In this paper, DOA estimation is an important research area in array signal processing. The performance of adaptive beamforming methods are known to degrade severly in the presence of uncertainty in the signal sight direction. We propose a novel adaptive beamforming with a Bayesian method. Bayesian method a posterior probability density DOA estimator has been shown to perform excellently. The p...

A true adaptive nonlinear model predictive control (MPC) algorithm must address the issue of robustness to model uncertainty while the estimator is evolving. Unfortunately, this may not be achieved without introducing extra degree of conservativeness and/or computational complexity in the controller calculations. To attenuate this problem, we employ a finite time identifier and propose an adapt...

Adaptive mixing control (AMC) is a recently developed control scheme for uncertain plants, where the control action coming from a bank of precomputed controller is mixed based on the parameter estimates generated by an on-line parameter estimator. Even if the stability of the control scheme, also in the presence of modeling errors and disturbances, has been shown analytically, its transient per...

‎we extend the method of adaptive two-stage sequential sampling to‎‎include designs where there is more than one criteria is used in‎‎deciding on the allocation of additional sampling effort‎. ‎these‎‎criteria‎, ‎or conditions‎, ‎can be a measure of the target‎‎population‎, ‎or a measure of some related population‎. ‎we develop‎‎murthy estimator for the design that is unbiased estimators for‎‎t...

This paper presents an indirect adaptive control scheme for a pH process, which is diicult to control due to its nonlinear dynamics with uncertainties. A nonlinear controller is designed using a backstepping technique which can deal with a large class of nonlinear systems with parametric uncertainty. The nonlinear controller is combined with a parameter estimator to estimate the concentrations ...

This paper reports new contributions to the advancement of wind power uncertainty forecasting beyond the current state-of-the-art. A new kernel density forecast (KDF) method applied to the wind power problem is described. The method is based on the Nadaraya-Watson estimator, and a time-adaptive version of the algorithm is also proposed. Results are presented for different casestudies and compar...

Actual performance of sequential decision-making problems can be extremely sensitive to errors in the models, and this research addressed the role of robustness in coping with this uncertainty. The first part of this thesis presents a computationally efficient sampling methodology, Dirichlet Sigma Points, for solving robust Markov Decision Processes with transition probability uncertainty. A Di...

We present two robust detectors for nonstationary random signals that belong to p-point uncertainty classes, one being based on an estimator-correlator approach, the other using the deflection criterion. Apart of stable performance, these robust detectors have the advantage of requiring only reduced prior knowledge. Using local cosine bases, we provide an intuitive and highly efficient timefreq...

This paper addresses the problem of position tracking control of robot manipulators in the presence of parametric uncertainty and additive periodic disturbances. Specifically, a self tuning, Lyapunov-based adaptive controller with desired dynamics compensation term and a disturbance estimator has been designed to ensure that the link position tracking error converges to zero asymptotically, des...

In this paper, a new guidance law is designed to improve the performance of a homing missiles guidance system in terminal phase. For this purpose first of all, the two dimensions equations of motion are formulated, then the approximation dynamic of missile control loop is added to these equations which are nonlinear whit unmatched uncertainty. Then, a new adaptive back-stepping method is develo...