Model-following Neuro-adaptive approach for robust control of high performance aircrafts

Based on dynamic inversion, a relatively straightforward approach is presented in this paper for nonlinear flight control design of high performance aircrafts, which does not require the normal and lateral acceleration commands to be first transferred to body rates before computing the required control inputs. This leads to substantial improvement of the tracking response. Promising results are obtained from six degree-offreedom simulation studies of F-16 aircraft, which are found to be superior as compared to an existing approach (which is also based on dynamic inversion). The new approach has two potential benefits, namely reduced oscillatory response (including elimination of non-minimum phase behavior) and reduced control magnitude. Next, a model-following neuron-adaptive design is augmented the nominal design in order to assure robust performance in the presence of parameter inaccuracies in the model. Note that in the approach the model update takes place adaptively online and hence it is philosophically similar to indirect adaptive control. However, unlike a typical indirect adaptive control approach, there is no need to update the individual parameters explicitly. Instead the inaccuracy in the system output dynamics is captured directly and then used in modifying the control. This leads to faster adaptation, which helps in stabilizing the unstable plant quicker. The robustness study from a large number of simulations shows that the adaptive design has good amount of robustness with respect to the expected parameter inaccuracies in the model.

Stability of control systems with time delay

To find the approximate stability limit on the forward gain in control systems with small time delay, this note suggests approximating the exponential in the characteristic equation by the first few terms of its series and using the Routh–Hurwitz criterion. This approximation avoids all the time-consuming graphical work and gives a somewhat pessimistic maximum bound for the gain constant.

A neural network based automatic generation control design through reinforcement learning

This paper presents the design and implementation of a learning controller for the Automatic Generation Control (AGC) in power systems based on a reinforcement learning (RL) framework. In contrast to the recent RL scheme for AGC proposed by us, the present method permits handling of power system variables such as Area Control Error (ACE) and deviations from scheduled frequency and tie-line flows as continuous variables. (In the earlier scheme, these variables have to be quantized into finitely many levels). The optimal control law is arrived at in the RL framework by making use of Q-learning strategy. Since the state variables are continuous, we propose the use of Radial Basis Function (RBF) neural networks to compute the Q-values for a given input state. Since, in this application we cannot provide training data appropriate for the standard supervised learning framework, a reinforcement learning algorithm is employed to train the RBF network. We also employ a novel exploration strategy, based on a Learning Automata algorithm,for generating training samples during Q-learning. The proposed scheme, in addition to being simple to implement, inherits all the attractive features of an RL scheme such as model independent design, flexibility in control objective specification, robustness etc. Two implementations of the proposed approach are presented. Through simulation studies the attractiveness of this approach is demonstrated.

A study of epileptogenic network structures in rat hippocampal cultures using first spike latencies during synchronization events

Study of hypersynchronous activity is of prime importance for combating epilepsy. Studies on network structure typically reconstruct the network by measuring various aspects of the interaction between neurons and subsequently measure the properties of the reconstructed network. In sub-sampled networks such methods lead to significant errors in reconstruction. Using rat hippocampal neurons cultured on a multi-electrode array dish and a glutamate injury model of epilepsy in vitro, we studied synchronous activity in neuronal networks. Using the first spike latencies in various neurons during a network burst, we extract various recurring spatio-temporal onset patterns in the networks. Comparing the patterns seen in control and injured networks, we observe that injured networks express a wide diversity in their foci (origin) and activation pattern, while control networks show limited diversity. Furthermore, we note that onset patterns in glutamate injured networks show a positive correlation between synchronization delay and physical distance between neurons, while control networks do not.

A study of epileptogenic network structures in rat hippocampal cultures using first spike latencies during synchronization events

Study of hypersynchronous activity is of prime importance for combating epilepsy. Studies on network structure typically reconstruct the network by measuring various aspects of the interaction between neurons and subsequently measure the properties of the reconstructed network. In sub-sampled networks such methods lead to significant errors in reconstruction. Using rat hippocampal neurons cultured on a multi-electrode array dish and a glutamate injury model of epilepsy in vitro, we studied synchronous activity in neuronal networks. Using the first spike latencies in various neurons during a network burst, we extract various recurring spatio-temporal onset patterns in the networks. Comparing the patterns seen in control and injured networks, we observe that injured networks express a wide diversity in their foci (origin) and activation pattern, while control networks show limited diversity. Furthermore, we note that onset patterns in glutamate injured networks show a positive correlation between synchronization delay and physical distance between neurons, while control networks do not.

