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In this work, a multiparametric family of iterative vectorial fourth-order methods free of Jacobian matrices is proposed. A convergence analysis of this family is carried out as well as a study of its efficiency. Several numerical experiments are made in order to compare the behaviour of the proposed family with other competitive methods of the literature. [ABSTRACT FROM AUTHOR]

Magnetically coupled resonant wireless power transfer (MCR‐WPT) system with constant power load (CPL), finds extensive applications in military, industrial, and medical treatment. However, this system can easily exhibit complex non‐linear behaviors under certain parameter conditions due to the influences of constant power loads, switching devices, and feedback controllers. These behaviors limit the effectiveness of controllers and reduce the efficiency and stability of the system. It is necessary to study the non‐linear characteristics of the MCR‐WPT system with CPL. The stroboscopic discrete mapping of the MCR‐WPT system with CPL is established. Then the system's non‐linear dynamics are analyzed theoretically using a bifurcation diagram, the maximal Floquet multipliers, and the maximal Lyapunov exponent spectrum obtained from the proposed discrete mapping. The results show that the MCR‐WPT system with CPL will exhibit rich non‐linear dynamics with the variation of the power of CPL, such as cyclic fold bifurcation, Neimark–Scaker bifurcation, border collision bifurcations, chaos, etc. The excellent alignment of experimental and theoretical outcomes in corresponding states confirms the accuracy of the proposed discrete mapping and the nonlinear analysis of the system. The results of this study can provide a reference for selecting parameters for an actual MCR‐WPT system with CPL. [ABSTRACT FROM AUTHOR]

The article discusses the application of fuzzy control theory in non-linear networked systems to enhance security against cyber-attacks. It focuses on various control strategies, such as resilient fuzzy control synthesis and adaptive control, to address security issues like denial-of-service attacks and deception attacks. The research presented in the article covers a wide range of topics related to control and security of information physical systems, resilient fuzzy control, and adaptive control in non-linear multi-agent systems and energy security management. The special issue includes 17 peer-reviewed papers that explore innovative approaches to address security challenges in non-linear networked systems. [Extracted from the article]

One of the most common methods to protect electronic and telecommunication systems in a high‐power electromagnetic (HPEM) environment is using a conductive enclosure. Amplifiers are the vital parts in transmitter and receiver systems as the main components of a telecommunication system, so investigating the effects of HPEM waveform on their performance is very important. The Presence of apertures in the body of the conductive enclosure, as well as non‐linear elements such as transistors in amplifiers, adds to the complexity of examining the coupling of electromagnetic fields on the performance of amplifiers. Considering the importance of the topic, in this study, the impact of the HPEM waveform on the power amplifier inside the conductive enclosure has been investigated. By using the finite difference delay modelling (FDDM) numerical method as a stable method followed by the vector fitting (VF) method, the electromagnetic problem has become a circuit problem, which allows examining the response of the power amplifier and any circuit, including non‐linear elements against the interference field. Responses of bipolar linear and non‐linear components in the perforated conductive enclosure by the proposed method have been compared with the results obtained by computer simulation technology microwave studio (CST‐MWS) for verification. [ABSTRACT FROM AUTHOR]

Abstract Magnetically coupled resonant wireless power transfer (MCR‐WPT) system with constant power load (CPL), finds extensive applications in military, industrial, and medical treatment. However, this system can easily exhibit complex non‐linear behaviors under certain parameter conditions due to the influences of constant power loads, switching devices, and feedback controllers. These behaviors limit the effectiveness of controllers and reduce the efficiency and stability of the system. It is necessary to study the non‐linear characteristics of the MCR‐WPT system with CPL. The stroboscopic discrete mapping of the MCR‐WPT system with CPL is established. Then the system's non‐linear dynamics are analyzed theoretically using a bifurcation diagram, the maximal Floquet multipliers, and the maximal Lyapunov exponent spectrum obtained from the proposed discrete mapping. The results show that the MCR‐WPT system with CPL will exhibit rich non‐linear dynamics with the variation of the power of CPL, such as cyclic fold bifurcation, Neimark–Scaker bifurcation, border collision bifurcations, chaos, etc. The excellent alignment of experimental and theoretical outcomes in corresponding states confirms the accuracy of the proposed discrete mapping and the nonlinear analysis of the system. The results of this study can provide a reference for selecting parameters for an actual MCR‐WPT system with CPL.

Abstract One of the most common methods to protect electronic and telecommunication systems in a high‐power electromagnetic (HPEM) environment is using a conductive enclosure. Amplifiers are the vital parts in transmitter and receiver systems as the main components of a telecommunication system, so investigating the effects of HPEM waveform on their performance is very important. The Presence of apertures in the body of the conductive enclosure, as well as non‐linear elements such as transistors in amplifiers, adds to the complexity of examining the coupling of electromagnetic fields on the performance of amplifiers. Considering the importance of the topic, in this study, the impact of the HPEM waveform on the power amplifier inside the conductive enclosure has been investigated. By using the finite difference delay modelling (FDDM) numerical method as a stable method followed by the vector fitting (VF) method, the electromagnetic problem has become a circuit problem, which allows examining the response of the power amplifier and any circuit, including non‐linear elements against the interference field. Responses of bipolar linear and non‐linear components in the perforated conductive enclosure by the proposed method have been compared with the results obtained by computer simulation technology microwave studio (CST‐MWS) for verification.

