Machine Learning Control For Highly Reconfigurable High-Order Systems
Final rept. 1 Jul 2011-29 Sep 2014
TEXAS ENGINEERING EXPERIMENT STATION COLLEGE STATION
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This Grant addressed four focus areas toward advancing the state-of-the-art in learning control theory of robust and adaptive non-equilibrium control of highly nonlinear, higher-order, reconfigurable systems 1. Extend Approximate Dynamic Programming ADP techniques to control of nonlinear, multiple time scale, non-affine systems in an Adaptive Control framework. 2. Develop solution techniques for Markov Decision Problems MDP that scale to continuous state and control spaces with constraints 3. Extend MDP techniques to solve multi-agent co-ordination and control problems in a decentralized fashion. 4. Develop solution techniques that scale to continuous state-space Partially Observable Markov Decision Problems POMDP and their multi-agent generalizations. The work produced the first significant results in the nonlinear control of multiple time-scale control in the last 25 years, and additionally made significant contributions to the analysis and control of systems that are non-affine in control, and non-minimum phase. The work also developed sampling based feedback planning techniques for the solution of Markov Decision Problems MDP and Partially Observed MDPs POMDP.