334 research outputs found

    Time delay Duffing’s systems: chaos and chatter control

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    Prescription of rhythmic patterns for legged locomotion

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    As the engine behind many life phenomena, motor information generated by the central nervous system (CNS) plays a critical role in the activities of all animals. In this work, a novel, macroscopic and model-independent approach is presented for creating different patterns of coupled neural oscillations observed in biological central pattern generators (CPG) during the control of legged locomotion. Based on a simple distributed state machine, which consists of two nodes sharing pre-defined number of resources, the concept of oscillatory building blocks (OBBs) is summarised for the production of elaborated rhythmic patterns. Various types of OBBs can be designed to construct a motion joint of one degree-of-freedom (DOF) with adjustable oscillatory frequencies and duty cycles. An OBBs network can thus be potentially built to generate a full range of locomotion patterns of a legged animal with controlled transitions between different rhythmic patterns. It is shown that gait pattern transition can be achieved by simply changing a single parameter of an OBB module. Essentially this simple mechanism allows for the consolidation of a methodology for the construction of artificial CPG architectures behaving as an asymmetric Hopfield neural network. Moreover, the proposed CPG model introduced here is amenable to analogue and/or digital circuit integration

    Simulation of Cutting Process – Modeling and Applications

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    A novel model reference adaptive control approach investigation for power electronic converter applications

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    This paper demonstrates the viability and effectiveness of a novel adaptive control approach applied to power electronic converters. A methodology based on the formulation of a Lyapunov-based approach is showcased to represent the operation of a new adaptive controller for the regulation of two power converter topologies (Buck and Boost). The models of the Buck and Boost converter topologies include the parasitic parameters that represent the non-ideal components. The basic idea of the control approach is to demonstrate adaptive stabilization for the proposed non-linear system. The most important design specification to stabilize the system is to track the reference trajectory in such a way that the error on the output variable converges asymptotically to zero. This adaptation mechanism is explicitly designed so that the asymptotic stability of the equilibrium condition is guaranteed according to the Lyapunov theorem and sensitivity theory. The details of the design algorithm are explained in the paper. The proposed control approach has been compared to other Lyapunov-based control techniques proposed in literature for the same non-ideal converters. The results show that the proposed controller provides better level of robustness and performance than the other wellestablished Lyapunov based controllers. To verify the effectiveness of the controller in real time, a test bench has been set up with prototypes of both converters and the controller has been implemented using the Arduino microcontroller and the control system driven through the Matlab/Simulink platform

    ARTIFICIAL INTELLIGENCE APPLIED TO THE STUDY OF CONSCIOUS PERCEPTIVE STATES

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    My PhD research consists of the processing of signals from a 14-electrode EEG system, connected to immersive glasses that allow for a realistic visual experience and for the investigation of the brain network in order to identify signal features corresponding to different perceptive and cognitive stimuli. The aim of the research is to implement a procedure that identifies correspondences among EEG signals and chaotic attractors. The chaotic attractors can be defined as a trajectory of a dynamical system, contained in a defined volume of phase space. A dynamical system can have chaotic behavior, i.e. an organized (but not periodic) behavior sensitive to the initial conditions. EEG signals can be considered dynamical systems. In this work a custom Artificial Neural Network (ITSOM) processes individual signals or many signals simultaneously. The sequence of the ITSOM winning nodes tends to repeat itself creating a time series of chaotic attractors. The ITSOM attributes similar codes to attractors emerging from similar brain states, perceptions and emotions. These attractors are isomorphic to the attractors in which the corresponding dynamical system (the signal time series) is evolving and univocally characterize the input element that produces them. If the attractors are chaotic, this means that the signals are individually self-organized or, by examining more signals together, there is a form of coherence among signals. The ITSOM network memorizes the time series of the winning nodes. The cumulative scores for each input are normalized following the z standardized variable distribution. Attractors are labeled with a binary code that univocally identifies them, and the flexibility of the Artificial Neural Network allows attributing the same codes to similar dynamical events. During the experiment, the subject is looking at the screen while different shades of colors, yellow, red and blue are displayed. Each stimulation lasts five seconds, between stimuli there is a black screen, used to reset the previous color stimuli. The collected results show, as forecast, many correspondences among binary codes coming from similar stimuli. The thesis provides a detailed description of these results

    Synchronization and prediction of chaotic dynamics on networks of optoelectronic oscillators

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    The subject of this thesis is the exploration of chaotic synchronization for novel applications including time-series prediction and sensing. We begin by characterizing the nonlinear dynamics of an optoelectronic time-delayed feedback loop. We show that synchronization of an accurate numerical model to experimental measurements provides a way to assimilate data and forecast the future of deterministic chaotic behavior. Next, we implement an adaptive control method that maintains isochronal synchrony for a network of coupled feedback loops when the interaction strengths are unknown and time-varying. Control signals are used as real-time estimates of the variations present within the coupling paths. We analyze the stability of synchronous solutions for arbitrary coupling topologies via a modified master stability function that incorporates the adaptation response dynamics. Finally, we show that the master stability function, which is derived from a set of linearized equations, can also be experimentally measured using a two-node network, and it can be applied to predict the convergence behavior of large networks

    On a Planar Dynamical System Arising in the Network Control Theory

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    We study the structure of attractors in the two-dimensional dynamical system  that appears in the network control theory. We provide description of the attracting set and follow changes this set suffers under the changes of positive parameters ” and Θ.

    Self-Organized Intelligent Robust Control Based on Quantum Fuzzy Inference

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