Microgrid (MG) control is crucial for efficient, reliable, and sustainable energy management in distributed energy systems. Genetic Algorithm-based energy management systems (GA-EMS) can optimally control MGs by solving complex, non-linear, and non-convex problems but may struggle with real-time application due to their computational demands. Model Predictive Control (MPC)-based EMS, which predicts future behaviour to ensure optimal performance, usually depends on linear models. This paper introduces a novel Genetic Predictive Control (GPC) method that combines a GA and MPC to enhance resource allocation, balance multiple objectives, and adapt dynamically to changing conditions. Integrating GAs with MPC improves the handling of non-linearities and non-convexity, resulting in more accurate and effective control. Comparative analysis reveals that GPC significantly reduces excess power production, improves resource allocation, and balances cost, emissions, and power efficiency. For example, in the Mutation–Random Selection scenario, GPC reduced excess power to 76.0 W compared to 87.0 W with GA; in the Crossover-Elitism scenario, GPC achieved a lower daily cost of USD 113.94 versus the GA's USD 127.80 and reduced carbon emissions to 52.83 kg CO2e compared to the GA's 69.71 kg CO2e. While MPC optimises a weighted sum of objectives, setting appropriate weights can be difficult and may lead to non-convex problems. GAs offer multi-objective optimisation, providing Pareto-optimal solutions. GPC maintains optimal performance by forecasting future load demands and adjusting control actions dynamically. Although GPC can sometimes result in higher costs, such as USD 113.94 compared to USD 131.90 in the Crossover–Random Selection scenario, it achieves a better balance among various metrics, proving cost-effective in the long term. By reducing excess power and emissions, GPC promotes economic savings and sustainability. These findings highlight GPC's potential as a versatile, efficient, and environmentally beneficial tool for power generation systems. [ABSTRACT FROM AUTHOR]

Hospital Pneumatic Systems, specializing in pneumatic systems, are among the most essential components for hospitals. It offers efficient and cost-effective solutions to problems related to the transportation of various materials in hospitals. However, in existing systems, the need for compressed air is met without worrying about cost control and without depending on the sample transported, and this not only makes the system inefficient but also may cause sample degradation. The main purpose of this study is to provide speed/pressure control according to the type of material transported to eliminate the disadvantages of existing systems such as energy use and sample degradation. In this study, a new mathematical model is presented that can be used to make more energyefficient hospital pneumatic systems. Although there are many studies on various pneumatic systems in the literature, there is not enough for the control of hospital pneumatic systems. According to the results obtained in this study, the system parameters were determined and the mathematical model of the system was obtained by using the Multivariate nonlinear regression method. A genetic algorithm was used to test the validity of the obtained mathematical model and to optimize the coefficient of the input parameters of the model. It is expected that this proposed model will contribute to the use of hospital pneumatic systems and provide a scientific and practical solution to the proposed mathematical model. The proposed mathematical model provides up to 43% more efficient transportation over the currently used system that has been tested. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical modeling approach utilizing a fuzzy modeling framework for fixed-wing aircraft systems with the goal of creating a highly desirable mathematical representation for model-based control design applications. The starting point is a mathematical model comprising fifteen non-linear ordinary differential equations representing the dynamic and kinematic behavior applicable to a wide range of fixed-wing aircraft systems. Here, the proposed mathematical modeling framework is applied to the AIRBUS A310 model developed by ONERA. The proposed fuzzy modeling framework takes advantage of sector non-linearity red techniques to recast all the non-linear terms from the original model to a set of combined fuzzy rules. The result of this fuzzification is a more suitable mathematical description from the control system design point of view. Therefore, the combination of this fuzzy model and the wide range of control techniques available in the literature for such kind of models, like parallel and non-parallel distributed compensation control laws using linear matrix inequality optimization, enables the development of control algorithms that guarantee stability conditions for a wide range of operations points, avoiding the classical gain scheduling schemes, where the stability issues can be extremely challenging. [ABSTRACT FROM AUTHOR]

Abstract Research on the physical memristive system is vital for realizing widespread applications of the memristor in practical engineering. In this article, a family of the asymmetric generalized passive voltage‐controlled memristive system (AGPVCMS) is proposed; also the mathematical model of the AGPVCMS is established and its remarkable memristive characteristics are analysed. Especially, the AGPVCMS only consists of the diode bridge and LR filter, however it can enhance its asymmetry as well as display various asymmetrical pinched hysteresis loops in the voltage–current plane, via expediently changing the number of the diodes on different bridge arms. In addition, the effectiveness of the AGPVCMS is verified by the consistency between theoretical and experimental results.

