Improved Genetic Algorithm for Finite-Horizon Optimal Control of Nonlinear Systems

Optimal control has been a very attractive and desirable feature for many dynamic and static systems, An effective online technique for finite-horizon nonlinear control problem is offered in this paper. The idea of the proposed technique is to combine the differential State Dependent Riccati equation filter algorithm and the finite-horizon SDRE technique. Genetic algorithm is used to calculate the optimal weighting matrices. Unlike the linear techniques that are used for linearized systems, the proposed technique is effective for wide range of operating points. Simulation results are given to demonstrate the effectiveness of the offered technique.

[1]  Tayfun Çimen,et al.  Survey of State-Dependent Riccati Equation in Nonlinear Optimal Feedback Control Synthesis , 2012 .

[2]  Bor-Sen Chen,et al.  A structure-specified H∞ optimal control design for practical applications: a genetic approach , 1998, IEEE Trans. Control. Syst. Technol..

[4]  Ruxandra Botez,et al.  Speed and altitude optimization on the FMS CMA-9000 for the Sukhoi Superjet 100 using genetic algorithms , 2013 .

[5]  A. Heydari,et al.  Path Planning Using a Novel Finite Horizon Suboptimal Controller , 2013 .

[6]  Dawid Zydek,et al.  Nonlinear Optimal Control Using Finite-Horizon State Dependent Riccati Equation Combined with Genetic Algorithm , 2016, ICONS 2016.

[7]  Tayfun Çimen.  Development and validation of a mathematical model for control of constrained non-linear oil tanker motion , 2009 .

[8]  Ali Heydari,et al.  Approximate closed-form solutions to finite-horizon optimal control of nonlinear systems , 2012, 2012 American Control Conference (ACC).

[9]  Roberto Salvador Felix Patron,et al.  Flight trajectories optimization under the influence of winds using genetic algorithms , 2013 .

[10]  Tayfun Çimen,et al.  State-Dependent Riccati Equation (SDRE) Control: A Survey , 2008 .

[11]  Mohd Wazir Mustafa,et al.  Optimal location and signal selection of UPFC device for damping oscillation , 2011 .

[12]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[13]  M. Razzaghi,et al.  Solution of the matrix Riccati equation for the linear quadratic control problems , 1998 .

[14]  M. W. Mustafa,et al.  Power system damping using GA based fuzzy controlled SVC device , 2009, TENCON 2009 - 2009 IEEE Region 10 Conference.

[15]  Zoran Gajic,et al.  Solving the Matrix Differential Riccati Equation: A Lyapunov Equation Approach , 2010, IEEE Transactions on Automatic Control.

[16]  Ruxandra Botez,et al.  New Robust Control Analysis Methodology for Lynx Helicopter and Cessna Citation X Aircraft using Guardian Maps, Genetic Algorithms and LQR Theories Combinations , 2014 .

[17]  Zoran Gajic,et al.  Successive approximation procedure for steady-state optimal control of bilinear systems , 1995 .

[18]  A. Garulli,et al.  Positive Polynomials in Control , 2005 .

[19]  J. Cloutier.  State-dependent Riccati equation techniques: an overview , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[20]  Mark F. Bramlette,et al.  A Comparative Evaluation of Search Methods Applied to Parametric Design of Aircraft , 1989, ICGA.

[21]  Mukhtar Fatihu Hamza,et al.  COMPARISON OF GA AND LQR TUNING OF STATIC VAR COMPENSATOR FOR DAMPING OSCILLATIONS , 2012 .

[22]  D. Subbaram Naidu,et al.  Nonlinear Position Control of DC Motor Using Finite-Horizon State Dependent Riccati Equation , 2014, ICSEng.

[23]  D. Naidu,et al.  Nonlinear Finite-Horizon Regulation and Tracking for Systems with Incomplete State Information Using Differential State Dependent Riccati Equation , 2014 .

[24]  David E. Goldberg,et al.  Control system optimization using genetic algorithms , 1992 .