Presence Of Inputs Research Articles

Least-squares parameter estimation of linear systems with noisy input–output data

This paper studies the computational efficiency of the bias-eliminated least-squares (BELS) method recently proposed for estimating linear systems in the presence of input and output noises. It is found that the BELS method based on expanding the denominator polynomial of the system transfer function by two dimensions may involve some redundant computations due to its handling of an augmented system model in its estimation scheme. To improve the computational efficiency, a direct estimation scheme is proposed to identify the underlying noisy input–output system. Numerical results show that the computational cost can be considerably reduced using such a new estimation scheme.

Robust Adaptive Variable Structure Control of a Flexible Spacecraft Containing Input Nonlinearity/Dead-Zone

Fine attitude control and stabilization are key technologies required for modern flexible spacecraft, whose missions, such as stereoscopic mapping, require high pointing accuracy and stabilization. However, in the realistic environment, knowledge about system parameters such as inertia matrix and modal frequencies are usually not known, and various disturbances also occur. Therefore, disturbance rejection control strategies that are also robust to parametric uncertainty are of great interest in spacecraft applications. Variable structure control (VSC) is an efficient control technique that is applicable to a wide class of nonlinear systems subject to modeling uncertainty and external disturbances. In those works, VSC is limited to systems with full-state feedback. In practical application, however, fullstate measurement might be neither possible nor feasible. The problem of designing output feedback controllers via VSC to stabilize multi-variable plants has been investigated by Wang and Yallapragada. In addition, the inputs of a system are usually restricted by its physical structure and energy consumption, which also gives the system inputs such nonlinear characteristics as saturation, dead-zone, etc. The existence of input nonlinearity is a source of degradation or even worse, instability in system performance. Hsu et al. have proposed VSC schemes for uncertain dynamics systems with dead-zone nonlinearity for solving regulation problems. These control schemes can possess insensitivity to matching uncertainties and disturbances. However, information on the upper boundaries of uncertainty is required, which is generally not available or too expensive to assess. Adaptive approaches may offer a simple and effective tool to solve this problem. The contribution of this paper is the design of a nonlinear controller to achieve attitude maneuvering of a three-axis stabilized flexible spacecraft under parametric uncertainty, external disturbances and control input nonlinearity/deadzone. The proposed controller ensures the global reaching condition of the sliding mode of an uncertain system in the presence of input nonlinearity/dead-zone without the limitation of knowing the boundaries of uncertainty and the perturbations in advance. Numerical simulations show that precise attitude control and vibration suppression can be accomplished using the derived controller for both asymptotically and exponentially stable design cases.

Right atrial electrical isolation: Left atrial connection to the atrioventricular node

espite extensive research, the electrical connections beween each atrium and the AV node are incompletely unerstood. Some histologic and electrophysiologic evidence ndicates the presence of inputs from both the right and left tria to the AV node in humans and animal models. We present a case in which the right atrium was inadertently isolated electrically by radiofrequency energy rom both the left atrium and the AV node, without any mpairment of the connection between the left atrium and he AV node or of AV node function. To our knowledge, his is the first case to describe an anatomic disconnection etween the left atrium and a large part of the right atrium or the entire right atrium) after a right atrial ablation and eparate electrophysiologic connections between the two tria and the AV node.

Nonlinear observer design for a general class of discrete-time nonlinear systems with real parametric uncertainty

This paper is a geometric study of the local observer design for a general class of discrete-time nonlinear systems with real parametric uncertainty. Explicitly, we study the observer design problem for a general class of discrete-time nonlinear systems with real parametric uncertainty and with an input generator (exosystem). In this paper, we show that for the classical case, when the state equilibrium does not change with the parametric uncertainty, and when the plant output is purely a function of the state, there is no local asymptotic observer for the plant. Next, we show that in sharp contrast to this case, for the general case of problems where we allow the state equilibrium to change with the parametric uncertainty, there typically exist local exponential observers even when the plant output is purely a function of the state. We also present a characterization and construction procedure for local exponential observers for the general class of discrete-time nonlinear systems with real parametric uncertainty under some stability assumptions. We also show that for the general class of discrete-time nonlinear systems considered, under some stability assumptions, the existence of local exponential observers in the presence of inputs implies, and is implied by the existence of local exponential observers in the absence of inputs. Finally, we generalize our results to a general class of discrete-time nonlinear systems with input generator, and with exogenous disturbance.

