Proceedings ArticleDOI
Optimal control of affine nonlinear discrete-time systems
Travis Dierks,S. Jagannthan +1 more
- pp 1390-1395
TLDR
In this article, direct neural dynamic programming techniques are utilized to solve the Hamilton Jacobi-Bellman equation in real time for the optimal control of general affine nonlinear discrete-time systems.Abstract:
In this paper, direct neural dynamic programming techniques are utilized to solve the Hamilton Jacobi-Bellman equation in real time for the optimal control of general affine nonlinear discrete-time systems. In the presence of partially unknown dynamics, the optimal regulation control problem is addressed while the optimal tracking control problem is addressed in the presence of known dynamics. Each design entails two portions: an action neural network (NN) that is designed to produce a nearly optimal control signal, and a critic NN which evaluates the performance of the system. Novel weight update laws for the critic and action NN's are derived, and all parameters are tuned online. Lyapunov techniques are used to show that all signals are uniformly ultimately bounded (UUB) and that the output of the action NN approaches the optimal control input with small bounded error. Simulation results are also presented to demonstrate the effectiveness of the approach.read more
Citations
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Journal ArticleDOI
Online Optimal Control of Affine Nonlinear Discrete-Time Systems With Unknown Internal Dynamics by Using Time-Based Policy Update
TL;DR: The Hamilton-Jacobi-Bellman equation is solved forward-in-time for the optimal control of a class of general affine nonlinear discrete-time systems without using value and policy iterations and the end result is the systematic design of an optimal controller with guaranteed convergence that is suitable for hardware implementation.
Journal ArticleDOI
Online adaptive policy learning algorithm for H∞ state feedback control of unknown affine nonlinear discrete-time systems.
TL;DR: An online adaptive policy learning algorithm (APLA) based on adaptive dynamic programming (ADP) is proposed for learning in real-time the solution to the Hamilton-Jacobi-Isaacs (HJI) equation, which appears in the H∞ control problem.
Journal ArticleDOI
Stochastic optimal control of unknown linear networked control system in the presence of random delays and packet losses
TL;DR: The proposed stochastic optimal control method uses an adaptive estimator (AE) and ideas from Q-learning to solve the infinite horizon optimal regulation of unknown NCS with time-varying system matrices and produces an optimal control scheme that operates forward-in-time manner for unknown linear systems.
Proceedings ArticleDOI
Optimal control of affine nonlinear continuous-time systems
TL;DR: In this article, the optimal regulation and tracking control of affine nonlinear continuous-time systems with known dynamics is undertaken using a novel single online approximator (SOL)-based scheme.
Journal ArticleDOI
Neural Network-Based Optimal Adaptive Output Feedback Control of a Helicopter UAV
TL;DR: An optimal controller design via an output feedback for trajectory tracking of a helicopter UAV, using a neural network (NN) is introduced, utilizing the backstepping methodology.
References
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TL;DR: The real and complex number system as discussed by the authors is a real number system where the real number is defined by a real function and the complex number is represented by a complex field of functions.
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Optimal Control
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TL;DR: In this paper, the authors discuss a variety of adaptive critic designs (ACDs) for neuro-control, which are suitable for learning in noisy, nonlinear, and nonstationary environments They have common roots as generalizations of dynamic programming for neural reinforcement learning approaches.
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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.
Journal ArticleDOI
Online learning control by association and reinforcement
Jennie Si,Yu-Tsung Wang +1 more
TL;DR: In this article, a generic online learning control system based on the fundamental principle of reinforcement learning or more specifically neural dynamic programming is presented. But the authors focus on a systematic treatment for developing a generic RL control system.