We consider a chain of integrators system that has an uncertain delay in the input. Also, there is a measurement noise in the feedback channel that only noisy output is available. We develop a new output feedback control scheme along with amplification such that the ultimate bounds of all states and output of the controlled system can be made arbitrarily small. We note that the condition imposed on the sensor noise is quite general over the existing results such that the sensor noise is uncertain and is only required to be bounded by a known bound. The benefit of our control method is shown via an example.

Sensor noise prevents the exact measurement of output, which makes it difficult to guarantee the ultimate bound of the actual output and states, which is smaller than the sensor noise amplitude. Even worse, the time-varying delay in the input does not guarantee the boundedness of the actual output and states under sensor noise. In this letter, our considered system is a chain of integrators in which time-varying delay exists in the input and there is an additive form of sensor noise in the output measurement. To guarantee the arbitrarily small ultimate bound of the actual output and states, we newly propose an adaptive output feedback controller whose gain is tuned on-line. The merits of our control method over the existing results are clearly shown in the example.

We consider an output feedback control problem of a chain of integrators under sensor noise. The sensor noise enters the output feedback channel in an additive form. A similar problem has been addressed most recently in [9], but their result has been developed only under AC sensor noise. We generalize the result of [9] by allowing the sensor noise to include both AC and DC components. With our new output feedback controller, we show that the ultimate bounds of all states can be made arbitrarily small. We show the generality of our result over [9] via an example.

In this letter, we consider a control problem of a chain of integrators where there is an uncertain delay in the input and sensor noise. This is an output feedback control result over [10] in which a state feedback control is suggested. The several generalized features are: i) output feedback control is developed instead of full state feedback control, ii) uncertain delay in the input is allowed, iii) all states are derived to be arbitrarily small under uncertain sensor noise.

In this letter, we consider a control problem of a chain of integrators by output feedback under sensor noise. First, we introduce a measurement output feedback controller which drives all states and output of the considered system to arbitrarily small bounds. Then, we suggest a measurement output feedback controller coupled with a switching gain-scaling factor in order to improve the transient response and retain the same arbitrarily small ultimate bounds as well. An example is given to show the advantage of the proposed control method.

In this letter, we consider a control problem of a chain of integrators with a delay in the input under measurement feedback. While there are several control results for our considered system, they have not dealt with any of measurement feedback problems. Our proposed controller is coupled with a low-pass filter such that it can attenuate the sensor noise effect and reduce the ultimate bounds of the controlled systems states. Our result shows that the proposed method has clear benefit over the existing results.

The contribution of the paper is two-fold: Firstly, a review of the point set registration literature is given, and secondly, a novel covariance weighted least squares formulation of the multiple view point set registration problem is presented. Point data for surface registration is commonly obtained by non-contact, 3D surface sensors such as scanning laser range finders or structured light systems. Our formulation allows the specification of anisotropic and heteroscedastic (point dependent) 3D noise distributions for each measured point. In contrast, previous algorithms have generally assumed an isotropic sensor noise model, which cannot accurately describe the sensor noise characteristics. For cases where the point measurements are heteroscedastically and anisotropically distributed, registration results obtained with the proposed method show improved accuracy over those produced by an unweighted least squares formulation. Results are presented for both synthetic and real data sets to demonstrate the accuracy and effectiveness of the proposed technique.