Control Theory Research Articles

Design, optimization, simulation, and implementation of a 3D printed soft robotic peristaltic pump

Abstract This study presents an innovative approach to fluidic pumping using soft robotics, designed to circulate fluid through soft conduits for delicate environments like blood streams where traditional peristaltic pumps may not be feasible. A novel soft robotic peristaltic pump is optimized and implemented, featuring 3D printed ring-shaped actuators and a PDMS pipe housing a Newtonian fluid. The design includes a three-stage actuator ring structure, actuated sequentially for peristaltic motion. A parametric finite element model predicts the required pressure, and the Mooney-Rivlin 5 Parameters hyper-elastic material model ensures accurate material properties. Optimization uses response surface analysis in Minitab and MATLAB Simulink Simscape simulations to achieve maximum flow rate with minimal power and pressure. Experimental validation confirms the simulations, achieving an optimal flow rate of 0.27 ml s−1 at a 450 ms cycle, with minor discrepancies due to friction and measurement errors. This study demonstrates the scalability of linearly sequenced soft squeeze actuators into an effective pump, validated by both simulation and experiments. Future applications include medical devices addressing deep venous thrombosis, with further research exploring control theory for optimization and comparing performance with conventional pumps to enhance practical applicability.

Offline supervisory control synthesis: taxonomy and recent developments

AbstractThis paper surveys recent advances in supervisory control theory since its 30th anniversary in 2017. We performed a systematic literature search and selected 272 relevant papers, with a focus on automata-based approaches. These were fitted into coherent narratives, based in part on a taxonomy of supervisor synthesis methods. The obtained papers show the fast and vast progress of the field, where recent research covers a wide range of new synthesis optimization techniques, different aspects such as fault tolerance, security, and timing, and new and diverse application domains. This survey refrains from in-depth descriptions of techniques, instead focusing on high-level contributions of recent works, how they relate to each other, and future directions for research.

Efficient implementation of quantum permutation algorithm using a polar SrO molecule in pendular states

Quantum algorithms offer more enhanced computational efficiency in comparison to their classical counterparts when solving specific tasks. In this study, we implement the quantum permutation algorithm utilizing a polar molecule within an external electric field. The selection of the molecular qutrit involves the utilization of field-dressed states generated through the pendular modes of SrO. Through the application of multi-target optimal control theory, we strategically design microwave pulses to execute logical operations, including Fourier transform, oracle U f operation, and inverse Fourier transform within a three-level molecular qutrit structure. The observed high fidelity of our outcomes is intricately linked to the concept of the quantum speed limit, which quantifies the maximum speed of quantum state manipulation. Subsequently, we design the optimized pulse sequence to successfully simulate the quantum permutation algorithm on a single SrO molecule, achieving remarkable fidelity. Consequently, a quantum circuit comprising a single qutrit suffices to determine permutation parity with just a single function evaluation. Therefore, our results indicate that the optimal control theory can be well applied to the quantum computation of polar molecular systems.

Effects of a dog activity tracker on owners' walking: a community-based randomised controlled trial.

A promising strategy to increase population physical activity is through promotion of dog walking. Informed by multi-process action control and nascent dog-walking theory, we examined the effectiveness of a 3-month technology-based (dog tracker) 2-arm randomised controlled dog-walking intervention to increase dog-owner daily physical activity in the general community in Sydney, Australia. 37 participants were allocated to the intervention group (mean age = 43.2 [SD 11.9]) and 40 to the control group (mean age = 42.3 [SD 11.9]). Both groups averaged more than 10,500 steps/day at baseline. There was no evidence of within- or between-group physical activity differences across timepoints. The results remained consistent after exclusion of participants who had data collected during COVID-19 lockdowns. Compared with baseline, both groups had significant increases in sedentary time during the post-intervention, and 6 month follow-up. The absence of significant differences between-group physical activity differences may be attributable to the ceiling effect of both groups already being sufficiently active. These results provide useful guidance to future studies intended to assess the efficacy of technology-based dog-walking interventions. Future dog-walking interventions should specifically target physically inactive dog owners. ACTRN12619001391167 (10/10/2019); Retrospectively registered.

