Journal ArticleDOI
Data-Driven Robust Approximate Optimal Tracking Control for Unknown General Nonlinear Systems Using Adaptive Dynamic Programming Method
TLDR
A novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method and a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method.Abstract:
In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.read more
Citations
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Journal ArticleDOI
Data-Based Techniques Focused on Modern Industry: An Overview
TL;DR: The main objective of this paper is to review and summarize the recent achievements in data-based techniques, especially for complicated industrial applications, thus providing a referee for further study on the related topics both from academic and practical points of view.
Journal ArticleDOI
Policy Iteration Adaptive Dynamic Programming Algorithm for Discrete-Time Nonlinear Systems
Derong Liu,Qinglai Wei +1 more
TL;DR: It is shown that the iterative performance index function is nonincreasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman equation and it is proven that any of the iteratives control laws can stabilize the nonlinear systems.
Journal ArticleDOI
Optimal tracking control of nonlinear partially-unknown constrained-input systems using integral reinforcement learning
Hamidreza Modares,Frank L. Lewis +1 more
TL;DR: This formulation extends the integral reinforcement learning (IRL) technique, a method for solving optimal regulation problems, to learn the solution to the OTCP, and it also takes into account the input constraints a priori.
Journal ArticleDOI
Reinforcement Q-learning for optimal tracking control of linear discrete-time systems with unknown dynamics
Bahare Kiumarsi,Frank L. Lewis,Hamidreza Modares,Ali Karimpour,Mohammad Bagher Naghibi-Sistani +4 more
TL;DR: A novel approach based on the Q -learning algorithm is proposed to solve the infinite-horizon linear quadratic tracker (LQT) for unknown discrete-time systems in a causal manner and the optimal control input is obtained by only solving an augmented ARE.
Journal ArticleDOI
Leader-Based Optimal Coordination Control for the Consensus Problem of Multiagent Differential Games via Fuzzy Adaptive Dynamic Programming
TL;DR: For the first time, GFHMs are used to approximate the solutions (value functions) of the coupled HJ equations, based on policy iteration algorithm, and the approximation solution is utilized to obtain the optimal coordination control.
References
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Book
Optimal Control
TL;DR: Reading optimal control frank l lewis solution manual ebook pdf 2019 is extremely useful because you could get enough detailed information in the book technology has.
Journal ArticleDOI
Reinforcement learning and adaptive dynamic programming for feedback control
Frank L. Lewis,Draguna Vrabie +1 more
TL;DR: This work describes mathematical formulations for reinforcement learning and a practical implementation method known as adaptive dynamic programming that give insight into the design of controllers for man-made engineered systems that both learn and exhibit optimal behavior.
Journal ArticleDOI
Nearly optimal control laws for nonlinear systems with saturating actuators using a neural network HJB approach
Murad Abu-Khalaf,Frank L. Lewis +1 more
TL;DR: It is shown that the constrained optimal control law has the largest region of asymptotic stability (RAS) and the result is a nearly optimal constrained state feedback controller that has been tuned a priori off-line.
Journal ArticleDOI
Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem
TL;DR: An online algorithm based on policy iteration for learning the continuous-time optimal control solution with infinite horizon cost for nonlinear systems with known dynamics, which finds in real-time suitable approximations of both the optimal cost and the optimal control policy, while also guaranteeing closed-loop stability.
Journal ArticleDOI
Discrete-Time Nonlinear HJB Solution Using Approximate Dynamic Programming: Convergence Proof
TL;DR: It is shown that HDP converges to the optimal control and the optimal value function that solves the Hamilton-Jacobi-Bellman equation appearing in infinite-horizon discrete-time (DT) nonlinear optimal control.
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