Performance Analysis of Auction-Based Distributed Shop-Floor Control Schemes from the Perspective of the Communication System

A growing level of interest in academia and industry centers on the paradigm of distributed shop-floor control in which task and resource allocation in the manufacturing system is accomplished in a distributed manner through message passing and auction-based decision making among autonomous entities. Due to the prominent role played by the communication system in this paradigm, it is important to consider the requirements and performance characteristics of the communication system during the design and evaluation of distributed shop-floor control schemes. In this paper, we propose a two-phase methodology for analyzing auction-based shop-floor control schemes from the perspective of the communication system. In the first phase, the control scheme is modeled as a closed queueing network and performance measures related to the auctioning process and the communication system are obtained rapidly using asymptotic bounding analysis and mean value analysis. Control schemes identified as attractive in the first phase are then evaluated in greater detail during the second phase, using a discrete event simulation model. We illustrate this methodology using two-class and four-class control schemes and discuss insights learned about the impact of various control-scheme-related factors on the performance of the auctioning process and the communication system.     

An intelligent control method based on fuzzy logic for a robotic testing system for the human spine

In previous biomechanical studies of the human spine, we implemented a hybrid controller to investigate load-displacement characteristics. We found that measurement errors in both position and force caused the controller to be less accurate than predicted. As an alternative to hybrid control, a fuzzy logic controller (FLC) has been developed and implemented in a robotic testing system for the human spine. An FLC is a real-time expert system that can emulate part of a human operator's knowledge by using a set of action rules. The FLC provides simple but robust solutions that cover a wide range of system parameters and can cope with significant disturbances. It can be viewed as a heuristic and modular way of defining a nonlinear, table-based control system. In this study, an FLC is developed which uses the force difference and the change in force difference as the input parameters, and the displacement as the output parameter. A rule-table based on these parameters is designed for the controller Experiments on a physical model composed of springs demonstrate the improved performance of the proposed method.     

Control of neural synchrony using channelrhodopsin-2: a computational study

In this paper, we present an optical stimulation based approach to induce 1:1 in-phase synchrony in a network of coupled interneurons wherein each interneuron expresses the light sensitive protein channelrhodopsin-2 (ChR2). We begin with a transition rate model for the channel kinetics of ChR2 in response to light stimulation. We then define "functional optical time response curve (fOTRC)" as a measure of the response of a periodically firing interneuron (transfected with ChR2 ion channel) to a periodic light pulse stimulation. We specifically consider the case of unidirectionally coupled (UCI) network and propose an open loop control architecture that uses light as an actuation signal to induce 1:1 in-phase synchrony in the UCI network. Using general properties of the spike time response curves (STRCs) for Type-1 neuron model (Ermentrout, Neural Comput 8:979-1001, 1996) and fOTRC, we estimate the (open loop) optimal actuation signal parameters required to induce 1:1 in-phase synchrony. We then propose a closed loop controller architecture and a controller algorithm to robustly sustain stable 1:1 in-phase synchrony in the presence of unknown deviations in the network parameters. Finally, we test the performance of this closed-loop controller in a network of mutually coupled (MCI) interneurons.     

MODEM: a multi-agent hierarchical structure to model the human motor control system

In this study, based on behavioral and neurophysiological facts, a new hierarchical multi-agent architecture is proposed to model the human motor control system. Performance of the proposed structure is investigated by simulating the control of sit to stand movement. To develop the model, concepts of mixture of experts, modular structure, and some aspects of equilibrium point hypothesis were brought together. We have called this architecture MODularized Experts Model (MODEM). Human motor system is modeled at the joint torque level and the role of the muscles has been embedded in the function of the joint compliance characteristics. The input to the motor system, i.e., the central command, is the reciprocal command. At the lower level, there are several experts to generate the central command to control the task according to the details of the movement. The number of experts depends on the task to be performed. At the higher level, a “gate selector” block selects the suitable subordinate expert considering the context of the task. Each expert consists of a main controller and a predictor as well as several auxiliary modules. The main controller of an expert learns to control the performance of a given task by generating appropriate central commands under given conditions and/or constraints. The auxiliary modules of this expert learn to scrutinize the generated central command by the main controller. Auxiliary modules increase their intervention to correct the central command if the movement error is increased due to an external disturbance. Each auxiliary module acts autonomously and can be interpreted as an agent. Each agent is responsible for one joint and, therefore, the number of the agents of each expert is equal to the number of joints. Our results indicate that this architecture is robust against external disturbances, signal-dependent noise in sensory information, and changes in the environment. We also discuss the neurophysiological and behavioral basis of the proposed model (MODEM).     