Family formation and health-related behavior: The case of meal skipping

Using four rounds of nationally representative U.S. panel survey data (NLSY97) and fixed-effects models, this study examines whether and how transitions to marriage/cohabitation/parenthood are associated with gender-specific changes in meal-skipping patterns. Two theoretical perspectives, social control and time scarcity, provide different explanations of how family formation relates to meal skipping. However, empirical research examining the association using a nationally representative sample is scarce. Results indicate that the transitions to romantic relationships (either marriage or cohabitation) are associated with less frequent meal skipping, consistent with social control theory, but in ways that differ by gender. The transition to marriage is associated with reduced meal skipping only for men, whereas the transition to cohabitation is associated with reduced meal skipping to some extent for women. The transition to parenthood has distinctive consequences for women’s meal patterns. Specifically, having two or more children at home is associated with increased meal skipping only for women, which can be explained by the time scarcity perspective. This study reveals that the consequences of family formation on health-related behaviors are gender specific and not universally positive, highlighting women’s persistent role as the primary caregiver for the family.

Multilevel modeling and control of dynamic systems.

The underlying changes of complex dynamic systems are affected by numerous highly interdependent components with nonlinear interactions and cannot be suitably predicted by merely analyzing their individual parts. Recent technological developments (reducing the size of actuators and sensors, increasing speed and productivity) make it possible to monitor and handle systems on time, even during transient processes. In many practical applications, to effectively study rapidly evolving systems, their structure has to be examined at three levels: Micro-Scale (Short-Term), Meso-Scale (Intermediate-Term), and Macro-Scale (Long-Term). This multiscale framework provides a powerful tool for understanding and managing the intricate dynamics of complex systems. Macro-scale and micro-scale approaches are conventionally utilized in general system modeling and control theory, especially within the fields of networked control and multi-agent systems. By leveraging the meso-scale perspective, it is possible to balance detailed component analysis with overarching system behaviors, thereby enhancing the efficiency and effectiveness of operational strategies in complex systems. This paper presents and discusses a formal meso-level depiction of dynamics and control, assuming a finite number of attractors form and remain stable over time progress. Integral characteristics and dynamics of clusters are defined, simplifying control synthesis while maintaining problem formulation. A significant challenge is the model error due to changing cluster structures linked to randomized estimation and optimization algorithms valid under unknown disturbances. To demonstrate the practicality of this framework, the approach is applied to control tasks of a group of robots, leading to scalable and effective control strategies.

PEMFC Gas-Feeding Control: Critical Insights and Review

Proton exchange membrane fuel cells (PEMFCs) are currently a relatively mature type of hydrogen energy device due to their high efficiency and low noise compared to traditional power devices. However, there are still challenges that hinder the large-scale application of PEMFCs. One key challenge lies in the gas supply system, which is a complex, coupled nonlinear system. Therefore, an effective control strategy is essential for the efficient and stable operation of the gas control system. This paper aims to provide a comprehensive and systematic overview of the control strategies for PEMFC anode and cathode supply systems based on an analysis of 182 papers. The review covers modern control theories and optimization algorithms, including their design, objectives, performance, applications, and so on. Additionally, the advantages and disadvantages of these control methods are thoroughly evaluated and summarized.

Social Group Dynamics and Its Role in the Phenomenon of Bullying in Secondary Schools

This study aims to analyze the dynamics of social groups and their role in the phenomenon of bullying in secondary schools. The main focus is to understand how interactions between groups, social norms, and hierarchical structures within groups affect the prevalence and forms of bullying. The method used is a qualitative approach with a systematic literature review. Relevant sources were identified through academic databases using specific keywords, then evaluated and analyzed using thematic analysis. The results of the study indicate that social group dynamics play a significant role in the phenomenon of bullying. Rigid hierarchical group structures, norms that support dominance, and intergroup competition tend to increase the risk of bullying. Conversely, groups with more egalitarian structures and inclusive values tend to show lower levels of bullying. The role of group leaders and bystanders was also found to be crucial in shaping responses to bullying. Contextual factors such as school transitions and the use of social media also influence the dynamics of bullying. Analysis using Social Identity Theory and Social Control Theory revealed that bullying is often rooted in the need to strengthen group identity and respond to weak social ties. These findings emphasize the importance of a holistic approach in bullying prevention and intervention efforts, which considers the complexity of group dynamics and the broader social context.

Energy Associated With Dynamic Network Changes in Patients With Multiple Sclerosis and Cognitive Impairment.

