Control Theory Research Articles

Mathematical Modelling and Deep Learning Techniques for Predicting Cardiovascular Disease

Putting together mathematical models and deep learning has become a strong way to identify cardiovascular disease (CVD) in recent years. This paper looks at how dynamical systems, control theory, and machine learning methods can work together to make CVD forecast more accurate and reliable. To improve the ability of diagnostic tools to predict the future by using mathematical models like differential equations to describe how the heart works and deep learning architectures like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for feature extraction. This method considers both the time patterns of how the heart works and the non-linear complexity of risk factors like high blood pressure, high cholesterol, and family tendencies. The most important part of our method is creating a multi-stage prediction model. This model starts with differential equations that show how heart rate and blood pressure change over time. Next, it uses deep learning models that have been trained on large CVD datasets. Normalization and feature engineering methods are used to prepare the information. Key measures like age, BMI, and cholesterol levels are used as inputs. Metrics like precision, sensitivity, specificity, and F1-score are used to judge how well the model works. Early findings indicate that combining mathematical models with deep learning makes predictions a lot more accurate than using only standard machine learning models. In particular, adding time dynamics to the model lets it find hidden trends in physiological data that simple models often miss. Deep learning also lets the system learn on its own how different risk factors are related to each other, making it a strong tool for finding CVD early and predicting how bad it will be. The research shows that a mix of approaches could make personalized medicine better by giving doctors accurate and easy to understand prediction tools. In the future, researchers will focus on making these models work better so it can be used in real time in hospital settings. This could help lower the number of people who get cardiovascular disease around the world.

Mathematical Techniques for Autonomous Vehicle Navigation Systems

Advanced mathematical methods are used a lot in autonomous car guidance systems to make sure they work correctly, reliably, and efficiently. This abstract talks about some of the most important mathematics methods used to create and improve these systems. One important method is probabilistic robots, which uses Bayesian filters, such as the Kalman filter and its nonlinear versions (Extended Kalman Filter and Unscented Kalman Filter), to estimate a vehicle's state and make sense of sensor data that isn't always clear or loud. Path planning algorithms, like A and Dijkstra's algorithm, are needed to find the best routes. Sampling-based methods, like Rapidly-exploring Random Trees (RRT), can help with problems in high-dimensional space. Control theory is a very important part of keeping a car stable and following the direction you want it to take. Model Predictive Control (MPC), for example, is used a lot because it can handle control jobs with multiple variables while considering the system's behavior. To model how a vehicle moves, differential equations and dynamical systems theory are used to show how control inputs affect how the vehicle acts over time. Also, methods that combine data from different sources like LiDAR, cameras, and GPS are very important for making an accurate and complete picture of the world. Optimization methods improve tracking even more by adjusting the path of the car, cutting down on energy use, and shortening trip times. Besides these methods, machine learning and deep learning are being added to guidance systems more and more to help them make better decisions and be more flexible in settings that are changing quickly and are very complicated. These models can learn from very large datasets by finding trends and making predictions that are important for tasks like finding objects, understanding scenes, and making decisions on their own. Autonomous car tracking systems are getting smarter, more capable, and more reliable by using both old-fashioned math methods and new, cutting-edge machine learning methods together. This makes it possible for them to be widely used in real life.

Promoters as social controls to facilitate knowledge sharing, conflict management and innovation project portfolio performance

Organizational control theory emphasizes aligning members' actions with organizational goals through formal and social controls. In innovation management, formal controls like project management or stage‐gate processes are limited to measurable and standardized areas. For situations that are complex, unpredictable and highly idiosyncratic, social controls need to be applied for alignment. Knowledge sharing and competitive conflicts are two such innovation‐related phenomena that elude formal control and instead need social controls for alignment. In this research, we propose that promoters—individuals who actively support innovation—serve as social control agents. While previous research has shown the positive effect of single promoters on individual innovation project success, we propose a comprehensive implementation of all four promoter roles, and we test their joint effect on the intermediate outcomes of knowledge sharing and competitive conflict management facilitating innovation project portfolio performance. Drawing on cross‐industry data of 125 firms collected from 986 employees, this article provides empirical evidence that comprehensive implementation of all four promoter roles enhances knowledge sharing and reduces competitive conflicts, thereby improving innovation project portfolio performance. The research reframes promoters as social control agents, emphasizing their collective necessity for managing complex organizational issues and contributing to overall project success.