Design of a neurofuzzy controller with simplified architecture

This work presents the design of a neurofuzzy controller with simplified architecture that minimizes the processing time used in several stages associated with systems and processes modelling. The basic procedures of fuzzification and defuzzification are very simplified, whereas the inference procedures are computed in a direct way. The simplified architecture has allowed a fast and easy configuration of the neurofuzzy controller, as consequence, the control rules that define the control actions are obtained automatically. To validate the proposed approach, this neurofuzzy system is used in an industrial application for fluid flow control.     

A Microfluidic Device for Preparing Next Generation DNA Sequencing Libraries and for Automating Other Laboratory Protocols That Require One or More Column Chromatography Steps

Library preparation for next-generation DNA sequencing (NGS) remains a key bottleneck in the sequencing process which can be relieved through improved automation and miniaturization. We describe a microfluidic device for automating laboratory protocols that require one or more column chromatography steps and demonstrate its utility for preparing Next Generation sequencing libraries for the Illumina and Ion Torrent platforms. Sixteen different libraries can be generated simultaneously with significantly reduced reagent cost and hands-on time compared to manual library preparation. Using an appropriate column matrix and buffers, size selection can be performed on-chip following end-repair, dA tailing, and linker ligation, so that the libraries eluted from the chip are ready for sequencing. The core architecture of the device ensures uniform, reproducible column packing without user supervision and accommodates multiple routine protocol steps in any sequence, such as reagent mixing and incubation; column packing, loading, washing, elution, and regeneration; capture of eluted material for use as a substrate in a later step of the protocol; and removal of one column matrix so that two or more column matrices with different functional properties can be used in the same protocol. The microfluidic device is mounted on a plastic carrier so that reagents and products can be aliquoted and recovered using standard pipettors and liquid handling robots. The carrier-mounted device is operated using a benchtop controller that seals and operates the device with programmable temperature control, eliminating any requirement for the user to manually attach tubing or connectors. In addition to NGS library preparation, the device and controller are suitable for automating other time-consuming and error-prone laboratory protocols requiring column chromatography steps, such as chromatin immunoprecipitation.     

Stance phase control of above-knee prostheses: knee control versus SACH foot design

The mobility of above-knee amputees (A/K) is limited, in part, due to the performance of A/K prostheses during the stance phase. Currently stance phase control of most conventional A/K prostheses can only be achieved through leg alignment and choice of the SACH (Solid Ankle Cushioned Heel) foot. This paper examines the role of the knee controller in relation to a SACH foot during the stance phase of level walking. The three-dimensional gait mechanics were measured under two stance phase conditions. In the first set of trials, the amputee used a prosthesis with a conventional knee controller that allowed the amputee to maintain the knee joint in full extension during the stance phase. In the second set of trials, the prosthetic knee, during stance, echoed the modified kinematics of the amputee's sound (intact) knee that had been recorded during the previous sound stance phase. Analysis and interpretation of the data indicate the following: (1) SACH foot design can strongly influence the walking mechanics independent of the knee controller; (2) knee controller design and SACH foot design are mutually interdependent; and (3) normal kinematics imposed on the prosthetic knee does not necessarily produce normal hip kinematics (e.g. reduce the abnormal rise in the prosthetic side hip trajectory). Future research is necessary to explore and exploit the interdependency of prosthetic knee control and foot design.     

Bio-inspired adaptive feedback error learning architecture for motor control

This study proposes an adaptive control architecture based on an accurate regression method called Locally Weighted Projection Regression (LWPR) and on a bio-inspired module, such as a cerebellar-like engine. This hybrid architecture takes full advantage of the machine learning module (LWPR kernel) to abstract an optimized representation of the sensorimotor space while the cerebellar component integrates this to generate corrective terms in the framework of a control task. Furthermore, we illustrate how the use of a simple adaptive error feedback term allows to use the proposed architecture even in the absence of an accurate analytic reference model. The presented approach achieves an accurate control with low gain corrective terms (for compliant control schemes). We evaluate the contribution of the different components of the proposed scheme comparing the obtained performance with alternative approaches. Then, we show that the presented architecture can be used for accurate manipulation of different objects when their physical properties are not directly known by the controller. We evaluate how the scheme scales for simulated plants of high Degrees of Freedom (7-DOFs).     

