Fuzzy-enhanced adaptive control for flexible drive system with friction using genetic algorithms

When a mechatronic system is in slow speed motion, serious effect of nonlinear friction plays a key role in its control design. In this paper, a stable adaptive control for drive systems including transmission flexibility and friction, based on the Lyapunov stability theory, is first proposed. For ease of design, the friction is fictitiously assumed as an unknown disturbance in the derivation of the adaptive control law Genetic algorithms are then suggested for learning the structure and parameters of the fuzzy-enhancing strategy for the adaptive control to improve system's transient performance and robustness with respect to uncertainty. The integrated fuzzy-enhanced adaptive control is well tested via computer simulations using the new complete dynamic friction model recently suggested by Canudas de Wit et al. for modeling the real friction phenomena. Much lower critical velocity of a flexible drive system that determines system's low-speed performance bound can be obtained using the proposed hybrid control strategy

Stable adaptive fuzzy control with TSK fuzzy friction estimation for linear drive systems

This paper considers the control of a linear drive system with friction and disturbance compensation. A stable adaptive controller integrated with fuzzy model-based friction estimation and switching-based disturbance compensation is proposed via Lyapunov stability theory. A TSK fuzzy model with local linear friction models is suggested for real-time estimation of its consequent local parameters. The parameters update law is derived based on linear parameterization. In order to compensate for the effects resulting from estimation error and disturbance, a robust switching law is incorporated in the overall stable adaptive control system. Extensive computer simulation results show that the proposed stable adaptive fuzzy control system has very good performances, and is potential for precision positioning and trajectory tracking control of linear drive systems

Fuzzy modeling and control for conical magnetic bearings using linear matrix inequality

A general nonlinear model with six degree-of-freedom rotor dynamics and electromagnetic force equations for conical magnetic bearings is developed. For simplicity, a T-S (Takagi Sugeno) fuzzy model for the nonlinear magnetic bearings assumed no rotor eccentricity is first derived, and a fuzzy control design based on the T-S fuzzy model is then proposed for the high speed and high accuracy control of the complex magnetic bearing systems. The suggested fuzzy control design approach for nonlinear magnetic bearings can be cast into a linear matrix inequality (LMI) problem via robust performance analysis, and the LMI problem can be solved efficiently using the convex optimization techniques. Computer simulations are presented for illustrating the performance of the control strategy considering simultaneous rotor rotation tracking and gap deviations regulation

Hierarchical fuzzy control for C-axis of CNC turning centers using genetic algorithms

A combined PD and hierarchical fuzzy control is proposed for the low-speed control of the C-axis of CNC turning centers considering the effects of transmission flexibility and complex nonlinear friction. Learning of the hierarchical structure and parameters of the suggested control strategy is carried out by using the genetic algorithms. The proposed algorithm consists of two phases: the first one is to search the best hierarchy, and the second to tune the consequent center values of the constituent fuzzy logic systems into the hierarchy. For the least total control rule number, the hierarchical fuzzy controller is chosen to include only the simple two-input/one-output fuzzy systems, and both binary and decimal genes are used for the selection, crossover and mutation of the genetic algorithm. The proposed approach is validated by the computer simulation. Each generation consists of 30 individuals: ten reproduced from its parent generation, ten generated by crossover, and the other ten by mutation. In the simulations, the C-axis is assumed to be driven by a vector-controlled AC induction motor, and the dynamic friction model suggested by Canudas de Wit et al. in 1995 is used

Recurrent neuro-fuzzy modeling and fuzzy MDPP control for flexible servomechanisms

This paper considers the nonlinear system identification and control for flexible servomechanisms. A multi-step-ahead recurrent neuro-fuzzy model consisting of local linear ARMA (autoregressive moving average) models with bias terms is suggested for approximating the dynamic behavior of a servomechanism including the effects of flexibility and friction. The RLS ( recursive least squares) algorithm is adopted for obtaining the optimal consequent parameters of the rules. Within each fuzzy operating region, a local MDPP ( minimum degree pole placement) control law with integral action can be constructed based on the estimated local model. Then a fuzzy controller composed of these local MDPP controls can be easily constructed for the servomechanism. The techniques are illustrated using computer simulations

