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On the optimal control of a linear neutral differential equation arising in economics
In this paper, we apply two optimization methods to solve an optimal control problem of a linear neutral differential equation (NDE) arising in economics. The first one is a variational method, the second follows a dynamic programming approach. Due to the infinite dimensionality of the NDE, the second method requires the reformulation of the latter as an ordinary differential equation in an appropriate abstract space. It is shown that the resulting HJB equation admits a closed-form solution, allowing for a much finer characterization of the optimal dynamics compared to the alternative variational method. The latter is clearly limited by the nontrivial nature of asymptotic analysis of NDEs.Neutral differential equations; economic dynamics; optimal control; calculus of variations; dynamic programming; infinite dimension
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μ΄ μΈκ°μ§ λ¬Έμ μ λ°©λ²λ‘ μμ μ μλ νΉμ§λ€μ λ€μκ³Ό κ°μ΄ μμ½ν μ μλ€: 첫λ²μ§Έλ‘, μ€μΌμ€λ§ λ¬Έμ μμ closed-loop νΌλλ°± ννμ ν΄λ₯Ό μ μν μ μμλ€. μ΄λ κΈ°μ‘΄ μ§μ λ²μμ μ»μ μ μμλ ννλ‘μ, κ°ννμ΅μ κ°μ μ λΆκ°ν μ μλ μΈ‘λ©΄μ΄λΌ μκ°ν μ μλ€. λλ²μ§Έλ‘ κ³ λ €ν νμλ¨κ³ μ μ΄ λ¬Έμ μμ, λμ κ³νλ²μ 무νμ°¨μ ν¨μκ³΅κ° μ΅μ ν λ¬Έμ λ₯Ό ν¨μ κ·Όμ¬ λ°©λ²μ ν΅ν΄ μ νμ°¨μ 벑ν°κ³΅κ° μ΅μ ν λ¬Έμ λ‘ μνν μ μλ λ°©λ²μ λμ
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Όλ¬Έμ μ€μλ€. λ§μ§λ§ λ¬Έμ λ μμ λ¨κ³ λμ μ΅μ ν λ¬Έμ μ΄λ€. λμ μ΅μ ν λ¬Έμ μμ λ°μνλ μ μ½ μ‘°κ±΄νμμ κ°ννμ΅μ μννκΈ° μν΄, μ-μλ λ―ΈλΆλμ κ³νλ² (primal-dual DDP) λ°©λ²λ‘ μ μλ‘ μ μνμλ€. μμ μ€λͺ
ν μΈκ°μ§ λ¬Έμ μ μ μ©λ λ°©λ²λ‘ μ κ²μ¦νκ³ , λμ κ³νλ²μ΄ μ§μ λ²μ λΉκ²¬λ μ μλ λ°©λ²λ‘ μ΄λΌλ μ£Όμ₯μ μ€μ¦νκΈ° μν΄ μ¬λ¬κ°μ§ 곡μ μμ λ₯Ό μ€μλ€.Sequential decision making problem is a crucial technology for plant-wide process optimization. While the dominant numerical method is the forward-in-time direct optimization, it is limited to the open-loop solution and has difficulty in considering the uncertainty. Dynamic programming method complements the limitations, nonetheless associated functional optimization suffers from the curse-of-dimensionality. The sample-based approach for approximating the dynamic programming, referred to as reinforcement learning (RL) can resolve the issue and investigated throughout this thesis. The method that accounts for the system model explicitly is in particular interest. The model-based RL is exploited to solve the three representative sequential decision making problems; scheduling, supervisory optimization, and regulatory control. The problems are formulated with partially observable Markov decision process, control-affine state space model, and general state space model, and associated model-based RL algorithms are point based value iteration (PBVI), globalized dual heuristic programming (GDHP), and differential dynamic programming (DDP), respectively.
