Self-tuning Property Research Articles

Stabilization against gravity and self-tuning of an elastic variable-length rod through an oscillating sliding sleeve

An elastic rod, straight in its undeformed state, has a mass attached at one end and a variable length, due to a constraint at the other end by a frictionless sliding sleeve. The constraint is arranged with the sliding direction parallel to a gravity field, in a way that the rod can freely slip inside of the sleeve, when the latter is not moving. In this case, the free fall of the mass continues until the rod is completely injected into the constraint. However, when the sliding sleeve is subject to a harmonic transverse vibration, it is shown that the fall of the mass and the rod injection are hindered by the presence of a configurational force developing at the sliding sleeve and acting oppositely to gravity. During the dynamic motion, such a configurational force is varying in time because it is associated with the variable bending moment at the sleeve entrance. It is (experimentally, analytically, and numerically) demonstrated that, in addition to the states of complete injection or ejection of the elastic rod (for which the mass falls down or is thrown out), a stable sustained oscillation around a finite height can be realized. This ‘suspended motion’ is the signature of a new attractor, that arises by the constraint oscillation. This behaviour shares similarities with parametric oscillators, as for instance the Kapitza inverted pendulum. However, differently from the classical parametric oscillators, the ‘suspended’ configuration of the rod violates equilibrium and the stabilization occurs through a transverse mechanical input, instead of a longitudinal one. By varying the sliding sleeve oscillation amplitude and frequency within specific sets of values, the system spontaneously adjusts the sustained motion through a self-tuning of the rod’s external length. This self-tuning property opens the way to the design of vibration-based devices with extended frequency range.

Self‐tuning fuzzy‐PI‐based current control algorithm for doubly fed induction generator

This study presents a fuzzy-proportional–integral (PI) controller having self-tuning property for current control of doubly fed induction generator (DFIG). Two fuzzy-PI-based controllers: first one is used for controlling rotor current of DFIG, whereas the other come up with optimal output-scaling factor for the former are employed to carry out the algorithm. The proposed controller is robust with different operating conditions and parameter changes because of having adaptive feature. The performance of the control algorithm both transient and steady-state conditions are tested with several experiments performed various operating points. The results achieved from both simulation and experimental tests verify that the implemented method is superior to steady-state and transient response at all operations as exhibits robustness to wind speed and parameter variations.

Content adaptive fast motion estimation based on spatio-temporal homogeneity analysis and motion classification

In video coding, research is focused on the development of fast motion estimation (ME) algorithms while keeping the coding distortion as small as possible. It has been observed that the real world video sequences exhibit a wide range of motion content, from uniform to random, therefore if the motion characteristics of video sequences are taken into account before hand, it is possible to develop a robust motion estimation algorithm that is suitable for all kinds of video sequences. This is the basis of the proposed algorithm. The proposed algorithm involves a multistage approach that includes motion vector prediction and motion classification using the characteristics of video sequences. In the first step, spatio-temporal correlation has been used for initial search centre prediction. This strategy decreases the effect of unimodal error surface assumption and it also moves the search closer to the global minimum hence increasing the computation speed. Secondly, the homogeneity analysis helps to identify smooth and random motion. Thirdly, global minimum prediction based on unimodal error surface assumption helps to identify the proximity of global minimum. Fourthly, adaptive search pattern selection takes into account various types of motion content by dynamically switching between stationary, center biased and, uniform search patterns. Finally, the early termination of the search process is adaptive and is based on the homogeneity between the neighboring blocks. Extensive simulation results for several video sequences affirm the effectiveness of the proposed algorithm. The self-tuning property enables the algorithm to perform well for several types of benchmark sequences, yielding better video quality and less complexity as compared to other ME algorithms. Implementation of proposed algorithm in JM12.2 of H.264/AVC shows reduction in computational complexity measured in terms of encoding time while maintaining almost same bit rate and PSNR as compared to Full Search algorithm.

Cosmological constant is probably adjustable in brane worlds

In the brane world scenario, possessing the self-tuning property of the cosmological constant, we study the probability amplitude for an initial state of the Universe to transform to final states with some curvatures. In the Kim, Kyae, and Lee model, there exists a finite range of parameters such that the transition amplitude to a near flat universe is exponentially dominated by the smallness of curvature, $\ensuremath{\gtrsim}{e}^{\mathrm{positive}\text{ }\mathrm{number}/{\overline{\ensuremath{\Lambda}}}^{2}}$, i.e., it is dominated by an almost flat universe of $|\overline{\ensuremath{\Lambda}}|<ϵ$.