Effect of SST Variation on ITCZ in APE Simulations

The effect of meridional variation of sea surface temperature (SST) on tropical atmospheric circulation is analyzed using Aqua-planet Experiment (APE) simulations. The meridional SST gradient around the narrow SST peak in CONTROL simulation favours a strong and single equatorial Intertropical Convergence Zone (ITCZ, defined by the maximum of zonally averaged total precipitation) in all APE models. In contrast, flat equatorial SST peak (FLAT simulation) favours split/double ITCZs flanking the SST maximum, in the majority of the APE models. Although there is reasonable agreement for SST sensitivity of ITCZ among the APE models in CONTROL, there exists disparity among them in FLAT case. Similarly, while the total and convective precipitation responses are consistent among the models, the large-scale precipitation response shows considerable inter-model variations in FLAT case. The APE intercomparison indicates that the occurrence and positioning of the ITCZ are primarily related to boundary layer moisture convergence as a response to the meridional variation of SST. Furthermore, the meridional gradient of tropospheric temperature is found to be an important factor that can influence the positioning of ITCZ. FLAT SST distribution is found to be similar to the observed distribution over the Indian region during summer season. Models that yield double ITCZs in this case simulate an easterly jet over the equatorial region (similar to 15 degrees equatorward of the ITCZ). This is analogous to the Tropical Easterly Jet (TEJ), which is a unique feature observed over the Indian region during summer monsoon season, with its core at 12 degrees N, equatorward of the seasonal convergence zone centered along 25 degrees N. In these models, positive meridional temperature gradient and the associated easterly shear in the atmosphere strengthened by moisture convergence penetrate up to the upper troposphere, with which TEJ is in thermal wind balance.

STAT-1 expression is regulated by IGFBP-3 in malignant glioma cells and is a strong predictor of poor survival in patients with glioblastoma Laboratory investigation

Object. Insulin-like growth factor binding proteins (IGEBPs) have been implicated in the pathogenesis of glioma. In a previous study the authors demonstrated that IGFBP-3 is a novel glioblastoma biomarker associated with poor survival. Since signal transducer and activator of transcription 1 (STAT-1) has been shown to be regulated by IGFBP-3 during chondrogenesis and is a prosurvival and radioresistant molecule in different tumors, the aim in the present study was to explore the functional significance of IGFBP-3 in malignant glioma cells, to determine if STAT-1 is indeed regulated by IGFBP-3, and to study the potential of STAT-1 as a biomarker in glioblastoma. Methods. The functional significance of IGFBP-3 was investigated using the short hairpin (sh)RNA gene knockdown approach on U251MG cells. STAT-1 regulation by IGFBP-3 was tested on U251MG and U87MG cells by shRNA gene knockdown and exogenous treatment with recombinant IGFBP-3 protein. Subsequently, the expression of STAT-1 was analyzed with real-time reverse transcription polymerase chain reaction (RT-PCR) and immunohistochemistry (IHC) in glioblastoma and control brain tissues. Survival analyses were done on a uniformly treated prospective cohort of adults with newly diagnosed glioblastoma (136 patients) using Kaplan-Meier and Cox regression models. Results. IGFBP-3 knockdown significantly impaired proliferation, motility, migration, and invasive capacity of U251MG cells in vitro (p < 0.005). Exogenous overexpression of IGFBP-3 in U251MG and U87MG cells demonstrated STAT-1 regulation. The mean transcript levels (by real-time RT-PCR) and the mean labeling index of STAT-1 (by IHC) were significantly higher in glioblastoma than in control brain tissues (p = 0.0239 and p < 0.001, respectively). Multivariate survival analysis revealed that STAT-1 protein expression (HR 1.015, p = 0.033, 95% CI 1.001-1.029) along with patient age (HR 1.025, p = 0.005, 95% CI 1.008-1.042) were significant predictors of shorter survival in patients with glioblastoma. Conclusions. IGFBP-3 influences tumor cell proliferation, migration, and invasion and regulates STAT-1 expression in malignant glioma cells. STAT-1 is overexpressed in human glioblastoma tissues and emerges as a novel prognostic biomarker.

