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This article is concerned with the adaptive-event-triggered filtering problem as it relates to a class of nonlinear discrete-time systems characterized by interval Type-2 fuzzy models. The system under investigation is susceptible to Markovian switching and deception attacks. It is proposed to implement an improved event-triggering mechanism to reduce the unnecessary signal transmissions on the communication channel and formulate the extended dissipativity specification to quantify the transient dynamics of filtering errors. By resorting to the linear matrix inequality approach and using the information on upper and lower membership functions, stochastic analysis establishes sufficient conditions for the existence of the desired filter, ensuring the mean-squared stability and extended dissipativity of the augmented filtering system. Further, an optimization-based algorithm (PSO) is proposed for computing filter gains at an optimal level of performance. The developed scheme was finally tested through experimental numerical illustrations based on a single-link robot arm and a lower limbs system. [ABSTRACT FROM AUTHOR]

Fuzzy theory is widely studied and applied. This article introduces an adaptive control scheme for a class of non-linear systems with Markov jump switching. The introduced scheme supposes that the system is submitted to external disturbances under imperfect premise matching. By using discrete-time Takagi–Sugeno fuzzy models, a sliding mode observer-based control scheme is utilized to estimate unmeasured states of the system. We build two fuzzy switching manifolds for the disturbance and sliding mode observer systems. Then, a linear matrix inequality-based criterion is developed using slack matrices. This criterion proves that the sliding mode dynamics are robustly admissible under an H-infinity performance often used in control theory. Hence, new adaptive sliding mode controllers are synthesized for the disturbance and sliding mode observer systems. This allows the reachability of pre-designed sliding surfaces to be guaranteed. Finally, experimental numerical illustrations on a bio-economic system and a tunnel diode circuit are presented to show potential applications, as well as validating the effectiveness of the scheme proposed in the present investigation. [ABSTRACT FROM AUTHOR]

This paper examines the fault-tolerant control problem for discrete-time descriptor systems that are susceptible to intermittent actuator failures, nonlinear sensor data, and probability-based missing data. The discrete-time non-homogeneous Markov chain was adopted to describe the stochastic behavior of actuator faults. Moreover, Bernoulli-distributed stochastic variables with known conditional probabilities were employed to describe the practical features of random sensor non-linearity and missing data. In this study, the output signals were quantized and a dynamic output feedback controller was synthesized such that the closed-loop system was stochastically admissible and satisfied the strictly (Q , S , R) - γ -dissipative performance index. The theoretical developments are illustrated through numerical simulations of an infinite machine bus. [ABSTRACT FROM AUTHOR]

In this paper, the problem of asynchronous sliding mode control (SMC) is addressed for discrete-time singular Markovian jump systems (DSMJSs) with external disturbances and randomly occurring nonlinearities. Moreover, the states of DSMJSs are unavailable for measurement, and the synchronisation between the controller and the system modes is not surely ensured. Our attention is mainly focused on solving the control problem by proposing an observer-based adaptive sliding mode controller for such a class of complex systems. First, a non-fragile observer is designed not only to estimate the system states but also to relax the multiplicative gain variations and to construct an appropriate mode-dependent sliding mode surface function. Then, by assuming that the bounds of nonlinear terms are unknown, an adaptive SMC control law is synthesised to drive the system trajectories onto the specified sliding surface and completely compensate for the influences of the external disturbances and fluctuations of the controller and observer gains. Sufficient conditions are established to ensure that the closed-loop system is stochastically admissible with a γ level of the mixed H ∞ /passive performance, and to provide the observer and controller gain matrices. Finally, to numerically substantiate the efficacy of the proposed control scheme, two examples are given. [ABSTRACT FROM AUTHOR]

The efficient solution of the time-varying quaternion matrix inverse (TVQ-INV) is a challenging but crucial topic due to the significance of quaternions in many disciplines, including physics, engineering, and computer science. The main goal of this research is to employ the higher-order zeroing neural network (HZNN) strategy to address the TVQ-INV problem. HZNN is a family of zeroing neural network models that correlates to the hyperpower family of iterative methods with adjustable convergence order. Particularly, three novel HZNN models are created in order to solve the TVQ-INV both directly in the quaternion domain and indirectly in the complex and real domains. The noise-handling version of these models is also presented, and the performance of these models under various types of noises is theoretically and numerically tested. The effectiveness and practicality of these models are further supported by their use in robotic motion tracking. According to the principal results, each of these six models can solve the TVQ-INV effectively, and the HZNN strategy offers a faster convergence rate than the conventional zeroing neural network strategy. [ABSTRACT FROM AUTHOR]