Motion Estimation for Content Adaptive Video Compression

Motion estimation is a multistep process that encompasses techniques such as motion vector prediction, determination of search range and search patterns, and identification of termination criteria. Each of these techniques has several diversions that may suit a particular set of video characteristics. It would be hard to conceive a universal algorithm that can perform well for all kinds of video contents. However, if important characteristics of a video sequence can be identified and utilized for adjusting various steps of motion estimation, one can design an adjustable algorithm that can tune its parameters to suit the video at hand. A multistage motion estimation algorithm that includes a pre-stage for analyzing the motion characteristics of a video sequence online is proposed. This stage predicts the motion vector field (MVF) from the previous coded frame, and clusters macroblocks into background and foreground regions based on the predicted MVF. The information from the pre-stage are passed on to the next stage, which includes a mathematical model for block distortion surface to estimate the distance from the current search point to the global optimal position. This allows the motion estimation stage to adjust its search strategies accordingly. The search is performed on a precise area according to the statistical properties of the motion vector prediction error, separately for foreground and background regions. The proposed algorithm, including the pre-stage, is fast and, therefore, is suitable for online and real-time encoding. Extensive simulation results obtained for several video sequences affirm the effectiveness of the proposed algorithm. The self-tuning property enables the algorithm to perform well for several types of benchmark sequences, yielding better video quality without exceeding the time complexity as compared to the other predictive motion estimation algorithms.

Vibration Suppression Using Single Neuron-Based PI Fuzzy Controller and Fractional-Order Disturbance Observer

An approach is proposed for vibration suppression in a two-inertia system using an integration of a fractional-order disturbance observer and a single neuron-based PI fuzzy controller. The former is used to obtain disturbance estimate and generate compensation signal, and the latter is utilized to realize outer loop control. Fractional-order disturbance observer has a wider range to select a suitable tradeoff between robustness and vibration suppression, because introduction of fractional calculus makes universe of relative degree of Q-filter is expanded from integer domain to real-number domain. For the single neuron-based PI fuzzy controller, a single neuron makes up a PI controller and such a controller is embedded in each cell of the fuzzy control table. Thus, the fuzzy control table is changed into a controller matrix and it constructs a nonlinear adaptive controller with parameter self-tuning property. Experimental results illustrate that the integration of fractional-order disturbance observer and single neuron-based PI fuzzy controller can improve the performance of disturbance attenuation and system robustness

Self-T-dual brane cosmology and the cosmological constant problem

We consider a codimension-one brane embedded in a gravity-dilaton bulk action, whosesymmetries are compatible with T-duality along the space-like directions parallel to thebrane, and the bulk time-like direction. The equations of motions in the string frame allowfor a smooth background obtained by the union of two symmetric patches of AdS space.The Poincaré invariance of the solution appears to hold independently of the value of thebrane vacuum energy, through a self-tuning property of the dilaton ground state. Moreover,the effective cosmology displays a bounce, at which the scale factor does notshrink to zero. Finally, by exploiting the T-duality symmetry, we show how toconstruct an ever-expanding Universe, along the lines of the Pre-Big-Bang scenario.

On the reduced variance overparametrized pole‐assignment self‐tuning regulator

The self-tuning property of the reduced variance overparametrized pole-assignment self-tuning regulator is shown to imply partial system identifiability. The specified closed-loop poles for pole assignment occur asymptotically but the asymptotic closed-loop zero locations generally differ from those which are necessary for correct variance reduction. This implies that an explicit self-tuning algorithm should be used to implement the overparametrized pole-assignment self-tuning regulator in order to achieve correct variance reduction. © 1997 John Wiley & Sons, Ltd.

Self-tuning PID control: A multivariable derivation and application

In this paper, a multivariable self-tuning controller with a proportional plus integral plus derivative (PID) structure is derived. The algorithm features a combination of the self-tuning property, in which the controller parameters are tuned automatically on-line, and also the structure of a multivariable PID controller, making it more favourable for use in industry. The algorithm is applied to a microcomputer based multi-input multi-output (MIMO) furnace. Some experiments are conducted to observe the ability of the controller in the temperature control of MIMO furnace under set-point changes and its relative robustness as compared with a fixed-tuned multivariable PID (FTMPID) controller. The experimental results prove that the controller is capable of giving a good control result for the process.

Decentralized Adaptive Control Based on Delta Model Representation

The contribution deals with decentralized control of multi input-multi output continuous-time dynamic systems. The model of the controlled plant as well as the control design are formulated in a delta operator form. The decentralization of a MIMO plant is performed in the matrix fraction description. Control synthesis consists of local and global control loops. The local control design is obtained via Diophantine equations in delta polynomials. The global control strategy ensures asymptotic stability of the entire control system. The self-tuning (adaptive) property is based on real-time recursive identification. Proposed strategies are demonstrated by the simulation of control of a non-linear chemical plant.

Further Results on Least Squares Based Adaptive Minimum Variance Control

Based on the recently established results on self-tuning regulators originally proposed by Åström and Wittenmark, this paper presents various novel and extended results on least squares based adaptive minimum variance control for linear stochastic systems. These results establish self-optimality, self-tuning property, and the best possible convergence rate of the control law in a variety of situations of interest.

