Principles Of Control Theory Research Articles

A Network Control Theory of Dynamic Systems Approach to Personalize Therapy

Contemporary latent disease models of psychopathology have shown limited clinical utility and the efficacy of conventional treatments have been disappointing. An alternative approach offers the network approach and a dynamic systems perspective to psychopathology and treatment change. To understand and modify dynamic systems, engineering and mathematics have been relying on principles of network control theory. This article will discuss the application of network control theory of dynamic systems approach to personalize therapy. Network control theory can be used as a guide for personalizing treatment by choosing the most promising intervention strategy targeting the change processes based on the network structure. A composit case illustration will demonstrate the principles and application of network control theory to therapy in practice within the framework of process-based therapy. In conclusion, a network control theory of dynamic systems approach is highly relevant and applicable to clinical science.

Ultra-Broadband Mode (De)Multiplexer on Thin-Film Lithium Niobate Platform Adopting Phase Control Theory.

Mode (de)multiplexers (MDMs) serve as critical foundational elements within systems for facilitating high-capacity communication, relying on mode conversions achieved through directional coupler (DC) structures. However, DC structures are challenged by dispersion issues for broadband mode coupling, particularly for high-order modes. In this work, based on the principles of phase control theory, we have devised an approach to mitigate the dispersion challenges, focusing on a thin-film lithium niobate-on-onsulator (LNOI) platform. This solution involves integrating a customized inverse-dispersion section into the device architecture, offsetting minor phase shifts encountered during the mode coupling process. By employing this approach, we have achieved broadband mode conversion from TE0 to TE1 and TE0 to TE2 within a 300 nm wavelength range, and the maximum deviations were maintained below -0.68 dB and -0.78 dB, respectively. Furthermore, the device exhibited remarkably low crosstalk, reaching down to -26 dB.

Differentiable programming for gradient-based control and optimization in physical systems

This paper presents an exploration of the application of control theory, particularly utilizing a gradient-based algorithm, to automate and optimize the operation of photovoltaic panels and refrigeration systems in warehouse environments. The study emphasizes achieving coordination between energy generation and consumption, specifically harnessing surplus solar energy for efficient refrigeration. The complex interplay between fluctuating solar irradiance, thermal dynamics of the warehouse, and refrigeration needs underscores the significance of control theory in designing algorithms to dynamically adjust PV panel output and refrigeration system operation. The paper discusses foundational control theory principles, proposes a tailored framework for warehouse operations, and highlights the potential for sustainable energy practices. This paper explores the use of data-driven approaches based on NeuralODEs vs classical ones using physics equations.

Fusion dynamical systems with machine learning in imitation learning: A comprehensive overview

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. A persistent challenge in IL lies in extending generalization from historical demonstrations, enabling the acquisition of new skills without re-teaching. Dynamical system-based IL (DSIL) emerges as a significant subset of IL methodologies, offering the ability to learn trajectories via movement primitives and policy learning based on experiential abstraction. This paper emphasizes the fusion of theoretical paradigms, integrating control theory principles inherent in dynamical systems into IL. This integration notably enhances robustness, adaptability, and convergence in the face of novel scenarios. This survey aims to present a comprehensive overview of DSIL methods, spanning from classical approaches to recent advanced approaches. We categorize DSIL into autonomous dynamical systems and non-autonomous dynamical systems, surveying traditional IL methods with low-dimensional input and advanced deep IL methods with high-dimensional input. Additionally, we present and analyze three main stability methods for IL: Lyapunov stability, contraction theory, and diffeomorphism mapping. Our exploration also extends to popular policy improvement methods for DSIL, encompassing reinforcement learning, deep reinforcement learning, and evolutionary strategies. The primary objective is to expedite readers’ comprehension of dynamical systems’ foundational aspects and capabilities, helping identify practical scenarios and charting potential future directions. By offering insights into the strengths and limitations of dynamical system methods, we aim to foster a deeper understanding among readers. Furthermore, we outline potential extensions and enhancements within the realm of dynamical systems, outlining avenues for further exploration.

