Tamer Inanc


Tamer Inanc received M.S. and Ph.D. degrees in Electrical Engineering from the Pennsylvania State University, PA in 1996 and 2002, respectively. Between 2002 and 2004, he was a postdoctoral scholarship at the California Institute of Technology, Pasadena, CA. He started working as an Assistant Professor in 2004 at the Electrical and Computer Engineering Department at UofL. He is currently Professor at the same department. Dr. Inanc received the President's Distinguished Teaching Professor Award (2022), the Speed School Excellence in Teaching Award (2021), the Delphi Center “Innovations in Technology Award for Teaching and Learning” (2008) and the Kentuckiana Metroversity Instructional Development Award (2006). His research interests center on control systems, model identification, autonomous robotics, and applications of system identification and control systems to biomedical problems.


  • B.S. in Electrical & Electronics Engr, Dokuz Eylul University, 1991
  • M.S. in Electrical Engineering, The Pennsylvania State University, 1996
  • Ph.D. in Electrical Engineering, The Pennsylvania State University, 2002


Radial basis function interpolation and galerkin projection for direct trajectory optimization and costate estimation- 2021

<span>This work presents a novel approach combining radial basis function (RBF) interpolation with Galerkin projection to efficiently solve general optimal control problems. The goal is to develop a highly flexible solution to optimal control problems, especially nonsmooth problems involving discontinuities, while accounting for trajectory accuracy and computational efficiency simultaneously. The proposed solution, called the RBF-Galerkin method, offers a highly flexible framework for direct transcription by using any interpolant functions from the broad class of global RBFs and any arbitrary discretization points that do not necessarily need to be on a mesh of points. The RBF-Galerkin costate mapping theorem is developed that describes an exact equivalency between the Karush-Kuhn-Tucker (KKT) conditions of the nonlinear programming problem resulted from the RBF-Galerkin method and the discretized form of the first-order necessary conditions of the optimal control problem, if a set of discrete conditions holds. The efficacy of the proposed method along with the accuracy of the RBF-Galerkin costate mapping theorem is confirmed against an analytical solution for a bang-bang optimal control problem. In addition, the proposed approach is compared against both local and global polynomial methods for a robot motion planning problem to verify its accuracy and computational efficiency.</span>

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