The ‘simulation hypothesis’ is an intriguing explanation for cognition, and holds that ‘thinking consists of simulated interaction with the environment’ ([4], p.242). However, the neuroscientific proof for a simulation mechanism in the brain is indirect. In this paper we present a minimal-model approach to investigate the ‘simulation hypothesis’. Our minimal model is called ACP? and is an exten...

This paper proposes a wavelet neurocontroller capable of self-adaptation and self-organization for uncertain systems controlled with semi-active devices, ideal candidates for control of large-scale civil structures. A condition on the sliding surface for cantilever-like structures is defined. The issue of applicability of the control solution to largescale civil structures is made the central t...

The interaction of brain, body and environment can result in complex behavior with rich dynamics even for relatively simple agents. Such dynamics are, however, often difficult to analyze. In this article we explore the case of a simple simulated robotic agent, equipped with a reactive neurocontroller and an energy level, that the agent has been evolved to re-charge. A dynamical systems analysis...

Essentially, motive behind using control system is to generate suitable control signal for yielding desired response of a physical process. Control of synchronous generator has always remained very critical in power system operation and control. For certain well known reasons power generators are normally operated well below their steady state stability limit. This raises demand for efficient a...

The problem of decentralized consensus control of a network of heterogeneous nonlinear systems is formulated as an optimal tracking problem and a solution is proposed using an approximate dynamic programming based neurocontroller. The neurocontroller training comprises an initial offline training phase and an online re-optimization phase to account for the fact that the reference signal subject...

An approach to robotics called layered evolution and merging features from the subsumption architecture into evolutionary robotics is presented, and its advantages are discussed. This approach is used to construct a layered controller for a simulated robot that learns which light source to approach in an environment with obstacles. The evolvability and performance of layered evolution on this t...

We introduce a technique for quantifying and then exploiting uncertainty in nonlinear stochastic control systems. The approach is suboptimal though robust and relies upon the approximation of the forward and inverse plant models by neural networks, which also estimate the intrinsic uncertainty. Sampling from the resulting Gaussian distributions of the inversion based neurocontroller allows us t...