Studies on the effects of varying secondary gas properties in a low entrainment ratio supersonic ejector

A dilution cum purge ejector for application in fuel cells represents a domain of ejector operation involving low entrainment ratio with differing secondary and primary gas; which is hardly investigated and a cohesive design framework is not readily available. We comprehensively study a constant area ejector using analytical, experimental and numerical tools at low entrainment ratio (0.004-0.065) with Air, Helium and Argon as secondary gas while the primary gas is Air. For the first time, limits of operating parameters used in control volume method to design the ejector are found to be highly dependent on the secondary molecular weight. The entrainment ratio in the ejector (low for Helium and high for Argon) is affected by the molecular weight and the static pressure within the ejector (low for Air and high for Argon & Helium) by the gamma of the secondary gas. Sufficient suction pressure (0.3-0.55 bar) is generated by the ejector thereby preventing any backflow of secondary gas at all primary stagnation pressures (1.5, 2.2 and 3.1 bar). Numerical results agree well with experimental results. The ejector is shown to completely dilute and purge the secondary flow, meeting all key design requirements. (C) 2014 Elsevier Ltd. All rights reserved.

Early postnatal exposure to lithium in vitro induces changes in AMPAR mEPSCs and vesicular recycling at hippocampal glutamatergic synapses

Lithium is an effective mood stabilizer but its use is associated with many side effects. Electrophysiological recordings of miniature excitatory postsynaptic currents (mEPSCs) mediated by glutamate receptor AMPA-subtype (AMPARs) in hippocampal pyramidal neurons revealed that CLi (therapeutic concentration of 1 mM lithium, from days in vitro 4-10) decreased the mean amplitude and mean rectification index (RI) of AMPAR mEPSCs. Lowered mean RI indicate that contribution of Ca2+-permeable AMPARs in synaptic events is higher in CLi neurons (supported by experiments sensitive to Ca2+-permeable AMPAR modulation). Co-inhibiting PKA, GSK-3 beta and glutamate reuptake was necessary to bring about changes in AMPAR mEPSCs similar to that seen in CLi neurons. FM1-43 experiments revealed that recycling pool size was affected in CLi cultures. Results from minimum loading, chlorpromazine treatment and hyperosmotic treatment experiments indicate that endocytosis in CLi is affected while not much difference is seen in modes of exocytosis. CLi cultures did not show the high KCl associated presynaptic potentiation observed in control cultures. This study, by calling attention to long-term lithium-exposure-induced synaptic changes, might have implications in understanding the side effects such as CNS complications occurring in perinatally exposed babies and cognitive dulling seen in patients on lithium treatment.

Structured model-following neuro-adaptive design for attitude maneuver of rigid bodies

A new structured model-following adaptive approach is presented in this paper to achieve large attitude maneuvers of rigid bodies. First, a nominal controller is designed using the dynamic inversion philosophy. Next, a neuro- adaptive design is proposed to augment the nominal design in order to assure robust performance in the presence of parameter inaccuracies as well as unknown constant external disturbances. The structured approach proposed in this paper (where kinematic and dynamic equations are handled separately), reduces the complexity of the controller structure. From simulation studies, this adaptive controller is found to be very effective in assuring robust performance.

Biotransformation of cholesterol to diosgenin by freely suspended and immobilised cells of Dioscorea bulbifera L.

The cell suspension cultures, established from the friable callus which was risen from the nodal segments of Dioscorea bulbifera L. in Murashige-Skoog (MS) medium supplemented with indole-3-butryic acid (20 mg L- 1), was examined for cell growth in MS medium fed with cholesterol (50 mg L- 1 and 100 mg L- 1) after 8, 10, 12, 14, 16, and 18 days of culture. The growth index of the cell suspension culture on the 8th day was 1.2 and gradually inclined to 1.9 on the 16th day and remained the same at the 18th day. There is no marked difference in the cell growth of cholesterol-treated and control cell suspension culture. The maximum accumulation of diosgenin was noticed on the 14th day in control and cholesterol-treated cell suspension culture and immobilised cell cultures. The highest concentration of diosgenin, 2.94% and 2.14% dry weight, was obtained in immobilised cell culture and cell suspension culture treated with 100 mg L- 1 cholesterol, respectively.

