Sivasubramanya N. Balakrishnan

In this paper, a novel event triggered neural network (NN) adaptive controller is presented for uncertain affine nonlinear systems. Controller design is based on an observer, called as Modified State Observer (MSO), which is used to approximate uncertainties online. State is sensed continuously yet sent on feedback network only when required, in aperiodic fashion. Lyapunov analysis is used to derive this condition which is dynamic in nature since it is based on tracking error. In this way ETNAC helps to not only saves communication cost but also computational efforts. MSO formulations have two tunable gains which let you do fast estimation without inducing high frequency oscillations in the system. A benchmark example of 2-link robotic manipulator is used to show the efficacy of the proposed controller.

As children grow, they gradually learn how to make decisions independently. However, decisions like choosing healthy but less-tasty foods can be challenging for children whose self-regulation and executive cognitive functions are still maturing. We propose a computational decision-making process in which children estimate their mother’s choices for them as well as their individual food preferences. By employing functional magnetic resonance imaging during real food choices, we find that the ventromedial prefrontal cortex (vmPFC) encodes children’s own preferences and the left dorsolateral prefrontal cortex (dlPFC) encodes the projected mom’s choices for them at the time of children’s choice. Also, the left dlPFC region shows an inhibitory functional connectivity with the vmPFC at the time of children’s own choice. Our study suggests that in part, children utilize their perceived caregiver’s choices when making choices for themselves, which may serve as an external regulator of decision-making, leading to optimal healthy decisions.

In proceedings of the conference held 13-17 January 2014, National Harbor, Maryland.Click on the DOI number to access this conference paper (may not be free).This paper presents some issues encountered while flight testing a Model Reference Adaptive dynamic inverse controller (MRAC), on a General Aviation (GA) fly-by-wire test bed. Adaptation was replaced with a Modified State Observer (MSO) method which shows promising results in simulation. The flight test results of the MRAC summarized in this paper show PLA surging issues due to adaptation during the commanded flight path angle and airspeed. To overcome this problem, MSO based adaptation methodology is adopted to control the longitudinal dynamics of a typical general aviation aircraft. The advantage of MSO is that it adapts to estimation error, not modeling or tracking error. The controller performance is evaluated for elevator and engine dynamics along with the thrust saturation limits. The elevator model considered in this study deals with a very large delay (200 msec) in the elevator servo. The thrust saturation depends on engine shaft horse power limits that simulate the flight test condition. A controlled flight simulation is carried out including the turbulence effects observed during flight. Controller design using the saturated thrust is used in flight test conditions, where the thrust generated by the engine is not an observable state. The simulation results showing the controller adaptability to flight path angle and airspeed commands are promising. The random PLA surge seen during the simulation and flight test of the baseline MRAC controller is not observed in the simulation results of MSO adaptation controller. Hardware-In-Loop (HIL) Ground Tests and Flight test of MSO based controller is scheduled in the near future.This material is based upon work supported by NASA under award number NNXO9AP20A

This paper presents a web-based framework for interfacing product designers and fixture designers to fetch the benefits of early supplier involvement (ESI) to a reconfigurable assembly system (RAS). The interfacing of the two members requires four steps, namely, collaboration chain, fixture supplier selection, knowledge share, and accommodation of service facilities so as to produce multiple products on a single assembly line. The interfacing not only provokes concurrency in the activities of product and fixture designer but also enables the assembly systems to tackle the spatial and generational variety. Among the four stages of interfacing, two steps are characterized by optimization issues, one from the product customer side and the other from the fixture designer side. To impart promptness in the optimization and hence the interaction, computationally economic tools are also presented in the paper for both of the supplier selection and fixture design optimization.

In this paper, approximate dynamic programming (ADP) based design tools are developed for adaptive control of aircraft control under nominal and damaged conditions. Nominal control of the system is computed with a single network adaptive critic (SNAC) derived through principles of ADP. Convergence of SNAC training is shown by reducing it to solving a set of nonlinear algebraic equations in weights. Unlike many adaptive control approaches, we develop approximate optimal control expressions to handle uncertainties. Uncertainties are calculated with an online neural network with guaranteed convergence. Longitudinal dynamics of an aircraft is used to illustrate the working of the developed algorithms.