Patients with multiple sclerosis (MS) often experience cognitive impairment, and this is related to structural disconnection and subsequent functional reorganization. It is unclear how specific patterns of functional reorganization might make it harder for cognitively impaired (CI) patients with MS to dynamically adapt how brain regions communicate, which is crucial for normal cognition. We aimed to identify dynamic functional network patterns that are relevant to cognitive impairment in MS and investigate whether these patterns can be explained by altered energy costs. Resting-state functional and diffusion MRI was acquired in a cross-sectional design, as part of the Amsterdam MS cohort. Patients with clinically definitive MS (relapse-free) were classified as CI (≥2/7 domains Z < -2), mildly CI (MCI) (≥2/7 domains Z < -1.5), or cognitively preserved (CP) based on an expanded Brief Repeatable Battery of Neuropsychological Tests. Functional connectivity states were determined using k-means clustering of moment-to-moment cofluctuations (i.e., edge time series), and the resulting state sequence was used to characterize the frequency of transitions. Control energy of the state transitions was calculated using the structural network with network control theory. Imaging and cognitive data were available for 95 controls and 330 patients (disease duration: 15 years; 179 CP, 65 MCI, and 86 CI). We identified a "visual network state," "sensorimotor network state," "ventral attention network state," and "default mode network state." CI patients transitioned less frequently between connectivity states compared with CP (β = -5.78; p = 0.038). Relative to the time spent in a state, CI patients transitioned less from a "default mode network state" to a "visual network state" (β = -0.02; p = 0.004). The CI patients required more control energy to transition between states (β = 0.32; p = 0.007), particularly for the same transition (β = 0.34; p = 0.049). This study showed that it costs more energy for MS patients with cognitive impairment to dynamically change the functional network, possibly explaining why these transitions occur less frequently. In particular, transitions from a default mode network state to a visual network state were relevant for cognition in these patients. To further study the order of events leading to these network disturbances, future work should include longitudinal data across different disease stages.

A Bayesian-Based Credible and Reliable Control Method for Active Vibration Suppression of Structures with Uncertain Parameters

As a classical design method for addressing uncertainty in control systems, robust control is designed with the goal of complete reliability of the control system. Nevertheless, the pursuit of robustness can inadvertently give rise to considerable performance degradation, rendering the resultant closed-loop control system excessively conservative in its design. Reliability-based design methods serve as an effective approach for managing uncertainties, which will achieve a balance between the robustness and performance of the active control system by taking advantage of the characteristic of reliability that allows for a certain level of permissible failure probability. The accurate quantification of uncertainty is the key to achieving reliable control, but common uncertainty quantification methods cannot achieve credible quantification of uncertainty in small samples and update the distribution of new samples. In this paper, a nonprobabilistic Bayesian inference based interval uncertainty quantification method is proposed. This method updates the distribution of interval boundaries and radii by introducing a small number of samples, and obtains interval parameters under a given confidence level. On the basis of uncertainty quantification, the dynamic reliability of the vibration system is derived, and nonprobabilistic reliability is introduced into the design of active controllers through optimization strategies. The reliable control strategy aims to select the optimal controller parameters on a reliable path with a given credibility, in order to solve the problem of overly conservative robust control theory. Two numerical examples were used to verify the feasibility and applicability of this method.

Speed controller design for turboshaft engine using a high-fidelity AeroThermal model

Abstract One of the main objectives of this research is to construct a high-fidelity aerothermal model for controlling the turboshaft engine power turbine rotational speed. Firstly, a high-fidelity aerothermal model of General Electric T700 is formed. The turboshaft engine aerothermal model is simulated and compared to engine design point data on MATLAB/Simulink environment. Thermodynamic equations and some algebraic equations are used. In predicting compressor mass flow rate, the adaptive neuro-fuzzy inference system method is preferred. A propeller model is also added to the turbo shaft engine aerothermal model and to keep the power turbine rotational speed at a constant value, a Proportional Integral Derivative controller is designed and applied. Open-loop and closed-loop simulations are conducted and successful outcomes are achieved. Results indicate that the differences between simulation and actual engine data are within acceptable limits. The suggested model can be readily updated and expanded to control additional parameters of the engine. PACS 2010 Classification: Control theory in mathematical physics, 02.30. Yy, Computer modeling and simulation, 07.05.Tp, 05.70.Ce Thermodynamic functions and equations of state.