Nonlinear observer design for a general class of nonlinear systems with real parametric uncertainty

This paper is a geometric study of the local observer design for a general class of nonlinear systems with real parametric uncertainty. Explicitly, we study the observer design problem for a general class of nonlinear systems with real parametric uncertainty and with an input generator (exosystem). In this paper, we show that for the classical case, when the state equilibrium does not change with the parametric uncertainty, and when the plant output is purely a function of the state, there is no local asymptotic observer for the plant. Next, we show that in sharp contrast to this case, for the general case of problems where we allow the state equilibrium to change with the parametric uncertainty, there typically exist local exponential observers even when the plant output is purely a function of the state. We also present a characterization and construction procedure for local exponential observers for the general class of nonlinear systems with real parametric uncertainty under some stability assumptions. We also show that for the general class of nonlinear systems considered, under some stability assumptions, the existence of local exponential observers in the presence of inputs implies, and is implied by, the existence of local exponential observers in the absence of inputs. Finally, we generalize our results to a general class of nonlinear systems with input generator, and with exogenous disturbance.

Improved local learning rule for information maximization and related applications

For a neural network comprising feedforward and lateral connections, a local learning rule is proposed that causes the lateral connections to learn directly the inverse of a covariance matrix. In contrast to earlier work, the rule involves just one processing pass through the lateral connections for each input presentation, and consists of a simple anti-Hebbian term. This provides an effective and simple method for online network learning algorithms that implement optimization principles, drawn from statistics or from information or control theory, for which a running estimate of the covariance matrix inverse is useful. An application to infomax learning (mutual information maximization) in the presence of input and output noise is used to illustrate the method.

An estimation study for control of a lattice model of thin film deposition

The deposition of a thin film is a dynamic process that is complex and subject to unmeasured disturbances. When the dynamics and final material properties are dominated by atomic scale phenomena, it is difficult to apply existing optimization and control tools to improve process repeatability and to optimize the resulting material properties. Currently, feedback loops are used to control sensed quantities, and optimization has been applied to macroscopic reactor conditions like fluid flow. Ultimately, one would like to directly control the material properties that determine the performance of an integrated circuit or MEMS device, even when the property of interest is not directly measured. In this simulation study, several estimation strategies are explored to infer surface roughness from a measurement of step density. The dynamics of the growth process are represented by an atomic scale kinetic Monte-Carlo simulation, whose computational demands are then reduced through model reduction. The reduced order model is used in the estimator, which is then applied to the full Monte-Carlo simulation. The surface roughness estimate is used with a feedback controller, which corrects for temperature offset and modeling error in the presence of input and output noise.

Modified Smith Predictor Design for a Reclaimer

In this paper, a modified Smith predictor in the presence of input and output disturbances as well as modeling uncertainty is investigated. The modified Smith predictor includes the feedforward Smith predictor and the compensator that approximates the inverse of time-delay. Assuming a multiplicative uncertainty, a robust stability criterion and a compensator design criterion are derived. The modified Smith predictor is applied to a reclaimer, which is used in the raw yard of a steel plant and has a large output time-delay. The system identification method is used to find out a nominal model. The uncertainty bound is quantified in the frequency domain through an uncertainty quantification method. Simulation results are provided.

On the role of input and output spillovers when R&D projects are risky

This paper analyzes the impact of input and output spillovers on the expected effective cost reductions in a two-stage model of R&D where all R&D projects are risky. It is shown that, relative to the deterministic case, output spillovers tend to reduce expected cost reductions whereas input spillovers tend to increase investment in R&D and hence expected cost reductions. In particular, the relations between cost reductions in the presence of input and output spillovers known from deterministic models may be reversed under certain parameter constellations.