Research on the physical memristive system is vital for realizing widespread applications of the memristor in practical engineering. In this article, a family of the asymmetric generalized passive voltage‐controlled memristive system (AGPVCMS) is proposed; also the mathematical model of the AGPVCMS is established and its remarkable memristive characteristics are analysed. Especially, the AGPVCMS only consists of the diode bridge and LR filter, however it can enhance its asymmetry as well as display various asymmetrical pinched hysteresis loops in the voltage–current plane, via expediently changing the number of the diodes on different bridge arms. In addition, the effectiveness of the AGPVCMS is verified by the consistency between theoretical and experimental results. [ABSTRACT FROM AUTHOR]

The article presents the analysis and stability program of the closed nonlinear system frequency converter-brushless motor "(FC-BM)", which differs significantly from the analysis of linear systems. First of all, this is because the stability property of a nonlinear system depends on the initial conditions and external influences: for some input signals, the system will be stable, while for others it becomes unstable. Consequently, the stability criteria developed in the linear theory cannot be applied to their analysis. The stability of a non-linear automatic control system means that small changes in the input signal or disturbances, initial conditions, or plant parameters will not take the output variable beyond a sufficiently small neighborhood of the equilibrium point or limit cycle. Since several equilibrium positions can exist for a non-linear system, stability should be analyzed in the vicinity of each of them. This complicates the task of research. [ABSTRACT FROM AUTHOR]

This work presents the design, development, and performance evaluation of a cost‐effective and practical single‐phase power quality analyzer (SPPQA) using Pyboard microcontroller and Python‐to‐Python interface. The SPPQA offers comprehensive measurements, including voltage/current harmonics up to the seventh order, frequency, voltage and current root mean square values, total power, active/reactive power, power factor, voltage and current crest factor, as well as voltage/current total harmonic distortion. To ensure the accuracy of the SPPQA, a testing procedure was conducted using a test bench equipped with a non‐linear load and a 220‐V power source operating at a frequency of 50 Hz. The obtained results, such as VTHD${{V}_{THD}}$ and ITHD${{I}_{THD}}$, are compared with measurements obtained from classical approaches and devices, validating the authenticity and reliability of the SPPQA's performance. The SPPQA demonstrates considerable accuracy and reliability in its measurements, establishing it as an efficient and practical tool for power quality assessment. The ability to measure the seven harmonics of voltage and current, alongside other critical parameters, positions the SPPQA as a practical asset in the academic, industrial, and commercial sectors. The affordability and practicality of the SPPQA make it practical in both academic research and practical scenarios. Utilizers can gain deeper information into power quality through its measurements and harmonics analysis, while also enhancing industrial processes and ensuring a reliable power supply in commercial settings. [ABSTRACT FROM AUTHOR]

This article deals with modelling and a flatness-based robust trajectory tracking scheme for a two degrees of freedom helicopter, which is subject to four types of tail rotor disturbances to validate the control scheme robustness. A mathematical model of the system, its differential flatness and a differential parametrization are obtained. The flat filtering control is designed for the system control with a partially known model, assuming the non-modelled dynamics and the external disturbances (specially the tail rotor ones) to be rejected by means of an extended state model (ultra-local model). Numerical and experimental assessments are carried out on a characterized prototype whose yaw angle (ψ), given by the z axis, is in free form, while the pitch angle (θ), which results from rotation about the y axis, is mechanically restricted. The proposed controller performance is tested through a set of experiments in trajectory tracking tasks with different disturbances in the tail rotor, showing robust behaviour for the different disturbances. Besides, a comparison study against a widely used controller of LQR type is carried out, in which the proposed controller achieves better results, as illustrated by a performance index.

Abstract From an engineering point of view, non‐linear systems are essential to the operation of control systems, because all systems actually have a non‐linear state in nature. In reality, there are many different kinds of non‐linear systems hidden by this negative definition. For successful analysis and control, the identification of non‐linear systems using unknown models is typically necessary. Till now, numerous approaches are developed for identifying non‐linear systems, but it cannot be employed with a large number of components. Moreover, system identification is typically restricted to output and input signals alone, also such systems are rarely used in reality. This is the primary justification for using non‐linear systems in this research. So, this research proposed a non‐linear model of system identification for large‐scale systems under the consideration of two systems: bilinear system and Volterra system. Therefore, a novel algorithm named Self Adaptive Penguin Search Optimization (SAPeSO) is introduced to attain the system characteristics properly and minimize the output variation. Finally, the effectiveness of the proposed work is compared with existing works in terms of various error measures. This research mainly focuses on the application‐oriented engineering problems. In particular, the Mean Absolute Error (MAE) of the proposed work for the Volterra system at 4000 samples is 18.83%, 14.05%, 8.88%, 29.72%, 19.91%, and 6.70% which is better than the existing bald eagle search (BES), arithmetic optimization algorithm (AOA), whale optimization algorithm (WOA), nonlinear autoregressive moving average with exogenous inputs‐ frequency response function + principal component analysis (NARMAX‐FRF+PCA), Global Gravitational Search Algorithm‐Assisted Kalman Filter (CGS‐KF), and sparse regression and separable least squares method (SR‐SLSM) methods, respectively. Finally, the error is minimum for the proposed model when compared with the other traditional approaches.