Approximation Properties of Chlodovsky-Type Two-Dimensional Bernstein Operators Based on (p, q)-Integers

In the present study, we introduce the two-dimensional Chlodovsky-type Bernstein operators based on the (p,q)-integer. By leveraging the inherent symmetry properties of (p,q)-integers, we examine the approximation properties of our new operator with the help of a Korovkin-type theorem. Further, we present the local approximation properties and establish the rates of convergence utilizing the modulus of continuity and the Lipschitz-type maximal function. Additionally, a Voronovskaja-type theorem is provided for these operators. We also investigate the weighted approximation properties and estimate the rate of convergence in the same space. Finally, illustrative graphics generated with Maple demonstrate the convergence rate of these operators to certain functions. The optimization of approximation speeds by these symmetric operators during system control provides significant improvements in stability and performance. Consequently, the control and modeling of dynamic systems become more efficient and effective through these symmetry-oriented innovative methods. These advancements in the fields of modeling fractional differential equations and control theory offer substantial benefits to both modeling and optimization processes, expanding the range of applications within these areas.

Design and optimization of PD control system based on saturation

Abstract. Designing PI, PD, and PID controllers is challenging, but it is an inevitable step if engineers want their systems to perform well. Consequently, control theory was purposed and the system became more complicated. Unfortunately, saturation is hard to prevent. The degree of saturation is an important aspect of the electrical signal process. This paper investigates the concepts of saturation for PD,PI, and PID control. It involves the discussion of properties of passive components, excessive input/output concerning the design maximum or minimum, and the saturation function. The main purpose of the paper is to give an insight into how to prevent control saturation. The investigation will be mainly based on current control theory, research processes from other papers, and the online simulations. To avoid saturation, applying analysis on the gain of all controllers and saturation function is crucial.

A Comprehensive Review of Artificial Intelligence and Machine Learning in Control Theory

Abstract: Traditional control methods, such as proportional-integral-derivative (PID) controllers and linear-quadratic regulators (LQRs), have proven effective for linear and well-modeled systems. However, these methods often perform poorly in nonlinear, complex and dynamic environments. The paper aims to investigate the modern control systems by integrating artificial intelligence (AI) techniques, such as machine learning (ML), reinforcement learning (RL), deep learning, and fuzzy logic, to enhance their adaptive, robust, and predictive capabilities. And it reviews the literature and analyzes AI integration in control systems. The proposed strategies include supervised learning for trajectory optimization and fault detection, reinforcement learning for optimal control in dynamic environments, neural networks for complex nonlinear function approximation, and fuzzy logic for handling uncertainty and imprecise inputs. AI techniques significantly enhance the ability to tackle nonlinear problems and dynamic changes, demonstrating superior performance in applications like self-driving cars adapting to various road conditions and optimal energy distribution in smart grids. Despite the challenges of computational complexity, scalability, and the safety and reliability in the implementation of interpretable AI models, this paper suggests that hybrid approaches combining traditional control and AI techniques, along with the evolution of interpretable AI and convergence with quantum control, hold great promise for advancing AI-driven control systems.

Distributed Model Predictive Control Cooperative Guidance Law for Multiple UAVs

Aiming at the problem of multiple unmanned aerial vehicles (UAVs) cooperatively intercepting a maneuvering target, this paper proposes a cooperative guidance law with less energy consumption and a newly accurate time-to-go estimation algorithm in the two-dimensional (2D) plane. Firstly, based on the relative motion equations between UAVs and the target on the 2D plane, the line-of-sight (LOS) direction and the LOS normal direction models are established. Then, based on the distributed model predictive control (DMPC) theory, DMPC cooperative guidance laws are designed in two directions. This guidance law can ensure that all UAVs intercept the maneuvering target at the expected LOS angle at the same time and reduce the energy consumption during the guidance process. Then, a new time-to-go estimation algorithm is designed, which can reduce the time-to-go estimation error and improve the cooperative accuracy. Finally, the simulation results show that the DMPC cooperative guidance law reduces energy consumption by more than 50% compared to other guidance laws and the proposed time-to-go estimation algorithm improves the accuracy by 200% compared to traditional methods.

