Portugal and 369 from China. Comparative analysis is conducted to evaluate attitudes toward luxury consumption tendencies in both Chinese and Portuguese samples. The results indicate that social influence, conspicuous consumption, and brand image have a positive effect on luxury consumption tendencies. Moreover, luxury consumption tendencies are positively correlated with life satisfaction, the urge to buy, and luxury spending. Differences between Portuguese and Chinese consumers are observed, attributed to cultural variations. This study enhances our understanding of consumer behavior in luxury markets by elucidating influential relationships that impact consumer decision-making and life satisfaction, while also highlighting cultural influences.

Control of a three-wheeled omnidirectional mobile robot via a mixed FTB/$$\mathcal H_\infty $$ approach

AbstractIn this paper, the problem of guidance and motion control of mobile robots is addressed and solved within the novel framework of the mixed finite-time/$$\mathcal H_\infty $$ H ∞ control theory of nonlinear quadratic systems (NLQSs). Starting from a NLQS describing the dynamics of omnidirectional mobile platforms, the main tasks performed for controlling in closed loop the motion of omnidirectional robots can be conveniently formulated as a mixed finite-time/$$\mathcal H_\infty $$ H ∞ control problem. A robust motion controller, which can effectively rejects disturbances deviating the robot platform from a planned path, can be designed after choosing a linear state-feedback structure for the controller. The synthesis problem is solved through some sufficient conditions contemplating both norm-bounded disturbances and sets constraining initial and terminal conditions, together with a finite-time bound on the output transient. Therefore, for all the allowable uncertainties, in presence of nonzero initial conditions and exogenous disturbance inputs which are possible within an unstructured environment, the motion control tasks can be accomplished through optimal $$\mathcal H_\infty $$ H ∞ performance by simultaneously guaranteeing that the NLQS, which governs in closed loop the robot platform, is finite-time bounded. Finally, the applicability and control performance of the design approach have been evaluated through numerical simulations.

Anthropomorphic Tendon-Based Hands Controlled by Agonist-Antagonist Corticospinal Neural Network.

This article presents a study on the neurobiological control of voluntary movements for anthropomorphic robotic systems. A corticospinal neural network model has been developed to control joint trajectories in multi-fingered robotic hands. The proposed neural network simulates cortical and spinal areas, as well as the connectivity between them, during the execution of voluntary movements similar to those performed by humans or monkeys. Furthermore, this neural connection allows for the interpretation of functional roles in the motor areas of the brain. The proposed neural control system is tested on the fingers of a robotic hand, which is driven by agonist-antagonist tendons and actuators designed to accurately emulate complex muscular functionality. The experimental results show that the corticospinal controller produces key properties of biological movement control, such as bell-shaped asymmetric velocity profiles and the ability to compensate for disturbances. Movements are dynamically compensated for through sensory feedback. Based on the experimental results, it is concluded that the proposed biologically inspired adaptive neural control system is robust, reliable, and adaptable to robotic platforms with diverse biomechanics and degrees of freedom. The corticospinal network successfully integrates biological concepts with engineering control theory for the generation of functional movement. This research significantly contributes to improving our understanding of neuromotor control in both animals and humans, thus paving the way towards a new frontier in the field of neurobiological control of anthropomorphic robotic systems.

Energy-Aware Adaptive Scaling of Server Farms for NFV With Reliability Requirements

Auto-scaling techniques aim to keep the right number of active servers for the current load: if this number is too small we risk service disruption, but if it is too large we waste resources. Despite the interest in the efficient operation of this type of systems, no prior work has addressed auto-scaling techniques for Network Function Virtualization (NFV) with stringent reliability requirements such as those envisioned in 5G (5 or 6 nines). To achieve such levels of reliability, we need to account for both the activation delay until servers become available (i.e., the wake-up or activation time) and the fallible nature of servers (which may fail with some probability). In this paper, we build on control theory to design an auto-scaling technique for a server farm for NFV that guarantees certain reliability while minimizing the number of active resources. We show that the use of well-established tools from control theory results in convergence times much shorter than those obtained with state-of-the-art reinforcement learning techniques. This shows that, despite the current trend to apply machine learning to all sorts of networking problems, there may be some cases where other techniques (such as control theory) can be more suitable.