Predictive control of constrained nonlinear systems via LPV linear embeddings

AbstractThis paper analyzes the applicability of convex MPC schemes, synthesized for LPV polytopic systems, to nonlinear plants. The nonlinear systems under consideration are those whose trajectories can be embedded within those of a polytopic LPV discrete‐time system. It is postulated that the latter belongs to a polytopic family of linear systems, each member of which is parameterized by the value that a parameter vector assumes in the unit simplex. Such a parameter can be measured on‐line and exploited for feedback while a bound on its rate of change is known and exploited for predictions. Different customizations and improvements of a recently introduced MPC scheme for LPV systems are presented and contrasted in terms of their numerical burdens and control performance. The proposed predictive controllers are proved to quadratically stabilize LPV polytopic systems, as well as any other embedded non‐linear system, in the presence of input and state constraints. Copyright © 2003 John Wiley & Sons, Ltd.

On Bias Compensated Recursive Least-Squares Algorithm for FIR Adaptive Filtering

It is known that recursive least squares (RLS) algorithm or least mean square (LMS) algorithm leads to biased FIR filter coefficients in the presence of input and output noise. In this paper, a new type of bias compensated recursive least squares (BCRLS) algorithm is proposed to produce consistent results for adaptive FIR filtering in the input and output noise case. The proposed algorithm introduces an auxiliary estimator to estimate unknown input noise variance. Owing to this, the bias of the RLS solution due to the input noise can be compensated to yield the consistent filter coefficients. Computational simulations indicate that the proposed algorithm is very efficient

Thalamo-cortical connections of areas 3a and M1 in marmoset monkeys.

The present investigation is part of a broader effort to examine cortical areas that contribute to manual dexterity, reaching, and grasping. In this study we examine the thalamic connections of electrophysiologically defined regions in area 3a and architectonically defined primary motor cortex (M1). Our studies demonstrate that area 3a receives input from nuclei associated with the somatosensory system: the superior, inferior, and lateral divisions of the ventral posterior complex (VPs, VPi, and VPl, respectively). Surprisingly, area 3a receives the majority of its input from thalamic nuclei associated with the motor system, posterior division of the ventral lateral nucleus of the thalamus (VL), the mediodorsal nucleus (MD), and intralaminar nuclei including the central lateral nucleus (CL) and the centre median nucleus (CM). In addition, sparse but consistent projections to area 3a are from the anterior pulvinar (Pla). Projections from the thalamus to the cortex immediately rostral to area 3a, in the architectonically defined M1, are predominantly from VL, VA, CL, and MD. There is a conspicuous absence of inputs from the nuclei associated with processing somatic inputs (VP complex). Our results indicate that area 3a is much like a motor area, in part because of its substantial connections with motor nuclei of the thalamus and motor areas of the neocortex (Huffman et al. [2000] Soc. Neurosci. Abstr. 25:1116). The indirect input from the cerebellum and basal ganglia via the ventral lateral nucleus of the thalamus supports its role in proprioception. Furthermore, the presence of input from somatosensory thalamic nuclei suggests that it plays an important role in somatosensory and motor integration.

Asymptotic stabilization of linear plants in presence of input and output saturations

We are considering the problem of controlling an arbitrary linear plant with saturating input and output. We investigate the stability of the control system resulting from such a plant in closed loop with a given saturating linear regulator. Sufficient conditions for global asymptotic stability are formulated using tools from the input–output approach.

