On optimal control of a brownian motion

This paper is concerned with the optimal control of a Brownian motion on R⁺. The process is controlled by switching control action to affect the variance and the drift. There is a cost which depends on the state of the process and the action used to operate the system. There is another cost incurred instantaneously to switch the control action. A quasi-variational inequality is solved as the dynamic programming equation for a long run average cost criterion.     

On a sufficient transversality condition for infinite horizon optimal control problems

We prove that, for a class of optimal control problems with infinite horizon, convergence to zero for the co-state variable when time goes to infinity is a sufficient transversality condition.     

On the role of optimality functions in numerical optimal control

We present a survey of optimality conditions in optimality function form and discuss their role in establishing that discretized optimal control problems are consistent approximations to the original optimal control problems.     

State constraints in optimal control. The degeneracy phenomenon

In this paper we study the degeneracy phenomenon arising in optimal control problems with state constraints. It is shown that this phenomenon occurs because of the incompleteness of the standard variants of Pontryagin's maximum principle for problems with state constraints. The new maximum principle containing some additional information about the behavior of the Hamiltonian at the endtimes is developed. As application we obtain some sufficient and necessary conditions for nondegeneracy and pointwise nontriviality of the maximum principle. The results obtained envelope the optimal control problems with systems described by differential inclusions and ordinary differential equations.     

Minimizing control variation in nonlinear optimal control

In any real system, changing the control signal from one value to another will usually cause wear and tear on the system’s actuators. Thus, when designing a control law, it is important to consider not just predicted system performance, but also the cost associated with changing the control action. This latter cost is almost always ignored in the optimal control literature. In this paper, we consider a class of optimal control problems in which the variation of the control signal is explicitly penalized in the cost function. We develop an effective computational method, based on the control parameterization approach and a novel transformation procedure, for solving this class of optimal control problems. We then apply our method to three example problems in fisheries, train control, and chemical engineering.     

Active fault detection and control: Unified formulation and optimal design

A new unified formulation of the active fault detection and control problem for discrete-time stochastic systems and its optimal solution are proposed. The problem formulation stems from the optimal stochastic control problem and includes important special cases: an active detector and controller, an active detector and input signal generator, and an active detector with a given input signal generator. The optimal solution is derived using the so-called closed loop information processing strategy. This strategy respects the influence of the current decision and/or input on the future behavior of the observed system, allows penalizing future wrong decisions, and improves the quality of fault detection. The proposed formulation and obtained solution also provide better understanding of the active fault detection and its relation to the optimal stochastic control. The results are illustrated in numerical examples.     

The relaxed optimal control problem for Mean-Field SDEs systems and application

The present study deals with a new approach of optimal control problems where the state equation is a Mean-Field stochastic differential equation, and the set of strict (classical) controls need not be convex and the diffusion coefficient depends on the term control. Our consideration is based on only one adjoint process, and the necessary conditions as well as a sufficient condition for optimality in the form of a relaxed maximum principle are obtained, with application to Linear quadratic stochastic control problem with mean-field type.     

二次型最优控制问题中的权矩阵与最优控制律

二次型最优的权矩阵选择是一个包含大量经验与技巧的问题．对此，研究权矩阵与最优控制律的关系：一个最优控制律，其对应的性能指标（权矩阵）是否唯一的问题．不论是单输入还是多输入情形，在系统能控性指数大于2，对应权矩阵的对角线元素有2个不为零的元素，且满足一定条件时．该性能指标对应的最优控制律，必有另一性能指标与之对应．     

Further results on the Bellman equation for optimal control problems with exit times and nonnegative Lagrangians

In a series of papers, we proved theorems characterizing the value function in exit time optimal control as the unique viscosity solution of the corresponding Bellman equation that satisfies appropriate side conditions. The results applied to problems which satisfy a positivity condition on the integral of the Lagrangian. This positive integral condition assigned a positive cost for remaining outside the target on any interval of positive length. In this note, we prove a new theorem which characterizes the exit time value function as the unique bounded-from-below viscosity solution of the Bellman equation that vanishes on the target. The theorem applies to problems satisfying an asymptotic condition on the trajectories, including cases where the positive integral condition is not satisfied. Our results are based on an extended version of “Barb lat's lemma”. We apply the theorem to variants of the Fuller problem and other examples where the Lagrangian is degenerate.     

Constrained optimal estimation and control

A requirement of many multi-input multi-output control and estimation problems is a special structure of the gain matrix. This paper presents a constrained optimal method by which it is possible to constrain some elements of the gain matrix and to optimize the behaviour using the remaining parameters. Only linear constraints are considered and an algorithm based on a modified Riccati equation is obtained. The asymptotic solution for the steady state can be reduced to a sequence of classical Riccati equations by using an iterative approach. Numerical techniques have been found and computer programmes developed. Numerical examples are given.     

A new solution property in optimal control: The lens

We consider a new property of an optimal control problem called a lens.A lens is an interior point in the state-control phase plane where—given the value of the state variable—there is only one control value satisfying the necessary optimality conditions and—given the value of the control variable—there is only one state value satisfying the necessary optimality conditions.We build a simple model that generates a lens and give necessary and sufficient conditions under which a lens occurs.     