Feedback–feedforward signal control with exogenous demand estimation in congested urban road networks

To cope with uncertain traffic patterns and traffic models, traffic-responsive signal control strategies in the literature are designed to be robust to these uncertainties. These robust strategies still require sensing infrastructure to implement traffic-responsiveness. In this paper, we take a novel perspective and show that it is possible to use the already necessary sensing infrastructure to estimate the uncertain quantities in real time. Specifically, resorting to the store-and-forward model, we design a novel network-wide traffic-responsive strategy that estimates the occupancy and exogenous demand in each link, i.e., entering (exiting) vehicle flows at the origins (destinations) of the network or within links, in real time. Borrowing from optimal control theory, we design an optimal linear quadratic control scheme, consisting of a linear feedback term, of the occupancy of the road links, and a feedforward component, which accounts for the varying exogenous vehicle load on the network. Thereby, the resulting control scheme is a simple feedback–feedforward controller, which is fed with occupancy and exogenous demand estimates, and is suitable for real-time implementation. Numerical simulations for the urban traffic network of Chania, Greece, show that, for realistic surges in the exogenous demand, the proposed solution significantly outperforms tried-and-tested solutions that ignore the exogenous demand.

Исследование многоуровневых моделей управления вагонопотоками в адрес припортовых станций с применением теории дифференциальных уравнений

Purpose: To consider the issue of sustainability of multi-level control systems for car flows to port railway stations in the conditions of intellectualization of transportation management processes. To assess the influence of three-level and two-level control systems for the operational work of port roads on the quality of planning the supply of trains to port stations under various modes of operational operation of railways. Determine the conditions for the stability of the car flow control system by constructing and studying the corresponding hard and soft mathematical models described by autonomous systems of ordinary linear differential equations. Methods: In this study, the methods of the theory of stability of solutions of differential equations, as well as the theory of control and dynamical systems are used. A phase space is used in a visual representation, a qualitative and quantitative analysis of the behavior of phase trajectories in it is performed, corresponding to undisturbed and perturbed solutions of systems of differential equations describing the constructed models of control systems. Results: A comparison of the characteristics of multistage car traffic control systems is given. A study of the stability of multilevel models of car traffic management in the context of the intellectualization of management functions is proposed. In the environment of the analytical computing system, the conditions of asymptotic stability in time of a two-level wagon traffic control system are found and studied. Practical importance: The results obtained by computational experiment make it possible to assess the stability of the functioning of port-side transport and technological systems in the context of changes in the organization of production and the intellectualization of transportation management processes. Computer mathematics systems make it possible to implement the heuristic component of research and obtain theoretically sound and visual solutions to problems of optimizing cargo and wagon traffic control modes.

Analysis of efficient discretization technique for nonlinear integral equations of Hammerstein type

Purpose This study focuses on investigating the numerical solution of second-kind nonlinear Volterra–Fredholm–Hammerstein integral equations (NVFHIEs) by discretization technique. The purpose of this paper is to develop an efficient and accurate method for solving NVFHIEs, which are crucial for modeling systems with memory and cumulative effects, integrating past and present influences with nonlinear interactions. They are widely applied in control theory, population dynamics and physics. These equations are essential for solving complex real-world problems. Design/methodology/approach Demonstrating the solution’s existence and uniqueness in the equation is accomplished by using the Picard iterative method as a key technique. Using the trapezoidal discretization method is the chosen approach for numerically approximating the solution, yielding a nonlinear system of algebraic equations. The trapezoidal method (TM) exhibits quadratic convergence to the solution, supported by the application of a discrete Grönwall inequality. A novel Grönwall inequality is introduced to demonstrate the convergence of the considered method. This approach enables a detailed analysis of the equation’s behavior and facilitates the development of a robust solution method. Findings The numerical results conclusively show that the proposed method is highly efficacious in solving NVFHIEs, significantly reducing computational effort. Numerical examples and comparisons underscore the method’s practicality, effectiveness and reliability, confirming its outstanding performance compared to the referenced method. Originality/value Unlike existing approaches that rely on a combination of methods to tackle different aspects of the complex problems, especially nonlinear integral equations, the current approach presents a significant single-method solution, providing a comprehensive approach to solving the entire problem. Furthermore, the present work introduces the first numerical approaches for the considered integral equation, which has not been previously explored in the existing literature. To the best of the authors’ knowledge, the work is the first to address this equation, providing a foundational contribution for future research and applications. This innovative strategy not only simplifies the computational process but also offers a more comprehensive understanding of the problem’s dynamics.

