Search results

Dispatching rule‐based scheduling is a kind of dynamic scheduling commonly used in real world applications. Because of the lack of scheduling objective, it cannot optimize the specific performances at which shop managers aim in the current production period. To overcome the limitations of the dispatching rule‐based scheduling, an iterative learning scheduling scheme is proposed in this paper. A scheduling objective function, which reflects the performance criteria in which the shop managers are most interested, is established and used to guide the optimization of the crucial performances. According to the value of the scheduling objective obtained from the last simulation period, the parameters are adjusted so as to decrease the objective during the next simulation period. Experimental results show that the iterative learning scheduling overcomes the limitations of the dispatching rule‐based scheduling and achieves higher performances.

This paper utilizes the improved particle swarm optimization (IPSO) with bounded constraints technique on velocity and positioning for adjusting the gains of a proportional-integral-derivative (PID) and iterative learning control (ILC) controllers. The purpose of this paper is to achieve precision motion through bettering control by this technique.

Actual platform positioning must avoid the occurrence of a large control action signal, undesirable overshooting, and preventing out of the maximum position limit. Several in-house experiments observation, the PSO mechanism is sometimes out of the optimal solution in updating velocity and updating position of particles, the system may become unstable in real-time applications. The proposed IPSO with new bounded constraints technique shows a great ability to stabilize nonminimum phase and heavily oscillatory systems based on new bounded constraints on velocity and positioning in PSO algorithm is evaluated on one axis of linear synchronous motor with a PC-based real-time ILC.

Simulations and experiment results show that the proposed controller can reduce the error significantly after two learning iterations. The developed method using bounded constraints technique provides valuable programming tools to practicing engineers.

The proposed IPSO-ILC-PID controller overcomes the shortcomings of conventional ILC-PID controller with fixed gains. Simulation and experimental results show that the proposed IPSO-ILC-PID algorithm exhibits great speed convergence and robustness. Experimental results confirm that the proposed IPSO-ILC-PID algorithm is effective and achieves better control in real-time precision positioning.

The purpose of this paper is to add precisions to a method, to demonstrate the convergence, to explain time and memory space complexities and new simulated results on a non‐linear partial derivative equation system governing corona‐electrostatic electric field for granular mixture separation.

The method converts the non‐linear partial derivative system into an iterative system of linear equations. Using the well‐known finite difference approximation, a numerical solution is computed very quickly.

The paper gives the truncated error and the approximation error to conclude to the convergence.

The paper shows the fast numerical solution leads to confidence in the numerical approximations for the comprehension of the phenomenon. Extends the corona‐electrostatic electric field for granular mixture separation to new geometries easily.

The purpose of this paper is to study the industrial separation of granular mixture by approximation of combined corona‐electrostatic electric field with feeder and inductive electrostatic electrode.

The original numerical method developed in Caron's Kybernetes paper is used and extended to an industrial construction. The method gives a mathematical model of an industrial process and converts the non‐linear partial derivative system into an iterative system of linear equations. Using the well‐known finite difference approximation, a numerical solution is computed very quickly.

In order to really obtain a computer‐aided numerical solution, it is necessary to define a really manageable approximation method. The new simulation results are detailed and show better results for the steady state in time processing and finally, to imagine improvements of the industrial processes.

The paper shows the fast numerical solution, which leads to confidence in the numerical approximations to imagine improvements of the industrial processes easily.

The purpose of this paper is to discuss a Multi-perspective approach to knowledge production in terms of a set of cybernetic concepts relevant to the approach; to describe a software system that computationally embodies the approach; and to articulate a research project that pragmatically employs the approach.

A definition is provided. The paper uses a survey methodology, exploring relevant cybernetic and contemporary technological concepts. An operational software mechanism (The Insight Engine) is discussed that enables the bridging of transdisciplinary concepts by a user in the service of accretive research –Recombinant Informatics.

Many cybernetic concepts are relevant to contemporary research into cognition and Neosentience research. More study needs to be undertaken related to historical BCL projects in terms of articulating relevance to contemporary research.

Future research seeks to extend the computational functionality of “The insight engine”, as well as uncover relevant BCL/cybernetic materials.

The software is unique in the field and already there is interest in its use by differing research communities including the Duke Institute for Brain Sciences, and at Stanford, research under Ian Hodder.

The Insight Engine has potential to be used as a multi-perspective tool for many different fields enabling different forms of distributed, transdisciplinary team-based research.

This text is valuable to researchers interested in new forms of interface, augmentation of thought and learning via computational approaches; and the development of bridges between novel research areas, including contemporary, historical BCL, and other cybernetic inquiry.

The purpose of this paper is to present a theory that applies Miller et al.’s (1960) Test-Operate-Test-Exit (TOTE) concept to the psychophysiology involved in electroencephalographic (EEG) biofeedback (BFB).

Six components are presented, namely, the teleological brain, attractors as the “test” in TOTEs, EEG production, positive and negative feedback, synaptogenesis and designated actor, and then integrated into a theoretical structure. Comparisons with the previous conceptualizations are discussed, and finally, suggestions for practical application and needed research are offered.

Previous theories neglected significant variables and promoted unverified conceptualizations. These issues are redressed with a psychophysiological, cybernetic theory.

