Abstract
When considering the concept of distributed intelligent control, three types of components can be defined: (i) fuzzy sensors which provide a representation of measurements as fuzzy subsets, (ii) fuzzy actuators which can operate in the real world based on the fuzzy subsets they receive, and, (iii) the fuzzy components of the inference. As a result, these elements generate new fuzzy subsets from the fuzzy elements that were previously used. The purpose of this article is to define the elements of an interoperable technology Fuzzy Applied Cell Control-soft computing language for the development of fuzzy components with distributed intelligence implemented on the DSP target. The cells in the network are configured using the operations of symbolic fusion, symbolic inference and fuzzy–real symbolic transformation, which are based on the concepts of fuzzy meaning and fuzzy description. The two applications presented in the article, Agent-based modeling and fuzzy logic for simulating pedestrian crowds in panic decision-making situations and Fuzzy controller for mobile robot, are both timely. The increasing occurrence of panic moments during mass events prompted the investigation of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations. Based on the research presented in the article, we propose a Fuzzy controller-based system for determining pedestrian flows and calculating the shortest evacuation distance in panic situations. Fuzzy logic, one of the representation techniques in artificial intelligence, is a well-known method in soft computing that allows the treatment of strong constraints caused by the inaccuracy of the data obtained from the robot’s sensors. Based on this motivation, the second application proposed in the article creates an intelligent control technique based on Fuzzy Logic Control (FLC), a feature of intelligent control systems that can be used as an alternative to traditional control techniques for mobile robots. This method allows you to simulate the experience of a human expert. The benefits of using a network of fuzzy components are not limited to those provided distributed systems. Fuzzy cells are simple to configure while also providing high-level functions such as mergers and decision-making processes.
Highlights
The increasing occurrence of moments of panic during mass events motivated the study of the impact of panic on crowd dynamics and the simulation of pedestrian flows in panic situations
The corresponding forces can be controlled for each individual and represent a different variety of behaviors that can be associated with panic situations, such as avoiding danger, crowding, and pushing [38,39]
From the point of view of the concept of distributed intelligent control, three types of components can be defined: Fuzzy sensors, which provide a representation of measurements as fuzzy subsets; Fuzzy actuators, which can act in a real world; depending on the fuzzy subsets; and they receive fuzzy components of inference, which can perform distributed fuzzy logic
Summary
The general trend is a rapid increase in the number of measurements and actions in the control of complex processes. No single microcontroller/processor is used to simultaneously perform the measurements, the decision, and the process control loop, switching to distributed intelligent processing. This intelligent processing is done through a spatial distribution, using certain logic, so that the cooperative solutions between the distributed processors can be identified. Communication processes between functional components should be limited; otherwise, the performance could decrease dramatically. The use of communication systems ensures the exchange of data between distributed processors, achieving the first stage of intelligence
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