Abstract With the increase in the proportion of new energy power generation in grids, there is an urgent need for the flexible operation of coal‐fired power plants. However, as the most efficient commercial coal‐fired power generation technology, the flexibility of double‐reheat ultra‐supercritical units is limited due to the steam temperature fluctuation in transient processes. To inhibit the steam temperature fluctuations, a data‐driven method, called long short‐term memory (LSTM) was introduced to model the dynamics of the steam temperatures of a 1000‐MW double‐reheat coal‐fired boiler. Moreover, an advanced controller was proposed by combining the LSTM‐based dynamic model and the model predictive control architecture. The results demonstrate that the LSTM model can be used to capture the time delay, multivariable coupling and non‐linear dynamics of the steam temperatures and the proposed controller exhibits excellent performance in steam temperature regulation. The superheated steam temperature fluctuation decreases from 15.4 to 9.8°C, the primary reheat temperature fluctuation decreases from 34.6 to 8.3°C, and the secondary reheat temperature fluctuation decreases from 41.4 to 8.8°C. The proposed method has good potential to stabilize steam temperatures during transient processes of wide‐range load cycling, and finally improves the flexibility of the double‐reheat ultra‐supercritical unit.

Microgrid (MG) control is crucial for efficient, reliable, and sustainable energy management in distributed energy systems. Genetic Algorithm-based energy management systems (GA-EMS) can optimally control MGs by solving complex, non-linear, and non-convex problems but may struggle with real-time application due to their computational demands. Model Predictive Control (MPC)-based EMS, which predicts future behaviour to ensure optimal performance, usually depends on linear models. This paper introduces a novel Genetic Predictive Control (GPC) method that combines a GA and MPC to enhance resource allocation, balance multiple objectives, and adapt dynamically to changing conditions. Integrating GAs with MPC improves the handling of non-linearities and non-convexity, resulting in more accurate and effective control. Comparative analysis reveals that GPC significantly reduces excess power production, improves resource allocation, and balances cost, emissions, and power efficiency. For example, in the Mutation–Random Selection scenario, GPC reduced excess power to 76.0 W compared to 87.0 W with GA; in the Crossover-Elitism scenario, GPC achieved a lower daily cost of USD 113.94 versus the GA’s USD 127.80 and reduced carbon emissions to 52.83 kg CO2e compared to the GA’s 69.71 kg CO2e. While MPC optimises a weighted sum of objectives, setting appropriate weights can be difficult and may lead to non-convex problems. GAs offer multi-objective optimisation, providing Pareto-optimal solutions. GPC maintains optimal performance by forecasting future load demands and adjusting control actions dynamically. Although GPC can sometimes result in higher costs, such as USD 113.94 compared to USD 131.90 in the Crossover–Random Selection scenario, it achieves a better balance among various metrics, proving cost-effective in the long term. By reducing excess power and emissions, GPC promotes economic savings and sustainability. These findings highlight GPC’s potential as a versatile, efficient, and environmentally beneficial tool for power generation systems.

This paper is concerned with the switched observer design for a class of systems subject to locally Lipschitz non-linearities. By performing a suitable description of the estimation error dynamics into a linear parameter varying (LPV) system representation, sufficient conditions for the existence of a switching output injection gain are proposed such that the asymptotic stability of the estimation error is guaranteed. These conditions can be conveniently expressed by means of linear matrix inequalities (LMIs), which are easily computationally tractable. A numerical example is provided to show the favorable performance achieved by the proposed observer, which can be applied to a large class of non-linear systems.

The article shows dynamics of value figures on market of digital finance assets. The research studies methods of the sensitivity theory in tasks of optimal management of non-linear systems for automated monitoring and controlling the portfolio of digital finance assets and crypto-currencies. The authors study process modeling in economic systems, set the task of optimal management by the system of non-linear differential equations and target functionality, describe the variation method of task resolving, the principle of invariability of non-linear systems and possibilities to model economic systems provided by the solution. The approach to investigating dynamics of economic systems based on the principle of maximum was analyzed. The article shows possible application of the approach for resolving optimization tasks and for methods of management with split and continuous character in conditions of uncertainty typical of economic systems. Functions of non-linear sensitivity are identified. The use of the sensitivity theory for tasks of optimal management of non-linear systems was studied, illustrated by estimating the vector of unknown parameters of dynamic system. The authors analyze the condition in tasks of economic modeling with regard to uncertainty caused by social and psychological reasons. The material provided in the article shows possibilities of using the method of sensitivity analysis for tasks of system decomposition and related transformations in systems of differential equations.

