4,941 research outputs found
Neural network optimal control for nonlinear system based on zero-sum differential game
summary:In this paper, for a class of the complex nonlinear system control problems, based on the two-person zero-sum game theory, combined with the idea of approximate dynamic programming(ADP), the constrained optimization control problem is solved for the nonlinear systems with unknown system functions and unknown time-varying disturbances. In order to obtain the approximate optimal solution of the zero-sum game, the multilayer neural network is used to fit the evaluation network, the execution network and the disturbance network of ADP respectively. The Lyapunov stability theory is used to prove the uniform convergence, and the system control output converges to the neighborhood of the target reference value. Finally, the simulation example verifies the effectiveness of the algorithm
Minimax Iterative Dynamic Game: Application to Nonlinear Robot Control Tasks
Multistage decision policies provide useful control strategies in
high-dimensional state spaces, particularly in complex control tasks. However,
they exhibit weak performance guarantees in the presence of disturbance, model
mismatch, or model uncertainties. This brittleness limits their use in
high-risk scenarios. We present how to quantify the sensitivity of such
policies in order to inform of their robustness capacity. We also propose a
minimax iterative dynamic game framework for designing robust policies in the
presence of disturbance/uncertainties. We test the quantification hypothesis on
a carefully designed deep neural network policy; we then pose a minimax
iterative dynamic game (iDG) framework for improving policy robustness in the
presence of adversarial disturbances. We evaluate our iDG framework on a
mecanum-wheeled robot, whose goal is to find a ocally robust optimal multistage
policy that achieve a given goal-reaching task. The algorithm is simple and
adaptable for designing meta-learning/deep policies that are robust against
disturbances, model mismatch, or model uncertainties, up to a disturbance
bound. Videos of the results are on the author's website,
http://ecs.utdallas.edu/~opo140030/iros18/iros2018.html, while the codes for
reproducing our experiments are on github,
https://github.com/lakehanne/youbot/tree/rilqg. A self-contained environment
for reproducing our results is on docker,
https://hub.docker.com/r/lakehanne/youbotbuntu14/Comment: 2018 International Conference on Intelligent Robots and System
Issues on Stability of ADP Feedback Controllers for Dynamical Systems
This paper traces the development of neural-network (NN)-based feedback controllers that are derived from the principle of adaptive/approximate dynamic programming (ADP) and discusses their closed-loop stability. Different versions of NN structures in the literature, which embed mathematical mappings related to solutions of the ADP-formulated problems called “adaptive critics” or “action-critic” networks, are discussed. Distinction between the two classes of ADP applications is pointed out. Furthermore, papers in “model-free” development and model-based neurocontrollers are reviewed in terms of their contributions to stability issues. Recent literature suggests that work in ADP-based feedback controllers with assured stability is growing in diverse forms
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