The pursuit of substantive research needed to verify the theory would improve the scientific foundations for EEG BFB.

This theory shifts the designated actor in BFB to the participant’s brain, away from the BFB provider. EEG BFB is thus viewed as a means for neuronominalization driven by the brain’s attractor systems instead of as an intrusive intervention.

The theory proposes a much more participant-centric process than previous modes, which also promotes self-determination. The research validation needed for the theory could produce wider EEG BFB acceptance and application.

The theory is a complete departure from previous conceptualizations. It is the first instance of TOTE application to psychophysiological processes, and it is the first fully cybernetic conceptualization of EEG BFB.

Because of the multiple design elements and complicated relationship among design elements of complex products design, it is tough for designers to systematically and dynamically express and manage the complex products design process.

To solve these problems, a supernetwork model of complex products design is constructed and analyzed in this paper. First, the design elements (customer demands, design agents, product structures, design tasks and design resources) are identified and analyzed, then the sub-network of design elements are built. Based on this, a supernetwork model of complex products design is constructed with the analysis of the relationship among sub-networks. Second, some typical and physical characteristics (robustness, vulnerability, degree and betweenness) of the supernetwork were calculated to analyze the performance of supernetwork and the features of complex product design process.

The design process of a wind turbine is studied as a case to illustrate the approach in this paper. The supernetwork can provide more information about collaborative design process of wind turbine than traditional models. Moreover, it can help managers and designers to manage the collaborative design process and improve collaborative design efficiency of wind turbine.

The authors find a new method (complex network or supernetwork) to describe and analyze complex mechanical product design.

This paper aims to extend the previous contributions about data-driven control in aircraft control system from academy and practice, respectively, combining iteration and learning strategy. More specifically, after returning output signal to input part, and getting one error signal, three kinds of data are measured to design the unknown controller without any information about the unknown plant. Using the main essence of data-driven control, iterative learning idea is introduced together to yield iterative learning data-driven control strategy. To get the optimal data-driven controller, other factors are considered, for example, adaptation, optimization and learning. After reviewing the aircraft control system in detail, the numerical simulation results have demonstrated the efficiency of the proposed iterative learning data-driven control strategy.

First, considering one closed loop system corresponding to the aircraft control system, data-driven control strategy is used to design the unknown controller without any message about the unknown plant. Second, iterative learning idea is combined with data-driven control to yield iterative learning data-driven control strategy. The optimal data-driven controller is designed by virtue of power spectrum and mathematical optimization. Furthermore, adaptation is tried to combine them together. Third, to achieve the combination with theory and practice, our proposed iterative learning data-driven control is applied into aircraft control system, so that the considered aircraft can fly more promptly.

A novel iterative learning data-driven strategy is proposed to efficiently achieve the combination with theory and practice. First, iterative learning and data-driven control are combined with each other, being dependent of adaptation and optimization. Second, iterative learning data-driven control is proposed to design the flight controller for the aircraft system. Generally, data-driven control is more wide in our living life, so it is important to introduce other fields to improve the performance of data-driven control.

To the best of the authors’ knowledge, this new paper extends the previous contributions about data-driven control by virtue of iterative learning strategy. Specifically, iteration means that the optimal data-driven controller is solved as one recursive form, being related with one gradient descent direction. This novel iterative learning data-driven control has more advanced properties, coming from data driven and adaptive iteration. Furthermore, it is a new subject on applying data-driven control into the aircraft control system.

To study the application of three‐dimensional differential geometric (DG) guidance commands to a realistic missile defense engagement, and the application of the Newton's iterative algorithm to DG guidance problems.

The classical differential geometry theory is introduced firstly to transform all the variables in DG guidance commands from an arc length system to the time domain. Then, an algorithm for the angle‐of‐attack and the sideslip angle is developed by assuming the guidance curvature command and guidance torsion command equal to its corresponding value of current trajectory. Furthermore, Newton's iteration is utilized to develop iterative solution of the stated algorithm and the two‐dimensional DG guidance system so as to facilitate easy computation of the angle‐of‐attack and the sideslip angle, which are formulated to satisfy the DG guidance law.

DG guidance law is viable and effective in the realistic missile defense engagement, and it is shown to be a generalization of gain‐varying proportional navigation (PN) guidance law and performs better than the classical PN guidance law in the case of intercepting a maneuvering target. Moreover, Newton's iterative algorithm has sufficient accuracy for DG guidance problem.

Provides further study on DG guidance problem associated with its iterative solution.

This study proposes to develop and investigate different iterative solvers for non‐Newtonian flow equations.

Existing approaches for the time discretization of the flow equation and for an iterative solution of the discrete systems are discussed. Ideas for further development of existing preconditioners are proposed, implemented and investigated numerically.

A two‐level preconditioning, consisting of a transformation of the original system in the first step and subsequent preconditioning of the transformed system is suggested. The GMRES iterative method, which usually performs well when applied to academic problems, showed dissatisfactory performance for the type of industrial flow simulations investigated in this work. It was found that the BiCGStab method performed best in the tests presented here.

The iterative solvers considered here were investigated only for a certain class of polymer flows. More detailed studies for other non‐Newtonian flows should be carried out.

The work presented in this paper fills a gap related to the usage of efficient iterative methods for non‐Newtonian flow simulations.