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This paper addresses the identification problem of feedback nonlinear system with time delay (FNTD). A data-driven identification method for the FNTD system based on bidirectional long short-term memory (BiLSTM) networks is proposed. First, considering the long input and output data sequence and the existence of bidirectional features, the BiLSTM network is chosen for identification. In addition, in order to mine the complex nonlinear mapping relationship between input and output, the attention mechanism is introduced into the BiLSTM algorithm to highlight the influence of key factors. The Gaussian kernel function selects the optimal complexity model to enhance the extrapolation performance, and further improves the identification accuracy. Then, the attention-BiLSTM algorithm is proposed. In the simulation, a numerical example and two application examples are implemented. The results show that the attention-BiLSTM method can effectively identify the FNTD system, and is superior to LSTM and BiLSTM in terms of identification accuracy and speed. The proposed attention-BiLSTM method has the highest identification accuracy, and the RMSE is 5.57 % and 1.05 % lower than the LSTM and BiLSTM models, respectively. The R 2 value reaches 0.9997, which is 16.17 % and 0.77 % higher than LSTM and BiLSTM, respectively. [ABSTRACT FROM AUTHOR]

Spherical tanks have been predominantly used in process industries due to their large storage capability. The fundamental challenges in process industries require a very efficient controller to control the various process parameters owing to their nonlinear behavior. The current research work in this paper aims to propose the Approximate Generalized Time Moments (AGTM) optimization technique for designing Fractional-Order PI (FOPI) and Fractional-Order PID (FOPID) controllers for the nonlinear Single Spherical Tank Liquid Level System (SSTLLS). This system features a large dead time, and its real-time modeling generally represents a Single Input Single Output (SISO) model. However, in practice, the derived SISO model is often a First Order Plus Dead Time (FOPDT) model, necessitating an effective controller to maintain the tank's steady-state level. In this research, the proposed AGTM method, based on the conventional Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers, is compared with the FOPI and FOPID controllers for the nonlinear SSTLLS. The performance of these controllers is contrasted using metrics such as Integral Squared Error (ISE) and Integral Absolute Error (IAE), as well as time-domain characteristics containing Rise time, Peak time, Settling time, Peak overshoot, and Steady-state error. The implementation of the aforementioned controllers is done in simulation and real-time employing the MATLAB software environment and the Data Acquisition (DAQ) device National Instrument NI-DAQmx 6211. The simulation and experimental results demonstrate the exceptional performance of the designed Fractional-Order controllers based on the proposed method which offers an increased degree of freedom despite the more complex design process. [ABSTRACT FROM AUTHOR]

Tensegrity structures are geometric nonlinear systems and statically and kinematically indeterminate structures that require an initial shape-finding procedure to establish a self-equilibrium state. This paper presents a shape-finding algorithm requiring structure topology, strut force, cable force density, and a random initial estimate of node coordinates as input. The equilibrium of the structure is achieved by zeroing the nonlinear static equilibrium in which the generalized nodal coordinates are chosen as variables. The modified Newton-Raphson method is used to solve the nonlinear equilibrium system by decreasing the nonlinear least square function to ensure global convergence. The stability of the self-balancing structure was evaluated using the properties of the geometric and tangent stiffness matrix. Various numerical examples are presented to illustrate the method's effectiveness for 2-d and 3-d tensegrity structures with multiple states of self-stress. [ABSTRACT FROM AUTHOR]

As a result of this study, an improved direct torque control (DTC) method based on sliding mode control (SMC) was used to control the sensorless three-phase asynchronous motor, as well as a third-order cascade inverter has a frequency-modulated carrier (FM—Frequency Modulation) to reduce total harmonic distortion (THD) in torque and current, in order to achieve the best possible result. The rapid change in current and voltage on the load caused oscillations around the sliding surface (chattering) that damaged the actuators by means of rapid changes and large amplitudes of current and voltage. This study, therefore, proposed the use of nonlinear Tansig functions rather than classical sign functions as the control algorithm. Additionally, due to the fact that direct torque control (DTC) had the advantage that it was not sensitive to motor parameters, thereby ensuring that, even when nonlinear parameters change with time, they did not affect the whole closed system as a whole. Hence, radial basis function networks (RBFNs) were used to approximate magnetic fluxes and torques. A simulation and experiment were conducted by OPAL-RT equipment on Matlab/Simulink for a squirrel-cage rotor motor 1 hp, 150 rad/s with third-order cascade inverter that has a frequency-modulated carrier. In spite of the change in torque in the presence of noise, the system remained stable regardless of the set value from the lowest velocity 3 rad/s to the highest velocity 150 rad/s. This algorithm was simple, required little memory, and had high-order harmonic components of torque and low current (THD-Total Harmonic Distortion). The algorithm had been proven to be stable by Lyaponov. [ABSTRACT FROM AUTHOR]

