SINGULARLY PERTURBED METHODS IN THE THEORY OF OPTIMAL CONTROL OF SYSTEMS GOVERNED BY PARTIAL DIFFERENTIAL EQUATIONS

In this paper, the various problems associaled with the optimal control of systemsgoverned by partial differential equations are introduced by using singularly perturbedmethods for analysis based on stale equations, or the cost funtction and also stateequations defined in perturbed domains.

Linear Quadratic Optimal Control for Systems Governed by First-Order Hyperbolic Partial Differential Equations

This paper focuses on linear-quadratic(LQ)optimal control for a class of systems governed by first-order hyperbolic partial differential equations(PDEs).Different from most of the previous works,an approach of discretization-then-continuousization is proposed in this paper to cope with the infinite-dimensional nature of PDE systems.The contributions of this paper consist of the following aspects:(1)The differential Riccati equations and the solvability condition of the LQ optimal control problems are obtained via the discretization-then-continuousization method.(2)A numerical calculation way of the differential Riccati equations and a practical design way of the optimal controller are proposed.Meanwhile,the relationship between the optimal costate and the optimal state is established by solving a set of forward and backward partial difference equations(FBPDEs).(3)The correctness of the method used in this paper is verified by a complementary continuous method and the comparative analysis with the existing operator results is presented.It is shown that the proposed results not only contain the classic results of the standard LQ control problem of systems governed by ordinary differential equations as a special case,but also support the existing operator results and give a more convenient form of computation.

SINGULAR PERTURBATION OF OPTIMAL CONTROL FOR SEMILINEAR INTEGRO-DIFFERENTIAL EQUATIONS

The singular perturbation of optimal control for semilinear integro-differential equations is considered. Under the suitable conditions we prove that the given problem has a unique solution (ξ(t,ε, h0), η(t,ε, h0)), moreover, the optimal control u0(t) and the optimal cost J*(ε) are given as well.

Lax-Oleinik-Type Formulas and Efficient Algorithms for Certain High-Dimensional Optimal Control Problems

Two of the main challenges in optimal control are solving problems with state-dependent running costs and developing efficient numerical solvers that are computationally tractable in high dimensions.In this paper,we provide analytical solutions to certain optimal control problems whose running cost depends on the state variable and with constraints on the control.We also provide Lax-Oleinik-type representation formulas for the corresponding Hamilton-Jacobi partial differential equations with state-dependent Hamiltonians.Additionally,we present an efficient,grid-free numerical solver based on our representation formulas,which is shown to scale linearly with the state dimension,and thus,to overcome the curse of dimensionality.Using existing optimization methods and the min-plus technique,we extend our numerical solvers to address more general classes of convex and nonconvex initial costs.We demonstrate the capabilities of our numerical solvers using implementations on a central processing unit(CPU)and a field-programmable gate array(FPGA).In several cases,our FPGA implementation obtains over a 10 times speedup compared to the CPU,which demonstrates the promising performance boosts FPGAs can achieve.Our numerical results show that our solvers have the potential to serve as a building block for solving broader classes of high-dimensional optimal control problems in real-time.

Forward and Backward Mean-Field Stochastic Partial Differential Equation and Optimal Control

This paper is mainly concerned with the solutions to both forward and backward mean-field stochastic partial differential equation and the corresponding optimal control problem for mean-field stochastic partial differential equation. The authors first prove the continuous dependence theorems of forward and backward mean-field stochastic partial differential equations and show the existence and uniqueness of solutions to them. Then they establish necessary and sufficient optimality conditions of the control problem in the form of Pontryagin’s maximum principles. To illustrate the theoretical results, the authors apply stochastic maximum principles to study the infinite-dimensional linear-quadratic control problem of mean-field type. Further, an application to a Cauchy problem for a controlled stochastic linear PDE of mean-field type is studied.

Controlled Stochastic Partial Differential Equations for Rabbits on a Grassland

A new approach to modeling populations incorporating stochasticity,a random environment,and individual behavior is illustrated with a specific example of two interacting populations:rabbits and grass.The derivation of the system of stochastic partial differential equations(SPDEs)to show how the individual mechanisms of both populations are included.This model also has an unusual feature of a nonlocal term.The harvesting of the rabbit population is introduced as a control variable.