The computation of the time-varying matrix pseudoinverse has become crucial in recent years for solving time-varying problems in engineering and science domains. This paper investigates the issue of calculating the time-varying pseudoinverse based on full-rank decomposition (FRD) using the zeroing neural network (ZNN) method, which is currently considered to be a cutting edge method for calculating the time-varying matrix pseudoinverse. As a consequence, for the first time in the literature, a new ZNN model called ZNNFRDP is introduced for time-varying pseudoinversion and it is based on FRD. Five numerical experiments investigate and confirm that the ZNNFRDP model performs as well as, if not better than, other well-performing ZNN models in the calculation of the time-varying pseudoinverse. Additionally, theoretical analysis and numerical findings have both supported the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

In this research article, a simple four-dimensional (4D) chaotic dynamic system with uncomplicated structure and only one nonlinear term is introduced. The features of the proposed design have been conducted with some standard nonlinear dynamic analysis and mathematical tools which show the chaotic nature. One of the most important indicators for detecting complexity of the chaotic systems is the Kaplan-York dimension of the system. Moreover, one of the main criteria of chaotic systems is its simplicity due to the reduction of operating costs. Therefore, it seems necessary to design a system as simple as possible and with high complexity. In this research, a comparison has been made between the proposed system and similar chaotic systems, which has given noticeable results. For the practical implementation of the proposed design, the circuit analysis using Multisim software has been employed. The proposed scheme has been used in the application of image encryption to show the efficiency of the proposed chaotic system and standard encryption tests have been performed. The rest of the numerical results have been conducted using MATLAB/Simulink software. [ABSTRACT FROM AUTHOR]

In this paper, the problem of reliable control design with mixed H ∞ /passive performance is discussed for a class of Takagi–Sugeno TS fuzzy descriptor systems with time-varying delay, sensor failure, and randomly occurred non-linearity. Based on the Lyapunov theory, firstly, a less conservative admissible criterion is established by combining the delay decomposition and reciprocally convex approaches. Then, the attention is focused on the design of a reliable static output feedback (SOF) controller with mixed H ∞ /passive performance requirements. The key merit of the paper is to propose a simple method to design such a controller since the system output is subject to probabilistic missing data and noise. Using the output vector as a state component, an augmented model is introduced, and sufficient conditions are derived to achieve the desired performance of the closed-loop system. In addition, the cone complementarity linearization (CCL) algorithm is provided to calculate the controller gains. At last, three numerical examples, including computer-simulated truck-trailer and ball and beam systems are given to show the efficacy of our proposed approach, compared with existing ones in the literature. [ABSTRACT FROM AUTHOR]

In this study, control and fault detection are examined for Takagi–Sugeno (T–S) fuzzy continuous-time systems with conic-type nonlinearity. In a conic-type dynamic system, Takagi–Sugeno fuzzy models are applied. A conic-type non-linearity must lie in an n-dimensional hyper-sphere with an uncertain centre to satisfy the restrictive condition. A combined error system of estimation and control error is obtained by combining the observations and the controller. Our main goal is to design an observer and controller for fault detection that minimizes the impact of unknown inputs and faults on the system in terms of the $ H_{\infty } $ H ∞ performance. The Lyapunov function is used to construct the residual signal using an observer and a controller. Using Linear Matrix Inequalities (LMIs), we can express the performance conditions for $ H_{\infty } $ H ∞ . The scheme's effectiveness is demonstrated with numerical examples. [ABSTRACT FROM AUTHOR]

The stability of nonlinear systems in the control domain has been extensively studied using different versions of the algebraic Riccati equation (ARE). This leads to the focus of this work: the search for the time-varying quaternion ARE (TQARE) Hermitian solution. The zeroing neural network (ZNN) method, which has shown significant success at solving time-varying problems, is used to do this. We present a novel ZNN model called 'ZQ-ARE' that effectively solves the TQARE by finding only Hermitian solutions. The model works quite effectively, as demonstrated by one application to quadrotor control and three simulation tests. Specifically, in three simulation tests, the ZQ-ARE model finds the TQARE Hermitian solution under various initial conditions, and we also demonstrate that the convergence rate of the solution can be adjusted. Furthermore, we show that adapting the ZQ-ARE solution to the state-dependent Riccati equation (SDRE) technique stabilizes a quadrotor's flight control system faster than the traditional differential-algebraic Riccati equation solution. [ABSTRACT FROM AUTHOR]

