Event-based optimal guidance laws design for missile-target interception systems using fuzzy dynamic programming approach.

In this paper, the guidance system with unknown dynamics is modeled as a partially unknown zero-sum differential game system. Then, a periodic event-triggered optimal control algorithm is designed to interception target under a plug-n-play framework. To realize this algorithm, generalized fuzzy hyperbolic models are employed to construct the identifier-critic structure, where the online identifier is used to estimate unknown dynamics, meanwhile, the generalized fuzzy hyperbolic models-based critic network is utilized to approximate the cost function. Note that plug-n-play framework lets both the designed identifier and critic network work simultaneously, in other words, the prior system information is no longer required, which simplifies the network structure. Using the Lyapunov function method, the approximate optimal control strategy and corresponding weight updating laws are derived to guarantee that the closed-loop system and weight approximation errors are uniformly ultimately bounded, where an additional function is added to weight updating laws to release the requirement for admissible initial control. Finally, to compare the intercept effects and the utilization ratio of communication resources of the periodic event-triggered control algorithm and the common adaptive dynamic programming algorithm, the missile interception system is introduced as an example. • The guidance system with unknown dynamics is modeled as a partially unknown zero-sum differential game system. Then, the event-based optimal control strategy is designed to improve the utilization of data transmissions. • A GFHM-based identifier is proposed under a plug-n-play framework to approximate unknown nonlinear functions and eliminate the priori identification process. • This paper proposes the fuzzy ADP approach to design the optimal controller, where the single critic network is adopted to approximate the optimal performance index by using GFHMs. [ABSTRACT FROM AUTHOR]