Optimal boundary control of heat conduction problems on an infinite time domain by control parameterization

The present paper deals with an optimal boundary control problem in which the process of systems under consideration is governed by a linear parabolic partial differential equation over an infinite time interval. The objective of the paper is to determine the optimal boundary control that minimize a given energy-based performance measure. The performance measure is specified as a quadratic functional of displacement and a suitable penalty term involving the boundary controls. In order to determine the optimal boundary controls, the problem with boundary controls are converted into a problem with distributed controls. The modal space technique is then used to reduce the system into the optimal control of time invariant lumped parameter system. The associated system of uncoupled first order initial value problems is solved in terms of controllers. Next step deals with the computation of the control and trajectory of the linear time-invariant lumped parameter. For this we approximate the controllers by a finite number of orthogonal exponential zero-interpolants over the interval [0,∞). The resultant performance index after using the optimality condition leads to a system of linear algebraic equations. The suggested technique is easy to implement on digital computer. We provide a numerical example to demonstrate the applicability and efficiency of the proposed approach.     

An adaptive mesh method with variable relaxation time

Moving mesh partial differential equations have been widely used in the last decade for solving differential equations exhibiting large solution variations such as shock waves and boundary layers.In this paper, we have applied a dynamic adaptive method for solving time-dependent differential equations. The mesh velocities are governed by an equation in which a relaxation time is employed to move nodes in such a way that they remain concentrated in regions of rapid variation of the solution. A numerical example involving a blow-up problem shows the advantage of using a variable relaxation time over a fixed one.     

Adaptive pseudospectral methods for solving constrained linear and nonlinear time-delay optimal control problems

In this paper, we first develop an adaptive shifted Legendre–Gauss (ShLG) pseudospectral method for solving constrained linear time-delay optimal control problems. The delays in the problems are on the state and/or on the control input. By dividing the domain of the problem into a uniform mesh based on the delay terms, the constrained linear time-delay optimal control problem is reduced to a quadratic programming problem. Next, we extend the application of the adaptive ShLG pseudospectral method to nonlinear problems through quasilinearization. Using this scheme, the constrained nonlinear time-delay optimal control problem is replaced with a sequence of constrained linear-quadratic sub-problems whose solutions converge to the solution of the original nonlinear problem. The method is called the iterative-adaptive ShLG pseudospectral method. One of the most important advantages of the proposed method lies in the case with which nonsmooth optimal controls can be computed when inequality constraints and terminal constraints on the state vector are imposed. Moreover, a comparison is made with optimal solutions obtained analytically and/or other numerical methods in the literature to demonstrate the applicability and accuracy of the proposed methods.     

Modeling of pH neutralization process using fuzzy recurrent neural network and DNA based NSGA-II

In this paper, the Takagi–Sugeno fuzzy recurrent neural network (T–S FRNN) is applied to model a pH neutralization process. Since the accuracy and complexity of the network are two contradictory criteria for the T–S FRNN model, a DNA based NSGA-II is proposed to optimize the parameters of the model. In the DNA based NSGA-II, each individual is encoded with one nucleotide base sequence, modified DNA based crossover and mutation operators are designed to improve the searching ability of the algorithm, and crowding tournament selection is applied based on the Pareto-optimal fitness and the crowding distance. The study on the performance of test functions shows that the DNA based NSGA-II outperforms NSGA-II in the quality of the obtained Pareto-optimal solution. To verify the effectiveness of the established T–S FRNN model for the pH neutralization process, it is compared with two T–S FRNN models optimized with other methods. Comparison results show that the model optimized by DNA based NSGA-II is more accurate and the complexity of the network is acceptable.     

Differential games,partial-state stabilization,and model reference adaptive control

In this paper, we develop a unified framework to find state-feedback control laws that solve two-player zero-sum differential games over the infinite time horizon and guarantee partial-state asymptotic stability of the closed-loop system. Partial-state asymptotic stability is guaranteed by means of a Lyapunov function that is positive definite and decrescent with respect to part of the system state. The existence of a saddle point for the system?s performance measure is guaranteed by the fact that this Lyapunov function satisfies a partial differential equation that corresponds to a steady-state form of the Hamilton–Jacobi–Isaacs equation. In the second part of this paper, we show how our differential game framework can be applied not only to solve pursuit-evasion and robust optimal control problems, but also to assess the effectiveness of a model reference adaptive control law. Specifically, the model reference adaptive control architecture is designed to guarantee satisfactory trajectory tracking for uncertain nonlinear dynamical systems, whose matched nonlinearities are captured by the regressor vector. By modeling matched and unmatched nonlinearities, which are not captured by the regressor vector, as the pursuer?s and evader?s control inputs in a differential game, we provide an explicit characterization of the system?s uncertainties that do not disrupt the trajectory tracking capabilities of the adaptive controller. Two numerical examples illustrate the applicability of our results.     

