"Increased interest in complex interconnected systems like smart-grid, cyber manufacturing have attracted researchers to develop optimal adaptive control schemes to elicit a desired performance when the complex system dynamics are uncertain. In this dissertation, motivated by the fact that aperiodic event sampling saves network resources while ensuring system stability, a suite of novel event-sampled distributed near-optimal adaptive control schemes are introduced for uncertain linear and affine nonlinear interconnected systems in a forward-in-time and online manner.
First, a novel stochastic hybrid Q-learning scheme is proposed to generate optimal adaptive control law and to accelerate the learning process in the presence of random delays and packet losses resulting from the communication network for an uncertain linear interconnected system. Subsequently, a novel online reinforcement learning (RL) approach is proposed to solve the Hamilton-Jacobi-Bellman (HJB) equation by using neural networks (NNs) for generating distributed optimal control of nonlinear interconnected systems using state and output feedback. To relax the state vector measurements, distributed observers are introduced.
Next, using RL, an improved NN learning rule is derived to solve the HJB equation for uncertain nonlinear interconnected systems with event-triggered feedback. Distributed NN identifiers are introduced both for approximating the uncertain nonlinear dynamics and to serve as a model for online exploration. Next, the control policy and the event-sampling errors are considered as non-cooperative players and a min-max optimization problem is formulated for linear and affine nonlinear systems by using zero-sum game approach for simultaneous optimization of both the control policy and the event based sampling instants. The net result is the development of optimal adaptive event-triggered control of uncertain dynamic systems"--Abstract, page iv.
Sarangapani, Jagannathan, 1965-
Erickson, Kelvin T.
Landers, Robert G.
Bristow, Douglas A.
Le, Vy Koi
Electrical and Computer Engineering
Ph. D. in Electrical Engineering
National Science Foundation (U.S.)
Missouri University of Science and Technology. Intelligent Systems Center
Missouri University of Science and Technology
Journal article titles appearing in thesis/dissertation
- Distributed adaptive optimal regulation of uncertain large scale interconnected systems using hybrid Q-learning approach
- Event-triggered distributed approximate optimal state and output control of affine nonlinear interconnected systems
- Event-triggered distributed control of nonlinear interconnected systems using online reinforcement learning with exploration
- Adaptive optimal event-triggered control of linear dynamic systems
- Approximate optimal event-triggered control of nonlinear systems
xiii, 199 pages
© 2017 Vignesh Narayanan, All rights reserved.
Dissertation - Open Access
Electronic OCLC #
Narayanan, Vignesh, "Event-triggered near optimal adaptive control of interconnected systems" (2017). Doctoral Dissertations. 2587.