Adaptive compliance control of a multi‐legged stair‐climbing robot based on proprioceptive data

Purpose – The purpose of this paper is to describe an innovative compliance control architecture for hybrid multi‐legged robots. The approach was verified on the hybrid legged‐wheeled robot ASGUARD, which was inspired by quadruped animals. The adaptive compliance controller allows the system to cope with a variety of stairs, very rough terrain, and is also able to move with high velocity on flat ground without changing the control parameters. Design/methodology/approach – The paper shows how this adaptivity results in a versatile controller for hybrid legged‐wheeled robots. For the locomotion control we use an adaptive model of motion pattern generators. The control approach takes into account the proprioceptive information of the torques, which are applied on the legs. The controller itself is embedded on a FPGA‐based, custom designed motor control board. An additional proprioceptive inclination feedback is used to make the same controller more robust in terms of stair‐climbing capabilities. Findings – The robot is well suited for disaster mitigation as well as for urban search and rescue missions, where it is often necessary to place sensors or cameras into dangerous or inaccessible areas to get a better situation awareness for the rescue personnel, before they enter a possibly dangerous area. A rugged, waterproof and dust‐proof corpus and the ability to swim are additional features of the robot. Originality/value – Contrary to existing approaches, a pre‐defined walking pattern for stair‐climbing was not used, but an adaptive approach based only on internal sensor information. In contrast to many other walking pattern based robots, the direct proprioceptive feedback was used in order to modify the internal control loop, thus adapting the compliance of each leg on‐line.

Material uncertainty effect on vibration control of smart composite plate using polynomial chaos expansion

Polynomial Chaos Expansion with Latin Hypercube sampling is used to study the effect of material uncertainty on vibration control of a smart composite plate with piezoelectric sensors/actuators. Composite material properties and piezoelectric coefficients are considered as independent and normally distributed random variables. Numerical results show substantial variation in structural dynamic response due to material uncertainty of active vibration control system. This change in response due to material uncertainty can be compensated by actively tuning the feedback control system. Numerical results also show variation in dispersion of dynamic characteristics and control parameters with respect to ply angle and stacking sequence.

Control of Mean and Fluctuating Forces on a Circular Cylinder at High Reynolds Numbers

A narrow strip is used to control mean and fluctuating forces on a circular cylinder at Reynolds numbers from 2.0 x 10(4) to 1.0 x 10(5). The axes of the strip and cylinder are parallel. The control parameters are strip width ratio and strip position characterized by angle of attack and distance from the cylinder. Wind tunnel tests show that the vortex shedding from both sides of the cylinder can be suppressed, and mean drag and fluctuating lift on the cylinder can be reduced if the strip is installed in an effective zone downstream of the cylinder. A phenomenon of mono-side vortex shedding is found. The strip-induced local changes of velocity profiles in the near wake of the cylinder are measured, and the relation between base suction and peak value in the power spectrum of fluctuating lift is studied. The control mechanism is then discussed from different points of view.

Propiedades básicas de materiales soporte y parámetros de control en biofiltración.

[ES]En la problemática medioambiental generada por la contaminación atmosférica hay tres aspectos que marcan las directrices de actuación institucional: la presión social, la legislación vigente y la tecnología disponible. En cuanto a este último aspecto, la biofiltración es una tecnología eficaz, asequible y sostenible basada en la actividad biodegradadora de microorganismos específicos adheridos a la superficie de un material soporte que constituye el lecho del biofiltro. La elección de un material soporte adecuado es de especial importancia para asegurar el correcto funcionamiento de los biofiltros. Esta decisión está basada en las propiedades intrínsecas del material que deben ser analizadas previamente a su uso. En este proyecto se ha seleccionado cuales son estas propiedades básicas a partir de una revisión bibliográfica, destacando la capacidad de retención de humedad, superficie específica, porosidad, y estabilidad física y química del material. En este trabajo, también se han fijado los parámetros de control que deben ser medidos de forma rutinaria en un biofiltro para asegurar la eficacia del tratamiento de descontaminación. En base a la información bibliográfica recopilada, se ha concluido que los parámetros básicos son pH, temperatura, contenido de humedad del lecho y pérdidas de carga. A nivel experimental, se han medido las pérdidas de carga generadas en biofiltros empacados con tres materiales soportes que son de especial interés para una investigación posterior a desarrollar por el grupo Biofiltración de la Universidad del País Vasco (UPV/EHU). Durante el período de arranque inicial de los tres biofiltros, las pérdidas de carga medidas fueron muy bajas en los tres casos, aunque algo superiores cuando la alimentación era en sentido ascendente frente al flujo descendente. Sin embargo, cuando se midieron las pérdidas de carga bajo condiciones de encharcamiento del lecho, que es una de las situaciones más problemáticas en un biofiltro, todos los soportes presentaron un aumento puntual de la pérdida de carga aunque la tendencia en los días posteriores fue claramente descendente, recuperando valores de operación habituales. La comparativa del comportamiento frente a las pérdidas de carga permitió seleccionar el soporte más idóneo de los tres analizados, aunque los otros dos podrían ser alternativas viables en caso de sustitución.