Testing and Modeling of MR Damper and Its Application to SDOF Systems Using Integral Backstepping Technique

Magnetorheological dampers are intrinsically nonlinear devices, which make the modeling and design of a suitable control algorithm an interesting and challenging task. To evaluate the potential of magnetorheological (MR) dampers in control applications and to take full advantages of its unique features, a mathematical model to accurately reproduce its dynamic behavior has to be developed and then a proper control strategy has to be taken that is implementable and can fully utilize their capabilities as a semi-active control device. The present paper focuses on both the aspects. First, the paper reports the testing of a magnetorheological damper with an universal testing machine, for a set of frequency, amplitude, and current. A modified Bouc-Wen model considering the amplitude and input current dependence of the damper parameters has been proposed. It has been shown that the damper response can be satisfactorily predicted with this model. Second, a backstepping based nonlinear current monitoring of magnetorheological dampers for semi-active control of structures under earthquakes has been developed. It provides a stable nonlinear magnetorheological damper current monitoring directly based on system feedback such that current change in magnetorheological damper is gradual. Unlike other MR damper control techniques available in literature, the main advantage of the proposed technique lies in its current input prediction directly based on system feedback and smooth update of input current. Furthermore, while developing the proposed semi-active algorithm, the dynamics of the supplied and commanded current to the damper has been considered. The efficiency of the proposed technique has been shown taking a base isolated three story building under a set of seismic excitation. Comparison with widely used clipped-optimal strategy has also been shown.

Distributed nonlinear optimization algorithms for general network problems

There are a number of large networks which occur in many problems dealing with the flow of power, communication signals, water, gas, transportable goods, etc. Both design and planning of these networks involve optimization problems. The first part of this paper introduces the common characteristics of a nonlinear network (the network may be linear, the objective function may be non linear, or both may be nonlinear). The second part develops a mathematical model trying to put together some important constraints based on the abstraction for a general network. The third part deals with solution procedures; it converts the network to a matrix based system of equations, gives the characteristics of the matrix and suggests two solution procedures, one of them being a new one. The fourth part handles spatially distributed networks and evolves a number of decomposition techniques so that we can solve the problem with the help of a distributed computer system. Algorithms for parallel processors and spatially distributed systems have been described.There are a number of common features that pertain to networks. A network consists of a set of nodes and arcs. In addition at every node, there is a possibility of an input (like power, water, message, goods etc) or an output or none. Normally, the network equations describe the flows amoungst nodes through the arcs. These network equations couple variables associated with nodes. Invariably, variables pertaining to arcs are constants; the result required will be flows through the arcs. To solve the normal base problem, we are given input flows at nodes, output flows at nodes and certain physical constraints on other variables at nodes and we should find out the flows through the network (variables at nodes will be referred to as across variables).The optimization problem involves in selecting inputs at nodes so as to optimise an objective function; the objective may be a cost function based on the inputs to be minimised or a loss function or an efficiency function. The above mathematical model can be solved using Lagrange Multiplier technique since the equalities are strong compared to inequalities. The Lagrange multiplier technique divides the solution procedure into two stages per iteration. Stage one calculates the problem variables % and stage two the multipliers lambda. It is shown that the Jacobian matrix used in stage one (for solving a nonlinear system of necessary conditions) occurs in the stage two also.A second solution procedure has also been imbedded into the first one. This is called total residue approach. It changes the equality constraints so that we can get faster convergence of the iterations.Both solution procedures are found to coverge in 3 to 7 iterations for a sample network.The availability of distributed computer systems — both LAN and WAN — suggest the need for algorithms to solve the optimization problems. Two types of algorithms have been proposed — one based on the physics of the network and the other on the property of the Jacobian matrix. Three algorithms have been deviced, one of them for the local area case. These algorithms are called as regional distributed algorithm, hierarchical regional distributed algorithm (both using the physics properties of the network), and locally distributed algorithm (a multiprocessor based approach with a local area network configuration). The approach used was to define an algorithm that is faster and uses minimum communications. These algorithms are found to converge at the same rate as the non distributed (unitary) case.