Abstract
A novel reinforcement-learning based output-adaptive neural network (NN) controller, also referred as the adaptive-critic NN controller, is developed to track a desired trajectory for a class of complex nonlinear discrete-time systems in the presence of bounded and unknown disturbances. The controller includes an observer for estimating states and the outputs, critic, and two action NNs for generating virtual, and actual control inputs. The critic approximates certain strategic utility function and the action NNs are used to minimize both the strategic utility function and their outputs. All NN weights adapt online towards minimization of a performance index, utilizing gradient-descent based rule. A Lyapunov function proves the uniformly ultimate boundedness (UUB) of the closed-loop tracking error, weight, and observer estimation. Separation principle and certainty equivalence principles are relaxed; persistency of excitation condition and linear in the unknown parameter assumption is not needed. The performance of this adaptive critic NN controller is evaluated through simulation with the Daw engine model in lean mode. The objective is to reduce the cyclic dispersion in heat release by using the controller.
Recommended Citation
P. Shih et al., "Reinforcement Learning Based Output-Feedback Control of Nonlinear Nonstrict Feedback Discrete-Time Systems with Application to Engines," Proceedings of the American Control Conference, 2007. ACC'07, Institute of Electrical and Electronics Engineers (IEEE), Jul 2007.
The definitive version is available at https://doi.org/10.1109/ACC.2007.4283127
Meeting Name
American Control Conference, 2007. ACC'07
Department(s)
Electrical and Computer Engineering
Second Department
Computer Science
Third Department
Mechanical and Aerospace Engineering
Sponsor(s)
National Science Foundation (U.S.)
Keywords and Phrases
Lyapunov Methods; Adaptive Control; Closed Loop Systems; Feedback Function Approximation; Gradient Methods; Internal Combustion Engines; Learning (Artificial Intelligence); Learning Systems; Neurocontrollers; Nonlinear Control Systems; Observers; Discrete-time systems; Large scale systems
Document Type
Article - Conference proceedings
Document Version
Final Version
File Type
text
Language(s)
English
Rights
© 2007 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
Publication Date
01 Jul 2007
Included in
Aerospace Engineering Commons, Computer Sciences Commons, Electrical and Computer Engineering Commons, Mechanical Engineering Commons, Operations Research, Systems Engineering and Industrial Engineering Commons