Stochastic Optimal Control Analysis for HBV Epidemic Model with Vaccination

In this study, we explore the concept of symmetry as it applies to the dynamics of the Hepatitis B Virus (HBV) epidemic model. By incorporating symmetric principles in the stochastic model, we ensure that the control strategies derived are not only effective but also consistent across varying conditions, and ensure the reliability of our predictions. This paper presents a stochastic optimal control analysis of an HBV epidemic model, incorporating vaccination as a pivotal control measure. We formulate a stochastic model to capture the complex dynamics of HBV transmission and its progression to acute and chronic stages. By leveraging stochastic differential equations, we examine the model’s stationary distribution and asymptotic behavior, elucidating the impact of random perturbations on disease dynamics. Optimal control theory is employed to derive control strategies aimed at minimizing the disease burden and vaccination costs. Through rigorous numerical simulations using the fourth-order Runge–Kutta method, we demonstrate the efficacy of the proposed control measures. Our findings highlight the critical role of vaccination in controlling HBV spread and provide insights into the optimization of vaccination strategies under stochastic conditions. The symmetry within the proposed model equations allows for a balanced approach to analyzing both acute and chronic stages of HBV.

Hierarchical Control in Mechatronic Technological Systems

The topic of hierarchical control of technological machines is one of the most relevant in mechanical engineering technology. The most difficult issue in this area is the organization of interactions between different control levels, on the one hand, and the choice of automatic control methods for each of these control levels (control by deviation, control by disturbance, mixed control, etc.), on the other. In this article, in relation to machining technology, a method and corresponding device are proposed that make it possible to implement the control of cutting force parameters (axial cutting force and cutting torque) in an automatic control system for the deviation of cutting torque by changing the axial cutting force (lower level of control). The lower-level control ensures the required quality of the surface layer (surface integrity) of the machined parts. At the same time, the required dimensional accuracy of parts is ensured at the upper level of control, which is implemented by the CNC system of the machine. At the upper level, automatic control is carried out based on the deviation of the kinematic parameters of the movement of the working parts of the CNC machine (acceleration, speed, displacement). Control switching from upper to lower level and back is carried out without using a spindle linear axial movement sensor. Instead of this expensive sensor, a limit switch (a closed and opened pair of contacts) is used, which fixes the lowest axial position of the spindle (and cutting tool). Based on the signal of closing the specified contacts of the limit switch, a transition from the lower control level to the upper one is carried out. Thus, the upper-level system operates only when these contacts are closed, and the lower-level system operates only when they are open. In relation to the upper-level system, the lower-level control system implements the control “by disturbance” principle, also known in control theory as the “disturbance compensation principle”.

New stability theory of model predictive control: modified stage cost approach

This paper presents a promising new approach to establish the stability of finite receding horizon control with a terminal cost. Departing from the traditional approaches of using the property of the terminal cost or relaxed Lyapunov inequalities, this paper establishes its stability based on the property of a modified stage cost. First, we rotate the stage cost with the terminal cost. Then a one-step optimisation problem is defined based on this augmented stage cost. It is shown that a slightly modified Model Predictive Control (MPC) algorithm is stable if the value function of the augmented one-step cost (OSVF) is a Control Lyapunov Function (CLF). Stability for MPC algorithms with zero terminal cost or even negative terminal cost can be unified with this new approach. Combining it with the existing MPC stability theories, we are able to significantly relax the stability requirement on MPC and extend the stabilising MPC design space to the region that no existing MPC stability theories can cover. The proposed stage cost-based approach will help to further reduce the gap between stability theory and practical applications of MPC and other optimisation-based control methods.