With the increasing demand for electrical energy and the challenges related to its production, along with the need to be environmentally friendly to achieve sustainability for future generations, proton exchange membrane fuel cells (PEMFCs) are emerging as a clean energy source that can effectively replace conventional energy sources, in various fields of application and especially in the field of transportation exploiting electric vehicles (EVs). To improve the development and control of the PEMFCs, the precise determination of its mathematical model remains an essential task. Indeed, the accuracy of such a model depends on the ability to overcome the constraints associated with the nonlinearity and the numerous involved unknown parameters. The present paper proposes a new Dandelion Optimizer (DO) to accurately identify, for the first time, the parameters of the PEMFC model. The DO addresses the weaknesses of the majority of metaheuristic algorithms related to the self-adaptation of parameters, the stagnation of convergence to local minima, and the ability to refer to the whole population. The high ability of the proposed method is investigated using both steady-state and dynamic situations. The DO-based parameters estimation approach has been assessed through a specific comparative study with the most recently published techniques including GWO, GBO, HHO, IAEO, VSDE, and ABCDESC is performed using two typical PEMFC modules, namely 250 W PEMFC and NedStack PS6. The results obtained proved that the proposed approach obtained promising achievements and better performances comparatively with well-recognized and competitive methods. [ABSTRACT FROM AUTHOR]

In this work, a new finite element (FE) EDM model was established to estimate the material removal rate (MRR) and thermal stresses in the AISI 316L and AISI D2 steels. Numerical simulation of EDM process was performed using dynamic coupled thermo-mechanical analysis considering plane-strain assumption. Different thermal loading conditions were tested during numerical simulation of the discharge phase while altering heat input size function. Time-dependent plasma channel radius forms with and without discharge current effect have been investigated for each material. The appropriate discharge radius function was identified for each grade of steel based on the comparison of numerically predicted MRR-discharge current diagrams with experiments from previously published work. It was found that the heat input size functions that take into account the effect of discharge current are the most suitable for the prediction of the MRR. Moreover, thermal stresses were predicted via a cooling analysis. The confrontation between experiment and the computed thermal stress profiles for the AISI 316L in the same erosion regime was deemed satisfactory. [ABSTRACT FROM AUTHOR]

The accurate model of the solar PV system is the principal organ that describes the performance of this resource. Several approaches based on optimizing algorithms were considered valuable tools to illustrate the I–V curve for improving the photovoltaic models. Their electrical parameters are estimated using optimization algorithms referring to the experimental database or manufacturer's datasheet. This paper proposes a novel developed a photovoltaic model based on improved arithmetic optimization algorithm (IAOA) to extract the solar cell parameters. Also, an experimental test bench is presented for obtaining the measured illustration of the I–V characteristics. Thus, the root mean square error value that describes the difference between measured and estimated results is considered the objective function for two different models, the simple-diode model and the one-diode model. The proposed IAOA results are compared with other research papers and optimization algorithms. Furthermore, the evaluation of the proposed IAOA has been discussed considering several statistical analysis tests. The presented results show that the effectiveness and accuracy of IAOA results are excellent, and their I–V characteristics coincide with experimental data. Moreover, the results obtained by the proposed algorithm show its high superiority in optimizing the solar cell parameters under a variety of operating conditions. [ABSTRACT FROM AUTHOR]

Increasing energy needs, pollution of nature, and eventual depletion of resources have prompted humanity to obtain new technologies and produce energy using clean sources and renewables. In this paper, we design an advanced method to improve the performance of a sliding mode controller combined with control theory for a photovoltaic system. Specifically, we decouple the controlled output of the system from any perturbation source and assess the effectiveness of the results in terms of solution quality, closed-loop control stability, and dynamical convergence of the state variables. This study focuses on the climatic conditions that may affect the behavior of a solar energy plant to supply a motor with the highest possible efficiency and nominal operating conditions. The designed method enables us to obtain an optimal performance by means of advanced control techniques and a slime mould stochastic optimization algorithm. The efficiency and performance of this method are examined based on a benchmark model of a photovoltaic system via numerical analysis and simulation. [ABSTRACT FROM AUTHOR]

In this study, we consider eight stages per step family of explicit Runge-Kutta-Nyström pairs of orders eight and six. The pairs from this family effectively use eight stages for each step. The coefficients provided by such a method are much less than the number of non linear order conditions required to be solved. Thus, we traditionally apply various simplified assumptions in order to address this drawback. The assumptions taken in the family we consider here deliver a subsystem where all the coefficients are evaluated successively and explicitly with respect to five free parameters. We train (adjust) these free parameters in order to derive a certain pair that outperforms other similar pairs of orders 8 (6) in Keplerian type orbits, e.g., Kepler, perturbed Kepler, Arenstorf orbit, or Pleiades. Differential evolution technique is used for the training. The pair that we finally present offers about an additional digit of accuracy in a variety of orbits. [ABSTRACT FROM AUTHOR]