Human-inspired similarity control system: Enhancing line-following robot perception

Human-Inspired Control (HIC) holds promise for endowing machines with human-like cognition, decision-making, and adaptability. In this study, we employ a fusion of cognitive modeling, machine learning, and control theory as the foundational architecture and empirically validate its suitability for robot control within the realm of HIC. Fuzzy logic stands out as a viable approach for HIC control, wherein control rules can be devised drawing from human intuitive inspiration. Specifically, this study explores similarity inference control systems in robotics, with the objective of enhancing line-following control as an alternative to fuzzy systems. The experimental optimization results provide insights into the advantages and limitations of the similarity inference control system. Despite achieving performance comparable to that of traditional two-stage fuzzy control systems, careful consideration of noise sensitivity is paramount. While the similarity inference approach streamlines implementation and obviates the necessity for expert-designed fuzzy rules, its susceptibility to noise can compromise performance, particularly in noisy environments. These considerations are pivotal for the development of control systems aimed at mitigating noise sensitivity, enhancing task-specific performance, and ensuring the adaptability and robustness of line-following robots. To tackle this challenge, we both discuss and experimentally evaluate potential solutions and their applicability in this paper.

Fixed-time trajectory tracking control of a quadrotor UAV under time-varying wind disturbances: Theory and experimental validation

Abstract This paper adopts a fixed-time method to study the trajectory tracking issue of a quadrotor unmanned aerial vehicle (UAV) under time-varying wind disturbances. Firstly, in order to address the impact of time-varying wind disturbances on UAV, a fixed-time disturbance observer (FxTDO) is constructed to accurately estimate wind disturbances. Secondly, to improve control accuracy and convergence rate, the robust fixed-time controller is designed for the position and attitude system by combining the sliding mode control theory with fixed-time technique. Furthermore, it is rigorously analyzed that the tracking error of the observer and controller can converge to zero via Lyapunov criterion, and the convergence time is independent of the initial state.Finally, the effectiveness and robustness of the designed control strategy are verified by numerical simulations and actual flight experiments, providing an effective connection between control theory and practical applications.

Approaching lifelong learning: An integrated framework for explaining decision-making processes in personal and professional development

Background: Individual differences in commitment to lifelong learning, a process aimed at seizing opportunities for self-development, have not been extensively studied. Objective: Our aim is to provide a comprehensive understanding of the decision-making mechanisms involved in pursuing learning for self-development. Method: We conducted a literature review on the taxing nature of cognitive exertion and its impact on the inclination to engage in cognitively demanding tasks for learning, as well as individual differences in sensitivity to aversive or rewarding outcomes inherent in the learning process. Results: Our findings indicate that the Expected Value of Control (EVC) theory can elucidate the former, while research on approach-avoidance motivation can shed light on the latter. Conclusion: We propose and develop an integrated framework that incorporates both lines of research. This framework holds relevance for neuropsychology, experimental psychology, and education psychology, offering theoretical guidance for tailoring learning experiences to enhance engagement and commitment to self-development.

Optimal controller for S-CO2 compressor inlet conditioning

The small, simple, and effective characteristics of a Supercritical CO2 (S-CO2) power system make it an ideal candidate for distributed power generation. However, the distributed power generation will be required with constant changes in the operating conditions to meet the varying demand. Thus, it is necessary for the S-CO2 power system to demonstrate that it can operate safely and efficiently under all anticipated operating conditions. One of the key issues in the S-CO2 power system is controlling the compressor inlet conditions. This paper focused on the control of the compressor inlet temperature. In this paper, a system dynamic simulation code is used to model the precooler system for the compressor inlet control first. Based on the analysis results to obtain the transfer function and the state space, a linear quadratic controller was designed using optimal control theory. Since the controller was estimated to have steady-state error, a disturbance observer was also used. To validate the designed controller, experiments were conducted. The experimental results show that the proposed controller can be used to maintain the S-CO2 compressor inlet conditions under various transient scenarios without additional tuning.

Parameter identification of a reaction-diffusion predator-prey system based on optimal control theory

This paper takes a reaction-diffusion predator-prey system with ratio-dependent Holling III functional response function and Leslie-Gower term into consideration. First of all, the system model is proposed on the basis of basic biological assumptions and previous work, and the existence conditions of the equilibrium point of the system are discussed. Secondly, under the assumption of the existence of equilibrium point, the Turing instability necessary conditions induced by diffusion are investigated. Thirdly, optimal control theory is derived, and the adjoint system and the first-order optimization condition are established. Fourthly, parameter identification based on optimal control is studied, and the technique is extended to network structure. Finally, extensive numerical simulations, including Turing pattern, Normalized Population High Distribution Area (NPHDA) diagram and parameter identification, are carried out to illustrate and validate the analytical results. For the system dynamics phenomena, the results from different perspectives effectively demonstrate that the theoretical findings, numerical simulations and natural reality are identical. In terms of the parameter identification of continuous model and network model, the efficiency and accuracy of various algorithms are fully tested.