The proper orthogonal decomposition (POD) method is used to model the shell-motor-rotor coupled system with bearing nonlinearities to reduce the computational cost. The rotor system with 192 degrees of freedom is modeled by the finite element method for the coupled shell-motor-rotor structure. The basic principle of the POD method and its specific expression in the shell-motor-rotor coupled system are introduced. By using the POD method, the shell-motor-rotor coupled system is reduced to 20, 18 and 15 degrees of freedom, and the computation time is reduced by more than 94%. The time course curves and spectra of the simplified system and the original system are compared, which verifies the efficiency and accuracy of the POD method. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive filtering algorithm based on the symmetry Kernel Hyperbolic Tangent Mixed Error Criterion (KHTMC), aimed at addressing the identification of nonlinear systems under non-Gaussian noise environments. The algorithm optimizes signal processing by constructing a mixed cost function that combines the symmetry logarithmic square error and the hyperbolic tangent function and integrates it with the kernel adaptive filtering method. Simulation results show that, compared to existing kernel adaptive filtering algorithms, the KHTMC algorithm exhibits significant advantages in terms of convergence speed and steady-state mean square error. It demonstrates strong robustness and tracking performance, especially when dealing with mixed non-Gaussian noise. Therefore, this algorithm shows great potential in signal processing applications under complex noise conditions, offering a more reliable and efficient solution. [ABSTRACT FROM AUTHOR]

Previous works have analyzed finite/fixed-time tracking control for nonlinear systems. In these works, achieving the accurate time convergence of errors must be under the premise of known initial values and careful design of control parameters. Then, how to break through the constraints of initial values and design parameters for this issue is an unsolved problem. Motivated by this, we successfully studied prescribed-time tracking control for single-input single-output nonlinear systems with uncertainties. Specifically, we designed a state feedback controller on [ 0 , T p) , based on the backstepping method, to make the tracking error (TE) tend to zero at T p , in which T p is the arbitrarily selected prescribed-time. Furthermore, on [ T p , ∞) , another controller, similarly to that on [ 0 , T p) , was designed to keep TE within a precision after T p , while TE may not stay at zero. Therefore, on [ T p , ∞) , another new controller, based on sliding mode control, was built to ensure that TE stays at zero after T p. [ABSTRACT FROM AUTHOR]

This article presents a compact magnetic levitation energy harvester (MLEH) with tunable resonant frequency. Unlike many of the reported tunable harvesters with unknown tuning results, the proposed MLEH can be tuned toward designated resonant frequency values within its tuning range. The targeted tuning processes is realized by a nonlinear magnet repulsive force exerted on a Halbach magnet array, combined with a calibrated scaling system. At a sinusoidal acceleration of ±0.15 g, the maximum frequency tuning range of the proposed MLEH is 6.3 Hz (8.1–14.4 Hz), which is 77.8% of its resonant MLEH (8.1 Hz). At a frequency of 9.7 Hz, the output power is 462.1 μW and the calculated normalized power density is 496 μW cm−3 g−2. HIGHLIGHTS: • A magnetic levitation energy harvester (MLEH) with tunable resonant frequency based on a Halbach magnet array is proposed. • The MLEH has the advantages of low resonant frequency, wide tuning range, and simple tuning procedure. • It can be precisely tuned toward a specific target frequency in a one-off manner without the need for repeated frequency scanning. [ABSTRACT FROM AUTHOR]

This paper presents an extension of the Vector Fitting algorithm with the purpose of constructing compact behavioral models of weakly nonlinear circuits starting from frequency-domain input-output data. Using the concept of generalized transfer function provided by Volterra series theory for nonlinear systems, the algorithm approximates a given dataset of generalized transfer function samples with a black-box multivariate rational model. The fitted model can then be recast into a bilinear state-space form for time-domain analysis. Practical extraction of the required data samples can be carried out by measurement or harmonic balance analysis available in commercial solvers. Examples demonstrating the accuracy and efficiency of the behavioral models include a Low-Dropout Regulator and a Low Noise Amplifier.

