Algebraic Matrix Equation Research Articles

Numerical solutions for linear ordinary differential equation with variable coefficients using Haar wavelet method

Recently, wavelet analysis or wavelet transform has been developed as a mathematical instrument for many problems. In the present paper, the numerical technique is based on Haar wavelet collocation points and operational matrices over a new interval [-ϑ,ϑ]. So, we have established a new operational matrix of integration the Haar wavelets functions for a chosen domain from -ϑ to 0. The operational matrices of integration and product are utilized to convert the linear ordinary differential equations together with variable coefficients into algebraic matrix equation which can be resolved by MATLAB. Three numerical examples have been displayed which involving first order differential equations including variable coefficients. The results illustrate that the suggested approach is completely reasonable in comparison with correct solution. Moreover, the exactness of the variable can be enhanced by increasing the Haar wavelets resolution.

Approximating Solutions of Non-Linear Troesch’s Problem via an Efficient Quasi-Linearization Bessel Approach

Two collocation-based methods utilizing the novel Bessel polynomials (with positive coefficients) are developed for solving the non-linear Troesch’s problem. In the first approach, by expressing the unknown solution and its second derivative in terms of the Bessel matrix form along with some collocation points, the governing equation transforms into a non-linear algebraic matrix equation. In the second approach, the technique of quasi-linearization is first employed to linearize the model problem and, then, the first collocation method is applied to the sequence of linearized equations iteratively. In the latter approach, we require to solve a linear algebraic matrix equation in each iteration. Moreover, the error analysis of the Bessel series solution is established. In the end, numerical simulations and computational results are provided to illustrate the utility and applicability of the presented collocation approaches. Numerical comparisons with some existing available methods are performed to validate our results.

The Matrix Formulations And Algebraic Equations Using Scilab

The Matrix Formulations And Algebraic Equations Using Scilab

On the Hermitian and skew-Hermitian splitting-like iteration approach for solving complex continuous-time algebraic Riccati matrix equation

Some matrix equations are important in application and studying in various fields of sciences. One of them is continuous-time algebraic Riccati matrix equation that is an interesting subject for many researchers in engineering and sciences specially applied mathematics. In this work we try to apply Hermitian and skew-Hermitian splitting (HSS) like method to solve this equation. Then we analyze the convergence of the new iterative method in detail. Also we try to determine the optimum parameter in our method which minimizes the upper bounds of absolute error. In the end we test the new algorithm by solving some numerical examples.

Signaling Equilibria for Dynamic LQG Games With Asymmetric Information

In this article, we consider a finite-horizon dynamic game with two players who observe their types privately and take actions that are publicly observed. Players' types evolve as conditionally independent, controlled linear Gaussian processes, and players incur quadratic instantaneous costs. This forms a dynamic linear quadratic Gaussian game with asymmetric information. We show that under certain conditions, players' strategies that are linear in their private types, together with Gaussian beliefs, form a perfect Bayesian equilibrium (PBE) of the game. This is a signaling equilibrium due to the fact that future beliefs on players' types are affected by the equilibrium strategies. We provide a backward-forward algorithm to find the PBE. Each step of the backward algorithm reduces to solving an algebraic matrix equation for every possible realization of the state estimate covariance matrix. The forward algorithm consists of Kalman filter recursions, where state estimate covariance matrices depend on equilibrium strategies. We provide necessary and sufficient conditions for the existence of the solution of the algebraic matrix equation for scalar actions. Finally, we present numerical examples.

Optimal control of nonlinear stationary systems at infinite control time

The article presents a solution to the problem of control synthesis for dynamical systems described by linear differential equations that function in accordance with the integral-quadratic quality criterion under uncertainty. External perturbations, errors and initial conditions belong to a certain set of uncertainties. Therefore, the problem of finding the optimal control in the form of feedback on the output of the object is presented in the form of a minimum problem of optimal control under uncertainty. The problem of finding the optimal control and initial state, which maximizes the quality criterion, is considered in the framework of the optimization problem, which is solved by the method of Lagrange multipliers after the introduction of the auxiliary scalar function - Hamiltonian. The case of a stationary system on an infinite period of time is considered. The formulas that can be used for calculations are given for the first and second variations. It is proposed to solve the problem of control search in two stages: search of intermediate solution at fixed values of control and error vectors and subsequent search of final optimal control. The solution of -optimal control for infinite time taking into account the signal from the compensator output is also considered, as well as the solution of the corresponding matrix algebraic equations of Ricatti type.

