Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems Using Neurodynamic Programming

This paper presents an event-triggered near optimal control of uncertain nonlinear discrete-time systems. Event-driven neurodynamic programming (NDP) is utilized to design the control policy. A neural network (NN)-based identifier, with event-based state and input vectors, is utilized to learn the s...

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Bibliographic Details
Published in:	IEEE transaction on neural networks and learning systems 2016-09, Vol.27 (9), p.1801-1815
Main Authors:	Sahoo, Avimanyu, Hao Xu, Jagannathan, Sarangapani
Format:	Article
Language:	eng
Subjects:	Approximation methods Artificial neural networks Computer simulation Cost function Discrete-time systems Dynamical systems Event-triggered control (ETC) Hamilton–Jacobi–Bellman equation Mirrors Neural networks neural networks (NNs) neurodynamic programming (NDP) Nonlinear dynamics Nonlinearity Optimal control Policies Programming System dynamics
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Summary:	This paper presents an event-triggered near optimal control of uncertain nonlinear discrete-time systems. Event-driven neurodynamic programming (NDP) is utilized to design the control policy. A neural network (NN)-based identifier, with event-based state and input vectors, is utilized to learn the system dynamics. An actor-critic framework is used to learn the cost function and the optimal control input. The NN weights of the identifier, the critic, and the actor NNs are tuned aperiodically once every triggered instant. An adaptive event-trigger condition to decide the trigger instants is derived. Thus, a suitable number of events are generated to ensure a desired accuracy of approximation. A near optimal performance is achieved without using value and/or policy iterations. A detailed analysis of nontrivial inter-event times with an explicit formula to show the reduction in computation is also derived. The Lyapunov technique is used in conjunction with the event-trigger condition to guarantee the ultimate boundedness of the closed-loop system. The simulation results are included to verify the performance of the controller. The net result is the development of event-driven NDP.
ISSN:	2162-237X 2162-2388