Learning optimal adaptation strategies in unpredictable motor tasks.

Picking up an empty milk carton that we believe to be full is a familiar example of adaptive control, because the adaptation process of estimating the carton's weight must proceed simultaneously with the control process of moving the carton to a desired location. Here we show that the motor system initially generates highly variable behavior in such unpredictable tasks but eventually converges to stereotyped patterns of adaptive responses predicted by a simple optimality principle. These results suggest that adaptation can become specifically tuned to identify task-specific parameters in an optimal manner.

A framework for distributed intelligent automation systems developments

This paper proposes a pragmatic framework that has been developed for classifying and analyzing developments in distributed automation and information systems - especially those that have been labeled intelligent systems for different reasons. The framework dissects the different stages in the standard feedback process and assesses distribution in terms of the level of granularity of the organization that is being considered. The framework has been found to be useful in comparing and assessing different distributed industrial control paradigms and also for examining common features of different development projects - especially those that might be sourced from different sectors or domains. © 2012 IFAC.

Generalization in Adaptation to Stable and Unstable Dynamics

Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization. © 2012 Kadiallah et al.

A framework for distributed intelligent automation systems developments

This chapter proposes a simple and pragmatic framework that has been developed for classifying and analyzing developments in distributed automation and information systems - especially those that have been labelled intelligent systems for different reasons. The framework dissects the different stages in the standard feedback process and assesses distribution in terms of the level of granularity of the organization that is being considered. The framework has been found to be useful in comparing and assessing different distributed industrial control paradigms and also for examining common features of different development projects - especially those that might be sourced from different sectors or domains. © Springer-Verlag Berlin Heidelberg 2013.

Optimizing the energy output of vertical axis wind turbines for fluctuating wind conditions

The control of a wind turbine to the mean wind speed in a gusty wind results in very poor performance. Fluctuations in wind speed with time constants shorter than the response time of a wind turbine results in operation away from optimum design conditions. The effectiveness of a turbine operating in a gusty wind is shown though the use of an unsteady performance coefficient, C e. This performance coefficient is similar in form to a power coefficient. However in order to accommodate unsteady effects, Ce is defined as a ratio of energy extracted to the total wind energy available over a set time period. The turbine's response to real wind data is modelled, in the first instance, by assuming a constant rotational speed operation. It is shown that a significant increase in energy production can be realized by demanding a Tip Speed Ratio above the steady state optimum. The constant speed model is then further extended to incorporate inertial and controller effects. Parameters dictating how well a turbine can track a demand in Tip Speed Ratio have been identified and combined, to form a non-dimensional turbine response parameter. This parameter characterizes a turbine's ability to track a demand in Tip Speed Ratio dependent on an effective gust frequency. A significant increase in energy output of 42% and 245% is illustrated through the application of this over-speed control. This is for the constant rotational speed and Tip Speed Ratio feedback models respectively. The affect of airfoil choice on energy extraction within a gusty wind has been considered. The adaptive control logic developed enables the application of airfoils demonstrating high maximum L/D values but sharp stalling characteristics to be successfully used in a VAWT design.

Reflex contributions to the directional tuning of arm stiffness

It has been shown that during arm movement, humans selectively change the endpoint stiffness of their arm to compensate for the instability in an unstable environment. When the direction of the instability is rotated with respect to the direction of movement, it was found that humans modify the antisymmetric component of their endpoint stiffness. The antisymmetric component of stiffness arises due to reflex responses suggesting that the subjects may have tuned their reflex responses as part of the feedforward adaptive control. The goal of this study was to examine whether the CNS modulates the gain of the reflex response for selective tuning of endpoint impedance. Subjects performed reaching movements in three unstable force fields produced by a robotic manipulandum, each field differing only in the rotational component. After subjects had learned to compensate for the field, allowing them to make unperturbed movements to the target, the endpoint stiffness of the arm was estimated in the middle of the movements. At the same time electromyographic activity (EMG) of six arm muscles was recorded. Analysis of the EMG revealed differences across force fields in the reflex gain of these muscles consistent with stiffness changes. This study suggests that the CNS modulates the reflex gain as part of the adaptive feedforward command in which the endpoint impedance is selectively tuned to overcome environmental instability. © 2008 Springer-Verlag Berlin Heidelberg.

Computational fluid-structure interaction of DGB parachutes in compressible fluid flow

This paper describes large-scale simulations of compressible flows over a supersonic disk-gap-band parachute system. An adaptive mesh refinement method is used to resolve the coupled fluid-structure model. The fluid model employs large-eddy simulation to describe the turbulent wakes appearing upstream and downstream of the parachute canopy and the structural model employed a thin-shell finite element solver that allows large canopy deformations by using subdivision finite elements. The fluid-structure interaction is described by a variant of the Ghost-Fluid method. The simulation was carried out at Mach number 1.96 where strong nonlinear coupling between the system of bow shocks, turbulent wake and canopy is observed. It was found that the canopy oscillations were characterized by a breathing type motion due to the strong interaction of the turbulent wake and bow shock upstream of the flexible canopy. Copyright © 2010 by ASME.

Adaptive agent-based manufacturing control and its application to flow shop routing control

The manufacturing industry is currently facing unprecedented challenges from changes and disturbances. The sources of these changes and disturbances are of different scope and magnitude. They can be of a commercial nature, or linked to fast product development and design, or purely operational (e.g. rush order, machine breakdown, material shortage etc.). In order to meet these requirements it is increasingly important that a production operation be flexible and is able to adapt to new and more suitable ways of operating. This paper focuses on a new strategy for enabling manufacturing control systems to adapt to changing conditions both in terms of product variation and production system upgrades. The approach proposed is based on two key concepts: (1) An autonomous and distributed approach to manufacturing control based on multi-agent methods in which so called operational agents represent the key physical and logical elements in the production environment to be controlled - for example, products and machines and the control strategies that drive them and (2) An adaptation mechanism based around the evolutionary concept of replicator dynamics which updates the behaviour of newly formed operational agents based on historical performance records in order to be better suited to the production environment. An application of this approach for route selection of similar products in manufacturing flow shops is developed and is illustrated in this paper using an example based on the control of an automobile paint shop.