Зведення задачі мінімаксного керування лінійними нестаціонарними системами до – робастного шляхом динамічної гри

The problem of synthesis of minimax control for the dynamic, described by the linear system of differential equations (taking into account the state, controls, perturbations and initial conditions, with the given equation of observation inclusive) of objects functioning in accordance with the integral-quadratic quality criterion in uncertainty is solved in the work. External perturbations, errors, and initial conditions were assumed to belong to a number of uncertainties. The task of finding optimal control in the form of a feedback object that minimizes the performance criterion is presented in the form of a minimum maximal uncertainty control problem. In the absence of ready-made solution paths, this problem is reduced to a -control problem under the most unfavorable disturbances, and in addition to a dynamic game problem with zero sum and a certain price for the game, and a strategy for solving it is proposed that offers a way to new results. The problem of finding the optimal control and the initial state that maximize the quality criterion is considered in the framework of the optimization problem solved by the Lagrange multiplier method after introducing the auxiliary scalar function, the Hamiltonian. It is shown that to find the maximum value of the criterion, either the necessary condition of the extremum of the first kind can be used, which depends on the ratio of the first variation of the criterion and the first variations of the control vectors and the initial state, or also the necessary condition of the extremum of the second kind, which depends on the sign of the second variation. For the first and second variations, formulas are given that can be used for calculations. It is suggested to solve the control search problem in two steps: search for an intermediate solution at fixed values of control vectors and errors, and then search for final optimal control. Consideration is also given to solving -optimal control for infinite control time with respect to the signal from the compensator output, as well as solving the corresponding Riccati matrix algebraic equations.

The rate of convergence of an iterative‐computational algorithm for second‐kind nonlinear Volterra integral equations with weakly singular kernels

This paper attempts to present an iterative‐computational method for solving weakly singular nonlinear Volterra integral equations (WSNVIEs) of the second type based on rationalized Haar wavelets (RHWs). The proposed algorithm does not need to solve any linear or nonlinear system for evaluating the wavelet coefficients. After a brief introduction of rationalized Haar functions (RHFs), the computational matrices of integration and fractional integral are applied to reduce the approximation of integral equations to some matrix algebraic equations. Next, the error analysis of the problem by using the two‐dimensional iterated projection operator is offered. We will prove that the rate of convergence of the proposed algorithm is , where h is the iteration number and β is the contraction constance. The method for any WSNVIE of the second kind with 0 ≤ β < 1 is convergent. The proposed method is computationally attractive, and comparing the results obtained of the known technique is more efficient.

Combining Pontryagin's Principle and Dynamic Programming for Linear and Nonlinear Systems

To study optimal control and disturbance attenuation problems, two prominent-and somewhat alternative-strategies have emerged in the last century: dynamic programming (DP) and Pontryagin's minimum principle (PMP). The former characterizes the solution by shaping the dynamics in a closed loop (a priori unknown) via the selection of a feedback input, at the price, however, of the solution to (typically daunting) partial differential equations. The latter, instead, provides (extended) dynamics that must be satisfied by the optimal process, for which boundary conditions (a priori unknown) should be determined. The results discussed in this article combine the two approaches by matching the corresponding trajectories, i.e., combining the underlying sources of information: knowledge of the complete initial condition from DP and of the optimal dynamics from PMP. The proposed approach provides insights for linear as well as nonlinear systems. In the case of linear systems, the derived conditions lead to matrix algebraic equations, similar to the classic algebraic Riccati equations (AREs), although with coefficients defined as polynomial functions of the input gain matrix, with the property that the coefficient of the quadratic term of such equation is sign definite, even if the corresponding coefficient of the original ARE is sign indefinite, as it is typically the case in the H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> control problem. This feature is particularly appealing from the computational point of view, since it permits the use of standard minimization techniques for convex functions, such as the gradient algorithm. In the presence of nonlinear dynamics, the strategy leads to algebraic equations that allow us to (locally) construct the optimal feedback by considering the behavior of the closed-loop dynamics at a single point in the state space.

