Abstract
A novel Q-learning approach is presented for the design of an adaptive optimal regulator for linear large-scale interconnected system. The subsystems communicate among each other through a communication network while another communication network is inserted within the feedback loop of each subsystem. The network induced random delays and data dropouts of the network in the feedback are modelled along with the system dynamics. Stochastic Q-learning is used to adaptively learn the Q-function parameters with periodic and intermittent feedback. For efficient parameter learning with event-sampled feedback, a novel hybrid learning algorithm is proposed. Boundedness of the estimated parameters and asymptotic convergence of state vector in the mean square is achieved and it is demonstrated using Lyapunov stability analysis. Moreover, if the regression function of the QFE is persistently exciting (PE), the estimated parameters converge to their expected target values. The proposed analytical design is validated using a numerical example via simulation.
| Original language | American English |
|---|---|
| Journal | Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence (2015, Cape Town, South Africa) |
| DOIs | |
| State | Published - Dec 1 2015 |
Keywords
- Adaptive control systems
- Adaptive regulators
- Analytical design
- Artificial intelligence
- Asymptotic convergence
- Estimated parameter
- Large-scale interconnected systems
- Networked control systems
- Parameter estimation
- Persistency of excitation
- Q-learning approach
- Regression vectors
- Stochastic systems
Disciplines
- Electrical and Computer Engineering
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