Рифма в рамках средневекового крымскотатарского силлабического стиха

В предложенной статье представлен теоретический материал, который раскрывает сущность и особенности рифмы, подкреплённый необходимым материалом из крымскотатарской литературы.У запропонованій статті представлений теоретичний матеріал, що розкриває сутність і особливості рими, підкріплений необхідним матеріалом із кримськотатарської літератури.In offered article the theoretical material which opens essence and features of the rhyme, supported by a necessary material from crimean tatars literatures is submitted

Scattering mechanism in a step-modulated subwavelength metal slit: a multi-mode multi-reflection analysis

In this paper, the scattering/transmission inside a step-modulated subwavelength metal slit is investigated in detail. We firstly investigate the scattering in a junction structure by two types of structural changes. The variation of transmission and reflection coefficients depending on structural parameters are analyzed. Then a multi-mode multi-reflection model based on ray theory is proposed to illustrate the transmission in the step-modulated slit explicitly. The key parts of this model are the multi-mode excitation and the superposition procedure of the scatterings from all possible modes, which represent the interference and energy transfer happened at interfaces. The method we use is an improved modal expansion method (MEM), which is a more practical and efficient version compared with the previous one [Opt. Express 19, 10073 (2011)]. In addition, some commonly used methods, FDTD, scattering matrix method, and improved characteristic impedance method, are compared with MEM to highlight the preciseness of these methods.Comment: 25 pages, 9 figure

Integrated dopaminergic neuronal model with reduced intracellular processes and inhibitory autoreceptors

Dopamine (DA) is an important neurotransmitter for multiple brain functions, and dysfunctions of the dopaminergic system are implicated in neurological and neuropsychiatric disorders. Although the dopaminergic system has been studied at multiple levels, an integrated and efficient computational model that bridges from molecular to neuronal circuit level is still lacking. In this study, the authors aim to develop a realistic yet efficient computational model of a dopaminergic pre‐synaptic terminal. They first systematically perturb the variables/substrates of an established computational model of DA synthesis, release and uptake, and based on their relative dynamical timescales and steady‐state changes, approximate and reduce the model into two versions: one for simulating hourly timescale, and another for millisecond timescale. They show that the original and reduced models exhibit rather similar steady and perturbed states, whereas the reduced models are more computationally efficient and illuminate the underlying key mechanisms. They then incorporate the reduced fast model into a spiking neuronal model that can realistically simulate the spiking behaviour of dopaminergic neurons. In addition, they successfully include autoreceptor‐mediated inhibitory current explicitly in the neuronal model. This integrated computational model provides the first step toward an efficient computational platform for realistic multiscale simulation of dopaminergic systems in in silico neuropharmacology

A Statistical Study on the Morphology of Rays and Dynamics of Blobs in the Wake of Coronal Mass Ejections

In this paper, with a survey through the Large Angle and Spectrometric Coronagraph (LASCO) data from 1996 to 2009, we present 11 events with plasma blobs flowing outwards sequentially along a bright coronal ray in the wake of a coronal mass ejection. The ray is believed to be associated with the current sheet structure that formed as a result of solar eruption, and the blobs are products of magnetic reconnection occurring along the current sheet. The ray morphology and blob dynamics are investigated statistically. It is found that the apparent angular widths of the rays at a fixed time vary in a range of 2.1-6.6 (2.0-4.4) degrees with an average of 3.5 (2.9) degrees at 3 (4) Rs, respectively, and the observed durations of the events vary from 12 h to a few days with an average of 27 h. It is also found, based on the analysis of blob motions, that 58% (26) of the blobs were accelerated, 20% (9) were decelerated, and 22% (10) moved with a nearly-constant speed. Comparing the dynamics of our blobs and those that are observed above the tip of a helmet streamer, we find that the speeds and accelerations of the blobs in these two cases differ significantly. It is suggested that these differences of the blob dynamics stem from the associated magnetic reconnection involving different magnetic field configurations and triggering processes.Comment: 12 pages, 6 figures, accepted by Solar Physic