Dynamic evolution control for the DC/DC boost converter design and implementation

This paper introduces the design and hardware implementation of the dynamic evolution control for a DC/DC boost converter. The development of the controller aims to effectively regulate the output voltage of the DC/DC boost converter with a high degree of precision. A comprehensive examination of the duty cycle formula for the boost converter is conducted using a non-linear analysis approach. This study introduces a methodology for the synthesis of a converter controller utilizing the principles of dynamic evolution control theory. The converter's output consistently aligns with the target voltage by following the dynamic evolution path determined by the designed controller. The performance evaluation of the proposed boost converter controller utilizing dynamic evolution control is being validated through the utilization of MATLAB/Simulink simulation software. The effectiveness of the proposed controller is further validated through the presentation of hardware results. The performance of the controller was evaluated by varying the values of parameters k and m.

Improved model-free synergetic control of permanent magnet synchronous motor using unknown input observer

Parametric uncertainties and inverter nonlinearity exist in the permanent magnet synchronous motor (PMSM) drive system, which may lead to performance degradation or failure of model-based control. This paper presents an enhanced model-free synergetic control method for PMSMs, addressing the limitations of relying on accurate motor models in existing speed control strategies. The proposed approach aims to achieve robust control performance in the presence of parameter perturbations without the need for an accurate motor model. Firstly, a new ultra-local model of the speed loop for the PMSM is established based on a newly developed ultra-local theory. Subsequently, a model-free synergetic controller is designed using the principles of synergetic control theory. To address the issue of output chattering in the sliding mode observer used to estimate the unknown part of the ultra-local model, a synergetic observer method is proposed. Simulation results are presented and compared with those obtained using a proportional-integral (PI) controller and a conventional model-free sliding mode controller. The results demonstrate that the model-free synergetic controller exhibits robust performance and provides accurate estimation of the unknown part without output chattering.

Research step of PID control method of stepper motor based on improved fuzzy control algorithm

The significance of PID control within the management system of stepper motors is noteworthy; nonetheless, it is worth noting that stepper motors are susceptible to issues such as low power, step loss, and vibration. The conventional Proportional-Integral-Derivative (PID) control method is insufficient in addressing the control challenge specific to stepper motor management systems. Hence, this research work presents an enhanced fuzzy control method that integrates the principles of fuzzy control theory with traditional PID control theory. The integration of fuzzy control into the P ID control is undertaken to create a fuzzy controller that satisfies the demands of stepper motor control. Additionally, the division of indices is conducted in accordance with the specifications of the fuzzy controller in order to mitigate the disruptive elements of PID control. then, the use of fuzzy control rules is employed to achieve control over the stepper motor, resulting in the development of an enhanced scheme that is then subjected to rigorous validation. The present study employs a MATLAB simulation to compare the performance of the enhanced fuzzy control algorithm with that of the P-ID control method. The results demonstrate that the improved fuzzy control algorithm significantly enhances the stability and dynamic performance of the stepper motor. Superior to traditional Proportional-Integral-Derivative (PID) control.

Linking activity dyshomeostasis and sleep disturbances in Alzheimer disease.

The presymptomatic phase of Alzheimer disease (AD) starts with the deposition of amyloid-β in the cortex and begins a decade or more before the emergence of cognitive decline. The trajectory towards dementia and neurodegeneration is shaped by the pathological load and the resilience of neural circuits to the effects of this pathology. In this Perspective, I focus on recent advances that have uncovered the vulnerability of neural circuits at early stages of AD to hyperexcitability, particularly when the brain is in a low-arousal states (such as sleep and anaesthesia). Notably, this hyperexcitability manifests before overt symptoms such as sleep and memory deficits. Using the principles of control theory, I analyse the bidirectional relationship between homeostasis of neuronal activity and sleep and propose that impaired activity homeostasis during sleep leads to hyperexcitability and subsequent sleep disturbances, whereas sleep disturbances mitigate hyperexcitability via negative feedback. Understanding the interplay among activity homeostasis, neuronal excitability and sleep is crucial for elucidating the mechanisms of vulnerability to and resilience against AD pathology and for identifying new therapeutic avenues.