Hospital Pneumatic Systems, specializing in pneumatic systems, are among the most essential components for hospitals. It offers efficient and cost-effective solutions to problems related to the transportation of various materials in hospitals. However, in existing systems, the need for compressed air is met without worrying about cost control and without depending on the sample transported, and this not only makes the system inefficient but also may cause sample degradation. The main purpose of this study is to provide speed/pressure control according to the type of material transported to eliminate the disadvantages of existing systems such as energy use and sample degradation. In this study, a new mathematical model is presented that can be used to make more energy-efficient hospital pneumatic systems. Although there are many studies on various pneumatic systems in the literature, there is not enough for the control of hospital pneumatic systems. According to the results obtained in this study, the system parameters were determined and the mathematical model of the system was obtained by using the Multivariate nonlinear regression method. A genetic algorithm was used to test the validity of the obtained mathematical model and to optimize the coefficient of the input parameters of the model. It is expected that this proposed model will contribute to the use of hospital pneumatic systems and provide a scientific and practical solution to the proposed mathematical model. The proposed mathematical model provides up to %43 more efficient transportation over the currently used system that has been tested.

Abstract This work presents the design, development, and performance evaluation of a cost‐effective and practical single‐phase power quality analyzer (SPPQA) using Pyboard microcontroller and Python‐to‐Python interface. The SPPQA offers comprehensive measurements, including voltage/current harmonics up to the seventh order, frequency, voltage and current root mean square values, total power, active/reactive power, power factor, voltage and current crest factor, as well as voltage/current total harmonic distortion. To ensure the accuracy of the SPPQA, a testing procedure was conducted using a test bench equipped with a non‐linear load and a 220‐V power source operating at a frequency of 50 Hz. The obtained results, such as VTHD and ITHD, are compared with measurements obtained from classical approaches and devices, validating the authenticity and reliability of the SPPQA's performance. The SPPQA demonstrates considerable accuracy and reliability in its measurements, establishing it as an efficient and practical tool for power quality assessment. The ability to measure the seven harmonics of voltage and current, alongside other critical parameters, positions the SPPQA as a practical asset in the academic, industrial, and commercial sectors. The affordability and practicality of the SPPQA make it practical in both academic research and practical scenarios. Utilizers can gain deeper information into power quality through its measurements and harmonics analysis, while also enhancing industrial processes and ensuring a reliable power supply in commercial settings.

From an engineering point of view, non‐linear systems are essential to the operation of control systems, because all systems actually have a non‐linear state in nature. In reality, there are many different kinds of non‐linear systems hidden by this negative definition. For successful analysis and control, the identification of non‐linear systems using unknown models is typically necessary. Till now, numerous approaches are developed for identifying non‐linear systems, but it cannot be employed with a large number of components. Moreover, system identification is typically restricted to output and input signals alone, also such systems are rarely used in reality. This is the primary justification for using non‐linear systems in this research. So, this research proposed a non‐linear model of system identification for large‐scale systems under the consideration of two systems: bilinear system and Volterra system. Therefore, a novel algorithm named Self Adaptive Penguin Search Optimization (SAPeSO) is introduced to attain the system characteristics properly and minimize the output variation. Finally, the effectiveness of the proposed work is compared with existing works in terms of various error measures. This research mainly focuses on the application‐oriented engineering problems. In particular, the Mean Absolute Error (MAE) of the proposed work for the Volterra system at 4000 samples is 18.83%, 14.05%, 8.88%, 29.72%, 19.91%, and 6.70% which is better than the existing bald eagle search (BES), arithmetic optimization algorithm (AOA), whale optimization algorithm (WOA), nonlinear autoregressive moving average with exogenous inputs‐ frequency response function + principal component analysis (NARMAX‐FRF+PCA), Global Gravitational Search Algorithm‐Assisted Kalman Filter (CGS‐KF), and sparse regression and separable least squares method (SR‐SLSM) methods, respectively. Finally, the error is minimum for the proposed model when compared with the other traditional approaches. [ABSTRACT FROM AUTHOR]

With the increase in the proportion of new energy power generation in grids, there is an urgent need for the flexible operation of coal‐fired power plants. However, as the most efficient commercial coal‐fired power generation technology, the flexibility of double‐reheat ultra‐supercritical units is limited due to the steam temperature fluctuation in transient processes. To inhibit the steam temperature fluctuations, a data‐driven method, called long short‐term memory (LSTM) was introduced to model the dynamics of the steam temperatures of a 1000‐MW double‐reheat coal‐fired boiler. Moreover, an advanced controller was proposed by combining the LSTM‐based dynamic model and the model predictive control architecture. The results demonstrate that the LSTM model can be used to capture the time delay, multivariable coupling and non‐linear dynamics of the steam temperatures and the proposed controller exhibits excellent performance in steam temperature regulation. The superheated steam temperature fluctuation decreases from 15.4 to 9.8°C, the primary reheat temperature fluctuation decreases from 34.6 to 8.3°C, and the secondary reheat temperature fluctuation decreases from 41.4 to 8.8°C. The proposed method has good potential to stabilize steam temperatures during transient processes of wide‐range load cycling, and finally improves the flexibility of the double‐reheat ultra‐supercritical unit. [ABSTRACT FROM AUTHOR]

