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Optimization of sampling intervals for tracking control of nonlinear systems: A game theoretic approach.

07:00 EST 8th March 2019 | BioPortfolio

Summary of "Optimization of sampling intervals for tracking control of nonlinear systems: A game theoretic approach."

This paper presents a near optimal adaptive event-based sampling scheme for tracking control of an affine nonlinear continuous-time system. A zero-sum game approach is proposed by introducing a novel performance index. The optimal value function, i.e., the solution to the associatedHamilton-Jacobi-Issac (HJI) equation is approximated using a functional link neural network (FLNN) with event-based aperiodic state feedback information as inputs. The saddle point approximated optimal solution is employed to design the near optimal event-based control policy and the sampling condition. An impulsive weight update scheme is designed to guarantee local ultimate boundedness of the closed-loop parameters, which is analyzed via extension of Lyapunov stability theory for the impulsive hybrid dynamical systems. Zeno-freeness of the event-sampling scheme is enforced and its effect on stability is analyzed. Finally, numerical simulation results are included to corroborate the analytical design, which shows a 48.82% reduction of feedback communication and computational load.

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This article was published in the following journal.

Name: Neural networks : the official journal of the International Neural Network Society
ISSN: 1879-2782
Pages: 78-90

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