Finite-Time Adaptive Control for Electro-Hydraulic Braking Gear Transmission Mechanism with Unilateral Dead Zone Nonlinearity

Autonomous vehicles require more precise and reliable braking control, and electro-hydraulic braking (EHB) systems are better adapted to the development of autonomous driving. However, EHB systems inevitably suffer from unilateral dead zone nonlinearity, which adversely affects the position tracking control. Therefore, a finite-time adaptive control strategy was designed for unilateral dead zone nonlinearity. Initially, the unilateral dead zone nonlinearity was reformulated into a matched disturbance term and an unmatched disturbance term to reduce the adverse effects of disturbances, thereby enhancing system controllability. Then, the “complexity explosion” in the design of the control strategy was avoided by command filtering, and the design process of the controller was simplified. Furthermore, the finite-time control theory was employed to boost the system’s convergence speed, thereby enhancing control performance. In order to ensure the stability of the system under the dead zone disturbance, the unknown disturbance terms were estimated. The stability of the control strategy was validated through the finite-time stability theorem and the Lyapunov function. Eventually, simulations and hardware-in-the-loop (HIL) experiments validated the feasibility and availability of the finite-time adaptive control strategy.

Research on Coordinated Control of Vehicle Inertial Suspension Using the Dynamic Surface Control Theory

In the process of driving, the steering, braking, and driving conditions and different road conditions affect the vibration characteristics of the vehicle in the vertical, roll, and pitch directions. These factors greatly impact the riding comfort of the vehicle. Among them, the uneven distribution of vertical load between the left and right or the front and rear suspension is one of the important factors affecting the performance indicators of the vehicle’s roll angle acceleration and pitch angle acceleration. In order to improve the ride comfort of the vehicle in vertical, roll, and pitch motion, the inerter is introduced in this paper to form a new type of suspension structure with the “spring-damping” base element, inertial suspension. It breaks away from the traditional “spring-damping” base element of the inherent suspension structure. In this paper, the mechatronic inerter is taken as the actual controlled object, and the inertial suspension structure is considered as the controlled model based on the dynamic surface control theory and the pseudo-inverse matrix principle. Thus, the coordinated control of the inertial suspension can be achieved. Under random road input, compared with passive suspension, the ride comfort performance indicators of the vehicle with inertial suspension based on dynamic surface control are significantly improved. Finally, a Hardware-in-the-Loop (HiL) test of the controller based on dynamic surface control is carried out to verify that the performance of the vehicle inertial suspension using the dynamic surface control algorithm had improved in terms of vehicle ride comfort. The error between the experimental results and the simulation results is about 8%, which verifies the real-time performance and effectiveness of the dynamic surface controller in the real controller.

Fault Diagnosis of Maritime Equipment Using an Intelligent Fuzzy Framework

The task of automatically and intelligently diagnosing faults in marine equipment is of great significance due to the numerous duties that shipboard professionals must handle. Incorporating automated and intelligent systems on ships allows for more efficient equipment monitoring and better decision-making. This approach has attracted considerable interest in both academia and industry because of its potential for economic savings and improved safety. Several fault diagnosis methods are documented in the literature, often involving mathematical and control theory models. However, due to the inherent complexity of some processes, not all characteristics are precisely known, making mathematical modeling highly challenging. As a result, fault diagnosis often depends on data or heuristic information. Fuzzy logic theory is particularly well suited for processing this type of information. Therefore, this paper employs fuzzy models to diagnose faults in a marine pneumatic servo-actuated valve. The fuzzy models used in fault diagnosis are obtained from the data. These fuzzy models are identified for the normal operation of the marine pneumatic servo-actuated valve, and for each fault, predicting the system’s outputs from the inputs and outputs of the process. The proposed fault diagnosis framework analyzes the discrepancy signals between the outputs of the fuzzy models and the actual process outputs. These discrepancies, known as residuals, help in detecting and isolating equipment faults. The fault isolation process uses an intelligent decision-making approach to determine the specific fault in the system. This method is applied to diagnose abrupt faults in a marine pneumatic servo-actuated valve. The approach presented was used to detect and diagnose three very important faults in the operation of a marine pneumatic servo-actuated valve. The three faults were correctly detected and isolated, and no errors were detected in this detection and isolation process.

Observer-based decoupling control of air flow and pressure in fuel cell systems

This paper investigates the control of the air supply system for marine proton exchange membrane fuel cells. A mathematical model of the air supply system is established, and a sliding mode diagonal decoupling controller based on a state observer is proposed. The control effectiveness is validated through experiments and simulations. A decoupling control strategy for the air supply system of marine fuel cells is presented. First, the load input current of the fuel cell system is selected according to the load characteristics of the target vessel, ensuring that the load current meets the power level of the fuel cell system model. Second, the impact of the cathode pressure on the fuel cell system is analyzed. An observer-based measurement method is proposed to address the measurement issue of cathode pressure. On this basis, a decoupling control strategy combining a diagonal decoupling matrix and sliding mode variable structure control theory is proposed. Finally, the effectiveness of the decoupling controller is validated through Matlab/Simulink. The results show the proposed decoupling controller exhibits superior performance to existing controllers with environmental disturbances, continuous time-varying loads, and step loads. The maximum relative error is 2.95%, the average relative error is 0.022%, and the longest adjustment time is 0.5 s. These results indicate that the control performance of the proposed decoupling controller is superior to that of diagonal PI controllers and sliding mode controllers, thereby validating the superiority of the sliding mode decoupling controller. This article provides a useful method for the development and application of control technology for the air supply system of marine fuel cells.