Path planning is a crucial component for ensuring the safety and efficiency of flight missions, especially for fighter aircraft. To enhance the combat effectiveness of fighter aircraft, it is important to consider how to avoid danger sources an terrain obstacles, reduce fuel consumption, and utilize the aircraft's own performance to accomplish the mission objectives. In the modern battlefield environment, the shortest path is not the only criterion for planning, but also other factors such as the threat level to the aircraft, fuel consumption, mission completion time, and minimum turning radius. In this paper, the authors propose a multi‐constraint path planning method for fighter aircraft that incorporates these factors into an improved particle swarm algorithm. The authors transform the constraints of three‐dimensional terrain, threat source, fuel consumption, and mission time into an aggregated fitness function. The authors construct a limit curvature matrix to evaluate the feasibility of the generated path. The authors also introduce an adaptive adjustment strategy based on the activation function for the parameters in the particle swarm algorithm. The weights of each constraint are determined according to the actual demand. The experiment results show that the authors' method can efficiently plan the optimal path that satisfies the requirements. Compared with other improved particle swarm algorithms, the authors' method has higher optimal search efficiency and better convergence effect. The authors also provide optimal values for important parameters such as mission energy consumption, mission time, flight speed and others to support the overall mission planning. The authors' method has a certain practical application value. [ABSTRACT FROM AUTHOR]

Abstract The paper addresses the problem of accelerating predictive control of non‐linear system models augmented with Gaussian processes (GP‐MPC). Due to the non‐linear and stochastic prediction model, predictive control of GP‐based models requires to solve a stochastic optimization problem. Different model simplification methods have to be applied to reformulate this problem to a deterministic, non‐linear optimization task that can be handled by a numerical solver. As these problems are still complex, especially with exact moment calculations, real‐time implementation of GP‐MPC is extremely challenging. The existing solutions accelerate the computations at the solver level by linearizing the non‐linear optimization problem and applying sequential convexification. In contrast, this paper proposes a novel GP‐MPC solution approach that without linearization formulates a series of surrogate quadratic programs (QP‐s) to iteratively obtain the solution of the original non‐linear optimization problem. The first step is embedding the non‐linear mean‐variance dynamics of the GP‐MPC prediction model in a linear parameter‐varying (LPV) structure and rewriting the constraints in parameter‐varying form. By fixing the scheduling trajectory at a known variation (based on previously computed or initial state‐input trajectories), optimization of the input sequence for the remaining varying linear model reduces to a linearly constrained quadratic program. After solving the QP, the non‐linear prediction model is simulated for the new control input sequence and new scheduling trajectories are updated. The procedure is iterated until the convergence of the scheduling, that is, the solution of the QP converges to the solution of the original non‐linear optimization problem. By designing a reference tracking controller for a 4DOF robot arm, we illustrate that the convergence is remarkably fast and the approach is computationally advantageous compared to current solutions. The proposed method enables the application of GP‐MPC algorithms even with exact moment matching on fast dynamical systems and requires only a QP solver.

Abstract Path planning is a crucial component for ensuring the safety and efficiency of flight missions, especially for fighter aircraft. To enhance the combat effectiveness of fighter aircraft, it is important to consider how to avoid danger sources an terrain obstacles, reduce fuel consumption, and utilize the aircraft's own performance to accomplish the mission objectives. In the modern battlefield environment, the shortest path is not the only criterion for planning, but also other factors such as the threat level to the aircraft, fuel consumption, mission completion time, and minimum turning radius. In this paper, the authors propose a multi‐constraint path planning method for fighter aircraft that incorporates these factors into an improved particle swarm algorithm. The authors transform the constraints of three‐dimensional terrain, threat source, fuel consumption, and mission time into an aggregated fitness function. The authors construct a limit curvature matrix to evaluate the feasibility of the generated path. The authors also introduce an adaptive adjustment strategy based on the activation function for the parameters in the particle swarm algorithm. The weights of each constraint are determined according to the actual demand. The experiment results show that the authors’ method can efficiently plan the optimal path that satisfies the requirements. Compared with other improved particle swarm algorithms, the authors’ method has higher optimal search efficiency and better convergence effect. The authors also provide optimal values for important parameters such as mission energy consumption, mission time, flight speed and others to support the overall mission planning. The authors’ method has a certain practical application value.

Purpose: In this paper, the horizontal response of a vertical transportation (elevator) model with 4 degrees of freedom, excited by deformations in the rail guide, is numerically investigated, introducing in the system a Non-linear Energy Sink (NES), considered as an absorber of non-linear dynamic vibration, that acts simultaneously as a vibration absorber and as a source of power generation. Methods and Results: The levels of vibration and comfort in the elevator cabin, the efficiency of using a non-linear dynamic vibration absorber to reduce cabin vibrations, and its potential for generating energy are analyzed numerically. To consider the lateral displacement of the cabin, an external disturbance that represents the deformations and misalignment of the guide rails and the increase in the elevator speed is adopted, associating the lateral acceleration levels of the elevator with the comfort levels of the passengers according to ISO 2631 and BS 6841. Conclusions: The results of the numerical simulations demonstrated that the configuration of the appropriate parameters of the NES is essential to guarantee a better level of comfort to the passengers and the production of energy and that the increase in the speed associated with the use of the absorber of non-linear dynamic vibrations can improve the comfort level by reducing accelerations in the cabin. [ABSTRACT FROM AUTHOR]

