Neural event-triggered optimal filtering co-design of Markovian jump systems with hidden mode detections
Chao Ma, Yanfeng Lu, Wei Li Wu · Transactions of the Institute of Measurement and Control · 2023
The modified H ∞ filtering problem for a class of Markovian jump systems with unknown nonlinear dynamics is investigated in the work by developing the neural event-triggered filter co-design method. Moreover, the true system modes are assumed to be inaccessible such that the estimated jumping modes are utilized for the mode-dependent filters. In particular, a novel event-triggered mechanism is introduced to improve filtering communication efficiency, where the unknown nonlinearity approximation is conducted by a neural network. By virtue of employing Lyapunov–Krasovskii method, sufficient filtering conditions are constructed to ensure the optimal H ∞ performance under the mean-square framework, based on which desired mode-dependent filter gains, event-triggering, and neural network parameters are co-designed with an aid of matrix techniques. Illustrative simulations with two practical examples are finally carried out to validate the usefulness and advantages of our developed approach.