A predefined-time sliding mode adaptive control method (PDTSMAC)for nonlinear system is proposed in the presence of parameters unknown, external disturbances and arbitrary initial values. Firstly, the expected trajectory of the system is extended to the arrival process with characters of predefined-time convergence and the accurate tracking process of completely tracking the desired trajectory, the design principle of extended trajectory is given; Then, an extreme learning machine (ELM) with exponential convergence of external weights is designed to compensate the uncertainties of the system, and a sliding mode adaptive controller with predefined-time convergence is constructed based on a predefined-time convergent sliding mode surface. The stability of the closed-loop system is proved theoretically. The simulation results show that the control strategy can ensure that the construction robot in arbitrary initial state converges to the extended desired trajectory within the predefined-time, and realizes the complete and accurate tracking of the preset desired trajectory, and the trajectory tracking error is less than 0.008. [ABSTRACT FROM AUTHOR]

An interesting field of studying nonlinear systems is their sensitivity study. With sensitivity study the most influential parameters on a system can be obtained and then the simplification and improvement of the model will be possible. In this paper the global sensitivity study of a Duffing-type vibration system is carried out with Sobol’s variance-based method taking the root mean square of acceleration and the maximum acceleration as output variables. With the sensitivity study it was observed that the parameters of the excitation signal like the amplitude and the angular velocity are the most influential. It was also found that a single parameter has less influence on the system than the parameter combinations. The aim of the research is to carry out the global sensitivity study of a relatively simple nonlinear system. The study is the basis for further research tasks in order to perform the sensitivity study of more complex systems.

The current work investigates a recently introduced unidirectional wave model, applicable in science and engineering to understand complex systems and phenomena. This investigation has two primary aims. First, it employs a novel modified Sardar sub-equation method, not yet explored in the literature, to derive new solutions for the governing model. Second, it analyzes the complex dynamical structure of the governing model using bifurcation, chaos, and sensitivity analyses. To provide a more accurate depiction of the underlying dynamics, they use quantum mechanics to explain the intricate behavior of the system. To illustrate the physical behavior of the obtained solutions, 2D and 3D plots, along with a phase plane analysis, are presented using appropriate parameter values. These results validate the effectiveness of the employed method, providing thorough and consistent solutions with significant computational efficiency. The investigated soliton solutions will be valuable in understanding complex physical structures in various scientific fields, including ferromagnetic dynamics, nonlinear optics, soliton wave theory, and fiber optics. This approach proves highly effective in handling the complexities inherent in engineering and mathematical problems, especially those involving fractional-order systems. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive iterative learning control (ILC) scheme for nonlinear systems with iteration-varying trial lengths under external morphologically-similar disturbances, utilising inter-trial iteration and real-time correction. To attenuate external quasi-periodic disturbances and non-repetitive uncertainties, and further achieve better tracking performance along iteration and time axes, the proposed scheme combines the iteration-to-iteration proportional-type (P-type) ILC with the within-iteration P-type scheme. The tracking error with dead zone property and zero filling treatment is constructed. In addition, as opposed to the existing two-dimensional (2D) ILC works, the integrated framework is formed through the connection of adaptive weights, which are calculated by the adaptive weight determination method based on the ideology of the Kalman filter. The convergence of the algorithm is proved based on the contraction mapping principle. Compared with the traditional ILC schemes, illustrative and applicational simulations are provided to demonstrate the effectiveness and the superiority of the proposed framework. [ABSTRACT FROM AUTHOR]

The present study proposes an approximate analytical solution to a nonlinear system of time-fractional partial differential equations. The Sadik residual power series method, which integrates the two-part Sadik integral transform with the residual power series technique, is employed to solve the fractional differential equation in the Caputo sense. Nonlinear problems with known and unknown solutions are examined to demonstrate the capacity of the technique. Numerical simulations and 3D visualizations are conducted for various values of the fractional order to further understand the solution's behavior. Additionally, the results are validated against exact solutions or existing methodologies to ensure their reliability and accuracy. A key advantage of the proposed method is its ability to generate results without the need for Adomian polynomials, perturbation techniques, discretization, or linearization, enabling a more efficient. [ABSTRACT FROM AUTHOR]

Piezoelectric actuators (PEAs) are extensively used for scanning and positioning in scanning probe microscopy (SPM) due to their high precision, simple construction, and fast response. However, there are significant challenges for instrument designers due to their nonlinear properties. Nonlinear properties make precise and accurate control difficult in cases where position feedback sensors cannot be employed. However, the performance of PEA-driven scanners can be significantly improved without position feedback sensors if an accurate mathematical model with low computational costs is applied to reduce hysteresis and other nonlinear effects. Various methods have been proposed for modeling PEAs, but most of them have limitations in terms of their accuracy and computational efficiencies. In this research, we propose a variant DenseNet-type neural network (NN) model for modeling PEAs in an AFM scanner where position feedback sensors are not available. To improve the performance of this model, the mapping of the forward and backward directions is carried out separately. The experimental results successfully demonstrate the efficacy of the proposed model by reducing the relative root-mean-square (RMS) error to less than 0.1%. [ABSTRACT FROM AUTHOR]

