Robust Adaptive Critic Neural Network Control of a Class of Nonlinear Dynamic Systems

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Proposal Number: 0621924Proposal Title: Robust Adaptive Critic Neural Network Control of a Class of Nonlinear Dynamic SystemsPI Name: Sarangapani, JagannathanPI Institution: University of Missouri-RollaThe objective of this research is to develop optimal controllers using approximate dynamic programming for nonlinear discrete-time systems with uncertainties and disturbances. Supervised actor-critic neural network architectures will be used to solve the equations resulting from the receding horizon optimal control formulation. Intellectual Merit Intellectual merit of this research lies in realizing unified controller solutions to optimal control problems for uncertain nonlinear discrete-time systems that are important, yet difficult to solve. Outcome of this research is expected to advance the field of control in the area of robust adaptive control. Additionally, expected results will advance the state of the art in the field of neural network control since these investigations are based on new approximate dynamic programming formulations with neural network based solution structures. Broader ImpactsBroader impact of this research will be in the applications areas of fuel flexible engine and spark ignition engine control. Optimal control solutions to such problems could lead to the drastic reductions in engine-out emissions while improving efficiency and adaptability. Outreach activities planned in this proposal include using women and minorities and disseminating results through the newly established NSF Industry/University Cooperative Research Center Site. The project results will be presented through demonstrations to the area high schools in order to create early career interest in science and engineering.