14,822 research outputs found

    Dynamically Stable 3D Quadrupedal Walking with Multi-Domain Hybrid System Models and Virtual Constraint Controllers

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    Hybrid systems theory has become a powerful approach for designing feedback controllers that achieve dynamically stable bipedal locomotion, both formally and in practice. This paper presents an analytical framework 1) to address multi-domain hybrid models of quadruped robots with high degrees of freedom, and 2) to systematically design nonlinear controllers that asymptotically stabilize periodic orbits of these sophisticated models. A family of parameterized virtual constraint controllers is proposed for continuous-time domains of quadruped locomotion to regulate holonomic and nonholonomic outputs. The properties of the Poincare return map for the full-order and closed-loop hybrid system are studied to investigate the asymptotic stabilization problem of dynamic gaits. An iterative optimization algorithm involving linear and bilinear matrix inequalities is then employed to choose stabilizing virtual constraint parameters. The paper numerically evaluates the analytical results on a simulation model of an advanced 3D quadruped robot, called GR Vision 60, with 36 state variables and 12 control inputs. An optimal amble gait of the robot is designed utilizing the FROST toolkit. The power of the analytical framework is finally illustrated through designing a set of stabilizing virtual constraint controllers with 180 controller parameters.Comment: American Control Conference 201

    Parameter reduction in nonlinear state-space identification of hysteresis

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    Hysteresis is a highly nonlinear phenomenon, showing up in a wide variety of science and engineering problems. The identification of hysteretic systems from input-output data is a challenging task. Recent work on black-box polynomial nonlinear state-space modeling for hysteresis identification has provided promising results, but struggles with a large number of parameters due to the use of multivariate polynomials. This drawback is tackled in the current paper by applying a decoupling approach that results in a more parsimonious representation involving univariate polynomials. This work is carried out numerically on input-output data generated by a Bouc-Wen hysteretic model and follows up on earlier work of the authors. The current article discusses the polynomial decoupling approach and explores the selection of the number of univariate polynomials with the polynomial degree, as well as the connections with neural network modeling. We have found that the presented decoupling approach is able to reduce the number of parameters of the full nonlinear model up to about 50\%, while maintaining a comparable output error level.Comment: 24 pages, 8 figure

    Data-driven modelling of biological multi-scale processes

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    Biological processes involve a variety of spatial and temporal scales. A holistic understanding of many biological processes therefore requires multi-scale models which capture the relevant properties on all these scales. In this manuscript we review mathematical modelling approaches used to describe the individual spatial scales and how they are integrated into holistic models. We discuss the relation between spatial and temporal scales and the implication of that on multi-scale modelling. Based upon this overview over state-of-the-art modelling approaches, we formulate key challenges in mathematical and computational modelling of biological multi-scale and multi-physics processes. In particular, we considered the availability of analysis tools for multi-scale models and model-based multi-scale data integration. We provide a compact review of methods for model-based data integration and model-based hypothesis testing. Furthermore, novel approaches and recent trends are discussed, including computation time reduction using reduced order and surrogate models, which contribute to the solution of inference problems. We conclude the manuscript by providing a few ideas for the development of tailored multi-scale inference methods.Comment: This manuscript will appear in the Journal of Coupled Systems and Multiscale Dynamics (American Scientific Publishers

    Virtual Constraints and Hybrid Zero Dynamics for Realizing Underactuated Bipedal Locomotion

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    Underactuation is ubiquitous in human locomotion and should be ubiquitous in bipedal robotic locomotion as well. This chapter presents a coherent theory for the design of feedback controllers that achieve stable walking gaits in underactuated bipedal robots. Two fundamental tools are introduced, virtual constraints and hybrid zero dynamics. Virtual constraints are relations on the state variables of a mechanical model that are imposed through a time-invariant feedback controller. One of their roles is to synchronize the robot's joints to an internal gait phasing variable. A second role is to induce a low dimensional system, the zero dynamics, that captures the underactuated aspects of a robot's model, without any approximations. To enhance intuition, the relation between physical constraints and virtual constraints is first established. From here, the hybrid zero dynamics of an underactuated bipedal model is developed, and its fundamental role in the design of asymptotically stable walking motions is established. The chapter includes numerous references to robots on which the highlighted techniques have been implemented.Comment: 17 pages, 4 figures, bookchapte

    Lyapunov based reference model of tension control in a continuous strip processing line with multi-motor drive

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    The article describes design and experimental verification of a new control structure with reference model for a multi-motor drive of a continuous technological line in which the motors are mutually mechanically coupled through processed material. Its principle consists in creating an additional information by introducing a new suitable state variable into the system. This helps to achieve a zero steady-state control deviation of the tension in the strip. Afterwards, the tension controller is designed to ensure asymptotic stability of the extended system by applying the second Lyapunov method. The realized experimental measurements performed on a continuous line laboratory model confirm the advantages and correctness of the proposed control structure: it is simple, stable, robust against changes of parameters, invariant to operating disturbances and ensures a high-quality dynamics of the controlled system prescribed by the reference model. To demonstrate effectiveness of the design, the performance of the controller was compared with properties of a standard Proportional Integral Derivative/Proportional Integral (PID/PI) controller designed in frequency domain

    Compressed Passive Macromodeling

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    This paper presents an approach for the extraction of passive macromodels of large-scale interconnects from their frequency-domain scattering responses. Here, large scale is intended both in terms of number of electrical ports and required dynamic model order. For such structures, standard approaches based on rational approximation via vector fitting and passivity enforcement via model perturbation may fail because of excessive computational requirements, both in terms of memory size and runtime. Our approach addresses this complexity by first reducing the redundancy in the raw scattering responses through a projection and approximation process based on a truncated singular value decomposition. Then we formulate a compressed rational fitting and passivity enforcement framework which is able to obtain speedup factors up to 2 and 3 orders of magnitude with respect to standard approaches, with full control over the approximation errors. Numerical results on a large set of benchmark cases demonstrate the effectiveness of the proposed techniqu

    Implementation Of Internal Model Control (IMC) In Continuous Distillation Column.

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    Distillation columns have been widely used in chemical plants for separation process. The high nonlinearity and dynamic behavior of the column make them hard to control

    Direct control strategy for a four-level three-phase flying-capacitor inverter

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    A direct predictive control strategy is proposed for a three-phase four-level flying-capacitor (FC) inverter in this paper. The balancing of the FC voltages, a challenge in applications with small capacitors and low switching frequencies, is done without any modulation, simply using tables calculated offline. These allow the realization of fast-dynamics output currents with reduced dv/dt in the output voltages and reduced switching frequencies. Moreover, no interharmonics are created when operating at low switching frequencies and with reference currents containing multiple harmonic components, which is a key feature for active power filters. Simulations and experimental results are presented to demonstrate the excellent performance of the direct control strategy in comparison with a conventional pulsewidth-modulation control technique, mostly for operation at low switching frequencies
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