Maximizing the efficiency of photovoltaic (PV) systems relies heavily on employing efficient maximum power point tracking (MPPT) algorithms. This research focuses on the advancement of enhanced MPPT algorithms capable of achieving the maximum power point (MPP) under different climatic profiles. This paper proposes an adapted perturb and observe-based model predictive control (APO-MPC) strategy to validate the effectiveness of PV systems under three climatic situations. The APO algorithm incorporates variable step sizes to compute reference currents to reduce oscillations while maintaining a steady state in output power. The APO-MPC efficiently tracks and stabilizes output power by predicting future states using reference current and minimizing the cost function. This eliminated the necessity for expensive sensing and communication equipment and networks designed for directly measuring variations in solar irradiation. The computational burden of an algorithm is reduced using a simplified mathematical model of a boost converter and a one-step prediction approach. The PV panel and boost converter are modeled to get appropriate parameters for implementing the proposed algorithm. The system undergoes simulations using the MATLAB/Simulink environment, and multiple test cases are conducted under constant, rapid, and linearly changing irradiances. The outcomes demonstrate that the proposed APO-MPC MPPT algorithm outperforms APO, Kalman filter-based MPC (KMF-MPC), and other existing strategies in terms of stability, transient response time, overshoots, steady-state oscillations, and follow of reference trajectory under dynamic weather conditions. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach for achieving rapid synchronization between two memristor chaotic circuits (MCCs), both with and without noise perturbations. The proposed adaptive control strategy effectively handles the uncertainty in control gains by adhering to predesigned update law. Additionally, this protocol is non-chattering and differentiable, avoiding the use of conventional discontinuous functions such as signum and absolute value functions. This method successfully mitigates the tremors caused by discontinuous functions. We derive two sufficient criteria using finite-time Lyapunov and stochastic finite-time Lyapunov stability methods. Numerical results validate the theoretical analysis and demonstrate the influence of noise intensity on convergence speed. Furthermore, the results have an application in image encryption transmission. [ABSTRACT FROM AUTHOR]

This paper aims to propose a real-time detection method of wheel-rail conditions in a monorail vehicle-track nonlinear system based on vehicle vibration signals. Monorail rubber tires exhibit strong nonlinearity and three-dimensional elasticity, resulting in highly coupled vertical and lateral vibrations. As a result, traditional methods applied in railway system such as transfer function analysis are not suitable for back-calculation of road irregularities for this nonlinear system. Iterative simulation with numerous parameters is time-consuming, thus this paper proposes a parallel simulation approach fused with the genetic algorithm to shorten the calculation time and facilitate big data processing. The multi-rigid body model's simulation result can closely match the test data by intelligently modifying the vehicle parameters. This method overcomes the transfer function's limitations in nonlinear systems and the significant errors introduced by the simplified mathematical derivation method. It also overcomes the shortcoming of significant errors in the mathematical derivation method and disperses errors caused by simplifying the multi-rigid body dynamics and ensures calculation accuracy. Additionally, this paper highlights the application of artificial intelligence techniques in intelligent wheel eccentricity detection. It improves the traditional algorithm to shorten the calculation time and benefit big data processing. [ABSTRACT FROM AUTHOR]

This paper addresses the challenge of data-driven control of nonlinear systems, focusing on the limitations and capabilities of model reference Gaussian process regression (MR-GPR) and its evolved counterpart, model reference neural networks (MR-NN). MR-GPR, based on Gaussian processes renowned for their adaptability to diverse data structures, encounters scalability issues especially when handling large datasets. To address these limitations, this paper introduces MR-NN, an extension of MR-GPR, leveraging neural networks (NN) to manage large datasets and capture complex nonlinear dynamics effectively. We present a comprehensive evaluation of both methods through a classical control problem of the inverted pendulum, a system well-recognized for its nonlinear behavior. Numerical experiments are conducted to compare the methods in terms of control performance, computational efficiency, and reliability. [ABSTRACT FROM AUTHOR]

An adaptive control method based on immersion and invariance (I&I) is presented in a class of nonlinear systems with time-varying uncertain parameters. A parameter estimation law based on reference models using I&I is designed to accelerate the convergence of estimated parameters to the true value, enabling the closed-loop system to reach the predefined target system on the manifold more quickly and reducing the energy consumption of the system. The inherent integrability obstacles in I&I are overcome by using dynamic scaling techniques, reducing the complexity of controller design. Stability analysis of the closed-loop system demonstrates that the proposed control method can achieve asymptotic stability control of the target system, and verified the robustness of the closed-loop system in the face of external disturbances. Finally, simulations of attitude tracking control demonstrate the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