The contribution for each problem can be written as follows: First, for the scheduling problem, we developed the closed-loop feedback scheme which highlights the strength compared to the direct optimization method. In the second case, the regulatory control problem is tackled by the function approximation method which relaxes the functional optimization to the finite dimensional vector space optimization. Deep neural networks (DNNs) is utilized as the approximator, and the advantages as well as the convergence analysis is performed in the thesis. Finally, for the supervisory optimization problem, we developed the novel constraint RL framework that uses the primal-dual DDP method. Various illustrative examples are demonstrated to validate the developed model-based RL algorithms and to support the thesis statement on which the dynamic programming method can be considered as a complementary method for direct optimization method.1. Introduction 1
1.1 Motivation and previous work 1
1.2 Statement of contributions 9
1.3 Outline of the thesis 11
2. Background and preliminaries 13
2.1 Optimization problem formulation and the principle of optimality 13
2.1.1 Markov decision process 15
2.1.2 State space model 19
2.2 Overview of the developed RL algorithms 28
2.2.1 Point based value iteration 28
2.2.2 Globalized dual heuristic programming 29
2.2.3 Differential dynamic programming 32
3. A POMDP framework for integrated scheduling of infrastructure maintenance and inspection 35
3.1 Introduction 35
3.2 POMDP solution algorithm 38
3.2.1 General point based value iteration 38
3.2.2 GapMin algorithm 46
3.2.3 Receding horizon POMDP 49
3.3 Problem formulation for infrastructure scheduling 54
3.3.1 State 56
3.3.2 Maintenance and inspection actions 57
3.3.3 State transition function 61
3.3.4 Cost function 67
3.3.5 Observation set and observation function 68
3.3.6 State augmentation 69
3.4 Illustrative example and simulation result 69
3.4.1 Structural point for the analysis of a high dimensional belief space 72
3.4.2 Infinite horizon policy under the natural deterioration process 72
3.4.3 Receding horizon POMDP 79
3.4.4 Validation of POMDP policy via Monte Carlo simulation 83
4. A model-based deep reinforcement learning method applied to finite-horizon optimal control of nonlinear control-affine system 88
4.1 Introduction 88
4.2 Function approximation and learning with deep neural networks 91
4.2.1 GDHP with a function approximator 91
4.2.2 Stable learning of DNNs 96
4.2.3 Overall algorithm 103
4.3 Results and discussions 107
4.3.1 Example 1: Semi-batch reactor 107
4.3.2 Example 2: Diffusion-Convection-Reaction (DCR) process 120
5. Convergence analysis of the model-based deep reinforcement learning for optimal control of nonlinear control-affine system 126
5.1 Introduction 126
5.2 Convergence proof of globalized dual heuristic programming (GDHP) 128
5.3 Function approximation with deep neural networks 137
5.3.1 Function approximation and gradient descent learning 137
5.3.2 Forward and backward propagations of DNNs 139
5.4 Convergence analysis in the deep neural networks space 141
5.4.1 Lyapunov analysis of the neural network parameter errors 141
5.4.2 Lyapunov analysis of the closed-loop stability 150
5.4.3 Overall Lyapunov function 152
5.5 Simulation results and discussions 157
5.5.1 System description 158
5.5.2 Algorithmic settings 160
5.5.3 Control result 161
6. Primal-dual differential dynamic programming for constrained dynamic optimization of continuous system 170
6.1 Introduction 170
6.2 Primal-dual differential dynamic programming for constrained dynamic optimization 172
6.2.1 Augmented Lagrangian method 172
6.2.2 Primal-dual differential dynamic programming algorithm 175
6.2.3 Overall algorithm 179
6.3 Results and discussions 179
7. Concluding remarks 186
7.1 Summary of the contributions 187
7.2 Future works 189
Bibliography 192Docto
Differential Dynamic Programming for time-delayed systems
Trajectory optimization considers the problem of deciding how to control a
dynamical system to move along a trajectory which minimizes some cost function.
Differential Dynamic Programming (DDP) is an optimal control method which
utilizes a second-order approximation of the problem to find the control. It is
fast enough to allow real-time control and has been shown to work well for
trajectory optimization in robotic systems. Here we extend classic DDP to
systems with multiple time-delays in the state. Being able to find optimal
trajectories for time-delayed systems with DDP opens up the possibility to use
richer models for system identification and control, including recurrent neural
networks with multiple timesteps in the state. We demonstrate the algorithm on
a two-tank continuous stirred tank reactor. We also demonstrate the algorithm
on a recurrent neural network trained to model an inverted pendulum with
position information only.Comment: 7 pages, 6 figures, conference, Decision and Control (CDC), 2016 IEEE
55th Conference o
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