A multivariable self-tuning PID controller

Abstract A multivariable self-tuning controller with a proportional plus integral plus derivative (PID) structure is derived. The algorithm features a combination of the self-tuning property, in which the controller parameters are tuned automatically on-line, and also the structure of a multivariable PID controller, making it more favourable to be used in industry. A stability proof of the algorithm is also presented here. Comparison of the performance of the algorithm with a fixed, tuned, multivariable PID controller in the simulation examples demonstrates the effectiveness of the proposed algorithm.

A semi-infinite horizon LQ self-tuning regulator for ARMAX plants based on RLS

So far no extension of the celebrated self-tuning property of the RLS + GMV adaptive controller is available for the steady-state LQ stochastic control. The above extension is addressed in the paper. Namely, we pose the question: Is it possible to adaptively get the semi-infinite horizon LQ stochastic control for any ARMAX plant by using a finite number of predictors whose parameters are estimated by standard RLS? An adaptive control algorithm solving an underlying control problem that coincides with the semi-infinite LQ stochastic feedback is developed. It embodies a standard RLS separate identification of the parameters of T ≥ n + 1 predictors of the joint i/o process, n being the order of the ARMAX plant, together with an appropriate control synthesis rule. The proposed algorithm turns out to be a slightly modified version of MUSMAR with the capability of turning itself to the semi-infinite horizon LQ stochastic regulator.

On Self-tuning Property of Variational Adaptive Control Schemes

The theory of implicit models. developed in previous papers. is here used in order to define a class of adaptive algorithms based on m-step ahead.or infinite horizon. quadratic optimization. and on Recursive Least Squares identification. even in presence of systems having an ARMAX structure. This can be possible due to the fact that not the true system model but an implicit model is actually sought by the identification procedure. the latter being a model of the system which is correct only in certain closed-loop conditions. In the paper. it is shown that. via the identification of a single extended implicit model (which takes into account the presence of an external dither noise in the control law). and basing on almost no a-priori knowledge about the true system. it is possible to structure the adaptive algorithms in such a way that a single equilibrium point is allowed. coinciding with the optimum control situation

Adaptive deadbeat control of stochastic systems

This paper considers the use of a discrete-time deadbeat control action on systems affected by noise. Variations on the standard controller form are discussed and comparisons are made with controllers in which noise rejection is a higher priority objective. Both load and random disturbances are considered in the system description, although the aim of the deadbeat design remains as a tailoring of reference input variations. Finally, the use of such a deadbeat action within a self-tuning control framework is shown to satisfy, under certain conditions, the self-tuning property, generally though only when an extended form of least-squares estimation is incorporated.

Multistep Self-Tuning Prediction

An optimal multistep predictor based on the 1-step-ahead predictor is presented. It is shown that parallel predictors need not be implemented in order to predict the output of some stochastically disturbed system over a future horizon of sampling instants. When the parameters are not known the self-tuning property of the 1-step ahead predictor combined with a least-square recursive identification algorithm is inherited by the future predictions obtained on its bases. The structure complexity of the problem can thus be reduced to its minimal expression, i.e., the 1-step-ahead step-tuning predictor.

Reduced-variance pole-assignment self-tuning regulation

Abstract In this paper the standard explicit self-tuning pole-assignment regulator is generalized to allow the output and/or input variances to be reduced. This is achieved by a suitable overparametrization of the controller polynomials allowing scope for optimization. In the regular case, simulation results indicate that the self-tuning property remains valid, and this is proved in a particular case. A reduced-variance servo controller is briefly discussed. The algorithms can give significant reductions in variance for little extra computational effort.

Implicit Extended Pole-Assignment Self-Tuning Controller

In this paper, first we propose the definition of two linear system equivalence in self-tuning significance. Then we give an implicit extended pole-assignment self-tuning controller (IEPSC) which combines extended self-tuning controller with optimal tracking property. The IEPSC algorithm holds extended self-tuning property, has good tracking and convergence oroperties, and needs less computation than explicit pole-assignment self-tuning controller as well. Computer simulation proves these properties. And we nob only, under some usual conditions, prove the self-tuning property of IEPSC algorithm, but also discuss how to choose the surrogate noise to ensure the convergence of the correspondent cunt roller parameters. Furthermore this IEPSC algorithm is of novelty and validity.

Extended Self-Tuning - Practical Aspects

The paper discusses an extended self-tuning property which generalises previous self-tuning results by employing filtered data during parameter estimation. It is shown that the choice of filter has repercussions on the computational efficiency, convergence and set-point tracking characteristics of the resulting algorithms. The argument is developed in terms of pole-assignment and is supported by computer demonstrations and discussion of practical issues.

Self-tuning property of state-space self tuners

The basic assumption from implicit self-tuning theory is that, for self tuning to occur, the control input obtained from the estimated system model converges to the value whic would be obtained if the system parameters were known. As as direct result of this, only certain control strategies are acceptable. Here a general rule for the self-tuning property of pole-placement self tuners is obtained, and previous strategies are shown to be special cases of this.