Bayesian optimization for state engineering of quantum gases

Abstract State engineering of quantum objects is a central requirement for precision sensing and quantum computing implementations. When the quantum dynamics can be described by analytical solutions or simple approximation models, optimal state preparation protocols have been theoretically proposed and experimentally realized. For more complex systems such as interacting quantum gases, simplifying assumptions do not apply anymore and the optimization techniques become computationally impractical. Here, we propose Bayesian optimization based on multi-output Gaussian processes to learn the physical properties of a Bose-Einstein condensate within few simulations only. We evaluate its performance on an optimization study case of diabatically transporting the quantum gas while keeping it in its ground state. Within a few hundred executions, we reach a competitive performance to other protocols. While restricting this benchmark to the well known Thomas-Fermi approximation for straightforward comparisons, we expect a similar performance when employing more complex theoretical models, which would be computationally more challenging, rendering standard optimal control theory protocols impractical. This paves the way for efficient state engineering of complex quantum systems including mixtures of interacting gases or cold molecules.

Intelligent Vehicle Trajectory Tracking Based on Horizontal and Vertical Integrated Control

To address the common issues of accuracy and stability in trajectory tracking tasks for autonomous vehicles, this study proposes an innovative composite control strategy that skillfully integrates lateral and longitudinal dynamic control. For lateral control, model predictive control (MPC) theory is introduced to compute the front wheel steering angle that ensures optimal trajectory following. On the longitudinal control level, the vehicle’s acceleration and deceleration logic are finely tuned to ensure precise adherence to the preset speed trajectory. More importantly, by deeply integrating these two control methods, the comprehensive coordination of the vehicle’s lateral and longitudinal movements is achieved. To validate the effectiveness of the proposed control strategy, simulations were conducted using the CarSim and MATLAB/Simulink platforms. The analysis of the simulation results confirms that the proposed method effectively improves speed tracking stability and significantly enhances path tracking accuracy and overall driving stability.

The control costs of human brain dynamics

Abstract The human brain is a complex system with high metabolic demands and extensive connectivity that requires control to balance energy consumption and functional efficiency over time. How this control is manifested on a whole-brain scale is largely unexplored, particularly what the associated costs are. Using network control theory, here we introduce a novel concept, time-averaged control energy (TCE), to quantify the cost of controlling human brain dynamics at rest, as measured from functional and diffusion MRI. Importantly, TCE spatially correlates with oxygen metabolism measures from positron emission tomography, providing insight into the bioenergetic footing of resting state control. Examining the temporal dimension of control costs, we find that brain state transitions along a hierarchical axis from sensory to association areas are more efficient in terms of control costs and more frequent within hierarchical groups than between. This inverse correlation between temporal control costs and state visits suggests a mechanism for maintaining functional diversity while minimizing energy expenditure. By unpacking the temporal dimension of control costs, we contribute to the neuroscientific understanding of how the brain governs its functionality while managing energy expenses.

Dynamic analysis and application of network structure control in risk conduction in the industrial chain

According to control theory, a dynamical system is controllable if, with a suitable choice of inputs, it can be driven from any initial state to any desired final state within a finite time. Most dynamic characteristics of real networks are nonlinear, so achieving target control is more practical and necessary. The network’s control energy is also a problem that must be considered. Whether and how to control the complex system of the industrial chain has high theoretical and practical significance. In this study, we use the GARCH model, DCC model, and network structure control theory comprehensively to study the price fluctuation risk of the mining stock market from the perspective of the industry chain and network control dynamics and obtain interesting results. (1) Risk conduction among stocks has a prominent industry-driving effect, and the risk conduction ability of upper and middle stocks is stronger. (2) The risk regulation cost, time cost, and node number cost of the whole-industry chain are all higher than those of the two-tier chain, which indicates that the correlation complexity of the network has a positive relationship with risk control. (3) Key risk nodes play an essential role in risk control, so monitoring key stocks from the industrial chain perspective is necessary to control risks in time. This work can provide valuable suggestions for market regulators and policy-makers in terms of risk management and control.

Attitude Control System for Quadrotor Using Robust Monte Carlo Model Predictive Control

Monte Carlo Model Predictive Control (MCMPC) is a kind of non-linear Model Predictive Control (MPC) that determines control inputs using the Monte Carlo method. The Monte Carlo method used in MCMPC can handle discontinuous phenomena, and there have been reports of its application in control systems for quadrotors, where the objective is to maintain the stability of the attitude during collisions. However, as with conventional MPC, concerns remain about control performance degradation due to modeling errors and external disturbances such as unexpected wind gusts. In this study, we propose a novel Robust Monte Carlo Model Predictive Control (RMCMPC), which improves the robustness of MCMPC by considering the hypersurface known from the Sliding Mode Control (SMC) theory. The proposed RMCMPC is applied to a quadrotor attitude control system, and its effectiveness is validated through numerical simulations.