Control of the photonic crystal waveguide over the beam profile of vertical-cavity surface-emitting lasers

The control of the photonic crystal waveguide over the beam profile of vertical-cavity surface-emitting lasers is investigated. The symmetric slab waveguide model is adopted to analyze the control parameters, of the beam profile in the photonic-crystal vertical-cavity surface-emitting laser (PC-VCSEL). The filling factor (the ratio of the hole diameter to the lattice constant) and the etching depth control the divergence angle of the PC-VCSEL, and the low filling factor and the shallow etching depth are beneficial to achieve the low-divergence-angle beam. Two types of PC-VCSELs with different filling factors and etching depths are designed and fabricated. The experimental results show that the device with a lower filling factor and a shallower etching depth has a lower divergence angle, which agrees well with the theoretical predictions.

Vector Associative Maps: Unsupervised Real-time Error-based Learning and Control of Movement Trajectories

This article describes neural network models for adaptive control of arm movement trajectories during visually guided reaching and, more generally, a framework for unsupervised real-time error-based learning. The models clarify how a child, or untrained robot, can learn to reach for objects that it sees. Piaget has provided basic insights with his concept of a circular reaction: As an infant makes internally generated movements of its hand, the eyes automatically follow this motion. A transformation is learned between the visual representation of hand position and the motor representation of hand position. Learning of this transformation eventually enables the child to accurately reach for visually detected targets. Grossberg and Kuperstein have shown how the eye movement system can use visual error signals to correct movement parameters via cerebellar learning. Here it is shown how endogenously generated arm movements lead to adaptive tuning of arm control parameters. These movements also activate the target position representations that are used to learn the visuo-motor transformation that controls visually guided reaching. The AVITE model presented here is an adaptive neural circuit based on the Vector Integration to Endpoint (VITE) model for arm and speech trajectory generation of Bullock and Grossberg. In the VITE model, a Target Position Command (TPC) represents the location of the desired target. The Present Position Command (PPC) encodes the present hand-arm configuration. The Difference Vector (DV) population continuously.computes the difference between the PPC and the TPC. A speed-controlling GO signal multiplies DV output. The PPC integrates the (DV)·(GO) product and generates an outflow command to the arm. Integration at the PPC continues at a rate dependent on GO signal size until the DV reaches zero, at which time the PPC equals the TPC. The AVITE model explains how self-consistent TPC and PPC coordinates are autonomously generated and learned. Learning of AVITE parameters is regulated by activation of a self-regulating Endogenous Random Generator (ERG) of training vectors. Each vector is integrated at the PPC, giving rise to a movement command. The generation of each vector induces a complementary postural phase during which ERG output stops and learning occurs. Then a new vector is generated and the cycle is repeated. This cyclic, biphasic behavior is controlled by a specialized gated dipole circuit. ERG output autonomously stops in such a way that, across trials, a broad sample of workspace target positions is generated. When the ERG shuts off, a modulator gate opens, copying the PPC into the TPC. Learning of a transformation from TPC to PPC occurs using the DV as an error signal that is zeroed due to learning. This learning scheme is called a Vector Associative Map, or VAM. The VAM model is a general-purpose device for autonomous real-time error-based learning and performance of associative maps. The DV stage serves the dual function of reading out new TPCs during performance and reading in new adaptive weights during learning, without a disruption of real-time operation. YAMs thus provide an on-line unsupervised alternative to the off-line properties of supervised error-correction learning algorithms. YAMs and VAM cascades for learning motor-to-motor and spatial-to-motor maps are described. YAM models and Adaptive Resonance Theory (ART) models exhibit complementary matching, learning, and performance properties that together provide a foundation for designing a total sensory-cognitive and cognitive-motor autonomous system.

The effect of sub-optimal control and the spectral wave climate on the performance of wave energy converter arrays

A linear hydrodynamic model is used to assess the sensitivity of the performance of a wave energy converter (WEC) array to control parameters. It is found that WEC arrays have a much smaller tolerance to imprecision of the control parameters than isolated WECs and that the increase in power capture of WEC arrays is only achieved with larger amplitudes of motion of the individual WECs. The WEC array radiation pattern is found to provide useful insight into the array hydrodynamics. The linear hydrodynamic model is used, together with the wave climate at the European Marine Energy Centre (EMEC), to assess the maximum annual average power capture of a WEC array. It is found that the maximum annual average power capture is significantly reduced compared to the maximum power capture for regular waves and that the optimum array configuration is also significantly modified. It is concluded that the optimum configuration of a WEC array will be as much influenced by factors such as mooring layout, device access and power smoothing as it is by the theoretical optimum hydrodynamic configuration. © 2009 Elsevier Ltd.