The solutions of vibration control problems using artificial neural networks

This paper introduces an alternative method artificial neural networks (ANN) used to obtain numerical solutions of mathematical models of dynamic systems, represented by ordinary differential equations (ODEs) and partial differential equations (PDEs). The proposed trial solution of differential equations (DEs) consists of two parts: The initial and boundary conditions (BCs) should be satisfied by the first part. However, the second part is not affected from initial and BCs, but it only tries to satisfy DE. This part involves a feedforward ANN containing adjustable parameters (weight and bias). The proposed solution satisfying boundary and initial condition uses a feedforward ANN with one hidden layer varying the neuron number in the hidden layer according to complexity of the considered problem. The ANN having appropriate architecture has been trained with backpropagation algorithm using an adaptive learning rate to satisfy DE. Moreover, we have, first, developed the general formula for the numerical solutions of nth-order initial-value problems by using ANN.For numerical applications, the ODEs that are the mathematical models of linear and non-linear mass-damper-spring systems and the second- and fourth-order PDEs that are the mathematical models of the control of longitudinal vibrations of rods and lateral vibrations of beams have been considered. Finally, the responses of the controlled and non-controlled systems have been obtained. The obtained results have been graphically presented and some conclusion remarks are given.     

Model-Free distributed optimal consensus control of nonlinear multi-agent systems: A graphical game approach

In this paper, the optimal consensus control problem of nonlinear multi-agent systems(MASs) with completely unknown dynamics is considered. The problem is formulated in a differential graphical game approach which can be solved by Hamilton-Jacobi (HJ) equations. The main difficulty in solving the HJ equations lies in the nonlinear coupling between equations. Based on the Adaptive Dynamic Programming (ADP) technique, an VI-PI mixed HDP algorithm is proposed to solve the HJ equations distributedly. With the PI step, a suitable iterative initial value can be obtained according to the initial policies. Then, VI steps are run to get the optimal solution with exponential convergence rate. Neural networks (NNs) are applied to approximate the value functions, which makes the data-driven end-to-end learning possible. A numerical simulation is conducted to show the effectiveness of the proposed algorithm.     

Optimal control of stochastic functional neutral differential equations with time lag in control

In this work, we consider an optimal control problem of a class of stochastic differential equations driven by additive noise with aftereffect appearing in control. We develop a semigroup theory of the driving deterministic neutral system and identify explicitly the adjoint operator of the corresponding infinitesimal generator. We formulate the time delay equation under consideration into an infinite dimensional stochastic control system without time lag by means of the adjoint theory established. Consequently, we can deal with the associated optimal control problem through the study of a Hamilton–Jacob–Bellman (HJB) equation. Last, we present an example whose optimal control can be explicitly determined to illustrate our theory.     

On numerical solutions for hyperbolic–parabolic equations with the multipoint nonlocal boundary condition

A numerical method is proposed for solving multi-dimensional hyperbolic–parabolic differential equations with the nonlocal boundary condition in t and Dirichlet and Neumann conditions in space variables. The first and second order of accuracy difference schemes are presented. The stability estimates for the solution and its first and second orders difference derivatives are established. A procedure of modified Gauss elimination method is used for solving these difference schemes in the case of a one-dimensional hyperbolic–parabolic differential equations with variable coefficients in x and two-dimensional hyperbolic–parabolic equation.     

Uniform separable differential operators with parameters

In this paper we study boundary value problems for anisotropic partial differential-operator equations with parameters. The principal part of the appropriate differential operators are not self-adjoint. Several conditions for the uniform separability in weighted Banach-valued L_p-spaces are given. Sharp estimates for the resolvent of the corresponding differential operator are obtained. In particular the positivity and R-positivity of these operators are established. As an application we study the separability of degenerate DOEs, maximal regularity for degenerate abstract parabolic problem with parameters, the uniform separability of finite and infinite systems for degenerate anisotropic partial differential equations with parameters.     

Formulation of stable difference schemes for systems of initial-value partial differential equations

A general system of initial-value partial differential equations is classified into four categories based on the partial differential operators which define the equations. Specific combinations of the operators are termed “invariants” since they are common to all finite difference approximations to the system of equations. The “invariants” are used to a priori determine if one may formulate a stable difference approximation to a system of partial differential equations. This is in essence a numerical existence theory.     

The obstacle problem of integro-partial differential equations with applications to stochastic optimal control/stopping problem

This paper is devoted to existence and uniqueness of minimal mild super solutions to the obstacle problem governed by integro-partial differential equations. We first study the well-posedness and local Lipschitz regularity of L^p solutions (p?≥?2) to reflected forward-backward stochastic differential equations (FBSDEs) with jump and lower barrier. Then we show that the solutions to reflected FBSDEs provide a probabilistic representation for the mild super solution via a nonlinear Feynman–Kac formula. Finally, we apply the results to study stochastic optimal control/stopping problems.     

The numerical solution of physical problems modeled as a systems of differential-algebraic equations (DAEs)

In this paper, the numerical solution of differential-algebraic equations is considered by Padé approximation method. We applied this method to an example which is a physical model problem. Firstly the physical model problem has been converted to power series, then the numerical solution of physical model problem was put into Padé series form. Thus we obtained numerical solution of physical model problem.     