An iterative algorithm for coupled Riccati equations in continuous-time Markovian jump linear systems

In this paper, a novel implicit iterative algorithm with some tuning parameters is developed to solve the coupled algebraic Riccati matrix equation arising in the continuous-time Markovian jump linear systems. By introducing some tuning parameters in the proposed iterative algorithm, the current estimation for unknown variables is updated by using the information not only in the last step but also in the current iterative step and previous iterative steps. These tuning parameters can be appropriately chosen such that the proposed algorithm has faster convergence performance than some previous algorithms. It is shown that the proposed algorithm with zero initial conditions can monotonically converge to the unique positive semidefinite solution of the coupled Riccati matrix equation if the corresponding Markovian jump system is stabilisable. Finally, an example is provided to show the effectiveness of the developed algorithm.

Linear quadratic optimal control for a class of continuous-time nonhomogeneous Markovian jump linear systems in infinite time horizon

In this paper, the infinite time horizon linear quadratic optimal control problem is investigated for the continuous-time Itô stochastic Markovian jump linear systems (MJLSs) with time-varying transition rates. It is assumed that the time-varying transition rates of the MJLSs possess piecewise homogeneous time-varying property, which implies that the transition rates are time-varying in the whole time domain but they are time-invariant in some small time intervals. The variations of the transition rates in these small time intervals are considered to be in two cases: arbitrary variation and stochastic variation. With this, the considered system becomes a piecewise homogeneous Itô stochastic MJLS. The main contribution of this paper is that two linear quadratic optimal controllers in infinite time horizon are proposed for the above modeled continuous-time piecewise homogeneous Itô stochastic MJLS in the sense of arbitrary variation and stochastic variation, respectively. Moreover, the sufficient and necessary conditions for the existence of the designed controllers are established based on the existence of the unique positive definite solution of two coupled algebraic Riccati matrix equations. Finally, a simulation example is provided to illustrate the effectiveness of the proposed linear quadratic optimal controllers.

Solving Wiener–Hopf problems via an efficient iterative scheme

In the analysis of the fluid queues, it is necessary to obtain the nonnegative solution of a nonsymmetric algebraic Riccati matrix equation. Under suitable conditions, this solution can be obtained transforming algebraic Riccati equations into unilateral quadratic matrix equations. In this paper, we use an efficient iterative scheme to approximate a solution of this quadratic matrix equation. We improve the efficiency and the accuracy of Newton’s method, widely used in the literature. Moreover, a local convergence result is proved. Finally, we apply this efficient method to approximate the solution of a particular noisy Wiener–Hopf problem and we compare it with Newton’s method. Moreover, a predictor–corrector iterative scheme is constructed that improve the accessibility of the aforesaid method.

PIMOL: The Finite Difference Method of Lines Based on the Precise Integration Method for an Arbitrary Irregular Domain

The aim of this paper is to introduce a new semi-analytical method named precise integration method of lines (PIMOL), which is developed and used to solve the ordinary differential equation (ODE) systems based on the finite difference method of lines and the precise integration method. The irregular domain problem is mainly discussed in this paper. Three classical examples of Poisson’s equation problems are given, including one regular and two irregular domain examples. The PIMOL reduces a semi-discrete ODE problem to a linear algebraic matrix equation and does not require domain mapping for treating the irregular domain problem. Numerical results show that the PIMOL is a powerful method.

Kernel-Based Hamilton–Jacobi Equations for Data-Driven Optimal and H-Infinity Control

This paper presents a data-driven method for designing optimal controllers and robust controllers for unknown nonlinear systems. Mathematical models for the realization of the control are difficult to develop owing to a lack of knowledge regarding such systems. The proposed multidisciplinary method, based on optimal control theory and machine learning with kernel functions, facilitates designing appropriate controllers using a data set. Kernel-based system models are useful for representing nonlinear systems. An optimal and an H-infinity controller can be designed by solving Hamilton-Jacobi (HJ) equations, which unfortunately, are difficult to solve owing to the nonlinearity and complexity of the kernel-based models. The objective of this study consists of overcoming two challenges. The first challenge is to derive exact solutions to the HJ equations for a class of kernel-based system models. A key technique in overcoming this challenge is to reduce the HJ equations to easily solvable algebraic matrix equations, from which optimal and H-infinity controllers are designed. The second challenge is to control an unknown system using the obtained controllers, wherein the system is identified as a kernel-based model. Additionally, this study analyzes probabilistic stability of the feedback system with the proposed controllers. Numerical simulations demonstrate control performances of both the derived optimal and H-infinity controllers and stability of the feedback system.