Safe and Fault Tolerant Control of Industrial differential Drive Vehicles

differential drive vehicles are an integral of industrial operations, where precise control is essential for safety and efficiency. These vehicles often employ encoders for precise maneuvering through closed-loop control. In this scenario, system failures can result in uncontrolled movements, posing significant risks to human operators.To address this issue, this paper proposes a robust and fail-safe controller model that ensures vehicle safety, even in the face of multiple failure scenarios. The proposed model provides a comprehensive solution to address various potential problems encountered in industrial environments.Based on fault tolerance and control theory principles, our model incorporates innovative strategies to mitigate encoder failures, sensor malfunctions, and other related system faults. The design and performance of the controller are thoroughly analyzed to demonstrate its effectiveness in preventing catastrophic failures in industrial environments.

Bridging the gap between control theory and practice: From simple controller design to a practical microcontroller implementation

The practical implementation of control theory principles often presents challenges in bridging theoretical designs with real-world applications. This paper aims to fill this void by examining four distinct theoretical control techniques, namely the Kalman method, Dahlin method, PID, and Fractional order PID, and systematically implementing them using Arduino microcontroller boards. Theoretical foundations and design considerations are initially presented for each control method. Following that, comprehensive and sequential guidelines are given for converting these abstract ideas into tangible applications using microcontrollers. The text provides a detailed description of the hardware setup and software development process for each method. It focuses on practical aspects such as sensor interfacing, control algorithm implementation, and real-time constraints. An examination of these methods using practical implementation metrics such as response time, overshoot and stability allows us to gain a better understanding of how well each approach is suited for real-world applications. In summary, this study enhances our knowledge of the process of moving from theoretical control design to practical microcontroller implementation. It provides valuable guidance for engineers and researchers who want to apply control strategies in real-time embedded systems.

The minimum principle of hybrid optimal control theory

The minimum principle of hybrid optimal control theory

Assessing the benefit of variational quality control for assimilating Aeolus Mie and Rayleigh wind profiles in NOAA's global forecast system during tropical cyclones

AbstractIn this article, we show a “proof‐of‐concept” study to assess the utility of a variational quality control algorithm in increasing the number of assimilated Aeolus Mie‐cloudy and Rayleigh‐clear winds in National Oceanic and Atmospheric Administration (NOAA)'s global data assimilation and forecast system. The National Centers for Environmental Prediction (NCEP) Variational Quality Control (NCEP‐VQC) algorithm was tuned and applied during the minimization process. This type of quality control uses optimal control theory principles to treat outliers in the probability density function (PDF) of observational departure statistics, assuming that the observation errors follow a family of logistic distributions. In the case of Aeolus Mie‐cloudy and Rayleigh‐clear winds, the NCEP‐VQC algorithm permitted the relaxation of the gross error and one of the recommended ESA quality controls (reject Rayleigh‐clear observations below 850 hPa), assigned adaptive observation weights ranging from 0 to 1, and led to an increase in the number of retained Aeolus observations for the calculation of global analyses, which in turn improved the verification statistics on analyzed tropical storms This article discusses the advantage of implementing the NCEP‐VQC algorithm in the Aeolus data assimilation, the benefits of retaining more wind profiles that contribute to the analysis calculation, and shows improvements in the initialization and short‐term forecasts on several tropical cyclone cases.

