Integrated Intelligent and Predictive Control: A Multi-Agent Adaptive Type-2 Fuzzy Control Architecture

TL;DR

This paper introduces a two-layer multi-agent control system combining intelligent control and model-predictive control, utilizing novel type-2 fuzzy membership functions to enhance real-time control of complex systems with improved performance and low computation time.

Contribution

It presents a new multi-agent control architecture integrating intelligent and predictive control, and introduces probabilistic-fuzzy membership functions for type-2 fuzzy systems.

Findings

Probabilistic-fuzzy membership functions outperform existing fuzzy types.

The proposed architecture achieves better control performance than decentralized fuzzy control.

The system maintains low computation time while enhancing adaptivity and coordination.

Abstract

We propose a novel two-layer multi-agent architecture aimed at efficient real-time control of large-scale and complex-dynamics systems. The proposed architecture integrates intelligent control approaches (which have a low computation time and fit real-time applications) with model-predictive control (which takes care of the optimality requirements of control). The bottom control layer (intelligent-control module) includes several distributed intelligent-control agents, the design parameters of which are tuned by the top layer (model-predictive control module). The model-predictive control module fulfills two significant roles: looking ahead to the effects of the control decisions, and coordinating the intelligent-control agents of the lower control layer. The resulting multi-agent control system has a very low computation time, and provides adaptivity, control coordination, and aims at excellent performance. Additionally, we give a general treatment of type-2 fuzzy membership functions, and introduce two categories for them: probabilistic-fuzzy (which is a novel concept introduced in this paper) and fuzzy-fuzzy (which is a new treatment of the existing type-2 fuzzy membership functions). The performance of the proposed modeling and control approaches are assessed via a case study involving a simple urban traffic network: the results show that the novel concept of probabilistic-fuzzy membership function outperforms the type-1 and type-2 membership functions that have already been introduced in the literature. Furthermore, the proposed two-layer integrated multi-agent control architecture significantly outperforms a multi-agent decentralized fuzzy control system (without coordination among the agents), while requiring a comparable computation time.