Optimization of Controller Parameters for Energy Saving

Among various technologies to tackle the twin challenges of sustainable energy supply and climate change, energy saving through advanced control plays a crucial role in decarbonizing the whole energy system. Modern control technologies, such as optimal control and model predictive control do provide a framework to simultaneously regulate the system performance and limit control energy. However, few have been done so far to exploit the full potential of controller design in reducing the energy consumption while maintaining desirable system performance. This paper investigates the correlations between control energy consumption and system performance using two popular control approaches widely used in the industry, namely the PI control and subspace model predictive control. Our investigation shows that the controller design is a delicate synthesis procedure in achieving better trade-o between system performance and energy saving, and proper choice of values for the control parameters may potentially save a significant amount of energy

A simple feedback control for a chaotic oscillator with limited power supply

We proposed a simple feedback control method to suppress chaotic behavior in oscillators with limited power supply. The small-amplitude controlling signal is applied directly to the power supply system, so as to alter the characteristic curve of the driving motor. Numerical results are presented showing the method efficiency for a wide range of control parameters. Moreover, we have found that, for some parameters, this kind of control may introduce coexisting periodic attractors with complex basins of attraction and, therefore, serious problems with predictability of the final state the system will asymptote to. (c) 2006 Elsevier Ltd. All rights reserved.

On Nonlinear Dynamics and Control of A Particular Portal Frame Foundation Model, Excited by a Non-Ideal Motor

The nonlinear dynamic response and a nonlinear control method of a particular portal frame foundation for an unbalanced rotating machine with limited power (non-ideal motor) are examined. Numerical simulations are performed for a set of control parameters (depending on the voltage of the motor) related to the static and dynamic characteristics of the motor. The interaction of the structure with the excitation source may lead to the occurrence of interesting phenomena during the forward passage through the several resonance states of the systems. A mathematical model having two degrees of freedom simplifies the non-ideal system. The study of controlling steady-state vibrations of the non-ideal system is based on the saturation phenomenon due to internal resonance.

Application of passive control to energy harvester efficiency using a nonideal portal frame structural support system

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Can postural control performance be an indicator of truck drivers' sleep deprivation?

Long-haul drivers work in irregular schedules due to load delivery demands. In general, driving and sleeping occur at irregular times and, consequently, partial sleep deprivation and/or circadian misalignment may emerge and result in sleepiness at the wheel. In this way, the aim of this study was to verify changes in the postural control parameters of professional drivers after one-night working. Eight male truck drivers working at night - night drivers (ND) and nine day drivers (DD) volunteered to participate in this study. The night drivers' postural stability was assessed immediately before and after an approximately 430 km journey by two identical force platforms at departure and arrival sites. The DD group was measured before and after a day's work. An interaction effect of time of day and type of shift in both conditions: eyes open (p < 0.01) and eyes closed (p < 0.001) for amplitude of mediolateral movements was observed. Postural stability, measured by force platform, is affected by a night of work, suggesting that it could be an effect of circadian and homeostatic influences over postural control.

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.

Design and evaluation of an educational platform for implementing and testing bilateral control algorithms

This paper describes the design and evaluation of a new platform created in order to improve the learning experience of bilateral control algorithms in teleoperation. This experimental platform, developed at Universidad Politécnica de Madrid, is used by the students of the Master on Automation and Robotics in the practices of the subject called “Telerobotics and Teleoperation”. The main objective is to easily implement different control architectures in the developed platform and evaluate them under different conditions to better understand the main advantages and drawbacks of each control scheme. So, the student’s tasks are focused on adjusting the control parameters of the predefined controllers and designing new ones to analyze the changes in the behavior of the whole system. A description of the subject, main topics and the platform constructed are detailed in the paper. Furthermore, the methodology followed in the practices and the bilateral control algorithms are presented. Finally, the results obtained in the experiments with students are also shown.

Biologically inspired joint control for a humanoid robot

The GuRm is a 1.2m tall, 23 degree of freedom humanoid consuucted at the University of Queensland for research into humanoid robotics. The key challenge being addressed by the GuRw projcct is the development of appropriate learning strategies for control and coodinadon of the robot’s many joints. The development of learning strategies is Seen as a way to sidestep the inherent intricacy of modeling a multi-DOP biped robot. This paper outlines the approach taken to generate an appmpria*e control scheme for the joinis of the GuRoo. The paper demonsrrates the determination of local feedback control parameters using a genetic algorithm. The feedback loop is then augmented by a predictive modulator that learns a form of feed-fonward control to overcome the irregular loads experienced at each joint during the gait cycle. The predictive modulator is based on thc CMAC architecture. Results from tats on the GuRoo platform show that both systems provide improvements in stability and tracking of joint control.