We address the prescribed-time stability of a class of nonlinear system with uncertainty/disturbance. With the help of the parametric Lyapunov equation (PLE), we designed a state feedback control to regulate the full-state of a controlled system within prescribed time, independent of initial conditions. The result illustrated that the controlled state converges to zero as t approaches the settling time and remains zero thereafter. It was further proved that the controller is bounded by a constant that depends on the system state. A numerical example is presented to verify the validity of the theoretical results [ABSTRACT FROM AUTHOR]

Abstract This research introduces a new technique to control constrained nonlinear systems, named Lyapunov-based neural network model predictive control using a metaheuristic optimization approach. This controller utilizes a feedforward neural network model as a prediction model and employs the driving training based optimization algorithm to resolve the related constrained optimization problem. The proposed controller relies on the simplicity and accuracy of the feedforward neural network model and the convergence speed of the driving training based optimization algorithm. The closed-loop stability of the developed controller is ensured by including the Lyapunov function as a constraint in the cost function. The efficiency of the suggested controller is illustrated by controlling the angular speed of three-phase squirrel cage induction motor. The reached results are contrasted to those of other methods, specifically the fuzzy logic controller optimized by teaching learning-based optimization algorithm, the optimized PID with particle swarm optimization algorithm, the neural network model predictive controller based on particle swarm optimization algorithm, and the neural network model predictive controller using driving training based optimization algorithm. This comparative study showcase that the suggested controller provides good accuracy, quickness and robustness due to the obtained values of the mean absolute error, mean square error root mean square error, enhancement percentage, and computing time in the different simulation cases, and it can be efficiently utilized to control constrained nonlinear systems with fast dynamics.

The practical near impossibility of empirical attempts in estimating optimal controller gains makes the use of metaheuristics strategies inevitable to automatically obtain these gains by an iterative, heuristic simulation procedure. The convergence of the gains values to the local or global solutions occur with ease. In designing controllers for the Twin-Rotor MIMO System (TRMS), Jumping Spider Optimization Algorithm (JSOA), a novel neoteric population-based bio-inspired metaheuristic approach is used to obtain optimum values for the Proportional, Integral and Derivative (PID) controllers. With the k,p,i controller gains as the decision variables, the JSOA solution to a nonlinear multi-objective optimization problem subject to some intrinsic constraints spawned optimal values for the controllers’ variables. Counter to other algorithms (deterministic and stochastic) that get caught in local minima, JSOA evolved a solution after searchingly rummaging the entire solution search space in a vectorized fashion for an optimal value. Compared with several other versatile controllers (using GA, PSO, Pattern Search and Simulated Annealing), statistical results obtained showed JSOA technique provided a unique solution and found the gains of the PID controllers, marginally in relation to the others like optimization methods.

In this paper we study the well-posedness for the periodic Cauchy problem and the internal controllability of a one-dimensional system that describes the propagation of long water waves with small amplitude in the presence of surface tension. The well-posedness is proved by using the Fourier transform restriction method and the controllability is proved by using the moment method.