The ideal human community or "Eupsychia" envisioned by Abraham Maslow was a place inhabited by a thousand "self-actualizing people" who shared a devotion to certain higher values. These values were, for Maslow, universally human and biologically rooted, and they included truth, beauty, justice, and the ability to become the best that one was capable of becoming. In addition to imagining it, Maslow searched for Eupsychia in reality and thought he had found it in three California locations: Non-Linear Systems, a technology company; Synanon, a drug rehab center; and Esalen, a hippie retreat. Despite its dependence on shared values, for Maslow Eupsychia was not a perfect place, either in his imagination or in reality, and he realized that its inhabitants would need ways to confront strife and deal with their differences. I suggest that his utopian realism contains an important lesson for our own highly divided 21st-century American society. [ABSTRACT FROM AUTHOR]

This paper aims to study a specific kind of parallel robot: Spherical Parallel Manipulators (SPM) that are capable of unlimited rolling. A focus is made on the kinematics of such mechanisms, especially taking into account uncertainties (e.g. on conception & fabrication parameters, measures) and their propagations. Such considerations are crucial if we want to control our robot correctly without any undesirable behavior in its workspace (e.g. effects of singularities). In this paper, we will consider two different approaches to study the kinematics and the singularities of the robot of interest: symbolic and semi-numerical. By doing so, we can compute a singularity-free zone in the work- and joint spaces, considering given uncertainties on the parameters. In this zone, we can use any control law to inertially stabilize the upper platform of the robot. [ABSTRACT FROM AUTHOR]

The paper addresses the problem of accelerating predictive control of non‐linear system models augmented with Gaussian processes (GP‐MPC). Due to the non‐linear and stochastic prediction model, predictive control of GP‐based models requires to solve a stochastic optimization problem. Different model simplification methods have to be applied to reformulate this problem to a deterministic, non‐linear optimization task that can be handled by a numerical solver. As these problems are still complex, especially with exact moment calculations, real‐time implementation of GP‐MPC is extremely challenging. The existing solutions accelerate the computations at the solver level by linearizing the non‐linear optimization problem and applying sequential convexification. In contrast, this paper proposes a novel GP‐MPC solution approach that without linearization formulates a series of surrogate quadratic programs (QP‐s) to iteratively obtain the solution of the original non‐linear optimization problem. The first step is embedding the non‐linear mean‐variance dynamics of the GP‐MPC prediction model in a linear parameter‐varying (LPV) structure and rewriting the constraints in parameter‐varying form. By fixing the scheduling trajectory at a known variation (based on previously computed or initial state‐input trajectories), optimization of the input sequence for the remaining varying linear model reduces to a linearly constrained quadratic program. After solving the QP, the non‐linear prediction model is simulated for the new control input sequence and new scheduling trajectories are updated. The procedure is iterated until the convergence of the scheduling, that is, the solution of the QP converges to the solution of the original non‐linear optimization problem. By designing a reference tracking controller for a 4DOF robot arm, we illustrate that the convergence is remarkably fast and the approach is computationally advantageous compared to current solutions. The proposed method enables the application of GP‐MPC algorithms even with exact moment matching on fast dynamical systems and requires only a QP solver. This paper aims to discuss the approach of constrained modified feedback linearization model predictive control (CMFLMPC) for the spacecraft simulator. The simulation and experimental results demonstrate that the proposed hybrid controller has an insignificant calculative cost and facilitates the spacecraft to perform the regulation manoeuvre with sufficient precision in the presence of external torques and actuator saturations. [ABSTRACT FROM AUTHOR]

In passive target tracking, target motion parameters (TMP) i.e. range, course, and speed are estimated using bearing measurements. In the simulation mode, the accuracy of the estimated solution can be calculated, as true values are available. In the actual scenario, the true values of TMP are not readily obtainable. In this research, the focus is laid on the evolution of the acceptance criterion of the TMP in the actual scenario. Detailed mathematical modeling is carried out and an unscented Kalman filter (UKF) is used to estimate TMP. The state vector covariance matrix of UKF is utilized to express the errors in estimated TMP and an acceptance criterion is derived in terms of the standard deviation of errors in estimated TMP. Submarine to submarine tracking is carried out and scenarios are chosen accordingly. Several low angle on target bow (ATB) scenarios are used in the Monte-Carlo simulation and the results are presented. A similar procedure can be carried out for medium and high ATB scenarios and a submarine to ship or vice-versa passive target tracking. [ABSTRACT FROM AUTHOR]

This paper presents a mathematical modeling approach utilizing a fuzzy modeling framework for fixed-wing aircraft systems with the goal of creating a highly desirable mathematical representation for model-based control design applications. The starting point is a mathematical model comprising fifteen non-linear ordinary differential equations representing the dynamic and kinematic behavior applicable to a wide range of fixed-wing aircraft systems. Here, the proposed mathematical modeling framework is applied to the AIRBUS A310 model developed by ONERA. The proposed fuzzy modeling framework takes advantage of sector non-linearity red techniques to recast all the non-linear terms from the original model to a set of combined fuzzy rules. The result of this fuzzification is a more suitable mathematical description from the control system design point of view. Therefore, the combination of this fuzzy model and the wide range of control techniques available in the literature for such kind of models, like parallel and non-parallel distributed compensation control laws using linear matrix inequality optimization, enables the development of control algorithms that guarantee stability conditions for a wide range of operations points, avoiding the classical gain scheduling schemes, where the stability issues can be extremely challenging.

