7 research outputs found
Output-feedback online optimal control for a class of nonlinear systems
In this paper an output-feedback model-based reinforcement learning (MBRL)
method for a class of second-order nonlinear systems is developed. The control
technique uses exact model knowledge and integrates a dynamic state estimator
within the model-based reinforcement learning framework to achieve
output-feedback MBRL. Simulation results demonstrate the efficacy of the
developed method
Finite-horizon optimal control of linear and a class of nonlinear systems
Traditionally, optimal control of dynamical systems with known system dynamics is obtained in a backward-in-time and offline manner either by using Riccati or Hamilton-Jacobi-Bellman (HJB) equation. In contrast, in this dissertation, finite-horizon optimal regulation has been investigated for both linear and nonlinear systems in a forward-in-time manner when system dynamics are uncertain. Value and policy iterations are not used while the value function (or Q-function for linear systems) and control input are updated once a sampling interval consistent with standard adaptive control. First, the optimal adaptive control of linear discrete-time systems with unknown system dynamics is presented in Paper I by using Q-learning and Bellman equation while satisfying the terminal constraint. A novel update law that uses history information of the cost to go is derived. Paper II considers the design of the linear quadratic regulator in the presence of state and input quantization. Quantization errors are eliminated via a dynamic quantizer design and the parameter update law is redesigned from Paper I. Furthermore, an optimal adaptive state feedback controller is developed in Paper III for the general nonlinear discrete-time systems in affine form without the knowledge of system dynamics. In Paper IV, a NN-based observer is proposed to reconstruct the state vector and identify the dynamics so that the control scheme from Paper III is extended to output feedback. Finally, the optimal regulation of quantized nonlinear systems with input constraint is considered in Paper V by introducing a non-quadratic cost functional. Closed-loop stability is demonstrated for all the controller designs developed in this dissertation by using Lyapunov analysis while all the proposed schemes function in an online and forward-in-time manner so that they are practically viable --Abstract, page iv
Learning-based Predictive Control for Nonlinear Systems with Unknown Dynamics Subject to Safety Constraints
Model predictive control (MPC) has been widely employed as an effective
method for model-based constrained control. For systems with unknown dynamics,
reinforcement learning (RL) and adaptive dynamic programming (ADP) have
received notable attention to solve the adaptive optimal control problems.
Recently, works on the use of RL in the framework of MPC have emerged, which
can enhance the ability of MPC for data-driven control. However, the safety
under state constraints and the closed-loop robustness are difficult to be
verified due to approximation errors of RL with function approximation
structures. Aiming at the above problem, we propose a data-driven robust MPC
solution based on incremental RL, called data-driven robust learning-based
predictive control (dr-LPC), for perturbed unknown nonlinear systems subject to
safety constraints. A data-driven robust MPC (dr-MPC) is firstly formulated
with a learned predictor. The incremental Dual Heuristic Programming (DHP)
algorithm using an actor-critic architecture is then utilized to solve the
online optimization problem of dr-MPC. In each prediction horizon, the actor
and critic learn time-varying laws for approximating the optimal control policy
and costate respectively, which is different from classical MPCs. The state and
control constraints are enforced in the learning process via building a
Hamilton-Jacobi-Bellman (HJB) equation and a regularized actor-critic learning
structure using logarithmic barrier functions. The closed-loop robustness and
safety of the dr-LPC are proven under function approximation errors. Simulation
results on two control examples have been reported, which show that the dr-LPC
can outperform the DHP and dr-MPC in terms of state regulation, and its average
computational time is much smaller than that with the dr-MPC in both examples.Comment: The paper has been submitted at a IEEE Journal for possible
publicatio
Neural Network-Based Finite-Horizon Optimal Control of Uncertain Affine Nonlinear Discrete-Time Systems
In this paper, the finite-horizon optimal control design for nonlinear discrete-time systems in affine form is presented. In contrast with the traditional approximate dynamic programming methodology, which requires at least partial knowledge of the system dynamics, in this paper, the complete system dynamics are relaxed utilizing a neural network (NN)-based identifier to learn the control coefficient matrix. The identifier is then used together with the actor-critic-based scheme to learn the time-varying solution, referred to as the value function, of the Hamilton-Jacobi-Bellman (HJB) equation in an online and forward-in-time manner. Since the solution of HJB is time-varying, NNs with constant weights and time-varying activation functions are considered. To properly satisfy the terminal constraint, an additional error term is incorporated in the novel update law such that the terminal constraint error is also minimized over time. Policy and/or value iterations are not needed and the NN weights are updated once a sampling instant. The uniform ultimate boundedness of the closed-loop system is verified by standard Lyapunov stability theory under nonautonomous analysis. Numerical examples are provided to illustrate the effectiveness of the proposed method
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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