Nowadays, cars have become the mainstream means of transportation, and traffic accidents caused by cars often occur on the road. Therefore, its safety is very important and has also attracted the attention of the general public. The braking system of a car is a very important part of its composition and structure, which determines the smoothness and safety of the car. The automobile braking system is a complex nonlinear system, which has multiple inputs, multiple outputs, uncertainties and multiple interference sources. Due to the complex relationship between input, interference, and output, the uncertainty of internal and external parameters in automobiles makes it very difficult to maintain appropriate braking. In order to improve the stability and safety of automobiles, this article conducts research on artificial intelligence technology, collaborative control algorithms, and automobile braking systems. The aim is to strengthen automobile braking systems through artificial intelligence technology and design an intelligent braking system to ensure smooth and safe driving of automobiles. Experiments have shown that the intelligent braking system can maintain a constant speed and maintain relative stability during emergency braking. The intelligent braking system can automatically detect accident prone road sections for reminders and speed reduction, and also automatically detect the situation around the car to give a warning. The experimental results show that when the car's speed reaches 25m/s or above (90km/h), the warning distance of the system is nearly 150 meters, which can fully ensure the safety of the driver; when driving at low speeds, the warning distance would not be too long to avoid affecting the driver's driving experience. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms are computational techniques based on the collective behavior of swarms and the study of organisms acting in communities. These algorithms involve different types of organisms. Finding controller values for nonlinear systems is a challenging task using classical approaches. Hence, using metaheuristics to find the controller values of a twin rotor multi-input multi-output system (TRMS), one of the nonlinear systems studied in the literature, seems to be more appropriate than using classical methods. In this study, different types of metaheuristic algorithms were used to find the PID controller values for a TRMS, including a genetic algorithm (GA), a dragonfly algorithm, a cuckoo algorithm, a particle swarm optimization (PSO) algorithm, and a coronavirus optimization algorithm (COVIDOA). The obtained graphs were analyzed based on certain criteria for the main rotor and tail rotor angles to reach the reference value in the TRMS. The experimental results show that when the rise and settlement times of the TRMS are compared in terms of performance, the GA took 1.5040 s (seconds) and the COVIDOA took 9.59 s to increase the pitch angle to the reference value, with the GA taking 0.7845 s and the COVIDOA taking 2.4950 s to increase the yaw angle to the reference value. For the settling time, the GA took 11.67 s and the COVIDOA took 28.01 s for the pitch angle, while the GA took 14.97 s and the COVIDOA took 26.69 s for the yaw angle. With these values, the GA and COVIDOA emerge as the foremost algorithms in this context. [ABSTRACT FROM AUTHOR]

This research introduces a new technique to control constrained nonlinear systems, named Lyapunov-based neural network model predictive control using a metaheuristic optimization approach. This controller utilizes a feedforward neural network model as a prediction model and employs the driving training based optimization algorithm to resolve the related constrained optimization problem. The proposed controller relies on the simplicity and accuracy of the feedforward neural network model and the convergence speed of the driving training based optimization algorithm. The closed-loop stability of the developed controller is ensured by including the Lyapunov function as a constraint in the cost function. The efficiency of the suggested controller is illustrated by controlling the angular speed of three-phase squirrel cage induction motor. The reached results are contrasted to those of other methods, specifically the fuzzy logic controller optimized by teaching learning-based optimization algorithm, the optimized PID with particle swarm optimization algorithm, the neural network model predictive controller based on particle swarm optimization algorithm, and the neural network model predictive controller using driving training based optimization algorithm. This comparative study showcase that the suggested controller provides good accuracy, quickness and robustness due to the obtained values of the mean absolute error, mean square error root mean square error, enhancement percentage, and computing time in the different simulation cases, and it can be efficiently utilized to control constrained nonlinear systems with fast dynamics. [ABSTRACT FROM AUTHOR]

For regulating the Synchronous Generator (SG) output voltage, the Automatic Voltage Regulator (AVR) system is a significant device. This work propounds a survey on Optimization Algorithms (OAs) utilized for tuning the controller parameters on the AVR system. A device wielded for adjusting the SG's Terminal Voltage (TV) is named AVR. A Controller is utilized for improving stability and getting a superior response by mitigating maximum Over Shoot (OS), reducing Rise Time (RT), reducing Settling Time (ST), and enhancing Steady State Error (SSE) since output voltage has a slower response and instability. The controllers utilized here are Proportional-Integral-Derivative (PID), Intelligent Controller (IC), along with Fraction Order PID (FOPID). Owing to the occurrence of time delays, nonlinear loads, variable operating points, and others, OAs are wielded for tuning the controller. (a) Particle Swarm Optimization (PSO), (b) Genetic Algorithm (GA), (c) Gray Wolf Optimizer (GWO), (d) Harmony Search Algorithm (HSA), (e) Artificial Bee Colony (ABC), (f) Teaching Learned Based Optimization (TLBO), et cetera are the various sorts of OA. For enhancing the TV response along with stability, various OAs were tried by researchers. [ABSTRACT FROM AUTHOR]

A H ∞ robust adaptive nonlinear observer for state and parameter estimation of a class of Lipschitz nonlinear systems with disturbances is presented in this work. The objective is to estimate parameters and monitor the performance of nonlinear processes with model uncertainties. The behavior of the observer in the presence of disturbances is analyzed using Lyapunov stability theory and by considering an H ∞ performance criterion. Numerical simulations were carried out to demonstrate the applicability of this observer for a semi-active car suspension. The adaptive observer performed well in estimating the tire rigidity (as an unknown parameter) and induced disturbances representing damage to the damper. The main contribution is the proposal of an alternative methodology for simultaneous parameter and actuator disturbance estimation for a more general class of nonlinear systems. [ABSTRACT FROM AUTHOR]