Design of optimal strictly positive real controllers using numerical optimization for the control of flexible robotic systems

The design of optimal strictly positive real (SPR) controllers using numerical optimization is considered. We focus on how to parameterize the SPR controllers being optimized and the effect of parameterization. Minimization of the closed-loop H₂-norm is the optimization objective function. Various single-input single-output and multi-input multi-output controller parameterizations using transfer functions/matrices and state–space equations are considered. Depending on the controller form, constraints are enforced (i) using simple inequalities guaranteeing SPRness, (ii) in the frequency domain or, (iii) by implementing the Kalman–Yakubovich–Popov Lemma. None of the parameterizations we consider foster an observer-based controller structure. Simulated control of a single-link and a two-link flexible manipulators demonstrates the effectiveness of our proposed controller optimization formulations.     

An efficient solution of hamiltonian boundary value problems by combined gauss pseudospectral method with differential continuation approach

Our paper deals with an effective application of the pseudospectral method to solution of Hamiltonian boundary value problems in optimal control theory. The developed numerical methodology is based on the celebrated Gauss pseudospectral approach. The last one makes it possible to reduce the conventional Hamiltonian boundary value problem to an auxiliary algebraic system. The implementable algorithm we propose is computationally consistent and moreover, involves numerically tractable results for a relative small discretization grids. However, the solution of the obtained algebraic equations system may has a low convergence radius. We next use the differential continuation approach in order to weaken the necessity of the well-defined initial conditions for the above algebraic system. The presented solution procedure can be extremely useful when the generic shooting-type methods fail because of sensitivity or stiffness. We discuss some numerical results and establish the efficiency of the proposed methodology.     

Optimal finite-time passive controller design for uncertain nonlinear Markovian jumping systems

This paper studies the optimal finite-time passive control problem for a class of uncertain nonlinear Markovian jumping systems (MJSs). The Takagi and Sugeno (T–S) fuzzy model is employed to represent the nonlinear system with Markovian jump parameters and norm-bounded uncertainties. By selecting an appropriate Lyapunov-Krasovskii functional, it gives a sufficient condition for the existence of finite-time passive controller such that the uncertain nonlinear MJSs is stochastically finite-time bounded for all admissible uncertainties and satisfies the given passive control index in a finite time-interval. The sufficient condition on the existence of optimal finite-time fuzzy passive controller is formulated in the form of linear matrix inequalities and the designed algorithm is described as an optimization one. A numerical example is given at last to illustrate the effectiveness of the proposed design approach.     

时动边界上热通量重构正问题求解方法

具有Neumann边界条件的抛物型方程的初边值问题是偏微分方程研究领域的一类经典的问题。正问题是由已知的边界条件和初始条件来求区域温度场的问题。若所给边界是固定区域的称为定边界问题,而现实中又有一类问题其边界随时间变化,这样的问题称为动边界问题。本文对于时动边界上的热通量重构正问题的求解提出了人工边界的方法。在人工边界的基础上采用了位势理论方法求解此定解问题,并与前面已经给出的差分方法进行了比较。为了检验方法的可行性和两种方法的优劣,给出了数值模拟。     

A hybrid approximation scheme for discretizing constrained quadratic optimal control problems

In this paper, a composite Chebyshev finite difference method for solving linear quadratic optimal control problems with inequality constraints on state and control variables is introduced. This method is an extension of Chebyshev finite difference scheme and is based on a hybrid of block-pulse functions and Chebyshev polynomials using the well known Chebyshev–Gauss–Lobatto nodes. The excellent properties of hybrid functions are used to convert optimal control problem into a mathematical programming problem whose solution is much more easier than the original one. Various types of optimal control problems are investigated to demonstrate the effectiveness of the proposed approximation scheme. The method is simple, easy to implement and provides very accurate results.     

中小企业融资解困方式创新研究——基于基金资产配置的视角

中小企业融资解困一直是政策制定者亟待解决的问题及学者关注的课题.本文从基金资产配置的视角,展开了中小企业融资解困方式的创新研究.为研究方便,本文提出了存在非流动资产时机构投资者的最优资产配置模型.其中,对中小企业的融资视为非流程资产,而基金看作机构投资者.在模型中,机构投资者的目标函数是财富效用的期望增长率.本文将该类资产配置问题简化为有限时间上的控制问题,给出了价值函数满足的偏微分方程以及对应的初始条件、终止条件以及边界条件,求解了最高的财富效用期望增长率及对应的最优资产配置.文章的最大贡献是提出了一种创新研究方式,在某种程度上实现了基金、中小企业的双赢局面.     

Stability in mean of partial variables for stochastic reaction–diffusion systems with Markovian switching

This paper is denoted to investigating stability in mean of partial variables for stochastic reaction–diffusion equations with Markovian switching (SRDEMS). By transforming the integral of the trajectory with respect to spatial variables as the solution of the stochastic ordinary differential equations with Markovian switching (SODEMS) and using Itô formula, sufficient criteria on uniform stability in mean, asymptotic stability in mean, uniformly asymptotic stability in mean, exponential stability in mean of partial variables for SRDEMS are first derived. An example is presented to illustrate the effectiveness and efficiency of the obtained results.