Selection of microbial mutants tolerant to extreme environmental stress using continuous culture-control design

The design of controllers for a continuous selection technique (BOICS; Brown and Oliver, 1982) is considered. This technique is used to obtain microbial mutants that are tolerant to extreme environmental stress. Applications of BOICS have been hampered by the problem of controller design. In this paper, a modified implementation of BOICS is considered which has a number of practical advantages. A model-based approach to controller design is taken. The case in which the stress is due to an inhibitory substance in the growth environment is considered. The analysis is intended to be applicable to any reasonable combination of organism and inhibitor. Conventional linear and time-invariant controllers are considered. Guidelines for the selection of controller parameters' values are suggested. The application of these guidelines requires that certain process parameters' values be identified. Methods by which these parameters' values can be identified are suggested. Simulation results indicate that the resulting controllers perform satisfactorily. This is confirmed by experimental data from a model selection experiment. A recipe for the design of controllers is a necessary part of a protocol for BOICS. It is hoped that the solution to the controller design problem that is offered in this paper will encourage further applications for the technique.     

Fuzzy control of ethanol concentration its application to maximum glutathione production in yeast fed-batch culture

A fuzzy logic controller (FLC) for the control of ethanol concentration was developed and utilized to realize the maximum production of glutathione (GSH) in yeast fedbatch culture. A conventional fuzzy controller, which uses the control error and its rate of change in the premise part of the linguistic rules, worked well when the initial error of ethanol concentration was small. However, when the initial error was large, controller overreaction resulted in an overshoot.An improved fuzzy controller was obtained to avoid controller overreaction by diagnostic determination of "glucose emergency states" (i.e., glucose accumulation or deficiency), and then appropriate emergency control action was obtained by the use of weight coefficients and modification of linguistic rules to decrease the overreaction of the controller when the fermentation was in the emergency state. The improved fuzzy controller was able to control a constant ethanol concentration under conditions of large initial error.The improved fuzzy control system was used in the GSH production phase of the optimal operation to indirectly control the specific growth rate mu to its critical value mu(c). In the GSH production phase of the fed-batch culture, the optimal solution was to control mu to mu(c) in order to maintain a maximum specific GSH production rate. The value of mu(c) also coincided with the critical specific growth rate at which no ethanol formation occurs. Therefore, the control of mu to mu(c) could be done indirectly by maintaining a constant ethanol concentration, that is, zero net ethanol formation, through proper manipulation of the glucose feed rate. Maximum production of GSH was realized using the developed FLC; maximum production was a consequence of the substrate feeding strategy and cysteine addition, and the FLC was a simple way to realize the strategy. (c) 1993 John Wiley & Sons, Inc.     

A Model of Postural Control in Quiet Standing: Robust Compensation of Delay-Induced Instability Using Intermittent Activation of Feedback Control

The main purpose of this study is to compare two different feedback controllers for the stabilization of quiet standing in humans, taking into account that the intrinsic ankle stiffness is insufficient and that there is a large delay inducing instability in the feedback loop: 1) a standard linear, continuous-time PD controller and 2) an intermittent PD controller characterized by a switching function defined in the phase plane, with or without a dead zone around the nominal equilibrium state. The stability analysis of the first controller is carried out by using the standard tools of linear control systems, whereas the analysis of the intermittent controllers is based on the use of Poincaré maps defined in the phase plane. When the PD-control is off, the dynamics of the system is characterized by a saddle-like equilibrium, with a stable and an unstable manifold. The switching function of the intermittent controller is implemented in such a way that PD-control is ‘off’ when the state vector is near the stable manifold of the saddle and is ‘on’ otherwise. A theoretical analysis and a related simulation study show that the intermittent control model is much more robust than the standard model because the size of the region in the parameter space of the feedback control gains (P vs. D) that characterizes stable behavior is much larger in the latter case than in the former one. Moreover, the intermittent controller can use feedback parameters that are much smaller than the standard model. Typical sway patterns generated by the intermittent controller are the result of an alternation between slow motion along the stable manifold of the saddle, when the PD-control is off, and spiral motion away from the upright equilibrium determined by the activation of the PD-control with low feedback gains. Remarkably, overall dynamic stability can be achieved by combining in a smart way two unstable regimes: a saddle and an unstable spiral. The intermittent controller exploits the stabilizing effect of one part of the saddle, letting the system evolve by alone when it slides on or near the stable manifold; when the state vector enters the strongly unstable part of the saddle it switches on a mild feedback which is not supposed to impose a strict stable regime but rather to mitigate the impending fall. The presence of a dead zone in the intermittent controller does not alter the stability properties but improves the similarity with biological sway patterns. The two types of controllers are also compared in the frequency domain by considering the power spectral density (PSD) of the sway sequences generated by the models with additive noise. Different from the standard continuous model, whose PSD function is similar to an over-damped second order system without a resonance, the intermittent control model is capable to exhibit the two power law scaling regimes that are typical of physiological sway movements in humans.     