Two-cylinder Synchronous Electro-hydraulic Servo System and its Control Technology Development

Background: Electro-hydraulic servo control system synchronous control in industrial application is very extensive because the application of electro-hydraulic servo control system is mostly in a poor working environment, there are a variety of complex external factors. Objective: Improve the comprehensive performance indicators such as accuracy, speed, and stability of synchronous control of hydraulic cylinders Methods: In the process of synchronous movement, the typical hardware structure and control methods and algorithm strategies of the double hydraulic cylinder are analyzed in detail, and the basic principle of common hydraulic double cylinder synchronous control is summarized. Results: Through the comprehensive analysis of papers and patent documents related to the synchronous control of hydraulic twin cylinders, these papers and patented technologies study the control algorithm, and apply the new technology to the synchronous control of electro-hydraulic servo hydraulic cylinders to improve its comprehensive performance indicators such as work accuracy, speed and stability. Conclusion: Referring to the development trajectory of control algorithms in this regard, combined with new control theory and new technology, many achievements can be applied and researched in hydraulic cylinder synchronous control, and better develop the synchronous control system when double hydraulic cylinders and more hydraulic cylinders work together. By collating and analyzing the relevant research results, the two-cylinder synchronous control technology is comprehensively evaluated and prospected, which provides a valuable reference for the research and application in this field.

Energy allocation theory for bacterial growth control in and out of steady state

Efficient allocation of energy resources to key physiological functions allows living organisms to grow and thrive in diverse environments and adapt to a wide range of perturbations. To quantitatively understand how unicellular organisms utilize their energy resources in response to changes in growth environment, we introduce a theory of dynamic energy allocation that describes cellular growth dynamics by partitioning metabolizable energy into key physiological functions: growth, division, cell shape regulation, energy storage and loss through dissipation. By optimizing the energy flux for growth, we develop the equations governing the time evolution of cell morphology and growth rate in diverse environments. The resulting model accurately captures experimentally observed dependencies of bacterial cell size on growth rate, superlinear scaling of metabolic rate with cell size and predicts nutrient-dependent trade-offs between energy expended for growth, division and shape maintenance. By calibrating model parameters with experimental data for the model organism Escherichia coli , our model describes bacterial growth control in dynamic conditions, particularly during nutrient shifts and osmotic shocks. Integrating both the mechanical properties of the cell and underlying biochemical regulation, our model predicts the driving factors behind a wide range of observed morphological and growth phenomena with minimal added complexity.

Real-time predictive control assessment of low-water head hydropower station considering power generation and flood discharge

In the real-time operation of cascade reservoirs, when the discharge flow of the upstream power station changes frequently, the downstream power station with a low head and small storage capacity has to adjust the gate or turbine frequently to keep the water level safe. This paper proposes a real-time optimal scheduling model based on model predictive control theory(MPC), considering the interaction between power generation and flood discharge. Firstly, the correlation analysis is carried out between the outflow of the Zhentouba hydropower station(ZTB) and the inflow of the Shaping II Hydropower Station(SP), and the spatio-temporal hydraulic connection between the ZTB and SP is obtained. The fuzzy relationship between tail water level and discharge flow is accurately described using numerical simulation, considering the interaction between power generation and discharge. Secondly, based on the precise description of inflow and outflow, a high-precision water level rolling prediction model is constructed using the water balance principle. Finally, based on the MPC, the real-time control model of SP is constructed. The results show that the water level process is steadier, with fewer gate adjustments. Compared with the observed number of gate adjustments in 2020, the number of reservoir gate adjustments after model optimization is reduced by 73.26%. It improves the operation efficiency and safety of the hydropower station and provides a guidance basis for the optimal operation of the SP.