THE MAXIMUM PRINCIPLE FOR PARTIALLY OBSERVED OPTIMAL CONTROL OF FORWARD-BACKWARD STOCHASTIC SYSTEMS WITH RANDOM JUMPS

这份报纸为被 Brownian 运动和独立泊松随机两个都驾驶的随机的系统测量的提交向后学习部分观察的最佳的控制的问题。联合提交向后有某些古典凸的变化技术的随机的微分方程理论，必要最大的原则为部分观察的最佳的控制被证明，在控制域是 nonempty 的地方凸的集合。在某些凸状假设下面，作者也为上述的最佳的最佳的问题给最佳的控制的足够的条件。说明理论结果，作者也得出部分信息的一个例子线性二次的最佳的控制，并且由使用必要、足够的最大的原则发现相应最佳的控制的明确的表情。

Maximum Principle for Partially-Observed Optimal Control Problems of Stochastic Delay Systems

This paper is concerned with partially-observed optimal control problems for stochastic delay systems. Combining Girsanov's theorem with a standard variational technique, the authors obtain a maximum principle on the assumption that the system equation contains time delay and the control domain is convex. The related adjoint processes are characterized as solutions to anticipated backward stochastic differential equations in finite-dimensional spaces. Then, the proposed theoretical result is applied to study partially-observed linear-quadratic optimal control problem for stochastic delay system and an explicit observable control variable is given.

Approximate finite-horizon optimal control for input-affine nonlinear systems with input constraints

The finite-horizon optimal control problem with input constraints consists in controlling the state of a dynamical system over a finite time interval(possibly unknown)minimising a cost functional,while satisfying hard constraints on the input.In this framework,the minimum-time optimal control problem and some related problems are of interest for both theory and applications.For linear systems,the solution of the problem often relies upon the use of bang-bang control signals.For nonlinear systems,the“shape”of the optimal input is in general not known.The control input can be found solving a Hamilton–Jacobi–Bellman(HJB)partial differential equation(PDE):it typically consists of a combination of bang-bang controls and singular arcs.In this paper,a methodology to approximate the solution of the HJB PDE is proposed.This approximation yields a dynamic state feedback law.The theory is illustrated by means of two examples:the minimum-time optimal control problem for an industrial wastewater treatment plant and the Goddard problem,i.e.a maximum-range optimal control problem.

A New Computational Approach for Solving Optimal Control of Linear PDEs Problem

In this paper, we present a new computational approach for solving an internal optimal control problem, which is governed by a linear parabolic partial differential equation. Our approach is to approximate the PDE problem by a nonhomogeneous ordinary differential equation system in higher dimension. Then, the homogeneous part of ODES is solved using semigroup theory. In the next step, the convergence of this approach is verified by means of Toeplitz matrix. In the rest of the paper, the optimal control problem is solved by utilizing the solution of homogeneous part. Finally, a numerical example is given.

On Optimal Sparse-Control Problems Governed by Jump-Diffusion Processes

A framework for the optimal sparse-control of the probability density function of a jump-diffusion process is presented. This framework is based on the partial integro-differential Fokker-Planck (FP) equation that governs the time evolution of the probability density function of this process. In the stochastic process and, correspondingly, in the FP model the control function enters as a time-dependent coefficient. The objectives of the control are to minimize a discrete-in-time, resp. continuous-in-time, tracking functionals and its L2- and L1-costs, where the latter is considered to promote control sparsity. An efficient proximal scheme for solving these optimal control problems is considered. Results of numerical experiments are presented to validate the theoretical results and the computational effectiveness of the proposed control framework.

Optimal Control Using Microscopic Models for a Pollutant Elimination Problem

Optimal control problem with partial derivative equation(PDE) constraint is a numericalwise difficult problem because the optimality conditions lead to PDEs with mixed types of boundary values. The authors provide a new approach to solve this type of problem by space discretization and transform it into a standard optimal control for a multi-agent system. This resulting problem is formulated from a microscopic perspective while the solution only needs limited the macroscopic measurement due to the approach of Hamilton-Jacobi-Bellman(HJB) equation approximation. For solving the problem, only an HJB equation(a PDE with only terminal boundary condition) needs to be solved, although the dimension of that PDE is increased as a drawback. A pollutant elimination problem is considered as an example and solved by this approach. A numerical method for solving the HJB equation is proposed and a simulation is carried out.