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In this paper centralized and distributed model predictive control methods are used for the consensus problem of nonlinear multi-agent systems in the presence of fixed and time-varying obstacles. The distributed method is implemented in two ways: serial and parallel. Contractive constraints are applied to establish stability, and the convergence of agents to a consensus point is guaranteed. Obstacles are considered as the constraints of the optimization control problem. Using the proposed methods, the need for designing terminal ingredients that are essential parts of designing the conventional MPC for establishing stability is eliminated. Terminal ingredients are usually hard to design and have been used for low-order systems but the proposed methods reduce the complexity of the design and can be applied for higher-order systems. To show the effectiveness of the proposed methods, the simulation is provided for the consensus of a group of wheeled mobile robots for both centralized and distributed methods and the results are reported. • CMPC and DMPC are used for consensus of nonlinear multi-agent systems in the presence of fixed and time-varying obstacles. • The proposed distributed method is implemented in two ways: serial and parallel. • Contractive constraints are used to establish stability and the convergence of agents to a consensus point is guaranteed. • Parallel DMPC scheme in the presence of exogenous disturbances is studied. [ABSTRACT FROM AUTHOR]

In this work, a new methodology for the dynamic analysis of non-linear systems is developed by applying the Mamdani fuzzy model. With this model, parameters such as settling time, peak time and overshoot will be obtained. The dynamic analysis of non-linear fuzzy systems with triangular membership functions is performed, and linguistic variables describing overly complex or ill-defined phenomena are used to fit the model. Scaling factors will simplify the modification of the variables, making them easier to find the system model. The specifications of second-order characteristics in the time domain, such as overshoot and peak time, will be represented graphically. As a case study, the proposed methods are implemented to analyse the dynamics of a tank and a simple pendulum for first-order and second-order systems, respectively, where it is observed that the proposed methodology offers highly positive results. [ABSTRACT FROM AUTHOR]

This paper focuses on effective frequency regulation of isolated multi-area microgrid structures using polynomial functions extracted from the modelling of renewable sources. This paper highlights the necessity of state-space (SS) formation for intermittent renewables such as solar and wind for specified quantities compared with the standard functions. The approximate gain and time constants for the variable generating sources used in the literature cannot be rationalized while carrying out frequency regulation since it does not epitomize the system dynamics. Thus, the article recommends non-linear modelling of renewables for executing frequency regulation in MATLAB/Simulink. The supremacy of the proposed work in the presence of a 3-degree of freedom (DOF)-based fractional PID controller aided with a recently evolved Artificial Hummingbird Algorithm (AHA) is determined through the transient and sensitivity analysis. The augmented results proved the outstanding response of AHA-optimized 3DOF-FOPID-based systems when compared with the existing controllers under variable operating conditions. Stringent evaluation is executed in the presence of commonly used heuristic approaches for witnessing the robustness of the optimized controller. The proposed system's hardware-in-loop (HIL)-based implementation is also presented in the paper for a significant characterization of its working in the physical environment. [ABSTRACT FROM AUTHOR]

Purpose: While X-ray photon-counting detectors (PCDs) promise to revolutionize medical imaging, theoretical frameworks to evaluate them are commonly limited to incident fluence rates sufficiently low that the detector response can be considered linear. However, typical clinical operating conditions lead to a significant level of pile-up, invalidating this assumption of a linear response. Here, we present a framework that aims to evaluate PCDs, taking into account their non-linear behavior., Approach: We employ small-signal analysis to study the behavior of PCDs under pile-up conditions. The response is approximated as linear around a given operating point, determined by the incident spectrum and fluence rate. The detector response is subsequently described by the proposed perturbation point spread function (pPSF). We demonstrate this approach using Monte-Carlo simulations of idealized direct- and indirect-conversion PCDs., Results: The pPSFs of two PCDs are calculated. It is then shown how the pPSF allows to determine the sensitivity of the detector signal to an arbitrary lesion. This example illustrates the detrimental influence of pile-up, which may cause non-intuitive effects such as contrast/contrast-to-noise ratio inversion or cancellation between/within energy bins., Conclusions: The proposed framework permits quantifying the spectral and spatial performance of PCDs under clinically realistic conditions at a given operating point. The presented example illustrates why PCDs should not be analyzed assuming that they are linear systems. The framework can, for example, be used to guide the development of PCDs and PCD-based systems. Furthermore, it can be applied to adapt commonly used measures, such as the modulation transfer function, to non-linear PCDs., (© 2024 The Authors.)