We present a proportional–integral–derivative-based controller plus an adaptive slide surface to solve the trajectory tracking control problem for a fully actuated vessel with unknown parameters perturbed by slowly varying external unknown dynamics. The controller design assumes that the vessel moves at low speed and frequency, its physical parameters are unknown, and its state is measurable. The control approach ensures error tracking convergence toward a small vicinity at the origin. We conduct the corresponding stability analysis using the Lyapunov theory and saturation functions. We tested the controller through two numerical experiments—a turning ellipse maneuver and a rest-to-rest maneuver—where the vessel parameters were unknown, and we obtained satisfactory results. [ABSTRACT FROM AUTHOR]

The practical near impossibility of empirical attempts in estimating optimal controller gains makes the use of metaheuristics strategies inevitable to automatically obtain these gains by an iterative heuristic simulation procedure. The convergence of the gain values to the local or global solutions occur with ease. In designing controllers for the Twin-Rotor MIMO System (TRMS) Jumping Spider Optimization Algorithm (JSOA), a novel neoteric population-based bio-inspired metaheuristic approach is used to obtain optimum values for the Proportional Integral and Derivative (PID) controllers. With the kp, ki, kd controller gains as the decision variables, the JSOA solution to a nonlinear multi-objective optimization problem subject to some intrinsic constraints spawned optimal values for the controllers' variables. Counter to other algorithms (deterministic and stochastic) that get caught in local minima, JSOA evolved a solution after searchingly rummaging the entire solution search space in a vectorized fashion for an optimal value. Compared with several other versatile controllers (using GA, PSO, Pattern Search, and Simulated Annealing), statistical results obtained showed JSOA technique provided a unique solution and found the gains of the PID controllers marginally in relation to the others (optimization methods). [ABSTRACT FROM AUTHOR]

In this work, we present a method for the Global Asymptotic Stabilization (GAS) of an affine control chaotic Lorenz system, via admissible (bounded and regular) feedback controls, where the control bounds are given by a class of (convex) polytopes. The proposed control design method is based on the control Lyapunov function (CLF) theory introduced in [Artstein, 1983; Sontag, 1998]. Hence, we first recall, with parameters including those in [Lorenz, 1963], that these equations are point-dissipative, i.e. there is an explicit absorbing ball ℬ given by the level set of a certain Lyapunov function, V ∞ (x). However, since the minimum point of V ∞ (x) does not coincide with any rest point of Lorenz system, we apply a modified solution to the "uniting CLF problem" (to unify local (possibly optimal) controls with global ones, proposed in [Andrieu & Prieur, 2010]) in order to obtain a CLF V (x) for the affine system with minimum at a desired equilibrium point. Finally, we achieve the GAS of "any" rest point of this system via bounded and regular feedback controls by using the proposed CLF method, which also contains the following controllers: (i) damping controls outside ℬ , so they collaborate with the beneficial stable free dynamics, and (ii) (possibly optimal) feedback controls inside ℬ that stabilize the control system at "any" desired rest point of the (unforced) Lorenz system. [ABSTRACT FROM AUTHOR]

Considering the situation of limited resources in practical applications, it is significant to design control algorithms with high resource utilization rates for a class of nonlinear systems subject to mismatched disturbances. In contrast to common recursive methods, this paper proposes a novel event-triggered tracking control approach by co-designing the triggering event and the controller within a non-recursive design framework that combines disturbance estimation techniques and feedforward compensation strategies. Through rigorous Lyapunov stability analysis, the global boundedness of each state in the closed-loop system is demonstrated, and the absence of the Zeno phenomenon is further verified. A representative numerical simulation and a practical implementation for speed regulation of permanent magnet synchronous motor (PMSM) system confirm the effectiveness and simplicity of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper studies a class of input-saturated nonlinear systems with Lipschitz condition. We combine the fully actuated system approach with anti-windup control to design control system. The proposed control method has the advantages of both fully actuated system approach and anti-windup control. The main advantages are: (1) establishing a fully actuated system approach control framework based on anti-windup control. The controller of the nonlinear system can be written out immediately when a fully actuated system (FAS) model or a group of FAS models of the system is derived, simplifying the steps of control system analysis and design; (2) many actual systems are FAS models, and there is no need to convert the system into a state space model; (3) the anti-windup compensator works only when the actuator saturation occurs to ensure the stability of the control system. In fact, most of the time, the system operates in an unsaturated state, which means that in most cases, the system performance is not affected. A numerical simulation example is given to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The interaction of different time scales in predator-prey models has become a common research topic. In the present article, we concentrated on the dynamics of interactions at two time scales in a plankton-fish system. To investigate the effects of the two time scales on plankton-fish dynamics, we constructed a new parameter with a corrected type that differs from the traditional slow parameter. In addition, zooplankton's refuge from the predator and phytoplankton mortality due to competition are incorporated into the model. Positivity and boundedness of solutions were proved. We then discussed feasibility and stability conditions of the equilibrium. We used a variety of means to support the existence of chaos in the system. Hopf bifurcation conditions were also obtained. Chaos control in the plankton-fish model is one of the main motivations for this study. In the slow-variable parameter case, we explored the control mechanism of gestation delay on chaotic systems, which are calmed by different periodic solutions. Moreover, under seasonal mechanisms, external driving forces can stabilize the system from chaos to periodic oscillations. Meanwhile, the sliding mode control (SMC) approach quickly calms chaotic oscillations and stabilizes it to an internal equilibrium state. The necessary numerical simulation experiments support the theoretical results. [ABSTRACT FROM AUTHOR]