Robust Filtering for Discrete Systems with Unknown Inputs and Jump Parameters

The paper deals with robust filtering algorithms for discrete systems with unknown inputs (disturbances) and Markovian jump parameter. The proposed filtering algorithm is based on the separation principle, minimization of a quadratic criterion and the use of Kalman filters with unknown input and smoothing procedures. Solving a non-stationary problem is represented solving a two-point boundary value problem in kind of difference matrix equations. In the stationary case problem is represented matrix algebraic equations. Robustness ensures the stability of the filter dynamics when errors occur in identifying the jump parameter. An example is provided to illustrate the proposed approach, which showed that the use of smoothing procedures for estimating an unknown input improves the accuracy of estimates.

PIMOL: A New Semi-analytical Method Based on the Finite Difference Method of Lines and the Precise Integration Method

The aim of this paper is to introduce a new semi-analytical method, namely PIMOL (precise integration method of lines, the parametric finite difference method of lines based on the precise integration method), which is developed and used to solve the ordinary differential equation (ODEs) systems based on the finite difference method of lines and the precise integration method (PIM). Two examples of Poisson’s equation problems are given: a boundary value problem and an ODE eigenvalue problem. The PIMOL can effectively reduce a semi-discrete ODE problem to a linear algebraic matrix equation. Numerical results show that the PIMOL is a powerful method.

Iterative algorithms for discrete periodic Riccati matrix equations

In this paper, two iterative algorithms are constructed to obtain the positive definite solutions of the discrete periodic algebraic Riccati matrix equations. In these two algorithms, the estimation of the unknown matrices are updated by using the available estimation information at the current iteration step. The convergence properties of the proposed algorithms are also given. Finally, numerical examples are employed to illustrate the effectiveness of the proposed algorithms.

Disturbance Attenuation by Measurement Feedback in Nonlinear Systems via Immersion and Algebraic Conditions

In this paper, we consider the problem of disturbance attenuation with internal stability for nonlinear, input-affine systems via measurement feedback. The solution to the above-mentioned problem has been provided, three decades ago, in terms of the solution to a system of coupled nonlinear, first-order partial differential equations (PDEs). As a consequence, despite the rather elegant characterisation of the solution, the presence of PDEs renders the control design synthesis almost infeasible in practice. Therefore, to circumvent such a computational bottle-neck, in this paper we provide a novel characterisation of the exact solution to the problem that does not hinge upon the explicit computation of the solution to any PDE. The result is achieved by considering the immersion of the nonlinear dynamics into an extended system for which locally positive definite functions solving the required PDEs may be directly provided in closed form by relying only on the solutions to Riccati-like, state-dependent, algebraic matrix equations .

Accurate Extraction of Winding Inductances Using Dual Formulations Without Source Field Computation

Dual formulations are accurate in use for computing energy-related global quantities such as inductance and providing upper and lower bounds of the unknown true values of these global parameters, which is not possible if using a single formulation. Since traditional dual formulations result in totally different algebraic matrix equations, people have to develop two different finite element (FE) programs and solve the resultant two algebraic equation systems, respectively. In this paper, two practical dual formulations for open region magnetostatic problems, where the global FE matrices are exactly the same, are adopted for extracting the winding inductances. FE formulation and implementation details are presented. Practical examples having complex windings are solved using the proposed methods to showcase the effectiveness and accuracy. High-order FE basis functions are also used to enhance the solution accuracy. The proposed method is highly useful for medium-sized industrial applications by providing guaranteed inductance parameters.

Effects of parallel magnet bars and partially filled porous media on magneto-thermo-hydro-dynamic characteristics of pipe ferroconvection

In this experimental-numerical study, effects of transverse magnetic field of parallel magnet bars perpendicular to the fluid flow direction and partially filled porous media are investigated. Moreover, constant heat flux is applied on the outer surface of a circular straight horizontal pipe and concentric porous media is embedded inside it. Magnet bars are located right at the entrance of pipe and a thick layer of insulation is wrapped the pipe to prevent heat loss. In addition, a 3D numerical simulation is performed using CFD techniques to capture the influence of these two factors on the thermohydrodynamic behavior of ferrofluid flow. Hybrid scheme of finite volume method and SIMPLE algorithm are employed to discretize and solve governing equations as well as TDMA for solving the linear algebraic matrix equation. Mineral oil is used as a base fluid and ferrofluid with three different volume fractions 0.5, 1 and 2% are prepared for the experiment. Finally prepared experimental setup configuration in this investigation leads to this conclusion that in comparison with magnetic field, although porous media induce more unfavorable pressure loss, it will bring more heat transfer enhancement as well.