Event-Based Control for Discrete Polytopic LPV Systems With AI Inference

Abstract

This article presents an in-depth exploration of event-based control schemes, focusing on event-triggered control (ETC) and self-triggered control (STC) for discrete polytopic linear parameter-varying (LPV) systems with time-varying characteristics. The primary objective of the proposed ETC design is to ensure optimal system performance while minimizing the frequency of control updates. The article first introduces a standard ETC algorithm for discrete-time polytopic LPV systems, leveraging a parameter-varying Lyapunov function. Subsequently, a dynamic ETC algorithm is proposed to extend the inter-event time (IET) associated with the standard ETC. Furthermore, standard and dynamic STC algorithms are developed for the polytopic LPV systems to reduce the periodic sampling by evaluating the subsequent control update based on the preceding triggered state. The control gains are designed using multiobjective linear matrix inequalities (LMIs). Lastly, the efficacy of the developed control theory is demonstrated through its implementation on a distributed fog computing platform (DFCP) in warehouse applications. The experimental results demonstrate the effectiveness of auto-scaling fog computing nodes in processing vision data obtained from a mobile robot operating within a warehouse environment subject to service time constraints.