In a nonlinear control system, hysteresis exists usually as common characteristics. In addition, external output disturbances like modelling error, machine friction and so on also occur frequently. Both of them are considered to cause instability and unsatisfactory performance. In this paper, a practical nonlinear control system design is proposed so as to achieve the simultaneous control of input hysteresis and output disturbance. The system is based on RCF (right coprime factorization theory). Additionally, the proposed design has been applied to a soft robotic finger system and the results of simulations and practical experiments are exhibited, which show the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]

This paper describes the development and implementation of a virtual tool (TANQUES EN SERIE PID) of a system of two interconnected tanks made in Easy Java/JavaScript Simulations (EJS,), a tool that seeks to motivate learning control concepts in engineering programs. The simulated model corresponds to the non-linear system of interconnected tanks, which presents graphically the behavior of tank levels on an open loop, this application allows varying physical input parameters of the process, as the cross section of the tanks, resistivity constants of the valves and the input flow of process. The system is also presented in closed loop, presenting the behavior when a PID controller is implemented to the process, controller and time constants can be modified to enter a disturbance to the model and by doing this we are verifying the time response and system reaction when subjected to a perturbation. The tool is verified comparing the results using Simulink and equilibrium equations.

This paper presents an innovative approach for achieving rapid synchronization between two memristor chaotic circuits (MCCs), both with and without noise perturbations. The proposed adaptive control strategy effectively handles the uncertainty in control gains by adhering to predesigned update law. Additionally, this protocol is non-chattering and differentiable, avoiding the use of conventional discontinuous functions such as signum and absolute value functions. This method successfully mitigates the tremors caused by discontinuous functions. We derive two sufficient criteria using finite-time Lyapunov and stochastic finite-time Lyapunov stability methods. Numerical results validate the theoretical analysis and demonstrate the influence of noise intensity on convergence speed. Furthermore, the results have an application in image encryption transmission.

This paper introduces a new numerical technique that utilizes the Chebyshev collocation method to solve the nonlinear SIR mathematical model for dengue disease dynamics. The main goal of this study is the development of a highly accurate and efficient numerical approach that allows for the simulation of dengue fever infection and transmission mechanisms. The suggested methodology provides a strong and adaptable tool for studying the intricate dynamics of dengue fever, which is essential for improving our comprehension and control of this significant public health problem. The numerical approach is totally programmable and can be customized to incorporate a user-friendly interface for data input, hence enhancing its accessibility and practicality for researchers and public health practitioners. The conducted numerical simulations provide evidence of the accuracy and computational efficiency of the suggested technique. This unique numerical framework is positioned as a standard for managing and regulating a diverse array of biological problems. The creation of this sophisticated numerical tool signifies significant progress in the realm of dengue fever modeling. It offers a versatile and dependable platform to investigate different epidemiological scenarios and assess the possible effects of intervention efforts. The results of this study have significant consequences for enhancing dengue monitoring, safeguarding, and control initiatives in areas where the disease is prevalent.

Abstract The article presents an active fault-tolerant control scheme with an integral terminal sliding mode controller for the UAV systems. This scheme effectively addresses saturation issues, disturbances, and sensor and actuator faults. Initially, the quadcopter UAV's model is represented in state space form. Subsequently, an augmented system incorporating auxiliary states from sensor faults is developed. An adaptive sliding mode observer is proposed for estimating the actuator and sensor faults. The integral terminal sliding mode fault-tolerant control, designed for altitude and attitude regulation, relies on fault estimation data. In contrast, a cascade proportional-integral-derivative (PID) controller is employed for position control. Simulation results demonstrate the superiority of the proposed method over existing control algorithms.