Minimax deconvolution design of multirate systems with channel noises: a unified approach

The conventional signal reconstruction problem of multirate systems with channel noises can be cast as a robust multirate deconvolution design problem. We investigate a unified minimax approach for the robust deconvolution design of multirate systems. We discuss two typical multirate systems: the multirate filter bank system and the transmultiplexer system. We consider transmission noises resulting from quantization coding errors or external noises. The deconvolution filters for these systems that we derive are all IIR filters. The keypoint is converting the original robust deconvolution design problem to an equivalent minimax matching problem via polyphase decomposition and noble identities. Then, in spite of the presence of input signals and channel noises, we can solve this minimax matching problem by an optimization technique. The proposed method can be interpreted as designing an optimal multirate deconvolution filter such that the worst-case multirate system reconstruction error is minimized over all possible inputs and noises from the energy perspective. Therefore, our proposed design method is more robust than the conventional design method for multirate systems in the presence of uncertain input signals and channel noises. We present several numerical examples that show the good performance of our design method.

Frequency weighted optimal order reduction of digital filters

This paper focuses on the computation of an ℓ 2 optimal reduced order filter for a given stable discrete higher order filter in the presence of input and output frequency weightings. We first parameterize a set of stable reduced order filters through an orthogonal projection. We then propose two gradient based algorithms, namely the continuous and iterative algorithms, for computing an optimal reduced order filter. The algorithms are shown to possess nice properties including convergence. Examples are given to demonstrate the superior performance of the algorithms as compared with the conventional frequency weighted balanced truncation method.

The Interaction Between Quality Control and Production

A real options model of resource extraction is considered where management controls both the extraction rates as well as the quality of extracted material earmarked for processing into final product. The minimum quality of material acceptable for processing is called the cutoff grade. If the cutoff is too high, much of the extracted material will go to waste exacting an opportunity cost. If the cutoff is too low, then input capacity constraints will be fully utilized, but with poor revenues at output. The optimal strategy finds a balance between these considerations. It is found that the opportunity costs associated with extracting the marginal unit of resource play a crucial role in finding the optimal strategic balance. Particularly important is the realization that such opportunity costs have a structure analogous to a European call option on the commodity in question. The model is developed in the presence of input and output processing stage constraints, and the set of candidate optimal policies is identified. Simulations are then used to expound on the nature of the optimal policies and the important role played by the opportunity costs.

入出力雑音を受ける場合のMSEに基づくモデル次数の決定法

入出力雑音を受ける場合のMSEに基づくモデル次数の決定法

Convergent algorithms for frequency weighted L2 model reduction

This paper is concerned with computing an L 2-optimal reduced-order model for a given stable multivariable linear system in the presence of input and output frequency weightings. By parametrizing a class of reduced-order models in terms of an orthogonal projection and using manifold techniques as tools, both continuous and iterative algorithms are derived and their convergence properties are established. As an application, we show that an L 2 optimal reduced-order filter in the closed-loop sense can be computed using these algorithms.

Order-Determination of Transfer Function Model From Noisy Input-Output Signals

A regularization scheme is investigated to determine the order of a transfer function model in the presence of input and output noises. By introducing multiple regularization parameters to the corrected least squares (CLS) estimate, it is shown that the system order can be determined by comparing each eigenvalue and a corresponding optimal regularization parameter, which minimizes the mean-square error (MSE) of the regularized estimate. An asymptotic MSE is given, and an analytical expression of the optimal regularization parameters is also clarified. Furthermore, a data adaptive iterative algorithm is presented to estimate the system model order and noise variances simultaneously.

Rapid Chemical Reactions in Two Dimensions: Spatially Nonlocal Effects

Using a recent generalization of fluctuation theory that includes spatially nonlocal effects, we examine the influence of nonlocal chemical reactivities on rapid chemical reactions in two dimensions. We focus attention of the irreversible reaction A + A → products at low density, exploring the time dependence of the radial distribution of pairs of reactants, its asymptotic time dependence, and steady states in the presence of inputs. As in the case of three dimensions, we find that the nonlocal theory removes inherent problems with the local theory that occur at short times, gives the expected logarithmic divergence in the absence of inputs, but has well-behaved steady state properties in the presence of inputs. Thus, the nonlocal theory appears to provide a systematic method for treating rapid reactions in membranes and on surfaces at higher densities.