Optimization Design Method of Tendon-Sheath Transmission Path Under Curvature Constraint

The application requirements of the tendon-sheath mechanism in the field of precision machinery are becoming increasingly extensive. However, the contact friction between the tendon and sheath seriously affects the transmission accuracy. In the case of unavoidable friction, optimizing the tendon transmission path to reduce tension loss and elastic deformation has become an important research direction. In this article, the influence law of the tendon transmission path on the tension and displacement transmission is obtained using the two parameters related to the curvature of the transmission path: total bending angle and equivalent tendon length. Then, based on the optimal control theory and minimum principle, the different transmission path solutions of the minimum tension loss, the minimum tendon deformation, and the coupling of tension and displacement are obtained; the numerical optimization method verifies the correctness of the proposed theory. Finally, an optimal design of a tendon-constrained synchronous rotation mechanism for the manipulator is carried out, and the linkage performance is greatly improved by optimizing the transmission path.

Синтез трехканального ПИД-регулятора для управления трехзонной среднетемпературной печью

Currently, in metrological laboratories there is a need to transfer temperature units from standards of the highest category by reproducing the fixed points of the international temperature scale. The reproduction process of fixed point involves heating, melting, overheating and solidification of the metal and can sometimes take more than one working day. The purpose of this work was to create and practically test an algorithm that takes into account heat flows in a three-zone furnace for uniform heating and relatively fast reaching the solidification area. Over the past twenty years, several algorithms have been used making it possible to go through all four stages within one working day. This paper describes a practically tested algorithm for controlling three-zone furnaces for reproducing the fixed points of the temperature scale in the range from 156.5985 °C to 1084.62 °C. It takes into account the requirements for maintaining the minimum temperature gradient along the height of the fixed point cell. This algorithm has been used in the furnaces of the West Siberian branch of FSUE "VNIIFTRI". The controller model is based on the principles of automatic control theory using a multi-channel discrete proportional-integral-derivative or PID controller. The model was modified in accordance with the system structure. The main idea is to represent the model of a three-zone furnace as a three-channel control object with mutual influence between the channels. The corresponding controller model is a three-channel control system. In each channel of it there is a PID controller. Depending on the phase of the playback process, the three-channel controller changes its structure and tunes its coefficients. Such a structure made it possible to achieve sufficient productivity of the furnace.

Optimal Shortcuts to Adiabatic Control by Lagrange Mechanics.

We combined an inverse engineering technique based on Lagrange mechanics and optimal control theory to design an optimal trajectory that can transport a cartpole in a fast and stable way. For classical control, we used the relative displacement between the ball and the trolley as the controller to study the anharmonic effect of the cartpole. Under this constraint, we used the time minimization principle in optimal control theory to find the optimal trajectory, and the solution of time minimization is the bang-bang form, which ensures that the pendulum is in a vertical upward position at the initial and the final moments and oscillates in a small angle range.

Application of a time-delay SIR model with vaccination in COVID-19 prediction and its optimal control strategy.

In the classical infectious disease compartment model, the parameters are fixed. In reality, the probability of virus transmission in the process of disease transmission depends on the concentration of virus in the environment, and the concentration depends on the proportion of patients in the environment. Therefore, the probability of virus transmission changes with time. Then how to fit the parameters and get the trend of the parameters changing with time is the key to predict the disease course with the model. In this paper, based on the US COVID-19 epidemic statistics during calibration period, the parameters such as infection rate and recovery rate are fitted by using the linear regression algorithm of machine science, and the laws of these parameters changing with time are obtained. Then a SIR model with time delay and vaccination is proposed, and the optimal control strategy of epidemic situation is analyzed by using the optimal control theory and Pontryagin maximum principle, which proves the effectiveness of the control strategy in restraining the transmission of COVID-19. The numerical simulation results show that the time-varying law of the number of active cases obtained by our model basically conforms to the real changing law of the US COVID-19 epidemic statistics during calibration period. In addition, we have predicted the changes in the number of active cases in the COVID-19 epidemic in the USA over time in the future beyond the calibration cycle, and the predicted results are more in line with the actual epidemic data.

Electrophysiology-based Closed Loop Optogenetic Brain Stimulation Devices: Recent Developments and Future Prospects.

