Adaptive control of uncertain port-controlled Hamiltonian systems subject to actuator saturation

This paper deals with the problem of adaptive control of uncertain nonlinear port-controlled Hamiltonian systems subject to actuator saturation, and proposes a number of results on the control design. Firstly, the adaptive stabilization problem is studied, and a control design method is developed by using both the dissipative Hamiltonian structural and saturated actuator properties. Secondly, for the case that there are both parametric uncertainties and external disturbances in the AS systems, an adaptive H _∞ control design approach is presented. Finally, study of an example of power systems with simulations shows that the adaptive controller proposed in this paper is effective.     

An internet of things–based personal device for diabetes therapy management in ambient assisted living (AAL)

Diabetes therapy management in AAL environments, such as old people and diabetes patients homes, is a very difficult task since many factors affect a patient’s blood sugar levels. Factors such as illness, treatments, physical and psychological stress, physical activity, drugs, intravenous fluids and change in the meal plan cause unpredictable and potentially dangerous fluctuations in blood sugar levels. Right now, operations related to dosage are based on insulin infusion protocol boards, which are provided by physicians to the patients. These boards are not considering very influential factors such as glycemic index from the diet, consequently patients need to estimate the dosage leading to dose error, which culminates in hyperglycemia and hypoglycemia episode. Therefore, right insulin infusion calculation needs to be supported by the next generation of personal-care devices. For this reason, a personal device has been developed to assist and consider more factors in the insulin therapy dosage calculation. The proposed solution is based on Internet of things in order to, on the one hand, support a patient’s profile management architecture based on personal RFID cards and, on the other hand, provide global connectivity between the developed patient’s personal device based on 6LoWPAN, nurses/physicians desktop application to manage personal health cards, glycemic index information system, and patient’s web portal. This solution has been evaluated by a multidisciplinary group formed by patients, physicians, and nurses.     

Modeling,simulation, and practice of floor control for synchronous and ubiquitous collaboration

Floor control refers to the need for coordinating activities occurred in synchronously cooperating applications shared among collaborators. We address this for ubiquitous collaboration—the capability of multiple users to link together with disparate access device anytime and anywhere. Floor control has been studied for years but most researchers focus on relaxed coordination mechanisms with stationary devices that allow updates by any user on any object and resolve the uncoordinated updates. In this paper we present a floor control mechanism, called XGSP-Floor, which implements a coordination mechanism at application level for enabling users to consistently share the same resource in real time (synchronous collaboration) in ubiquitous collaboration environment. The implementation platform on cell phone devices may not be new. But we believe the implementation and experiment for XGSP-Floor on cell phone devices is a new challenge in ubiquitous collaboration environment even though the coordination mechanism can intuitively impose a tremendous overhead in worst case. We also describe the results of the modeling of XGSP-Floor and formal verification to prove the correctness of the modeling using Colored Petri Nets. We describe lessons learned and discuss future work.     

Subsegmental,segmental and suprasegmental processing of linear prediction residual for speaker information

This work processes linear prediction (LP) residual in the time domain at three different levels, extracts speaker information, and demonstrates their significance and also different nature for text-independent speaker recognition. The subsegmental analysis considers LP residual in blocks of 5 msec with shift of 2.5 msec to extract speaker information. The segmental analysis extracts speaker information by processing in blocks of 20 msec with shift of 2.5 msec. The suprasegmental speaker information is extracted by viewing in blocks of 250 msec with shift of 6.25 msec. The speaker identification and verification studies performed using NIST-99 and NIST-03 databases demonstrate that the segmental analysis provides best performance followed by subsegmental analysis. The suprasegmental analysis gives the least performance. However, the evidences from all the three levels of processing seem to be different and combine well to provide improved performance, demonstrating different speaker information captured at each level of processing. Finally, the combined evidence from all the three levels of processing together with vocal tract information further improves the speaker recognition performance.     

Adaptive control of straight worms without derivative measurement

In this paper we consider an adaptive control problem of finite DoF worm-like locomotion systems (WLLS) which contact the ground with Coulomb dry friction. Using a rough mathematical friction law the system is shown to belong to a system class that allows adaptive control. Gaits from the kinematic theory can be tracked by means of adaptive controllers. For this we introduce two different adaptive controllers for λ-tracking and focus on that one which is not based on the derivative of the output. We pay attention to the analysis of such systems and present some theoretical control investigations including proofs. Numerical simulations of tracking different reference signals under arbitrary choice of the system parameters demonstrate and illustrate that the introduced simple adaptive controllers work successfully and effectively. Current experiments are aimed at the justification of theoretical results.     

Smooth velocity approximation for constrained systems in real-time simulation

The rapidly increasing complexity of multi-body system models in applications like vehicle dynamics, robotics and bio-mechanics requires qualitative new solution methods to slash computing times for the dynamical simulation.     

Iterant recombination with one-norm minimization for multilevel Markov chain algorithms via the ellipsoid method

Recently, it was shown how the convergence of a class of multigrid methods for computing the stationary distribution of sparse, irreducible Markov chains can be accelerated by the addition of an outer iteration based on iterant recombination. The acceleration was performed by selecting a linear combination of previous fine-level iterates with probability constraints to minimize the two-norm of the residual using a quadratic programming method. In this paper we investigate the alternative of minimizing the one-norm of the residual. This gives rise to a nonlinear convex program which must be solved at each acceleration step. To solve this minimization problem we propose to use a deep-cuts ellipsoid method for nonlinear convex programs. The main purpose of this paper is to investigate whether an iterant recombination approach can be obtained in this way that is competitive in terms of execution time and robustness. We derive formulas for subgradients of the one-norm objective function and the constraint functions, and show how an initial ellipsoid can be constructed that is guaranteed to contain the exact solution and give conditions for its existence. We also investigate using the ellipsoid method to minimize the two-norm. Numerical tests show that the one-norm and two-norm acceleration procedures yield a similar reduction in the number of multigrid cycles. The tests also indicate that one-norm ellipsoid acceleration is competitive with two-norm quadratic programming acceleration in terms of running time with improved robustness.