Multi-agent replicator controller for sustainable vibration control of smart structures

Developed in the artificial intelligence community, an intelligent agent is an autonomous abstract or software entity that observes through sensors and acts upon an environment in an adaptive or intelligent manner. In a centralized control system, one central controller uses the global measurement data collected from all the sensors installed in the structure to make control decisions and to dispatch them to control devices. The centralized controller itself represents a single point of potential failure. To overcome this shortcoming, decentralized control is used to improve redundancy. This paper introduces three ideas to vibration control of smart structures: agent technology, replicator dynamics from evolutionary game theory, and energy minimization. It presents two new methods: 1) a single-agent Centralized Replicator Controller (CRC) and a decentralized Multi-Agent Replicator Controller (MARC) for vibration control of smart structures. The use of agents and a decentralized approach enhances the robustness of the entire vibration control system. The proposed control methodologies are applied to vibration control of a 3-story steel frame and a 20-story steel benchmark structure subjected to two sets of seismic loadings: historic earthquake accelerograms and artificial earthquakes and compared with the corresponding centralized and decentralized conventional Linear Quadratic Regulator (LQR) control algorithm

A Programmer-Interpreter Neural Network Architecture for Prefrontal Cognitive Control

There is wide consensus that the prefrontal cortex (PFC) is able to exert cognitive control on behavior by biasing processing toward task-relevant information and by modulating response selection. This idea is typically framed in terms of top-down influences within a cortical control hierarchy, where prefrontal-basal ganglia loops gate multiple input-output channels, which in turn can activate or sequence motor primitives expressed in (pre-)motor cortices. Here we advance a new hypothesis, based on the notion of programmability and an interpreter-programmer computational scheme, on how the PFC can flexibly bias the selection of sensorimotor patterns depending on internal goal and task contexts. In this approach, multiple elementary behaviors representing motor primitives are expressed by a single multi-purpose neural network, which is seen as a reusable area of "recycled" neurons (interpreter). The PFC thus acts as a "programmer" that, without modifying the network connectivity, feeds the interpreter networks with specific input parameters encoding the programs (corresponding to network structures) to be interpreted by the (pre-)motor areas. Our architecture is validated in a standard test for executive function: the 1-2-AX task. Our results show that this computational framework provides a robust, scalable and flexible scheme that can be iterated at different hierarchical layers, supporting the realization of multiple goals. We discuss the plausibility of the "programmer-interpreter" scheme to explain the functioning of prefrontal-(pre)motor cortical hierarchies