UNIQUENESS OF VISCOSITY SOLUTIONS OF STOCHASTIC HAMILTON-JACOBI EQUATIONS

This article is devoted to the study of fully nonlinear stochastic Hamilton-Jacobi (HJ) equations for the optimal stochastic control problem of ordinary differential equations with random coefficients. Under the standard Lipschitz continuity assumptions on the coefficients, the value function is proved to be the unique viscosity solution of the associated stochastic HJ equation.

部分信息下时滞倒向重随机系统线性二次最优控制问题

文章研究了一类含有时滞的部分信息下的倒向重随机系统线性二次最优控制问题,利用经典的凸变分技术和对偶方法讨论了当系统状态方程含有时滞变量时系统对应的最优控制的显示表达式,并利用经典的平行四边形法则证得最优控制的唯一性.

Performance of a Markovian neural network versus dynamic programming on a fishing control problem

Fishing quotas are unpleasant but efficient to control the productivity of a fishing site.A popular model has a stochastic differential equation for the biomass on which a stochastic dynamic programming or a Hamilton-Jacobi-Bellman algorithm can be used to find the stochastic control–the fishing quota.We compare the solutions obtained by dynamic programming against those obtained with a neural network which preserves the Markov property of the solution.The method is extended to a multi species model and shows that the Neural Network is usable in high dimensions.

年龄相关的种群空间扩散系统的广义解与收获控制

研究了由积分偏微分方程描述的年龄相关的种群空间扩散系统的收获控制问题.首先利用不动点方法证明了对于有界死亡率μ的系统广义解的存在性,但这是预备的结果.进一步,运用上述结果、先验估计和紧性定理,证明了对于在r=A附近无界的μ的系统解的存在惟一性.其次,利用类似方法得到系统最优收获控制的存在性.最后,利用G^ateax微分和Lions的变分不等式理论,推得了控制为最优的必要条件;从而得到了由积分偏微分方程和变分不等式构成的最优性组.最优性组能够确定最优控制.还建立了表征最优控制的Euler_Lagrange组.这些结果可为种群系统控制问题的实际研究作为理论参考.

基于简化Mumford-Shah模型的导航基准图适配区分割方法

基于景象匹配的飞行器导航数字基准地图中需要划分匹配稳定的区域,以提高导航系统航迹的可靠性.该文首先讨论了描述基准图局部区域匹配稳定性的局部匹配稳健度量指标,据此定义了适配区的概念.常规的基于相关函数局部匹配稳健度量需要大量的计算时间,难以实用化.为此,文中采用了三种可快速计算的匹配稳健度量指标:相关主峰曲率、可跟踪度及特征密度,以及相应的快速算法.同时,引入了一种基于简化Mumford-Shah模型的水平集算法进行适配区划分,通过演化由模型推导出的偏微分方程,就能得到适配区和非适配区的最优划分.最后对实际的导航基准图的适配区划分试验表明,该文的适配区分割方法不仅计算速度优于基于相关函数的方法,而且可以获得合理的适配区分布.

椭圆系统下最优控制的罚函数方法

讨论了椭圆系统的最优控制问题,首先给出要讨论的散度-旋度型方程,证明其在所选择的空间存在唯一解;其次选择合适的性能指标,运用Sobolve空间、变分法、泛函分析等理论证明了有约束问题最优解的存在性,并且利用罚函数的方法把有约束条件系统转化为无约束条件系统;最后证明了当罚参数趋于零时,有约束问题的解收敛于无约束问题的解以及约束问题解的梯度法的收敛性.

关于混凝土坝基渗流系统最优控制计算的乘子方法

研究了乘子方法应用于混凝土坝基渗流系统最优控制的计算，构造了其逼近程序，并证明了这种方法在适当的Ｈｉｌｂｅｒｔ空间中的收敛性．

年龄相关的半线性种群扩散系统的最优收获控制

研究年龄相关的半线性种群扩散系统的最优收获控制问题,证明了最优收获控制的存在性,得到了收获控制为最优的必要条件和由偏微分方程与变分不等式所构成的最优性组,由最优性组确定最优控制.