Robust control of underactuated wheeled mobile manipulators using GPI disturbance observers

This article describes the design of a linear observer–linear controller-based robust output feedback scheme for output reference trajectory tracking tasks in the case of nonlinear, multivariable, nonholonomic underactuated mobile manipulators. The proposed linear feedback scheme is based on the use of a classical linear feedback controller and suitably extended, high-gain, linear Generalized Proportional Integral (GPI) observers, thus aiding the linear feedback controllers to provide an accurate simultaneous estimation of each flat output associated phase variables and of the exogenous and perturbation inputs. This information is used in the proposed feedback controller in (a) approximate, yet close, cancelations, as lumped unstructured time-varying terms, of the influence of the highly coupled nonlinearities, and (b) the devising of proper linear output feedback control laws based on the approximate estimates of the string of phase variables associated with the flat outputs simultaneously provided by the disturbance observers. Simulations reveal the effectiveness of the proposed approach.

Nedd4 mediates control of an epithelial Na+ channel in salivary duct cells by cytosolic Na+

Epithelial Na+ channels are expressed widely in absorptive epithelia such as the renal collecting duct and the colon and play a critical role in fluid and electrolyte homeostasis. Recent studies have shown that these channels interact via PY motifs in the C terminals of their α, β, and γ subunits with the WW domains of the ubiquitin-protein ligase Nedd4. Mutation or deletion of these PY motifs (as occurs, for example, in the heritable form of hypertension known as Liddle’s syndrome) leads to increased Na+ channel activity. Thus, binding of Nedd4 by the PY motifs would appear to be part of a physiological control system for down-regulation of Na+ channel activity. The nature of this control system is, however, unknown. In the present paper, we show that Nedd4 mediates the ubiquitin-dependent down-regulation of Na+ channel activity in response to increased intracellular Na+. We further show that Nedd4 operates downstream of Go in this feedback pathway. We find, however, that Nedd4 is not involved in the feedback control of Na+ channels by intracellular anions. Finally, we show that Nedd4 has no influence on Na+ channel activity when the Na+ and anion feedback systems are inactive. We conclude that Nedd4 normally mediates feedback control of epithelial Na+ channels by intracellular Na+, and we suggest that the increased Na+ channel activity observed in Liddle’s syndrome is attributable to the loss of this regulatory feedback system.

Suppression of tumor necrosis factor-induced cell death by inhibitor of apoptosis c-IAP2 is under NF-κB control

Members of the NF-κB/Rel and inhibitor of apoptosis (IAP) protein families have been implicated in signal transduction programs that prevent cell death elicited by the cytokine tumor necrosis factor α (TNF). Although NF-κB appears to stimulate the expression of specific protective genes, neither the identities of these genes nor the precise role of IAP proteins in this anti-apoptotic process are known. We demonstrate here that NF-κB is required for TNF-mediated induction of the gene encoding human c-IAP2. When overexpressed in mammalian cells, c-IAP2 activates NF-κB and suppresses TNF cytotoxicity. Both of these c-IAP2 activities are blocked in vivo by coexpressing a dominant form of IκB that is resistant to TNF-induced degradation. In contrast to wild-type c-IAP2, a mutant lacking the C-terminal RING domain inhibits NF-κB induction by TNF and enhances TNF killing. These findings suggest that c-IAP2 is critically involved in TNF signaling and exerts positive feedback control on NF-κB via an IκB targeting mechanism. Functional coupling of NF-κB and c-IAP2 during the TNF response may provide a signal amplification loop that promotes cell survival rather than death.

Nonlinear control of heart rate variability in human infants.

Nonlinear analyses of infant heart rhythms reveal a marked rise in the complexity of the electrocardiogram with maturation. We find that normal mature infants (gestation greater than or equal to 35 weeks) have complex and distinctly nonlinear heart rhythms (consistent with recent reports for healthy adults) but that such nonlinearity is lacking in preterm infants (gestation > or = to 27 weeks) where parasympathetic-sympathetic interaction and function are presumed to be less well developed. Our study further shows that infants with clinical brain death and those treated with atropine exhibit a similar lack of nonlinear feedback control. These three lines of evidence support the hypothesis championed by Goldberger et al. [Goldberger, A.L., Rigney, D.R. & West, B.J. (1990) Sci. Am. 262, 43-49] that autonomic nervous system control underlies the nonlinearity and possible chaos of normal heart rhythms. This report demonstrates the acquisition of nonlinear heart rate dynamics and possible chaos in developing human infants and its loss in brain death and with the administration of atropine. It parallels earlier work documenting changes in the variability of heart rhythms in each of these cases and suggests that nonlinearity may provide additional power in characterizing physiological states.