Closed loop optogenetic brain stimulation enhances the efficacy of the stimulation by adjusting the stimulation parameters based on direct feedback from the target area of the brain. It combines the principles of genetics, physiology, electrical engineering, optics, signal processing and control theory to create an efficient brain stimulation system. To read the underlying neuronal condition from the electrical activity of neurons, a sensor, sensor interface circuit, and signal conditioning are needed. Also, efficient feature extraction, classification, and control algorithms should be in place to interpret and use the sensed data for closing the feedback loop. Finally, a stimulation circuitry is required to effectively control a light source to deliver light based stimulation according to the feedback signal. Thus, the backbone to a functioning closed loop optogenetic stimulation device is a well-built electronic circuitry for sensing and processing of brain signals, running efficient signal processing and control algorithm, and delivering timed light stimulations. This paper presents a review of electronic and software concepts and components used in recent closed-loop optogenetic devices based on neuro-electrophysiological reading and an outlook on the future design possibilities with the aim of providing a compact and easy reference for developing closed loop optogenetic brain stimulation devices.

Применение оптимального управления при определении режима обработки материалов концентрированными потоками энергии: краткий обзор

The analysis of approaches and ways of determining the processing mode (mechanical, local thermal, welding, thermal cutting, etc.) is given. It is shown that the task of determining the processing mode is the reverse, characterized by a violation of the causal relationship inherent in the real thermal process. Methods and principles of optimal control theory are more effective methods for solving inverse problems. The first problems of optimal control of thermal processes of processing materials with concentrated energy flows were posed and solved in 1976 at the Baykov Institute of Metallurgy of the USSR Academy of Sciences as one-dimensional problems of thermal conductivity. Two- and three-dimensional optimal control problems were posed later and solved using the same methods of the theory of optimal control of systems with distributed parameters. The application of the principles of optimal control and the capabilities of modern computer technology make it possible to reduce the number of field experiments in determining the processing mode and increase the efficiency of the technological processes being developed.

Measuring the performance of retailers during the COVID-19 pandemic: Embedding optimal control theory principles in a dynamic data envelopment analysis approach

Traditional retailers (bricks-and-mortar) have been continuously increasing online sales. However, not all retail companies were able to respond to the increasing sales with the same efficiency level as their competitors. This paper aims to propose a dynamic model – incorporating principles of Optimal Control Theory (OCT) into a Data Envelopment Analysis (DEA) model - for measuring the performance of retailing companies’ cost efficiency. It also aims to contribute through the application by investigating the impact of the pandemic on companies from the most prominent developing market in Latin America, Brazil. Twenty-one companies publicly traded in the São Paulo Stock Exchanges (B3) between the third quarter of 2018 (3Q2018) and the third quarter of 2020 (3Q2020) were investigated. Also, six measures - initial inventory cost (IIC), final inventory cost (FIC), net operating income (NOI), cost of goods sold (COGS), cost of the purchased product (CPP), and plant, property, and equipment (PPE) – were considered. In this way, the findings have implications for researchers and practitioners. Practitioners can discover which competitor(s) is (are) adopting the best practices at each operational aspect (e.g., inventory cost). Additionally, the proposed method can be replicated in other markets (developing or not) and for other categories of retailing companies (e.g., small- and middle-sized). Further research directions are presented, and their implications are discussed.

Dynamic Pricing and Logistics Service Decisions for Crowd Logistics Platforms with Social Delivery Capacity

With the development of sharing economy, more and more enterprises choose crowd logistics for distribution. Because the crowd logistics platform uses social freelancers, the service quality is difficult to guarantee. Considering the reward-penalty mechanism, dynamic differential game models are constructed to study the optimal pricing and quality of crowd logistics services under stochastic demand based on the optimal control theory and Pontryagin maximum principle. The numerical simulation results show that the optimal dynamic decisions change with the fluctuation of demand dynamically. Furthermore, the platform needs to adjust the value of the reward-penalty factor to ensure the level of service quality and revenue in different situations.