Mathematical insights into the influence of delay and external recruitment on coral-macroalgae system

As anthropogenic pressures on coral reef ecosystems continue to increase due to global warming and mass coral bleaching events, there is growing interest in developing conservation strategies to restore degraded coral reefs. One such approach is the transplantation of coral larvae onto degraded reefs. In this paper, we use a delayed coral-macroalgae model to explore the effects of external recruitment of coral. By analyzing the local and global stability of macroalgae-free equilibrium and coexistence equilibrium in the system, we find that sustained external recruitment of coral will favor coral competition with macroalgae. In addition, we choose delay as a bifurcation parameter and demonstrate that Hopf bifurcation may occur at a critical delay near the coexistence equilibrium. Interestingly, the delay may cause the globally asymptotically stable equilibrium in the ODE system to become unstable, resulting in the appearance of periodic solutions. Furthermore, we analyze population dynamics using optimal control theory and determine the effect of minimum external recruitment on the population dynamics. In the numerical simulation section, parameters of coral-macroalgae dynamics are estimated by using a 12-year (2005–2017) benthic cover dataset of coral reefs in the Gulf of Mannar, southeastern India. The theoretical results are validated and supported by numerical simulations.

Status of infection prevention and control in Cameroon healthcare facilities: lessons learned from the WHO COVID-19 scorecard tool under the hierarchy of control model

BackgroundInfection prevention and control (IPC) helps prevent disease transmission in healthcare facilities. There is a dearth of information on the implementation of IPC during the COVID-19 outbreak in Cameroon using the recommended WHO COVID-19 IPC scorecard tool. The present study assessed healthcare facilities’ compliance to IPC by continuous assessments, with an evaluation of the tool using the hierarchy of control theory. MethodsThis cross-sectional study was conducted in the 10 administrative regions of Cameroon by evaluating healthcare facilities prioritized by the Ministry of Public Health as high-risk facilities between March 2020 and November 2023. Comparisons were made regarding the facilities’ ownership, level and status. Results2,188 assessments from 1,358 healthcare facilities were collected. The median IPC scores at each evaluation were between the intermediate and advanced level, with a bias linked with decreasing selection of facilities. However, only 172 (13%) healthcare facilities achieved advanced IPC score (≥75%). Higher IPC scores were found in hospitals (p<0.001) and in private facilities (p=0.02). Predictors of good IPC compliance were hospital (OR=3.7, CI: 1.4-9.8) and private facility (OR=2.3, CI: 1.6-3.3). The tool met the five domains of the hierarchy of control model. ConclusionRepeated IPC assessments using recommended tools contribute to a better compliance of IPC by healthcare facilities in resources constrained settings.

Financial stress and leadership behavior: The role of leader gender.

Concern about personal finances is one of the most widespread and salient sources of stress. We advance our emerging understanding of the work-related impacts of financial stress by examining the consequences of personal financial stress on leadership behavior. Drawing on compensatory control theory, we propose that financial stress positively relates to abusive supervision via a lowered sense of personal control. Integrating social role theory, we propose that these effects are stronger for leaders who are men than leaders who are women. We test our model in a vignette-based study using a sample of leaders (N = 201) and a second multiwave, multisource field survey study among leaders and their subordinates (N = 119 leader-subordinate dyads). Across both studies, we found that financial stress was positively associated with abusive supervision via lack of control and that this relationship was stronger for men than women. In Study 2, we examined an alternative tend-and-befriend theoretical account, proposing that leaders who are women exhibit more communion-striving motivation and empathic leadership as a result of financial stress. We found some support for this alternative pathway, though not gender differences in it, and in doing so we uncovered novel outcomes of financial stress. Our results offer implications for supporting employee financial health and uncover a context wherein men (and their subordinates), rather than women, experience the costs of misalignment with societal gender expectations. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Research on assisted recovery algorithm of stall state based on signal guidance

Abstract Given the lack of quantitative guidance in the current stall recovery process, which leads to the pilot’s misoperation or untimely recovery, referring to the energy control theory of complex state recovery, a stall state-assisted recovery algorithm based on pitch angle, engine thrust, and roll angle signal guidance was determined. The structure pilot model and the kinematics simulation model of aircraft at a high angle of attack were established. The effectiveness of the algorithm has been verified by simulation analysis. The man-machine closed-loop simulation analysis results show that the algorithm can guide the pilot in effectively recovering from stall conditions under different stall scenarios and engine failure conditions.