Maximum principle for the optimal control of an ablation-transpiration cooling system with free final time and phase constraints

This paper is concerned with an optimal control problem of an abhtion-transpiration cooling control system with Stefan-Signorini boundary condition. As the continuation of the authors＇previous paper, the Dubovits Rii-Milyutin fimctional approach is again adopted in investigation of the Pontryagin＇ s maximun principle of the system. The necessary optimality condition is presented for the problem with free final horizon and phase constraints.     

Halo orbit mission correction maneuvers using optimal control

This paper addresses the computation of the required trajectory correction maneuvers for a halo orbit space mission to compensate for the launch velocity errors introduced by inaccuracies of the launch vehicle. By combining dynamical systems theory with optimal control techniques, we are able to provide a compelling portrait of the complex landscape of the trajectory design space. This approach enables automation of the analysis to perform parametric studies that simply were not available to mission designers a few years ago, such as how the magnitude of the errors and the timing of the first trajectory correction maneuver affects the correction ΔV. The impetus for combining dynamical systems theory and optimal control in this problem arises from design issues for the Genesis Discovery Mission being developed for NASA by the Jet Propulsion Laboratory.     

Local energy minimization in optimal train control

The calculation of optimal driving strategies for on-board control of freight trains is a challenging task. In this paper we calculate the critical switching points for a globally optimal strategy on a track with steep gradients using a new local energy minimization principle. The method has been used successfully in Australia to calculate optimal switching points and hence provide in-cab advice to train drivers on long-haul freight trains.     

Maxi mumprinciple for the optimal control of an ablation-transpiration cooling system with free final time and phase constraints

paper is concerned with an optimal co nt rol problem of an ablation-transpiration cooling control system with Stefan-Sig norini boundary condition.As the continuation of the authors'previous paper,the Dubovits Rii-Milyutin functional approach is again adopted in investigation of t he Pontryagin's maximun principle of the system.The necessary optimality condit ion is presented for the problem with free final horizon and phase constraints.     

Some second-order necessary conditions in optimal control

In optimal control problems any extremal arc which trivially satisfies the Maximum Principle, that is a first-order control variation produces no change in cost, is called singular. Higher-order conditions are then needed to check the optimality of such arcs. Using the Volterra series associated with the variation of the cost functional gives a new context for analyzing singular optimal control problems. A basic optimality criterion for a fixed terminal time Mayer problem is obtained which allows one to derive the necessary conditions for optimality in terms of Lie brackets of vector fields associated with the dynamics of the problem.     

Linear optimal control of systems with state and control variable delays

In this paper we announce a new rigorous ‘state space’ formulation for systems with delays in state and control variables. This is used to solve the linear-quadratic optimal control problem over both finite and infinite time horizons. In each case abstract operational Riccati equations are derived to characterize the feedback gains. This paper extends recent results of Vinter and Kwong.     

Analysis and synthesis of networked control systems: Topological entropy, observability, robustness and optimal control

This paper extends the concept of topological to the case of uncertain dynamical systems. We address problems of observability and optimal control via limited capacity digital communication channels. The main results are given in terms of inequalities between date rate of the communication channel and topological entropy of the open-loop system. Topological entropy is calculated for some classes of uncertain dynamical systems.     

Robust and optimal predictive control of the COVID-19 outbreak

We investigate adaptive strategies to robustly and optimally control the COVID-19 pandemic via social distancing measures based on the example of Germany. Our goal is to minimize the number of fatalities over the course of two years without inducing excessive social costs. We consider a tailored model of the German COVID-19 outbreak with different parameter sets to design and validate our approach. Our analysis reveals that an open-loop optimal control policy can significantly decrease the number of fatalities when compared to simpler policies under the assumption of exact model knowledge. In a more realistic scenario with uncertain data and model mismatch, a feedback strategy that updates the policy weekly using model predictive control (MPC) leads to a reliable performance, even when applied to a validation model with deviant parameters. On top of that, we propose a robust MPC-based feedback policy using interval arithmetic that adapts the social distancing measures cautiously and safely, thus leading to a minimum number of fatalities even if measurements are inaccurate and the infection rates cannot be precisely specified by social distancing. Our theoretical findings support various recent studies by showing that (1) adaptive feedback strategies are required to reliably contain the COVID-19 outbreak, (2) well-designed policies can significantly reduce the number of fatalities compared to simpler ones while keeping the amount of social distancing measures on the same level, and (3) imposing stronger social distancing measures early on is more effective and cheaper in the long run than opening up too soon and restoring stricter measures at a later time.     

Toward designing intelligent PDEs for computer vision: An optimal control approach

Many computer vision and image processing problems can be posed as solving partial differential equations (PDEs). However, designing a PDE system usually requires high mathematical skills and good insight into the problems. In this paper, we consider designing PDEs for various problems arising in computer vision and image processing in a lazy manner: learning PDEs from training data via an optimal control approach. We first propose a general intelligent PDE system which holds the basic translational and rotational invariance rule for most vision problems. By introducing a PDE-constrained optimal control framework, it is possible to use the training data resulting from multiple ways (ground truth, results from other methods, and manual results from humans) to learn PDEs for different computer vision tasks. The proposed optimal control based training framework aims at learning a PDE-based regressor to approximate the unknown (and usually nonlinear) mapping of different vision tasks. The experimental results show that the learnt PDEs can solve different vision problems reasonably well. In particular, we can obtain PDEs not only for problems that traditional PDEs work well but also for problems that PDE-based methods have never been tried before, due to the difficulty in describing those problems in a mathematical way.