The analysis and compensation of nonlinear sampled-data feedback systems.

"Research was supported by the United States Air Force through the Air Force Office of Scientific Research, Air Research and Development Command."

Specification and data presentation in linear control systems; [final report,

"EE 61-4. Research project PRF 30. Contract no. AF 29(600)-1933."

On adaptive control systems,

At head of title: Microwave Research Institute, Polytechnic Institute of Brooklyn, Systems and Control Group, R-735, PIB-663, contract no. DA-30-069-ORD-1560.

Sampled-data control systems /

Includes bibliography.

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.

Real-time control of a Tokamak plasma using neural networks

This paper presents results from the first use of neural networks for the real-time feedback control of high temperature plasmas in a Tokamak fusion experiment. The Tokamak is currently the principal experimental device for research into the magnetic confinement approach to controlled fusion. In the Tokamak, hydrogen plasmas, at temperatures of up to 100 Million K, are confined by strong magnetic fields. Accurate control of the position and shape of the plasma boundary requires real-time feedback control of the magnetic field structure on a time-scale of a few tens of microseconds. Software simulations have demonstrated that a neural network approach can give significantly better performance than the linear technique currently used on most Tokamak experiments. The practical application of the neural network approach requires high-speed hardware, for which a fully parallel implementation of the multi-layer perceptron, using a hybrid of digital and analogue technology, has been developed.

Comparison between conventional and stochastic pinning control

Theoretical developments on pinning control of complex dynamical networks have mainly focused on the deterministic versions of the model dynamics. However, the dynamical behavior of most real networks is often affected by stochastic noise components. In this paper the pinning control of a stochastic version of the coupled map lattice network with spatiotemporal characteristics is studied. The control of these complex dynamical networks have functional uncertainty which should be considered when calculating stabilizing control signals. Two feedback control methods are considered: the conventional feedback control and modified stochastic feedback control. It is shown that the typically-used conventional control method suffers from the ignorance of model uncertainty leading to a reduction and potentially a collapse in the control efficiency. Numerical verification of the main result is provided for a chaotic coupled map lattice network. © 2011 IEEE.

A Stochastic Control Approach to a Parabolic Equation, Reciprocal Processes

2000 Mathematics Subject Classi cation: 49L60, 60J60, 93E20.

Discrete-time positive periodic systems with state and control constraints

The aim of this paper is to provide an efficient control design technique for discrete-time positive periodic systems. In particular, stability, positivity and periodic invariance of such systems are studied. Moreover, the concept of periodic invariance with respect to a collection of boxes is introduced and investigated with connection to stability. It is shown how such concept can be used for deriving a stabilizing state-feedback control that maintains the positivity of the closed-loop system and respects states and control signals constraints. In addition, all the proposed results can be efficiently solved in terms of linear programming.

Object-Oriented Modeling Simulation and Control of Activated Sludge Process

Object-oriented modeling is spreading in current simulation of wastewater treatments plants through the use of the individual components of the process and its relations to define the underlying dynamic equations. In this paper, we describe the use of the free-software OpenModelica simulation environment for the object-oriented modeling of an activated sludge process under feedback control. The performance of the controlled system was analyzed both under normal conditions and in the presence of disturbances. The object-oriented described approach represents a valuable tool in teaching provides a practical insight in wastewater process control field.

Optimal control of non-linear systems through hybrid cell-mapping/artificial-neural-networks techniques

In this paper, a real-time optimal control technique for non-linear plants is proposed. The control system makes use of the cell-mapping (CM) techniques, widely used for the global analysis of highly non-linear systems. The CM framework is employed for designing approximate optimal controllers via a control variable discretization. Furthermore, CM-based designs can be improved by the use of supervised feedforward artificial neural networks (ANNs), which have proved to be universal and efficient tools for function approximation, providing also very fast responses. The quantitative nature of the approximate CM solutions fits very well with ANNs characteristics. Here, we propose several control architectures which combine, in a different manner, supervised neural networks and CM control algorithms. On the one hand, different CM control laws computed for various target objectives can be employed for training a neural network, explicitly including the target information in the input vectors. This way, tracking problems, in addition to regulation ones, can be addressed in a fast and unified manner, obtaining smooth, averaged and global feedback control laws. On the other hand, adjoining CM and ANNs are also combined into a hybrid architecture to address problems where accuracy and real-time response are critical. Finally, some optimal control problems are solved with the proposed CM, neural and hybrid techniques, illustrating their good performance.