Rise, fall and resurrection of chromosome territories: a historical perspective. Part II. Fall and resurrection of chromosome territories during the 1950s to 1980s. Part III. Chromosome territories and the functional nuclear architecture: experiments and models from the 1990s to the present

Part II of this historical review on the progress of nuclear architecture studies points out why the original hypothesis of chromosome territories from Carl Rabl and Theodor Boveri (described in part I) was abandoned during the 1950s and finally proven by compelling evidence forwarded by laser-uv-microbeam studies and in situ hybridization experiments. Part II also includes a section on the development of advanced light microscopic techniques breaking the classical Abbe limit written for readers with little knowledge about the present state of the theory of light microscopic resolution. These developments have made it possible to perform 3D distance measurements between genes or other specifically stained, nuclear structures with high precision at the nanometer scale. Moreover, it has become possible to record full images from fluorescent structures and perform quantitative measurements of their shapes and volumes at a level of resolution that until recently could only be achieved by electron microscopy. In part III we review the development of experiments and models of nuclear architecture since the 1990s. Emphasis is laid on the still strongly conflicting views about the basic principles of higher order chromatin organization. A concluding section explains what needs to be done to resolve these conflicts and to come closer to the final goal of all studies of the nuclear architecture, namely to understand the implications of nuclear architecture for nuclear functions.     

Disentangling the intertwined genetic bases of root and shoot growth in Arabidopsis

Root growth and architecture are major components of plant nutrient and water use efficiencies and these traits are the matter of extensive genetic analysis in several crop species. Because root growth relies on exported assimilate from the shoot, and changes in assimilate supply are known to alter root architecture, we hypothesized (i) that the genetic bases of root growth could be intertwined with the genetic bases of shoot growth and (ii) that the link could be either positive, with alleles favouring shoot growth also favouring root growth, or negative, because of competition for assimilates. We tested these hypotheses using a quantitative genetics approach in the model species Arabidopsis thaliana and the Bay-0 × Shahdara recombinant inbred lines population. In accordance with our hypothesis, root and shoot growth traits were strongly correlated and most root growth quantitative trait loci (QTLs) colocalized with shoot growth QTLs with positive alleles originating from either the same or the opposite parent. In order to identify regions that could be responsible for root growth independently of the shoot, we generated new variables either based on root to shoot ratios, residuals of root to shoot correlations or coordinates of principal component analysis. These variables showed high heritability allowing genetic analysis. They essentially all yielded similar results pointing towards two regions involved in the root--shoot balance. Using Heterogeneous Inbred Families (a kind of near-isogenic lines), we validated part of the QTLs present in these two regions for different traits. Our study thus highlights the difficulty of disentangling intertwined genetic bases of root and shoot growth and shows that this difficulty can be overcome by using simple statistical tools.     

Winching up heavy loads with a compliant arm: a new local joint controller

A closed kinematic chain, like an arm that operates a crank, has a constrained movement space. A meaningful movement of the chain’s endpoint is only possible along the free movement directions which are given implicitly by the contour of the object that confines the movement of the chain. Many technical solutions for such a movement task, in particular those used in robotics, use central controllers and force–torque sensors in the arm’s wrist or a leg’s ankle to construct a coordinate system (task frame formalism) at the local point of contact the axes of which coincide with the free and constrained movement directions. Motivated by examples from biology, we introduce a new control system that solves a constrained movement task. The control system is inspired by the control architecture that is found in stick insects like Carausius morosus. It consists of decentral joint controllers that work on elastic joints (compliant manipulator). The decentral controllers are based on local positive velocity feedback (LPVF). It has been shown earlier that LPVF enables contour following of a limb in a compliant motion task without a central controller. In this paper we extend LPVF in such a way that it is even able to follow a contour if a considerable counter force drags the limb away along the contour in a direction opposite to the desired. The controller extension is based on the measurement of the local mechanical power generated in the elastic joint and is called power-controlled relaxation LPVF. The new control approach has the following advantages. First, it still uses local joint controllers without knowledge of the kinematics. Second, it does not need a force or torque measurement at the end of the limb. In this paper we test power-controlled relaxation LPVF on a crank turning task in which a weight has to be winched up by a two-joint compliant manipulator.     

Evolutionary potential of hidden genetic variation

The ability of a population to respond to natural or artificial selection pressures is determined by the genetic architecture of the selected trait. It is now widely acknowledged that a substantial part of genetic variability can be buffered or released as the result of complex genetic interactions. However, the impact of hidden genetic diversity on phenotypic evolution is still not clear. Here, we argue that a common term to describe the impact of hidden genetic variation on phenotypic change is needed and will help to provide new insights into the contribution of different components of genetic architectures to the evolvability of a character. We introduce the 'genetic charge' concept, to describe how the architecture of a trait can be 'charged' with potential for evolutionary change that can later be 'discharged' in response to selection.     

Predictive feedback control and Fitts–law

Fitts’ law is a well established empirical formula, known for encapsulating the “speed-accuracy trade-off”. For discrete, manual movements from a starting location to a target, Fitts’ law relates movement duration to the distance moved and target size. The widespread empirical success of the formula is suggestive of underlying principles of human movement control. There have been previous attempts to relate Fitts’ law to engineering-type control hypotheses and it has been shown that the law is exactly consistent with the closed-loop step-response of a time-delayed, first-order system. Assuming only the operation of closed-loop feedback, either continuous or intermittent, this paper asks whether such feedback should be predictive or not predictive to be consistent with Fitts law. Since Fitts’ law is equivalent to a time delay separated from a first-order system, known control theory implies that the controller must be predictive. A predictive controller moves the time-delay outside the feedback loop such that the closed-loop response can be separated into a time delay and rational function whereas a non- predictive controller retains a state delay within feedback loop which is not consistent with Fitts’ law. Using sufficient parameters, a high-order non-predictive controller could approximately reproduce Fitts’ law. However, such high-order, “non-parametric” controllers are essentially empirical in nature, without physical meaning, and therefore are conceptually inferior to the predictive controller. It is a new insight that using closed-loop feedback, prediction is required to physically explain Fitts’ law. The implication is that prediction is an inherent part of the “speed-accuracy trade-off”.     

A Novel Robot System Integrating Biological and Mechanical Intelligence Based on Dissociated Neural Network-Controlled Closed-Loop Environment

We propose the architecture of a novel robot system merging biological and artificial intelligence based on a neural controller connected to an external agent. We initially built a framework that connected the dissociated neural network to a mobile robot system to implement a realistic vehicle. The mobile robot system characterized by a camera and two-wheeled robot was designed to execute the target-searching task. We modified a software architecture and developed a home-made stimulation generator to build a bi-directional connection between the biological and the artificial components via simple binomial coding/decoding schemes. In this paper, we utilized a specific hierarchical dissociated neural network for the first time as the neural controller. Based on our work, neural cultures were successfully employed to control an artificial agent resulting in high performance. Surprisingly, under the tetanus stimulus training, the robot performed better and better with the increasement of training cycle because of the short-term plasticity of neural network (a kind of reinforced learning). Comparing to the work previously reported, we adopted an effective experimental proposal (i.e. increasing the training cycle) to make sure of the occurrence of the short-term plasticity, and preliminarily demonstrated that the improvement of the robot’s performance could be caused independently by the plasticity development of dissociated neural network. This new framework may provide some possible solutions for the learning abilities of intelligent robots by the engineering application of the plasticity processing of neural networks, also for the development of theoretical inspiration for the next generation neuro-prostheses on the basis of the bi-directional exchange of information within the hierarchical neural networks.     

Convergence analysis of cascade error projection--an efficient learning algorithm for hardware implementation

In this paper, we present a mathematical foundation, including a convergence analysis, for cascading architecture neural network. Our analysis also shows that the convergence of the cascade architecture neural network is assured because it satisfies Liapunov criteria, in an added hidden unit domain rather than in the time domain. From this analysis, a mathematical foundation for the cascade correlation learning algorithm can be found. Furthermore, it becomes apparent that the cascade correlation scheme is a special case from mathematical analysis in which an efficient hardware learning algorithm called Cascade Error Projection(CEP) is proposed. The CEP provides efficient learning in hardware and it is faster to train, because part of the weights are deterministically obtained, and the learning of the remaining weights from the inputs to the hidden unit is performed as a single-layer perceptron learning with previously determined weights kept frozen. In addition, one can start out with zero weight values (rather than random finite weight values) when the learning of each layer is commenced. Further, unlike cascade correlation algorithm (where a pool of candidate hidden units is added), only a single hidden unit is added at a time. Therefore, the simplicity in hardware implementation is also achieved. Finally, 5- to 8-bit parity and chaotic time series prediction problems are investigated; the simulation results demonstrate that 4-bit or more weight quantization is sufficient for learning neural network using CEP. In addition, it is demonstrated that this technique is able to compensate for less bit weight resolution by incorporating additional hidden units. However, generation result may suffer somewhat with lower bit weight quantization.